Method for distinguishing between live and dead microbes in a sample

Abstract

The present invention relates to a method for differentiating between live and dead microorganisms in a sample by distinguishing between transcriptionally active and inactive microbial nucleic acid sequences. Specifically, the method is based on comparing the levels of nucleotide substitution in a sample cultured in the presence of an RNA labeling agent. The present invention also relates to a method for diagnosing microbial infections in a subject and to methods for assessing the risk of contamination of a sample by applying the discrimination method described herein.

Description

Method for distinguishing between live and dead microbes in a sample Field of invention The present invention relates to a method for discriminating between live and dead microbes in a sample by distinguishing between transcriptionally active and inert microbial nucleic acid sequences in the sample. In particular, the method according to the present invention is based on comparing the levels of nucleotide substitution in a sample cultured in the presence of an RNA labeling agent. The present invention further relates to a method for diagnosing microbial infections in a subject; and to methods for assessing the risk of contamination of a sample, implementing the method to distinguish according to the present invention. Background of the invention The ability to detect viruses in cells and, more generally, microbes, has numerous applications in the field of diagnostics, where it is useful in identifying infectious agents that lead, for example, to diseases; in biomedical research, where it conditions the interpretation of experimental results; or in the safety assessment of potentially contaminated samples; or the viability of microorganisms used in biotechnological processes. Beyond the now-standard detection technique based on the amplification of nucleic acid sequences specific to microorganisms, several techniques have emerged to circumvent the main limitation of amplification-only techniques: the inability to distinguish between dead and active (or replicative) microorganisms, including latent viruses. In the context of biological sample screening, the ability to make this distinction is crucial, as the presence of an active microbial agent can have different consequences than those associated with the presence of dead and / or inactive microbes, particularly viruses. These techniques can be based on the detection of specific sequences in replicating viruses: the presence of RNAs in the case of DNA viruses, the stoichiometry of positive-sense to negative-sense RNAs in the case of negative-sense single-stranded RNA viruses, the presence of negative-sense RNAs (antigenome) in the case of positive-sense single-stranded RNA viruses, and the presence of positive-sense spliced ​​DNA and RNA in the case of retroviruses. These techniques rely on reverse complement amplification, such as RT-PCR, or on RNA sequencing (RNA-Seq, also called whole-transcriptome shotgun sequencing) using high-throughput sequencing methods, particularly strand-specific RNA-Seq techniques. Current techniques still have several limitations: first, they cannot distinguish between contaminating RNAs, such as replication intermediates present regardless of the presence of active microbes, such as active viral particles, in the sample, and RNAs associated with the presence of replicating microbes, such as viruses, in the sample. The effectiveness of these techniques in detecting replicating double-stranded RNA viruses remains to be demonstrated. For positive-sense single-stranded RNA viruses, the small number of negative-sense RNA species produced limits the sensitivity of the available detection techniques. Furthermore, for negative-sense single-stranded RNA viruses, distinguishing between the positive-strand antigenome and specific transcripts is difficult and requires species-specific bioinformatics analysis. Another characteristic of RNA species associated with the presence of replicating viruses (and more broadly, replicating microbes) is that, unlike potentially contaminating RNAs, they are synthesized (i.e., in the host cell in the case of viruses; or directly within the bacteria, fungi, or similar organism in the case of other microbes). Several techniques have been described for labeling and purifying nascent transcripts (also called metabolic RNA labeling techniques). For example, the incorporation of 4-thiouridine (4sU) or other uridine analogs (BudR) has been used to purify nascent eukaryotic mRNA transcripts and / or investigate the dynamics of transcriptional networks (Herzog et al., 2017. Nat. Methods.14 (12): 1198). - 1204; Tani et al., 2012. ARN Biol.9 (10) : 1233-8 RIGGIO MP et al., EUROPEAN JOURNAL OF CLINICAL MICROBIOLOGY & INFECTIOUS DISEASES, , BERLIN, DE, vol. 29, no. 7, 8 May 2010, pages 823-834, refers to the molecular detection of transcriptionally active bacteria from defective hip prostheses removed during revision arthroplasty. In this specification, the inventors have designed, optimized, and validated a method for detecting virus-infected cells, including novel viral strains / species, based on the detection of viral RNA synthesis within cells of a biological sample using metabolic labeling. The inventors have further demonstrated that this method can also be readily implemented for the detection of any transcriptionally active microbe when based on the detection of microbial RNA synthesis in a sample using metabolic labeling. These include, for example, the detection of viral, bacterial, archaeal, fungal, or protozoan infections or contaminations by live microorganisms and the differentiation of carryover from dead microorganisms. Compendium The present invention is defined in the appended claims The invention relates to an in vitro method for distinguishing between live and dead microbes in a sample, comprising distinguishing between transcriptionally active and inert microbial nucleic acid sequences in the sample, wherein the method comprises the steps of: (a) sequencing a set of RNAs extracted from the sample, wherein the set of RNAs is obtained by culturing the sample in the presence of an RNA labeling agent and further subjecting the extracted RNAs to conditions that promote nucleotide substitution; thereby obtaining a set of sequence reads; (b) identify a matching microbial nucleic acid sequence by: (i) optionally, filtering of the sequence read set, (ii) optionally, assembly of the sequence reads into contigs, (iii) alignment of sequence reads or contigs thereof in a database comprising microbial nucleic acid sequences, (iv) identification of at least one matching microbial nucleic acid sequence mapped against at least one sequence read or contigo, (c) determine the number and / or rate of substituted nucleotides in the set of sequence reads or contigs that have been mapped against at least one matching microbial nucleic acid sequence in step (b)(iv) compared to a control sequence; and (d) conclude that at least one matching microbial nucleic acid sequence belongs to a living microbe if, in the set of sequence reads or contigs that have been mapped against at least one matching microbial nucleic acid sequence in step (b)(iv), the number and / or rate of substituted nucleotides in the set of sequence reads is greater than the number and / or rate of randomly substituted nucleotides in the control sequence, wherein the control sequence is the same matching microbial nucleic acid sequence found in the closest microbial strain identified in the nucleic acid sequence databases; and wherein the nucleotide substitution comprises the chemical modification of the RNAs; and furthermore, the reverse transcription of said chemically modified RNAs. The invention also relates to an in vitro method for diagnosing a microbial infection in a subject, comprising: (a) provide a previously obtained sample from the subject, (b) perform the in vitro method according to the invention on said sample, and (c) diagnose the subject as having a microbial infection if at least one of the identified matching microbial nucleic acid sequences belongs to a living microbe. The invention also relates to a method for assessing the risk of microbial contamination in a non-biological sample, comprising: (a) provide a non-biological sample, (b) performing the in vitro method according to the invention on said non-biological sample, and (c) conclude that the non-biological sample is at risk of contamination if at least one of the identified matching microbial nucleic acid sequences belongs to a living microbe. This disclosure also relates to an in vitro method for distinguishing between live and dead microbes in a sample, comprising distinguishing between transcriptionally active and inert microbial nucleic acid sequences in the sample, wherein the method comprises the steps of: (a) sequencing a first set of RNAs extracted from the sample, wherein the first set of RNAs is obtained by culturing the sample in the presence of an RNA labeling agent and further subjecting the extracted RNAs to conditions that allow nucleotide substitution; thus obtaining a first set of sequence reads; (b) comparing the number of substituted nucleotides in the first set of sequence reads mapped against at least one microbial nucleic acid sequence matching a control sequence; and (c) conclude that at least one matching microbial nucleic acid sequence belongs to a live microbe if the number of substituted nucleotides in the sequence reads mapped to that at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than the number of randomly substituted nucleotides in the control sequence. This document describes an in vitro method for distinguishing between live and dead microbes in a sample, comprising distinguishing between transcriptionally active and inert microbial nucleic acid sequences in the sample, wherein the method comprises the steps of: (a) sequencing a first set of RNAs extracted from the sample, wherein the first set of RNAs is obtained by culturing the sample in the presence of an RNA labeling agent and further subjecting the extracted RNAs to conditions that allow nucleotide substitution; thereby obtaining a first set of sequence reads; (b) comparing the number of substituted nucleotides in the first set of sequence reads mapped against at least one microbial nucleic acid sequence matching a control sequence; and (c) conclude that at least one matching microbial nucleic acid sequence belongs to a living microbe if the number of substituted nucleotides in the sequence reads that map to at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than the number of randomly substituted nucleotides in the control sequence, wherein the control sequence is not a second set of sequence reads that map to said at least one matching microbial nucleic acid sequence, said second set of sequence reads being obtained by sequencing a second set of RNAs obtained by culturing the sample in the absence of the RNA labeling agent. This document describes a control sequence that is selected from: - a second set of sequence reads that are mapped against said at least one matching microbial nucleic acid sequence, wherein the second set of sequence reads is obtained by sequencing a second set of RNAs obtained by culturing the sample in the absence of an RNA labeling agent; - a second set of sequence reads that maps against said at least one matching microbial nucleic acid sequence, wherein the second set of sequence reads is obtained by sequencing a second set of RNAs obtained by culturing the sample in the presence of an RNA labeling agent but without subjecting the extracted RNAs to conditions that allow nucleotide substitution; - a consensus microbial nucleic acid sequence, obtained from sequence reads or contigs of the first set of sequence reads that are mapped to at least one matching microbial nucleic acid sequence; - a sequence that corresponds to the same matching microbial nucleic acid sequence found in the closest microbial strain identified in nucleic acid sequence databases; and / or - an analogous sequence that corresponds to the same matching microbial nucleic acid sequence identified in the nucleic acid sequence databases. This document describes a control sequence that is selected from: - a second set of sequence reads that is mapped against said at least one matching microbial nucleic acid sequence, wherein the second set of sequence reads is obtained by sequencing a second set of RNAs obtained by culturing the sample in the presence of an RNA labeling agent but without subjecting the extracted RNAs to conditions that allow nucleotide substitution; - a consensus microbial nucleic acid sequence, obtained from sequence reads or contigs of the first set of sequence reads that are mapped to at least one matching microbial nucleic acid sequence; - a sequence that corresponds to the same matching microbial nucleic acid sequence found in the closest microbial strain identified in nucleic acid sequence databases; and / or - an analogous sequence that corresponds to the same matching microbial nucleic acid sequence identified in nucleic acid sequence databases. This document describes an in vitro method comprising: (a) sequencing a first and second set of RNAs extracted from the sample, wherein the first and second sets of RNAs are obtained by culturing the sample in the presence of an RNA labeling agent, thereby obtaining labeled RNAs, and wherein the first set of RNAs is obtained from the first fraction of the labeled RNAs that is subjected to conditions allowing nucleotide substitution, and the second set of RNAs is obtained from a second fraction of the labeled RNAs that is not subjected to conditions allowing nucleotide substitution, thereby obtaining a first and a second set of sequence reads, (b) compare the number of substituted nucleotides in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence with the number of substituted nucleotides in the second set of sequence reads mapped against at least one matching microbial nucleic acid sequence, and (c) conclude that at least one matching microbial nucleic acid sequence belongs to a living microbe if the number of substituted nucleotides in the sequence reads that map to at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than in the second set of sequence reads. This dissertation describes an in vitro method for distinguishing between infectious and non-infectious viral nucleic acid sequences in a cell sample, and comprises: (a) sequencing a first and a second set of RNAs extracted from a cell sample, wherein the first set of RNAs is obtained by culturing the cell sample in the presence of an RNA labeling agent and the second set of RNAs is obtained by culturing the cell sample in the absence of an RNA labeling agent, thereby obtaining a first and a second set of sequence reads, (b) identify at least one matching viral nucleic acid sequence that maps to at least one sequence read from the first set of sequence reads, (c) compare the number of substituted nucleotides in the sequence reads that are mapped against at least one matching viral nucleic acid sequence identified in the first and second sets of sequence reads, and (d) conclude that at least one matching viral nucleic acid sequence belongs to an infectious virus if the number of substituted nucleotides in the sequence reads that map to at least one matching viral nucleic acid sequence identified in the first set of sequence reads is greater than in the second set of sequence reads. This document describes an in vitro method for distinguishing between live and dead microbes in a sample, comprising: (a) sequencing a first and a second set of RNAs extracted from the sample, wherein the first set of RNAs is obtained by culturing the sample in the presence of an RNA labeling agent and the second set of RNAs is obtained by culturing the sample in the absence of an RNA labeling agent, thereby obtaining a first and a second set of sequence reads, (b) compare the number of substituted nucleotides in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence with the number of substituted nucleotides in the second set of sequence reads mapped against that at least one matching microbial nucleic acid sequence, and (c) conclude that at least one matching microbial nucleic acid sequence belongs to a living microbe if the number of substituted nucleotides in the sequence reads that map to at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than in the second set of sequence reads. In one embodiment, the first set of RNAs is obtained by culturing the sample in the presence of an RNA labeling agent, thus obtaining labeled RNAs, and further subjecting said labeled RNAs to nucleotide substitution. This document describes an in vitro method for distinguishing between live and dead microbes in a sample, comprising: (a) sequencing a first and a second set of RNAs extracted from the sample, wherein the first and second sets of RNAs are obtained by culturing the sample in the presence of an RNA labeling agent, thereby obtaining labeled RNAs, and wherein the first set of RNAs is obtained from the first fraction of the labeled RNAs that undergoes nucleotide substitution, and the second set of RNAs is obtained from a second fraction of the labeled RNAs that does not undergo nucleotide substitution, thereby obtaining a first and second set of sequence reads, (b) comparing the number of substituted nucleotides in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence with the number of substituted nucleotides in the second set of sequence reads mapped against that at least one matching microbial nucleic acid sequence, and (c) conclude that at least one matching microbial nucleic acid sequence belongs to a living microbe if the number of substituted nucleotides in the sequence reads that map to at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than in the second set of sequence reads. In one embodiment, the RNA labeling agent is a thiol-labeled RNA precursor. In one embodiment, the thiol-labeled RNA precursor is selected from the group comprising 4-thiouridine, 2-thiouridine, 2,4-dithiouridine, 2-thio-4-deoxyuridine, 5-carbetoxy-2-thiouridine, 5-carboxy-2-thiouridine, 5-(n-propyl)-2-thiouridine, 6-methyl-2-thiouridine, and 6-(n-propyl)-2-thiouridine, thereby obtaining thiouridine-labeled RNAs. In one embodiment, the thiol-labeled RNA precursor is 4-thiouridine. In one embodiment, the nucleotide substitution comprises chemically modifying the RNAs, preferably by alkylation, oxidative nucleophilic aromatic substitution or osmium-mediated transformation; more preferably by alkylation; and further reverse transcription of said chemically modified RNAs. In one embodiment, the second set of RNAs is obtained by culturing the cell sample in the presence of an RNA labeling agent, thus obtaining labeled RNAs, and subsequently alkylating said labeled RNAs. In one embodiment, the labeled RNAs are alkylated using an alkylating agent selected from the group comprising iodoacetamide, iodoacetic acid, N-ethylmaleimide, and 4-vinylpyridine. In one embodiment, the alkylating agent is preferably iodoacetamide. In one embodiment, the step of sequencing the RNAs extracted from the cell sample comprises: (i) reverse transcription of RNAs, thus obtaining a cDNA library, (ii) optionally, amplify said cDNA library, and (iii) sequence said cDNA library, preferably by next generation sequencing (NGS), deep sequencing or targeted sequencing of customized sequences. In one embodiment, the step of sequencing the RNAs extracted from the cell sample comprises: (i) reverse transcription of RNAs, thus obtaining a cDNA library, (ii) optionally, amplify said cDNA library, and (iii) sequence said cDNA library by next generation sequencing (NGS). In one embodiment, reverse transcription of RNAs converts uridine (U) to cytidine (C) instead of uridine (U) to thymidine (T) when the sample was cultured in the presence of an RNA labeling agent and / or when the labeled RNAs were subjected to nucleotide conversion. In one embodiment, reverse transcription of RNAs converts uridine (U) to cytidine (C) instead of uridine (U) to thymidine (T) when the cell sample was cultured in the presence of an RNA labeling agent. In one embodiment, the RNAs undergo first-strand synthesis with substitutions of adenine (A) for guanosine (G) and second-strand synthesis with substitutions of thymidine (T) for cytidine (C) following reverse transcription when the sample was cultured in the presence of an RNA labeling agent, preferably a thiol-labeled RNA precursor and / or when the labeled RNAs were further subjected to conditions that allowed nucleotide substitution. In one embodiment, the step of identifying at least one matching viral nucleic acid sequence that maps to at least one sequence read from the first set of sequence reads comprises: (i) optionally, filter the first set of sequence reads, (ii) optionally, assemble the sequence reads into contigs, (iii) aligning the sequence reads or contigs with a database comprising viral nucleic acid sequences, (iv) identify at least one matching viral nucleic acid sequence that maps to at least one sequence read or contigo, and (v) Optionally, realign the sequence reads or contigs with the matching viral nucleic acid sequence identified in step (iv), thereby determining a consensus viral nucleic acid sequence, thus identifying at least one consensus viral nucleic acid sequence. In one embodiment, at least one matching microbial nucleic acid sequence is identified by: (i) optionally, filtering the first and / or second set of sequence reads, (ii) optionally, assembling the sequence reads into contigs, (iii) aligning sequence reads or contigs with a database comprising microbial nucleic acid sequences, (iv) the identification of at least one matching microbial nucleic acid sequence that maps to at least one sequence read or contigo, and (v) Optionally, realigning the sequence reads or contigs with the matching microbial nucleic acid sequence identified in step (iv), thereby determining a consensus microbial nucleic acid sequence, where the consensus microbial nucleic acid sequence corresponds to the coincident microbial nucleic acid sequence. In one embodiment, at least one matching microbial nucleic acid sequence belongs to a living microbe if: - the number and / or rate of TC substitutions in sequence reads that map against at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than the number and / or rate of TC substitutions in the control sequence; and / or - The number and / or rate of TC substitutions in sequence reads that map to at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than the number and / or rate of TA and / or TG substitutions in the same sequence reads. In one embodiment, the at least one matching viral nucleic acid sequence belongs to an infectious virus if the number of TC substitutions in sequence reads that map to at least one matching viral nucleic acid sequence identified in the first set of sequence reads is greater than in the second set of sequence reads. In one embodiment, at least one matching microbial nucleic acid sequence belongs to a living microbe if: - the number and / or rate of TC substitutions in second-strand synthesis at sequence reads that map to at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than the number and / or rate of TC substitutions in second-strand synthesis at the control sequence; and / or - The number and / or rate of TC substitutions in second strand synthesis at sequence reads that map to at least one matching microbial nucleic acid sequence in the first set of sequence reads is greater than the number and / or rate of TA substitutions in second strand synthesis and TG substitutions in second strand synthesis at the same sequence reads. In one embodiment, the in vitro method comprises the following steps: (1) (i) sequencing the total unlabeled RNAs extracted from the cell sample, wherein the total unlabeled RNAs are obtained by culturing the cell sample in the absence of an RNA labeling agent, thereby obtaining a plurality of sequence reads, (ii) identify at least one matching viral nucleic acid sequence that maps to the sequence reads, and (iii) determine the number of substituted nucleotides in the sequence reads that are mapped against at least one identified matching viral nucleic acid sequence; and (2) (i) sequencing the labeled total RNAs extracted from the cell sample, wherein the labeled total RNAs are obtained by culturing the cell sample in the presence of a labeling agent, thereby obtaining a plurality of sequence reads, (ii) determine the number of substituted nucleotides in the sequence reads that are mapped against at least one matching viral nucleic acid sequence, (3) compare the number of substituted nucleotides determined in (1)(iii) and (2)(ii), and (4) conclude that the matching viral nucleic acid sequence belongs to an infectious virus if the number of substituted nucleotides determined in (2)(ii) is greater than the number of substituted nucleotides determined in (1)(iii). In one embodiment, the microbe is selected from the group comprising viruses, bacteria, archaea, fungi, and protozoa. The present invention also relates to an in vitro method for diagnosing a microbial infection in a subject, comprising: (a) provide a sample of the subject, (b) perform the claimed in vitro method to distinguish between live and dead microbes in said sample, and (c) diagnose that the subject has a microbial infection if at least one identified matching microbial nucleic acid sequence belongs to a live microbe. The present invention also relates to an in vitro method for diagnosing a viral infection in a subject, comprising: (a) provide a cell sample from the subject (b) performing the in vitro method for distinguishing between infectious and non-infectious viral nucleic acid sequences in a cell sample according to the present invention in said cell sample, and (c) diagnose that the subject has a viral infection if at least one matching viral nucleic acid sequence identified belongs to an infectious virus. This report also describes a method for treating a subject affected with a microbial infection, comprising: (a) provide a sample of the subject, (b) perform the in vitro method to distinguish between live and dead microbes in said sample, (c) diagnose that the subject has a microbial infection if at least one matching microbial nucleic acid sequence identified belongs to a living microbe, and (d) treat the subject if said subject was diagnosed to have a microbial infection in stage c). The present invention also relates to a method for assessing the risk of microbial contamination in a sample, comprising: (a) provide a sample, (b) perform the claimed in vitro method to distinguish between live and dead microbes in said sample, and (c) conclude that the sample is at risk of contamination if the at least one matching microbial nucleic acid sequence identified belongs to a live microbe. Definitions In the present invention, the following terms have the following meanings: "Around" or "approximately," as used herein, may mean within an acceptable range of error for the particular value determined by a person skilled in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measuring system. For example, "approximately" may mean within one standard deviation or more, in accordance with practice in the art. Alternatively, "approximately" preceding a number means plus or minus 10% of the value of that number. Alternatively, particularly with regard to biological systems or processes, the term may mean within an order of magnitude, within five times, and more preferably within two times, of a value.When particular values ​​are described in the application and claims, unless otherwise stated, the term "approximately" means that the value is to be assumed to be within an acceptable range of error for the particular value. "Amplification," as used herein, refers to the process of producing multiple copies, i.e., at least two copies, of a desired template sequence. Techniques for amplifying nucleic acids are well known to those skilled in the art and include both specific amplification methods and random amplification methods. "Biological specimen," as used herein, refers to any specimen obtained, obtainable, or otherwise derived from a subject. "Biological specimens" encompass "solid tissue specimens" and "fluid specimens." The term "solid tissue specimen" refers herein to a solid specimen of tissue isolated from any part of the body. Tissue specimens comprise cells that are not disaggregated and occur in large clusters. Examples of tissue specimens include, but are not limited to, biopsy specimens and autopsy specimens. The term "fluid specimen" refers herein to a specimen of fluid isolated from any part of the body.Examples of fluid samples include, but are not limited to, serum, plasma, whole blood, urine, saliva, breast milk, tears, sweat, joint fluid, cerebrospinal fluid, lymphatic fluid, sputum, mucus, pelvic fluid, synovial fluid, body cavity washings, eye brushings, skin scrapings, buccal swabs, vaginal swabs, vaginal cytology, rectal swabs, aspirates, semen, vaginal fluid, ascitic fluid, and amniotic fluid. In a preferred embodiment, a "biological sample" is a cell sample, i.e., any biological sample as described herein, comprising at least one cell. "cDNA library", as used in this dissertation, refers to a library composed of complementary DNAs that are reverse transcribed from mRNAs. "Contigo," as used in this document, refers to overlapping sequence reads. Typically, a contigo is a continuous nucleic acid sequence resulting from the reassembly of the small DNA fragments (sequence reads) generated by next-generation sequencing. In practice, the assembly software will search for pairs of overlapping sequence reads. Optionally, the assembly software can access nucleic acid or amino acid databases to "align and verify," thereby validating the sequence read assembly. Assembling sequences from pairs of overlapping sequence reads produces a longer contiguous read (contigo) of sequenced DNA. By repeating this process several times, initially with the first short pairs of sequence reads, then using progressively longer pairs resulting from previous assembly, longer contigs can be determined. "Deep sequencing," as used in this dissertation, refers to the sequencing of nucleic acids to a depth that allows each base to be read multiple times from independent nucleic acid molecules (e.g., a large number of template molecules are sequenced relative to the sequence length) and enables the simultaneous sequencing of thousands of molecules, thus allowing for the characterization of complex assemblies of nucleic acid molecules and increasing the accuracy of sequencing. Deep transcriptome sequencing, also known as RNA-Seq, provides both the sequence and frequency of the RNA molecule species present at any particular time in a given sample. "Expected value" or "e-value," as used herein, refers to a parameter that describes the number of sequence matches one might expect to see "by chance" when aligning sequence reads or contigs in a database of a particular size. The e-value decreases exponentially as the score of the matches increases. Essentially, the e-value describes the random background noise. For example, an e-value of 1 assigned to a match can be interpreted as meaning that, in a database of the current size, one might expect to see 1 match with a similar score simply by chance. The smaller the e-value, or the closer it is to zero, the more "significant" the match is. "Live microbe," as used herein, refers to any microbe that is transcriptionally active, i.e., capable of synthesizing RNAs, either on its own (such as, for example, in the case of bacteria, archaea, fungi, or protozoa) or after infecting a host cell (such as, for example, in the case of viruses). Live microbes include dormant microbes, i.e., inactive microbes that can be reactivated. It should be noted that dormant microbes, although inactive, exhibit basal transcriptional activity. Conversely, a "dead microbe" refers to a microbe that is not transcriptionally active, i.e., for which no transcribed genes are detected. In the context of the present invention, the method aims to distinguish between live microbes and inert microbial nucleic acid sequences, whether free in the sample or contained within a so-called dead microbe."Lysate", as used in this memory, refers to a liquid or solid collection of materials after a lysis procedure. "Lysis" (noun) or "lyse" (verb), as used herein, refers to the alteration of (or the act of altering) a biological sample to gain access to materials that would otherwise be inaccessible. When the biological sample is a cell, lysis refers to breaking the cell membrane, causing the cell contents to spill out. Methods of lysis are well known to those skilled in the art and include, but are not limited to, proteolytic lysis, chemical lysis, thermal lysis, mechanical lysis, and osmotic lysis. "Nucleic acid sequence primer" or "primer", as used herein, refers to an oligonucleotide that is capable of hybridizing or pairing with a nucleic acid sequence and serving as an initiation site for nucleotide polymerization under appropriate conditions, such as the presence of nucleoside triphosphate and a polymerization enzyme, such as DNA or RNA polymerase or reverse transcriptase, in an appropriate buffer and at a suitable temperature. "Oligonucleotide," as used herein, refers to a polymer of nucleotides, generally a single-stranded nucleotide polymer. In some embodiments, the oligonucleotide comprises from 2 to 500 nucleotides, preferably from 10 to 150 nucleotides, and preferably from 20 to 100 nucleotides. Oligonucleotides may be synthetic or enzymatically prepared. In some embodiments, oligonucleotides may comprise ribonucleotide monomers, deoxyribonucleotide monomers, or a mixture of both. "Microbe" or "microorganism", as used herein, refers to an organism, such as, without limitation, a virus, bacterium, archaea, fungus, or protozoan, that is likely to infect or contaminate a sample; and / or to generate, transmit, or carry a disease in a subject. "Polymerase chain reaction" or "PCR", as used herein, encompasses methods including, but not limited to, allele-specific PCR, asymmetric PCR, hot-start PCR, intersequence-specific PCR, methylation-specific PCR, mini-primer PCR, multiplex ligation-dependent probe amplification, multiplex PCR, quantitative PCR, nested PCR1, reverse transcription PCR and / or touchdown PCR.DNA polymerase enzymes suitable for amplifying nucleic acids include, but are not limited to, Stoffel fragment of Taq polymerase, Taq polymerase, DNA polymerase Advantage, AmpliTaq, AmpliTaq Gold, Titanium Taq polymerase, KlenTaq DNA polymerase, Platinum Taq polymerase, Accuprime Taq polymerase, Pfu polymerase, Pfu polymerase turbo, Vent polymerase, Vent exopolymerase, Pwo polymerase, 9Nm DNA polymerase, Therminator, Pfx DNA polymerase, Expand DNA polymerase, rTth DNA polymerase, DyNAzyme-EXT polymerase, Klenow fragment, DNA polymerase I, T7 polymerase, SequenaseTM, Tfi polymerase, T4 DNA polymerase, Bst polymerase, Bca polymerase, BSU polymerase, phi-29 DNA polymerase, and DNA polymerase Beta, or modified versions thereof. In one embodiment, the DNA polymerase has 3'-5' proofreading, i.e., exonuclease activity.In one embodiment, DNA polymerase has strand displacement activity; that is, DNA polymerase causes the dissociation of a paired nucleic acid from its complementary strand in a 5' to 3' direction, in conjunction with, and near, template-dependent nucleic acid synthesis. DNA polymerases such as E. coli DNA polymerase I, the Klenow fragment of DNA polymerase I, bacteriophage T7 or T5 DNA polymerase, and HIV reverse transcriptase are enzymes that possess both polymerase and strand displacement activity. Agents such as helicases can be used in conjunction with inducers that do not possess strand displacement activity to produce the strand displacement effect, i.e., the displacement of one nucleic acid coupled to the synthesis of another nucleic acid of the same sequence. Similarly, proteins such as RecA or the E. coli single-strand binding protein can also induce strand displacement.coli or from another organism could be used to produce or promote strand displacement, in conjunction with other inducing agents (Kornberg & Baker (1992). Chapters 4-6. In DNA replication (2nd ed., pp.113-225). New York: WH Freeman). "Random amplification techniques," as used herein, means the amplification of any nucleic acid present in a biological sample, regardless of its sequence. This includes, but is not limited to, multiple displacement amplification (MDA), random PCR, random amplification of polymorphic DNA (RAPD), or multiple hybridization-based loop-forming amplification (MALBAC) cycles. “Transcriptionally active microbial nucleic acid sequence,” as used herein, refers to a nucleic acid sequence belonging to a living microbe, i.e., a microbe expressing microbial genes, even if the microbe is dormant. Conversely, “inert microbial nucleic acid sequence,” as used herein, refers to a nucleic acid sequence belonging to an inactive microbe, i.e., a dead microbe. The term “inert microbial nucleic acid sequence” further refers to free nucleic acid sequences, i.e., outside of a microbe, whether intact or degraded / fragmented, but in either case, not active. "Transcriptionally active viral nucleic acid sequence," as used herein, refers to a nucleic acid sequence belonging to an active virus, i.e., a live virus expressing viral genes, even if the viral cycle is abortive, i.e., does not lead to the formation of virus particles (as in the case, for example, of latent viruses). Conversely, "inert viral nucleic acid sequence," as used herein, refers to a nucleic acid sequence belonging to an inactive virus, i.e., a dead virus or nucleic acids not associated with viral particles. "Reverse transcription," as used herein, refers to the replication of RNA using an RNA-directed DNA polymerase (reverse transcriptase, abbreviated "RT") to produce complementary DNA strands ("cDNA"). Reverse transcription of RNAs can be carried out using techniques well known to those skilled in the art, employing a reverse transcriptase enzyme and a mixture of four deoxyribonucleotide triphosphates (dNTPs), namely deoxyadenosine triphosphate (dATP), deoxycytidine triphosphate (dCTP), deoxyguanosine triphosphate (dGTP), and (deoxy)thymidine triphosphate (dTTP). In some embodiments, reverse transcription of RNAs comprises a first step of first-strand cDNA synthesis. Methods for first-strand cDNA synthesis are well known to those skilled in the art. First-strand cDNA synthesis reactions can use a combination of sequence-specific primers, oligo (dT) primers, or random primers. Examples of reverse transcriptase enzymes include, but are not limited to, M-MLV reverse transcriptase, SuperScript II (Invitrogen), SuperScript III (Invitrogen), SuperScript IV (Invitrogen), Maxima (ThermoFisher Scientific), ProtoScript II (New England Biolabs), and PrimeScript (ClonTech). "Sequence read," as used in this memory, refers to a sequence or data representing a sequence of nucleotide bases—in other words, the order of monomers in a nucleic acid sequence, which is determined by a sequencer. "Sequencer" or "sequencer," as used herein, refers to the apparatus used to determine the order of constituents in a biological polymer, such as a nucleic acid or a protein. Preferably, sequencers, within the meaning of the present invention, refer to next-generation sequencers. A "next-generation sequencer" may include several different sequencers based on different technologies, such as Illumina sequencing, Roche 454 sequencing, Ion Torrent sequencing, SOLiD sequencing, and the like. "Subject," as used herein, refers to a mammal, preferably a human being. In one embodiment, the subject is a pet, including, without limitation, a dog, cat, guinea pig, hamster, rat, mouse, ferret, rabbit, bird, or amphibian. In one embodiment, a subject may be a "patient," that is, a woman or man, an adult or a child, who is awaiting, or is receiving, medical care, or was / is / will be the subject of a medical procedure, or is being monitored for the development of a disease, disorder, or condition, particularly a viral, bacterial, archaeal, fungal, or protozoal infection. "Template" or "template sequence," as used herein, refers to a nucleic acid sequence for which amplification is desired. A template may comprise either DNA or RNA. In one embodiment, the template sequence is known. In another embodiment, the template sequence is unknown. The terminology used herein is intended to describe particular cases only and is not intended to be exhaustive. As used herein, the singular forms "a," "one," and "the" also include the plural forms, unless the context clearly indicates otherwise. Furthermore, to the extent that the terms "including," "containing," "having," "having," or variants thereof are used in the detailed description and / or in the claims, such terms are intended to be inclusive in a manner similar to the term "comprising." Detailed description The present invention relates to a method for distinguishing between live and dead microbes in a sample, preferably a cell sample. In particular, the method according to the present invention is based on distinguishing between transcriptionally active and inert microbial nucleic acid sequences in a sample, preferably a cell sample. The method of the present invention is particularly useful for distinguishing between (1) dead microbes, such as viruses, bacteria, archaea, fungi, or protozoa, and inert microbial sequences; and (2) active (or transcriptionally active) and latent microbes, such as viruses, bacteria, archaea, fungi, or protozoa. Therefore, it should be understood that the present method is readily applicable to the detection of any type of microbe and the distinction between live and dead microbes. In one embodiment, the microbe is selected from viruses, bacteria, archaea, fungi, and protozoa. In one realization, the microbe is a virus. Viruses are small infectious agents that replicate inside living cells and infect all kinds of life forms. The Baltimore classification of viruses is based on the mechanism of mRNA production. Viruses must generate mRNAs from their genomes to produce proteins and replicate, but different mechanisms are used to achieve this in each virus family. Viral genomes can be RNA or DNA, single-stranded (ss) or double-stranded (ds), and may or may not use reverse transcriptase. In addition, ssRNA viruses can be positive-sense (+) or negative-sense (-). This classification places viruses into seven groups: I. dsDNA viruses (such as, for example, adenovirus, herpesvirus or poxvirus), II. (+) ssDNA viruses (such as, for example, anelloviridae, bidnaviridae, circoviridae, geminiviridae, genomoviridae, inoviridae, microviridae, nanoviridae, parvoviridae, smacoviridae or spiraviridae), III. dsRNA viruses (such as, for example, reovirus), IV. (+) ssRNA viruses (such as, for example, picornavirus or togavirus), V. (-) ssRNA viruses (such as, for example, orthomyxovirus, rhabdovirus), VI. (+) ssRNA-RT viruses with a DNA life cycle intermediate (such as, for example, retroviruses), VIII. dsDNA-RT viruses, DNA with an RNA life cycle intermediate (such as, for example, hepadnaviruses). In one embodiment, the method according to the present invention is for distinguishing samples, preferably cell samples, containing transcriptionally active and inert viral nucleic acid sequences belonging to viruses selected from the group comprising or consisting of dsDNA viruses, (+) ssDNA viruses, dsRNA viruses, (+) ssRNA viruses, (-) ssRNA viruses, (+) ssRNA-RT viruses, and dsDNA-RT viruses. In one embodiment, the method according to the present invention is for distinguishing samples, preferably cell samples, containing transcriptionally active and inert viral nucleic acid sequences belonging to viruses selected from those described in the database of the International Committee on Taxonomy of Viruses (ICTV), preferably in ICTV Master Species List 2018b.v2 of May 31, 2019 (MSL#34). In one realization, the microbe is a bacterium. In one embodiment, the method according to the present invention is for distinguishing samples, preferably cell samples, containing transcriptionally active and inert bacterial nucleic acid sequences belonging to bacteria. Examples of bacteria include, but are not limited to, bacteria belonging to the families Acidobacteria, Actinobacteria, Aquificae, Bacteroidetes, Chlamydiae, Chlorobi, Chloroflexi, Chr and siogenetes, Cyanobacteria, Deferribacteres, Deinococcus-Thermus, Dictyoglomi, Fibrobacteres, Firmicutes, Fusobacteria, Gemmatimonadetes, Nitrospirae, Planctomycetes, Proteobacteria, Spirochaetes, Thermodesulfobacteria, Thermomicrobia, Thermotogae and Verrucomicrobia; including the subtaxa thereof. In a preferred embodiment, the bacterium is a Firmicute, preferably of the class Bacilli, more preferably of the subclass Mollicute, even more preferably of the genus Mycoplasma. In one realization, the microbe is an archaeon. In one embodiment, the method according to the present invention is for distinguishing samples, preferably cell samples, containing transcriptionally active and inert archaeal nucleic acid sequences belonging to archaea. Examples of archaea include, but are not limited to, archaea belonging to the families Aenigmarchaeota, Aigarchaeota, Altiarchaeia, Archaeoglobi, Asgardarchaeota, Bathyarchaeota, Crenarchaeota, Diapherotrites, Geoarchaeota, Halobacteria, Korarchaeota, Methanobacteria, Methanococci, Methanomicrobia, Methanopyri, Nanoarchaeota, Nanohaloarchaea, Parvarchaeota, Thalassoarchaeia, Thaumarchaeota, Thermococci, Thermoplasmata, and Woesearchaeota; including their subtaxa. In one realization, the microbe is a fungus. Fungi are eukaryotic organisms, including yeasts and molds, characterized by having chitin in their cell walls. In one embodiment, the method according to the present invention is for distinguishing samples, preferably cell samples, containing transcriptionally active and inert fungal nucleic acid sequences belonging to fungi. Examples of fungi include, but are not limited to, fungi belonging to the families Ascomycota, Basidiomycota, Entorrhizomycota, Glomeromycota, Mucoromycota, Calcarisporiellomycota, Mortierellomycota, Kickxellomycota, Entomophthoromycota, Olpidiomycota, Basidiobolomycota, Neocallimastigomycota, Chytridiomycota, and Blastocladiomycota; including the subtaxa thereof. In one realization, the microbe is a protozoan. In one embodiment, the method according to the present invention is for distinguishing samples, preferably cell samples, containing transcriptionally active and inert protozoan nucleic acid sequences belonging to protozoa. Examples of protozoa include, but are not limited to, protozoa belonging to the families Euglenozoa, Amoebozoa, Choanozoa, Microsporidia, and Sulcozoa; including the subtaxa thereof. In one embodiment, the sample is a biological sample. Examples of suitable biological samples include, but are not limited to, solid tissue samples and fluid samples. In one embodiment, the biological sample was obtained by sampling using minimally invasive or non-invasive approaches. In one embodiment, the biological sample was previously obtained from the subject, i.e., the methods according to the present invention are in vitro methods. In one embodiment, the biological sample is a cell sample. By "cell sample", we mean any biological sample as described herein, comprising at least one cell. In one embodiment, the biological sample is cultured. Therefore, the term "biological sample" includes cell or tissue cultures, preferably in vitro cell or tissue cultures, such as, for example, a cell or tissue culture isolated from a cytology sample, a tissue sample, or a biological fluid sample. In one embodiment, the method according to the present invention comprises an initial step of culturing the sample, preferably the cell sample, preferably culturing the cell sample in vitro. The cultivation of cell samples, particularly the cultivation of cells or tissues isolated from a cytology sample, a tissue sample, or a biological fluid sample, is well understood by those skilled in the technique. In one embodiment, the cell sample is seeded at a density that allows for exponential growth. In another embodiment, the biological sample is seeded with a confluence of approximately 50% to approximately 80%. The initial sample culture step is necessary (1) to allow the potential microbe (such as a virus, bacterium, archaeon, fungus, or protozoan) in the sample to transcribe RNAs (which is the key biological process used in the present method to distinguish between live and dead microbes) and (2) for metabolic labeling. If the microbe to be detected is not a self-replicating microbe (such as, for example, a virus, or some bacteria such as Mycoplasma), the sample will be a cell sample to allow the potential microbe to infect such cells and replicate. If the microbe is a self-replicating microbe (i.e., the microbe comprises or is itself a cell, such as, typically, a bacterium, archaeon, fungus, or protozoan), it is not mandatory for the sample to be a cell sample. In one embodiment, the sample is not a biological sample.In this case, the sample may be, for example, an environmental sample such as water, soil, air, and the like. Other examples of non-biological samples include food samples. Other examples of non-biological samples include the preservation medium. In one embodiment, the method for distinguishing between live and dead microbes, preferably viruses, bacteria, archaea, fungi, or protozoa, in a sample, preferably a cell sample, comprising distinguishing between transcriptionally active and inert microbial nucleic acid sequences, preferably from viruses, bacteria, archaea, fungi, or protozoa, in the sample, preferably the cell sample, comprises the steps of: (a) sequencing a first set of RNAs extracted from the sample, preferably from the cell sample, wherein the first set of RNAs is obtained by culturing the sample, preferably the cell sample, in the presence of an RNA labeling agent and further subjecting the extracted RNAs to conditions that allow nucleotide substitution; thus obtaining a first set of sequence reads; (b) compare the number of substituted nucleotides in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, with a control sequence; and (c) conclude that the at least one matching microbial sequence, preferably viral, bacterial, archaeal, fungal or protozoan, belongs to a living microbe, preferably virus, bacteria, archaea, fungus or rotizozoan, if the number of substituted nucleotides in the sequence reads that are mapped against said at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, in the set of sequence reads is greater than the number of randomly substituted nucleotides in the control sequence. The method of the invention is carried out under conditions that cause a reverse transcriptase enzyme to make errors (i.e., incorporate mismatched nucleotides), which are detected and compared by reference to a standard, consensus-based method of reverse transcription. These conditions include the presence, in the RNAs to be reverse transcribed, of tags such as thiol tags and / or nucleotide modifications using nucleotide substitution techniques. Examples of these conditions are further detailed below. As used in this memory, the term "mismatched nucleotide" refers to a nucleotide that is incorporated in a form of base pairing other than Watson-Crick. In one embodiment, the error rate of the reverse transcriptase enzyme is not linked to the fidelity of the reverse transcriptase enzyme. As used in this dissertation, the term "fidelity" with reference to a reverse transcriptase enzyme refers to the sequence accuracy maintained by the enzyme during the synthesis of DNA from RNA. Fidelity is inversely correlated with the error rate of reverse transcription. In one embodiment, the method according to the present invention comprises a sequencing step of a first set of RNAs extracted from the sample, preferably from the cell sample. In one embodiment, the method according to the present invention comprises a sequencing step of a first set of total RNAs extracted from the sample, preferably from the cell sample. In one embodiment, the method according to the present invention comprises a sequencing step of a first set of total messenger RNAs (mRNAs) extracted from the sample, preferably the cell sample. In one embodiment, the sequencing step of a first set of RNAs extracted from the sample, preferably from the cell sample, comprises one or more or all of the substeps of labeling the RNAs, lysing the cells, extracting the RNAs, substituting the nucleotides in the labeled RNAs, generating a cDNA library, amplifying the cDNA library, and sequencing the cDNA library. RNA labeling is normally performed in culture, during in vitro transcription, by adding a tag to the culture medium that is incorporated into the RNA transcripts, thus obtaining labeled RNAs. Alternatively, or additionally, RNA labeling can also be performed in culture without adding a tag that will be incorporated into the RNA transcripts, in cases where the sample from which the RNAs are extracted already contains such a tag, as will be detailed later. By "RNA transcript" is meant any newly synthesized RNA molecule. The labeling of RNA transcripts can be carried out using techniques well known to those skilled in the technique. Such techniques include, but are not limited to, those described in Schulz & Rentmeister (2014. Chembiochem. 15 (16): 2342-7), Huang & Yu (2013. Curr Protoc Mol Biol. Chapter 4: Unit 4.15) and Liu et al. (2016. Bioessays.38 (2): 192-200). Preferably, metabolic labeling of RNAs alters Watson-Crick base pairing and causes reverse transcription of the labeled RNAs to substitute nucleotides, i.e., to pair a labeled nucleotide with a non-Watson-Crick nucleotide. For example, a labeled uridine may pair with a guanosine instead of an adenine during first-strand synthesis. Consequently, a cytosine will be incorporated during second-strand synthesis, ultimately leading to a thymidine (T) to cytosine (C) substitution with respect to the initial nucleic acid sequence. In one embodiment, metabolic labeling of RNA transcripts is carried out by thiol labeling. Thiol labeling is a well-known technique that involves incorporating thiol-labeled RNA precursors into newly synthesized RNAs. Such techniques include, but are not limited to, those described in Clear et al. (2005).Nat Biotechnol.23(2):232–7; (2009. Nat. Methods 6(6) : 439–41), Garibaldi et al. (2017. Methods Mol Biol. 1648:169–176); (2017. Methods 120:39-48) and Herzog et al. (2017. Nat. Methods 14(12): 1198–1204). Examples of suitable thiol-labeled RNA precursors include, but are not limited to, 4-thiouridine, 2-thiouridine, 2, 4-dithiouridine, 2-thio-4-deoxyuridine, 5-carbethoxy-2-thiouridine, 5-carboxy-2-thiouridine, 5- (n-propyl); -2-thiouridine, 6-methyl-2-thiouridine, 6-(n-propyl)-2-thiouridine, 6-thioguanosine, 6-methylthioguanosine, 6-thioinosine and 6-methylthioinosine. In one embodiment, the thiol-labeled RNA precursor is a thiouridine derivative, preferably selected from the group comprising or consisting of 4-thiouridine (4sU), 2-thiouridine (2sU), 2,4-dithiouridine (2,4sU), 2-thio-4-deoxyuridine, 5-carbetoxy-2-thiouridine, 5-carboxy-2-thiouridine, 5-(n-propyl)-2-thiouridine, 6-methyl-2-thiouridine, and 6-(n-propyl)-2-thiouridine. In a preferred embodiment, the thiol-labeled RNA precursor is 4-thiouridine (sometimes abbreviated as "4sU" or "s4u"). In one embodiment, the thiol-labeled RNA precursor is delivered to the sample, preferably the cell sample, from the culture medium. In another embodiment, the thiol-labeled RNA precursor is added to the culture medium. Thiol-labeled RNA precursors, when added to the culture medium, can be imported into sample cells, preferably into cell samples (such as virus-infected cells or microbes that are themselves cells, e.g., bacteria, fungi, or protozoa), via specific transporters called Nucleoside Equilibrating Transporters (NETs). These transporters are almost ubiquitous in metazoans. In particular, 4-thiouridine can be imported into cells via Nucleoside Equilibrating Transporter 1 (NET1), encoded in humans by the SLC29A1 gene. In one embodiment, the new thiol-labeled RNA precursor is added to the culture every hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours or more. In one embodiment, the sample, preferably the cell sample, is cultured in a culture medium containing a thiol-labeled RNA precursor for a period of time ranging from approximately 2 hours to approximately 15 hours, preferably from approximately 4 hours to approximately 12 hours, preferably from approximately 6 hours to approximately 10 hours. In one embodiment, the sample, preferably the cell sample, is cultured in a culture medium containing the thiol-labeled RNA precursor for a first time period and a second time period, which comprises the addition of fresh thiol-labeled RNA precursor between the first and second time periods. In one embodiment, the first time period ranges from approximately 1 hour to approximately 10 hours, preferably from approximately 2 hours to approximately 8 hours, preferably from approximately 4 hours to approximately 6 hours, and preferably is approximately 6 hours. In one embodiment, the second time period ranges from approximately 1 hour to approximately 6 hours, preferably from approximately 2 hours to approximately 5 hours, preferably from approximately 3 hours to approximately 4 hours, and preferably is approximately 3 hours. Preferably, the thiol-labeled RNA precursor is non-toxic to the sample, preferably to the cell sample. In one embodiment, the thiol-labeled RNA precursor is delivered to the sample, preferably the cell sample, at a concentration that does not compromise cell viability. In one embodiment, a "concentration that does not compromise cell viability" ranges from approximately 1 µM to approximately 2 mM final, preferably from approximately 10 µM to approximately 1.5 mM final, preferably from approximately 100 µM to approximately 1 mM final, preferably from approximately 250 µM to approximately 1 mM final, preferably from approximately 500 µM to approximately 1 mM final, preferably from approximately 700 µM to approximately 900 µM final, preferably approximately 800 µM final of thiol-labeled RNA precursor. In one embodiment, the thiol-labeled RNA precursor is supplied to the sample, preferably the cell sample, directly from the microbe, preferably a virus, bacterium, archaeon, fungus, or protozoan. In another embodiment, the thiol-labeled RNA precursor is not added to the culture medium. Certain microbes are able to catalyze the biosynthesis of thiol-labeled RNA precursors, using enzymes such as, without limitation, 4-thiouridine synthetase (ThiI) (Müller et al., 1998. Nucleic Acids Res. 26 (11): 2606-10) or 2-thiouridine synthetase (MnmA) (Kambampati & Lauhon, 2003. Biochemistr y. 42 (4): 1109-1; Black & Dos Santos, 2015. J Bacteriol. 197 (11): 1952-62). In a specific embodiment where the sample comprises a microbe capable of catalyzing the biosynthesis of thiol-labeled RNA precursors, it may be advantageous to additionally supply the thiol-labeled RNA precursor to the sample, preferably the cell sample, from the culture medium. In this embodiment, the thiol-labeled RNA precursor supplied additionally from the culture medium may be the same as, or may be different from, the thiol-labeled RNA precursor supplied by the microbe. In this embodiment, the additional thiol-labeled RNA precursor supplied from the culture medium can be supplied as previously described herein (with regard to, but not limited to, the addition of the new thiol-labeled RNA precursor, concentration, time periods, etc.). Thiol-labeled RNA precursors and thiol-labeled RNAs are light-sensitive and prone to oxidation. Therefore, in one embodiment, RNA labeling is carried out in the dark or, at a minimum, under light protection. In another embodiment, RNA labeling is carried out in the presence of a reducing agent. Examples of suitable reducing agents include, but are not limited to, β-mercaptoethanol, dithiothreitol (DTT), tris(2-carboxyethyl)phosphine (TCEP), cysteine, N-acetylcysteine, cysteamine, sodium salt of 2-mercaptoethanesulfonic acid, dithioerythritol (DTE), and bis(2-mercaptoethyl)sulfone. Normally, lysing the sample cells, preferably the cell sample itself, aims to release the cell contents, particularly their RNAs. In one embodiment, lysing the sample cells may be optional, such as when the RNA content of the cells has already been released into the sample. In one embodiment, the cells are lysed by chemical lysis, mechanical lysis, proteolytic lysis, thermal lysis, and / or osmotic lysis. These cell lysis techniques are well known to those skilled in the art. In one embodiment, the cells are lysed in a suitable lysis solution. Lysis solutions may comprise various components, including salts, buffers, detergents, reducing agents, protease inhibitors, nuclease inhibitors, glycerol, sugars, and the like. A person skilled in the art has knowledge of lysis solutions and can easily design and / or select the appropriate lysis solution depending on the type of cells to be lysed. In one embodiment, cell lysis is performed in the presence of a ribonuclease (RNase) inhibitor. Occasionally, RNases may be released from cells during cell lysis, or they may be purified together with isolated RNA and thus compromise subsequent applications. Such RNase contamination can also be introduced via tips, tubes, and other reagents used in the procedures. RNase inhibitors are commercially available. Since thiol-labeled RNAs are light-sensitive and prone to oxidation, in one embodiment, cell lysis is carried out in the dark or, at a minimum, under protection from light. In another embodiment, cell lysis is carried out in the presence of a reducing agent. Examples of suitable reducing agents include, but are not limited to, β-mercaptoethanol, dithiothreitol (DTT), tris(2-carboxyethyl)phosphine (TCEP), cysteine, N-acetylcysteine, cysteamine, sodium salt of 2-mercaptoethanesulfonic acid, dithioerythritol (DTE), and bis(2-mercaptoethyl)sulfone. RNA extraction can be performed using techniques well-known to those skilled in the art. These techniques include, but are not limited to, chloroform-isoamyl alcohol extraction, phenol-chloroform extraction, alkaline extraction, guanidinium thiocyanate-phenol-chloroform extraction, anion exchange resin binding, silica matrices, glass particles, diatomaceous earth, magnetic particles made from various synthetic polymers, biopolymers, porous glass, and inorganic magnetic materials. Preferably, the extraction of RNAs is carried out by chloroform-isoamyl alcohol extraction, using, for example, chloroform:isoamyl alcohol 24:1. In one embodiment, the extracted RNAs are further precipitated. The precipitation of the RNAs can be carried out using techniques well known to those skilled in the art. Such techniques include isopropanol-ethanol precipitation, the TRIzol method (Chomczynski, 1993. Biotechniques.15 (3): 532-4, 536-7), and the Pine Tree method (Chang et al., 1993. Plant Mol. Biol. Report.11 (2): 113-116). Preferably, RNA precipitation is carried out by isopropanol-ethanol precipitation. Since thiol-labeled RNAs are light-sensitive and prone to oxidation, in one embodiment, RNA extraction is performed in the dark or, at a minimum, under light protection. In another embodiment, RNA extraction is carried out in the presence of a reducing agent. Examples of suitable reducing agents include, but are not limited to, β-mercaptoethanol, dithiothreitol (DTT), tris(2-carboxyethyl)phosphine (TCEP), cysteine, N-acetylcysteine, cysteamine, sodium salt of 2-mercaptoethanesulfonic acid, dithioerythritol (DTE), and bis(2-mercaptoethyl)sulfone. In one embodiment, the labeled RNAs undergo nucleotide substitution. In one embodiment, the labeled RNAs are subjected to conditions that allow nucleotide substitution. The terms "substitution", "conversion", and "transformation" may be used interchangeably to refer to the incorporation of unpaired nucleotides. Nucleotide substitution in labeled RNAs is carried out by chemically modifying the labeled RNAs and then performing reverse transcription of these chemically modified labeled RNAs. Therefore, the conditions that allow nucleotide substitution include chemical modification of the labeled RNAs and reverse transcription of these chemically modified labeled RNAs. Preferably, nucleotide conversion methods allow for altering the Watson-Crick base pairing in the labeled RNAs and induce reverse transcription of the labeled RNAs during cDNA synthesis to incorporate unpaired nucleotides—that is, to pair a labeled nucleotide with a non-Watson-Crick nucleotide. For example, a labeled uridine (such as a thiol-labeled uridine) can pair with a guanosine (G) instead of an adenine (A) during the synthesis of the first strand of cDNA. Consequently, a cytosine will be incorporated during the synthesis of the second strand, ultimately leading to a thymidine (T) substitution for cytosine (C) with respect to the initial nucleic acid sequence. Therefore, nucleotide substitution can be defined as the equivalent nucleotide substitution of first chain synthesis (i.e., the nucleotide substitution that occurs after the synthesis of the first chain); or as the equivalent nucleotide substitution of second chain synthesis (i.e., the nucleotide substitution that occurs after the synthesis of the second chain). In one embodiment, the labeled RNAs undergo an A-to-G (AG) substitution during first-strand synthesis. In another embodiment, the labeled RNAs undergo a T-to-C (TC) substitution during second-strand synthesis. In these embodiments, a labeled uridine (U) in the labeled RNA is therefore converted to cytosine (C) instead of thymidine (T) in the corresponding cDNA. Unless explicitly stated otherwise, the nucleotide substitutions listed in this document correspond to nucleotide substitutions from the synthesis of the second chain. Suitable chemical modifications of labeled RNAs include, but are not limited to, alkylation, oxidative nucleophilic aromatic substitution, osmium-mediated transformation, or any other method known to those skilled in the art. Alkylation of labeled RNAs can be carried out using techniques well known to experts in the field. Such techniques include, but are not limited to, those described in Herzog et al. (2017. Nat. Methods 14 (12): 1198-1204). Preferably, the alkylation of the labeled RNAs is carried out after the extraction of the RNAs as detailed earlier in this document. In one embodiment, alkylation of the labeled RNAs is carried out using an alkylating agent. Examples of suitable alkylating agents include, but are not limited to, iodoacetamide, iodoacetic acid, N-ethylmaleimide, and 4-vinylpyridine. In a preferred embodiment, the alkylating agent is iodoacetamide. A non-limiting example of alkylation treatment of labeled RNAs comprises adding to the labeled RNAs: from approximately 1 mM final to approximately 20 mM final, preferably from approximately 5 mM final to approximately 15 mM final, preferably approximately 10 mM final of iodoacetamide in 100% ethanol, from approximately 10 mM final to approximately 100 mM final, preferably from approximately 25 mM final to approximately 75 mM final, preferably approximately 50 mM final of a buffer at pH 8.0 (such as, for example, a sodium phosphate (NaPO4) buffer), of approximately 25% v / v, approximately 75% v / v, preferably of approximately 40% v / v, approximately 60% v / v, preferably approximately 50% v / v of DMSO. Since thiol-labeled RNAs are light-sensitive, in one embodiment, RNA alkylation is carried out in the dark or, at least, with protection from light. In one embodiment, RNA alkylation is not carried out in the presence of a reducing agent. In one embodiment, the RNA alkylation is stopped, i.e., halted at the end of the alkylation treatment. The alkylation treatment can be stopped using techniques well known to those skilled in the art. In one embodiment, the arrest of RNA alkylation is carried out using a reducing agent. Examples of suitable reducing agents include, but are not limited to, β-mercaptoethanol, dithiothreitol (DTT), tris(2-carboxyethyl)phosphine (TCEP), cysteine, N-acetylcysteine, cysteamine, sodium salt of 2-mercaptoethanesulfonic acid, dithioerythritol (DTE), and bis(2-mercaptoethyl)sulfone. A non-limiting example of stopping RNA alkylation comprises adding to alkylated RNAs from approximately 1 mM final to approximately 100 mM final, preferably from approximately 10 mM final to approximately 50 mM final, preferably from approximately 10 mM final to approximately 30 mM final, preferably approximately 20 mM final of dithiothreitol (DTT). The oxidative nucleophilic aromatic substitution of labeled RNAs can be carried out using techniques well known to those skilled in the art. Such techniques include, but are not limited to, those described in Schofield et al. (2018. Nat. Methods 15 (3): 221-225). Preferably, oxidative nucleophilic aromatic substitution of the labeled RNAs is carried out after the extraction of the RNAs as detailed above in this dissertation. In one embodiment, oxidative nucleophilic aromatic substitution of labeled RNAs is carried out using an oxidant and a nucleophile. Examples of suitable oxidants include, but are not limited to, sodium per- and sodium iodate (NaIO4), metachloroperoxybenzoic acid (mCPBA), sodium iodate (NaIO3), and hydrogen peroxide (H2O2). In a preferred embodiment, the alkylating agent is sodium per and sodium odate (NaIO4). Examples of suitable nucleophiles include, but are not limited to, 2,2,2-trifluoroethanamine (TFEA), hydrazine, benzylamine, ammonia, methoxyamine, 1,1-dimethylethylenediamine, aniline, and 4-(trifluoromethyl)benzylamine. In a preferred embodiment, the nucleophile is 2,2,2-trifluoroethanamine (TFEA). Since thiol-labeled RNAs are light-sensitive, in one embodiment, the oxidative aromatic-nucleophilic substitution of the RNA is carried out in the dark or, at least, with protection from light. In one embodiment, the oxidative nucleophilic aromatic substitution of RNA does not take place in the presence of a reducing agent. In one embodiment, the oxidative nucleophilic aromatic substitution of RNA is stopped, i.e., the oxidative nucleophilic aromatic substitution treatment is halted at the end. The stopping of the oxidative nucleophilic aromatic substitution treatment can be carried out using techniques well known to the expert in the technique. Osmium-mediated transformation of labeled RNAs can be carried out using techniques well known to experts in the field. Such techniques include, but are not limited to, those described in Riml et al., (2017. Angew Chem Int. Ed. Engl.56 (43): 13479-13483). Preferably, osmium-mediated transformation of the labeled RNAs is carried out after the extraction of the RNAs as detailed earlier in this document. In one embodiment, the osmium-mediated transformation of labeled RNA is carried out using osmium tetroxide (OsO4) and ammonia. Since thiol-labeled RNAs are light-sensitive, in one embodiment, the oxidative nucleophilic aromatic substitution of the RNA is carried out in the dark or, at least, with protection from light. In one embodiment, the osmium-mediated transformation of RNA does not take place in the presence of a reducing agent. In one embodiment, the osmium-mediated transformation of RNA is stopped, i.e., halted at the end of the oxidative nucleophilic aromatic substitution treatment. The arrest of osmium-mediated transformation treatment can be carried out using techniques well known to the expert in the technique. Generating cDNA libraries, especially for sequencing purposes, is part of the expertise of a technical expert. Kits for generating cDNA libraries are commercially available, including, but not limited to, the SMARTer Stranded Total RNA-Seq Kit (ClonTech), QuantSeq 3mRNA-Seq Librar and Prep Kit (Lexogen), Nextera XT DNA Librar and Prep Kit (Illumina), TruSeq Nano DNA Librar and Prep Kit (Illumina), NEBNext DNA Librar and Prep Master Mix (New England Biolabs), NEBNext Ultra DNA Librar and Prep Kit (New England Biolabs), and JetSeq DNA Librar and Preparation Kit (Bioline). In one embodiment, the generation of a cDNA library comprises some or all of the following substeps: reverse transcription of RNAs, which includes: synthesis of first-strand cDNA (thus obtaining a mixed double-stranded RNA-cDNA library), Optionally, removal of RNA templates (thus obtaining a single-stranded cDNA library), synthesis of second-stranded cDNA (thus obtaining a double-stranded cDNA library), and optionally, purification of the double-stranded cDNA library. Reverse transcription of RNAs is carried out using techniques well known to the expert in the technique, using a reverse transcriptase enzyme and a mixture of 4 deoxyribonucleotide triphosphates (dNTPs), namely deoxyadenosine triphosphate (dATP), deoxycytidine triphosphate (dCTP), deoxyguanosine triphosphate (dGTP) and (deoxy)thymidine triphosphate (dTTP). In particular, the methods for first-strand cDNA synthesis are well known to those skilled in the art. First-strand cDNA synthesis reactions can use a combination of sequence-specific, oligo(dT), or random primers. In one embodiment, the first-strand cDNA synthesis reaction uses oligo(dT) primers. In one embodiment, the first-strand cDNA synthesis reaction uses sequence-specific primers. In one embodiment, the first-strand cDNA synthesis reaction uses random primers. In one embodiment, the primers used for first-strand cDNA synthesis comprise a fixed nucleic acid sequence (comprising, for example, adapters and / or indexes used for sequencing) and a priming nucleic acid sequence (complementary to the RNA template). In one embodiment, the primers used for first-strand cDNA synthesis comprise a fixed sequence at the 5' end and a priming sequence at the 3' end. In one embodiment, the primers used for first-strand cDNA synthesis comprise a fixed sequence at the 3' end and a priming sequence at the 5' end. In particular, methods for removing RNA templates are well known to experts in the technique. RNA template removal can be carried out, for example, by incubating the mixed RNA-double-stranded cDNA library with RNase H. Reverse transcription of RNAs to generate a cDNA library can be performed randomly, i.e., using random primers and thus reverse transcribing all or most of the RNAs. Alternatively, reverse transcription of RNAs to generate a cDNA library can be performed in a targeted manner, i.e., using specific primers and thus creating a cDNA library of only customized sequences. In one embodiment, the generation of a cDNA library, particularly of reverse-transcribed RNAs, leads to nucleotide substitutions. Such nucleotide substitutions occur randomly in small numbers in any reverse-transcribed RNA in the absence of chemical modification. However, a sudden increase in such substitutions is observed during the reverse transcription of RNAs that were previously labeled and further chemically modified by techniques such as alkylation, oxidative nucleophilic aromatic substitution, osmium-mediated transformation, or similar methods, as described earlier in this document. This sudden increase in substitutions is shown in the "Examples" section below. cDNA library amplification can be carried out using methods well known to experts in the technique. cDNA library amplification can be performed randomly, i.e., using random primers and thus amplifying all or most of the cDNA library. Alternatively, cDNA library amplification can be performed in a targeted manner, i.e., using specific primers and thus amplifying only customized sequences within the cDNA library. cDNA library sequencing can be carried out using methods well-known to experts in the field. In one embodiment, cDNA library sequencing is performed using next-generation sequencing (NGS), deep sequencing, or targeted sequencing of customized sequences. Methods for NGS are known to experts in the technique and include, but are not limited to, paired-end sequencing, sequencing by synthesis, and single-read sequencing. Platforms for NGS are available and include, but are not limited to, Illumina MiSeq (Illumina), Ion Torrent PGM (ThermoFisher Scientific), PacBio RS (PacBio), Illumina GAIIx (Illumina), Illumina HiSeq 2000 (Illumina). The cDNA library sequencing step can be performed using commercially available kits, such as the MiSeq Reagent v2 Kit (Illumina). In one embodiment, sequencing the cDNA library yields a set of sequence reads. In one embodiment, the method according to the present invention comprises a step of comparing the number of substituted nucleotides in the first set of mapped sequence reads against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, with a control sequence. "Substituted nucleotides" means a nucleotide substituted for another with respect to the matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan. Normally, any nucleotide can be substituted for any other nucleotide, such as a thymidine substituted for a cytosine (TC), an adenine (TA), or a guanine (TG). The same applies to the substitution of adenine (A), cytosine (C), and guanine (G) for any one of the other three nucleotides. These substitutions occur randomly in small numbers, particularly during reverse transcription. However, the present invention is based on the sudden increase in these substitutions when the RNAs are pre-labeled and further subjected to chemical modification methods such as alkylation, oxidative nucleophilic aromatic substitution, osmium-mediated transformation, and the like. In one embodiment, the total number of substituted nucleotides in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, is compared to the total number of substituted nucleotides in the control sequence In one embodiment, the number of TC substitutions in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, is compared to the number of TC substitutions in the control sequence. In another embodiment, the nucleotide substitution rate in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, is compared to the nucleotide substitution rate in the control sequence. In one embodiment, the TC substitution rate in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, is compared to the TC substitution rate in the control sequence. The "substitution rate," as used herein, is calculated as the number of one or more given nucleotide substitutions (e.g., TC, or any other nucleotide substitution as defined above herein) divided by the total number of substitutions. Alternatively, the "substitution rate" may be calculated as the number of one or more given nucleotide substitutions divided by the total number of nucleotides in the sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan.In one embodiment, the ratio of the TC substitution rate between mapped sequence reads against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads, is compared to the ratio of the average substitution rate of all other nucleotides (i.e., all except TC) between mapped sequence reads against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads and in the control sequence. In one embodiment, the method comprises identifying at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, mapped against at least one sequence read. In one embodiment, the identification of at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, mapped against at least one sequence read comprises the substeps of filtering the set of reads, assembling the sequence reads into contigs, aligning the sequence reads or contigs in a database, identifying at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, mapped against at least one sequence read or contig, and realigning the sequence reads or contigs to the matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan. Filtering a sequence read set is part of the knowledge of an expert in the technique. In one embodiment, filtering a sequence read set may include, but is not limited to, removing duplicate sequence reads, removing low-quality sequence reads, removing homopolymers from sequence reads, removing fixed nucleic acid sequences from sequence reads (such as, for example, adapters and / or indices used for sequencing), discarding endogenous sequence reads (i.e., sequence reads that map a nucleic acid sequence belonging to the subject cell), discarding unwanted sequence reads (such as, for example, rRNA sequence reads and the like), and the like. This filtering can be carried out using software readily available to experts in the technique. Assembling a set of sequence reads into contigs is part of the expert's knowledge of the technique. This assembly of sequence readings in contigs can be carried out using software readily available to experts in the technique. Optionally, sequence reads or contigs can be translated into amino acid sequences. Aligning a set of sequence reads or contigs is part of the knowledge of those skilled in the technique. Such alignment can be performed using readily available software for those skilled in the technique. In one embodiment, the sequence reads or contigs are aligned in a microbial database, i.e., a database comprising nucleic acid sequences or microbial amino acid sequences, preferably viral, bacterial, archaeal, fungal, or protozoan (in the case where the sequence reads or contigs are translated into amino acid sequences). Such a database can be downloaded, for example, from the EMBL nucleotide sequence database. The identification of at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, (or matching amino acid sequence in the case of sequence reads or contigs being translated into amino acid sequences) mapped against at least one sequence read or contig is part of the knowledge of the expert in the technique. After aligning the set of sequence reads or contigs in a database, the matching sequences in that database are identified. In one embodiment, at least one matching sequence is identified (and therefore selected) based on an expected threshold value (e-value) obtained after alignment with the sequence reads or contigs. In one embodiment, a matching sequence is identified (and therefore selected) if the e-value obtained after alignment of that matching sequence with at least one sequence read or contig is less than 10⁻², preferably less than 5 x 10⁻³. Realigning sequence reads or contigs to at least one matched microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan (or a matched amino acid sequence if the sequence reads or contigs are translated into amino acid sequences), previously identified (and therefore selected), is part of the knowledge of a person skilled in the art. This realignment of sequence reads or contigs can be performed using software readily available to those skilled in the art. In one embodiment, after realignment, at least one final consensus sequence is determined from at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan (or matching amino acid sequence in the case where the sequence reads or contigs were translated into amino acid sequences) previously identified (and therefore selected). According to this disclosure, the control sequence is selected from: - a second set of sequence reads that are mapped against said at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, wherein the second set of sequence reads is obtained by sequencing a second set of RNAs obtained by culturing the sample, preferably the cell sample, in the absence of an RNA labeling agent; - a second set of sequence reads that are mapped against said at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, wherein the second set of sequence reads is obtained by sequencing a second set of RNAs obtained by culturing the sample, preferably the cell sample, in the presence of an RNA labeling agent but without subjecting the extracted RNAs to conditions that allow nucleotide substitution; - a consensus microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, obtained from sequence reads or contigs of the first set of sequence reads that are mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan; - a sequence that corresponds to the same matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoal, found in the closest microbial strain, preferably viral, bacterial, archaeal, fungal, or protozoal, identified in nucleic acid sequence databases; and / or - an analogous sequence that corresponds to the same matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, identified in nucleic acid sequence databases. In one embodiment, the control sequence is a second set of sequence reads that corresponds to said at least matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, wherein the second set of sequence reads is obtained by sequencing a second set of RNAs obtained by culturing the sample, preferably the cell sample, in the absence of an RNA labeling agent. In this implementation, the method comprises the following stages: (a) sequence a first and second set of RNAs extracted from the sample, preferably the cell sample, wherein the first set of RNAs is obtained by culturing the sample, preferably the cell sample, in the presence of an RNA labeling agent and the second set of RNAs is obtained by culturing the sample, preferably the cell sample, in the absence of an RNA labeling agent, thus obtaining a first and a second set of sequence reads, (b) comparing the number of substituted nucleotides in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, with the number of substituted nucleotides in the second set of sequence reads mapped against said at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, and (c) conclude that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, belongs to a living microbe, preferably virus, bacteria, archaea, fungus or protozoan, if the number of substituted nucleotides in the sequence reads that are mapped to at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, in the first set of sequence reads is greater than in the second set of sequence reads. Preferably, the first set of RNAs is obtained by culturing the sample, preferably the cell sample, in the presence of an RNA labeling agent, thus obtaining labeled RNAs, and then subjecting said labeled RNAs to nucleotide substitution as detailed earlier in this document. In one embodiment, the method comprises the steps of: (a) sequencing a first and second set of RNAs extracted from a sample, preferably a cell sample, wherein the first set of RNAs is obtained by culturing the sample, preferably the cell sample, in the presence of an RNA labeling agent and the second set of RNAs is obtained by culturing the sample, preferably the cell sample, in the absence of an RNA labeling agent, thus obtaining a first and a second set of sequence reads, (a) identify at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, that maps to at least one sequence read from the first set of sequence reads, (a) compare the number of substituted nucleotides in the sequence reads being mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, identified in the first and second sets of sequence reads, and (a) conclude that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, belongs to an active, living microbe, preferably virus, bacterium, archaea, fungus or protozoan, if the number of substituted nucleotides in the sequence reads that are mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, identified in the first set of sequence reads is greater than in the second set of sequence reads. In this embodiment, the method comprises a step of sequencing a first set of RNAs extracted from the sample, preferably the cell sample. In this embodiment, the sample, preferably the cell sample, was cultured in the presence of an RNA labeling agent. In this embodiment, the sequencing step of a first set of RNAs extracted from the sample, preferably the cell sample, comprises one or more or all of the substeps of labeling the RNAs, lysing the cells, extracting the RNAs, substituting the nucleotides in the labeled RNAs, generating a cDNA library, amplifying the cDNA library, and sequencing the cDNA library. These sub-steps are defined and detailed above in this document and are applied to the sequencing of a first set of RNAs. In this embodiment, the method comprises an additional step of sequencing a second set of RNAs extracted from the sample, preferably the cell sample. In this embodiment, the sample, preferably the cell sample, was cultured in the absence of an RNA labeling agent. In this embodiment, the sequencing step of a second set of RNAs extracted from the sample, preferably the cell sample, comprises one or more or all of the substeps of lysing the cells, extracting the RNAs, generating a cDNA library, amplifying the cDNA library, and sequencing the cDNA library. These substeps are defined and detailed earlier in this specification and apply to the sequencing of a second set of RNAs. In one embodiment, the control sequence is a second set of sequence reads that are mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, wherein the second set of sequence reads is obtained by sequencing a second set of RNAs obtained by culturing the sample, preferably the cell sample, in the presence of an RNA labeling agent but without subjecting the extracted RNAs to conditions that allow nucleotide substitution. In this implementation, the method comprises the following stages: (a) sequencing a first and a second set of RNAs extracted from the sample, preferably the cell sample, wherein the first and second sets of RNAs are obtained by culturing the sample, preferably the cell sample, in the presence of an RNA labeling agent, thereby obtaining labeled RNAs, and wherein the first set of RNAs is obtained from the first fraction of the labeled RNAs that is subjected to nucleotide substitution, and the second set of RNAs is obtained from a second fraction of labeled RNAs that is not subjected to nucleotide substitution, thereby obtaining a first and a second set of sequence reads, (b) comparing the number of substituted nucleotides in the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, with the number of substituted nucleotides in the second set of sequence reads mapped against said at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, and (c) conclude that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, belongs to a living microbe, preferably virus, bacteria, archaea, fungus or protozoan, if the number of substituted nucleotides in the sequence reads that are mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, in the first set of sequence reads is greater than in the second set of sequence reads. In one embodiment, the control sequence can be a consensus microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan. In one embodiment, a consensus microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, can be obtained from multiple sequence reads of the first set of sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan. Such a consensus sequence can be easily determined since it has been observed that not all targeted nucleotides are thio-tagged and / or substituted after the nucleotide substitution procedure.In fact, a sufficient number of target nucleotides are substituted to allow distinction according to the method of the present invention; but this number is still low enough to establish a consensus sequence. In one embodiment, the control sequence can be a nucleic acid sequence that corresponds to the same matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, but found in the closest microbial strain, preferably viral, bacterial, archaeal, fungal, or protozoan, identified in nucleic acid sequence databases. In one embodiment, the control sequence can be an analogous sequence that corresponds to the same matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, identified in nucleic acid sequence databases. In one embodiment, the method according to the present invention comprises a step of determining whether at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably a virus, bacterium, archaeon, fungus, or protozoan. In one embodiment, the living microbe, preferably a virus, bacterium, archaeon, fungus, or protozoan, is characterized by the taxonomic assignment of at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan. In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably virus, bacterium, archaea, fungus, or protozoan, if the total number of nucleotide substitutions in the sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads is greater than the total number of nucleotide substitutions in the control sequence. In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably virus, bacterium, archaea, fungus, or protozoan, if the number of TC substitutions in sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads is greater than the number of TC substitutions in the control sequence. In one embodiment, "the [...] number of [...] substitutions [...] in the first set of sequence reads is greater than the [...] number of substitutions in the control sequence" when the number of substitutions is twice as high, preferably three times as high, more preferably 4, 5, 6, 7, 8, 9, 10, 15, 20, 50, 100 times higher in the sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, in the first set of sequence reads than in the control sequence. In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably virus, bacterium, archaea, fungus, or protozoan, if the rate of nucleotide substitution in sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads is greater than the rate of nucleotide substitution in the control sequence. In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably virus, bacterium, archaea, fungus, or protozoan, if the TC substitution rate in sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads is greater than the TC substitution rate in the control sequence. As used in this document, the term "TC replacement rate" is defined by the following formula: In one embodiment, "the [...] substitution rate [...] in the first set of sequence reads is greater than the [...] substitution rate [...] in the control sequence" when the substitution rate is twice, preferably three times, more preferably 4, 5, 6, 7, 8, 9, 10, 15, 20, 50, 100 times greater in sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads than in the control sequence. In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably virus, bacteria, archaea, fungus, or protozoan, if the TC substitution rate is greater than the average substitution rates of all other nucleotides in the sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads. In one embodiment, "the substitution rate of TC is greater than the average substitution rates of all other nucleotides" when the substitution rate of TC is twice as high, preferably three times as high, more preferably 4, 5, 6, 7, 8, 9, 10, 15, 20, 50, 100 times higher than the average substitution rates of all other nucleotides. "Average substitution rates of all other nucleotides" means the average substitution rates of AC, AG, AT, CA, CG, CT, TA, TG, GA, GC, and GT. In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably virus, bacteria, archaea, fungus, or protozoan, if the TC substitution rate is greater than the average TA and TG substitution rates in sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads. In one embodiment, "the substitution rate of TC is greater than the average substitution rates of TA and TG" when the substitution rate of TC is twice as high, preferably three times as high, more preferably 4, 5, 6, 7, 8, 9, 10, 15, 20, 50, 100 times as high as the average substitution rates of TA and TG.In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably virus, bacterium, archaea, fungus, or protozoan, if the ratio of the TC substitution rate between the sequence reads mapped to at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads and in the control sequence is greater than the ratio of the average substitution rates of all other nucleotides between the sequence reads mapped to at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads and in the control sequence. In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably virus, bacterium, archaea, fungus, or protozoan, if the ratio of the TC substitution rate between sequence reads mapped to at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads and in the control sequence is greater than the ratio of the average TA and TG substitution rates between sequence reads mapped to at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads and in the control sequence. In one embodiment, it is concluded that at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, belongs to a living microbe, preferably a virus, bacterium, archaeon, fungus, or protozoan, if the TC substitution rate in sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the first set of sequence reads is greater than a threshold value. In this embodiment, the threshold value can be determined experimentally. In another embodiment, the threshold value is greater than the TC substitution rate in sequence reads mapped against at least one matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal, or protozoan, in the second set of sequence reads.In one embodiment, the threshold value is at least 2, preferably at least 2, 5, 3, 3, 5, 4, 4, 5, 5, 5, 5, 6, 6, 5, 7, 7, 5, 8, 8, 5, 9, 9, 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more. As used in this document, the term "TC substitution index" is defined by the following formula: Methods for distinguishing between live and dead microbes, preferably viruses, bacteria, archaea, fungi, or protozoa, in a sample, preferably a cell sample, comprising distinguishing between nucleic acid sequences of transcriptionally active and inert microbes, preferably viral, bacterial, archaeal, fungal, or protozoan, in the sample, preferably the cell sample, according to the present invention, are useful in several different applications. In fact, the risk of microbial contamination, particularly viral, bacterial, archaeal, fungal, or protozoal, is a major concern for biological products. This includes the risk of contamination of both Good Manufacturing Practice (GMP) facilities and the final pharmaceutical product. Virus testing of raw materials, cells, virus seeds, master / working banks, vaccine serum batches, and so on is key to pharmaceutical product safety. This is particularly critical for live vaccines, viral vectors for gene therapy, and cell therapy pharmaceuticals, as their production does not include downstream viral elimination stages. As a result, the safety of these products relies heavily on viral testing during the production process. All previously reported contaminations of cell culture-based products were due to unpredictable animal viruses that were not identified during viral testing of raw materials or production cells. In fact, conventional viral testing is limited because many viruses do not grow in cell lines used for in vitro testing or in rodents or eggs used for in vivo testing. The methods of the present invention offer an alternative approach to accurately analyze samples for contamination and to distinguish between live microbes, including dormant ones; and harmless contamination of inert microbial nucleic acid fragments (e.g., fragmented nucleic acids from microbes after inactivation by gamma radiation). Based on this, numerous industrial applications are foreseeable. In the field of vaccines, the control of inactivated vaccines is currently carried out by culturing the vaccine, which is considered inactivated, and then searching for the presence of live microbes. The methods of the present invention would allow the differentiation of live microbes from the background noise of the inert microbial nucleic acid sequence, which is inactivated and therefore harmless. Similarly, the methods according to the present invention can be readily implemented to detect contamination with live microbes in biological samples, such as raw materials (e.g., serum batches in the case of vaccines), cells, master / working banks, etc., as well as in blood cultures and other types of biological samples used for diagnosis. After antibiotic treatment in a subject, for example, it would be advisable to test the subject for the presence or absence of any remaining live microbes and thus identify any potentially treatment-resistant microbes. In the field of virus therapy, the methods according to the present invention can be implemented to test for the presence or absence of replicative revertant viruses in viral vectors, such as those used in, for example, gene therapy. The preservation medium can also be subjected to microbial contamination, and the methods according to the present invention can be readily used to check for such contamination before bringing it into contact with the sample to be preserved. The field of possibilities also extends to non-biological samples. For example, food safety is a major concern. Health scandals and the rise of a demand for quality- and safety-focused food resonate with the testing of food for microbial contamination. The methods of the present invention can solve this problem by providing a practical and definitive answer as to whether a food sample is contaminated by live microbes or not. Environmental samples can also be analyzed. For example, water and / or air conditioning systems are known to carry microbes. The methods according to the present invention can be implemented to confirm the presence or absence of such live microbes. Another objective of the present invention is a method for diagnosing a microbial infection, preferably viral, bacterial, archaeal, fungal or protozoal, in a subject. In one embodiment, the diagnostic method according to the present invention comprises a step of providing a sample, preferably a cell sample, from the subject. The diagnostic method according to the present invention further comprises a step of performing any one of the methods for distinguishing between transcriptionally active and inert microbial nucleic acid sequences, preferably viral, bacterial, archaeal, fungal or protozoan, in a sample, preferably a cell sample, according to the present invention. In one embodiment, the diagnostic method according to the present invention further comprises a step for diagnosing that the subject has a microbial infection, preferably viral, bacterial, archaeal, fungal or protozoal, if at least one identified matching microbial nucleic acid sequence, preferably viral, bacterial, archaeal, fungal or protozoan, belongs to a live microbe, preferably virus, bacteria, archaea, fungus or protozoa. This dissertation also describes a method for treating a microbial infection, preferably viral, bacterial, archaeal, fungal or protozoal, in a subject. In one embodiment, the method for treating a microbial infection comprises a step of performing a diagnostic method according to the present invention; and a step of treating the subject if said subject has been diagnosed as having a microbial infection, preferably viral, bacterial, archaeal, fungal, or protozoal. The means and methods for treating a microbial infection are well known to those skilled in the art and include, without limitation, the administration of at least one antiviral, antibacterial, antifungal, or antiprotozoal agent to the subject. Suitable examples of antiviral agents include, without limitation, those classified in the therapeutic subgroup J05 of the Anatomical Therapeutic Chemical Classification System. Additional examples include, but are not limited to, acemannan, acyclovir, acyclovir sodium, adamantanamine, adefovir, adenine arabinoside, alovudine, alvircept sudotox, amantadine hydrochloride, aranotine, arildone, atevirdine mesylate, avridine, cidofovir, cipamphylline, cytarabine hydrochloride, BMS 806, C31G, carrageenan, zinc salts, cellulose sulfate, cyclodextrins, dapivirine, delavirdine mesylate, desciclovir, dextrin 2-sulfate, didanosine, disoxaryl, dolutegravir, edoxudine, enviradene, envirozyme, etravirine, famciclovir, famotine hydrochloride, fiacitabine, fialuridine, fosarylate, foscarnet sodium, sodium phosphonet, FTC, ganciclovir, ganciclovir sodium, GSK 1265744, 9-2-hydroxy-ethoxy methylguanine, ibalizumab, idoxuridine, interferon, 5-iodo-2-deoxyuridine, IQP-0528,Kethoxal, lamivudine, lobucavir, maraviroc, memotine, pirodavir, penciclovir, raltegravir, ribavirin, rimantadine hydrochloride, rilpivirine (TMC-278), saquinavir mesylate, SCH-C, SCH-D, somantadine hydrochloride, sorivudine, statolon, stavudine, T20, tilorone hydrochloride, TMC120, TMC125, trifluridine, trifluorothymidine, tenofovir, tenofovir alefenamide, tenofovir disoproxil fumarate, tenofovir prodrugs, UC-781, UK-427, UK-857, valacyclovir, valacyclovir hydrochloride, vidarabine, vidarabine phosphate, vidarabine sodium phosphate, viroxime, zalcitabene zidovudine, zinviroxime, and combinations thereof. Suitable examples of antibacterial agents include, without limitation, those classified in the therapeutic subgroup J01 of the Anatomical Therapeutic Chemical Classification System. Additional examples include, but are not limited to, aminoglycosides (such as, for example, amikacin, gentamicin, kanamycin, neomycin, netilmicin, streptomycin, tobramycin, paromycin and the like), ansamycins (such as, for example, geldanamycin, herbycin and the like), carbapenems (such as, for example, loracarbef and the like), carbapenems (such as, for example, ertapenem, doripenem, imipenem, cilastatin, meropenem and the like), first-generation cephalosporins (such as, for example, cefadroxil, cefazolin, cephalothin, cephalexin and the like), second-generation cephalosporins (such as, for example, ceflachlor, cefamandole, cefoxitin, cefprozil, cefuroxime and the like), third-generation cephalosporins (such as, for example, cefixime, cefdinir,cefditoren, cefoperazone, cefotaxime, cefpodoxime, ceftazidime, ceftibuten, ceftizoxime, ceftriaxone and similar drugs), fourth-generation cephalosporins (such as, for example, cefepime and similar drugs), fifth-generation cephalosporins (such as, for example, ceftobiprole and similar drugs), glycopeptides (such as, for example, teicoplanin, vancomycin and similar drugs), macrolides (such as, for example, axithromycin, clarithromycin, dirithromycin, erythromycin, roxithromycin, troleandomycin, telithromycin, spectinomycin and similar drugs), monobactams (such as, for example, axtreonam and similar drugs), penicillins (such as, for example, amoxicillin, ampicillin, axlocillin, carbenicillin, cloxacillin, dicloxacillin, flucloxacillin, mezlocillin, methicillin, nafcillin, oxacillin, penicillin, peperacillin, ticarcillin and similar drugs), antibiotic polypeptides (such as, for example, bacitracin, colistin, polymyxin B and similar drugs), quinolones (such as, for example,ciprofloxacin, enoxacin, gatifloxacin, levofloxacin, lemefloxacin, moxifloxacin, norfloxacin, orfloxacin, trovafloxacin and similar drugs), sulfonamides (such as, for example, mafenide, prontosil, sulfacetamide, sulfamethizole, sulfanilamide, sulfasalazine, sulfisoxazole, trimethoprim, trimethoprim-sulfamethoxazole and similar drugs), tetracyclines (such as, for example, demeclocycline, doxycycline, minocycline, oxytetracycline, tetracycline and similar drugs), other antibiotics (such as, for example, arspenamine, chloramphenicol, clindamycin, lincomycin, ethambutol, fosfomycin, fusidic acid, furazolidone, isoniazid, linezolid, metronidazole, mupirocin, nitrofurantoin, platensimycin, pyrazinamide, quinupristin / dalfopristin, rifampin / rifampicin, tinidazole and similar drugs), and combinations thereof. Suitable examples of antifungal agents include, without limitation, those classified in the therapeutic subgroup J02 of the Anatomical Therapeutic Chemical Classification System. Additional examples include, but are not limited to, abafungin, albaconazole, amorolfine, amphotericin B, anidulafungin, atovaquone, biafungin, bifonazole, bromochlorosalicylanilide, butenafine, butoconazole, caspofungin, chlormidazole, chlorophenethanol, chlorphenesin, ciclopirox, cilofungin, citronella oil, clotrimazole, croconazole, crystal violet, dapsone, dimazole, eberconazole, econazole, efinaconazole, ethylparaben, fenticonazole, fluconazole, flucytosine, flutrimazole, phosphogluconazole, griseofulvin, haloprogin, hamicin, hexaconazole, isavuconazole, isoconazole, itraconazole, ketoconazole, lemongrass, lemon myrtle, luliconazole, micafungin, miconazole, naftifine, natamycin, neticonazole, nystatin, omoconazole, orange oil, oxiconazole, patchouli, pentamidine, polinoxylin, posaconazole, potassium iodide,ravuconazole, salicylic acid, selenium disulfide, sertaconazole, sodium thiosulfate, sulbentine, sulconazole, taurolidine, tavaborol, tea tree oil, terbinafine, terconazole, ticlatone, tioconazole, tolcyclate, tolnaftate, tribromometacresol, undecylenic acid, voriconazole, Whitfield's ointment and combinations thereof. Suitable examples of antiprotozoal agents include, without limitation, those classified in the therapeutic subgroup P01 of the Anatomical Therapeutic Chemical Classification System. Additional examples include, but are not limited to, albendazole, amodiaquine, amphotericin B, arstinol, artemether, artemisinin, artemotil, arterolan, artesunate, atovaquone, azanidazole, benznidazole, broxyquinoline, carnidazole, chiniophon, chlorhexidine, chloroquine, chlorproguanil, chlorquinaldol, clefamide, clindamycin, clioquinol, dehydroemetine, difetarsone, dihydroartemisinin, diiodohydroxyquinoline, diloxanide, doxycycline, eflornithine, emetine, etofamide, fexinidazole, fumagillin, furazolidone, glycobiarsol, halofantrine, hydroxychloroquine, iodoquinol, lumefantrine, mefloquine, meglumine antimoniate, melarsoprol, mepacrine, metronidazole, miltefosine, nifurtimox, nimorazol, nitazoxanide, ornidazole, pamaquine, paromomycin, pentamidine, fanquinone, piperaquine, primaquine, proguanil, propamidine,Propenidazole, pyrimethamine, pyronaridin, quinacrine, quinidine, quinine, secnidazole, sodium stibogluconate, sulfadiazine, sulfadoxine, sulfalene, sulfamethoxazole, suramin, tafenoquine, teclozan, tenonitrozole, tetracycline, tilbroquinol, tinidazole, trimethoprim, trimetrexate, and combinations thereof. Another objective of the present invention is a method for evaluating the risk of microbial contamination, preferably viral, bacterial, archaeal, fungal, or protozoal, in a sample. In one embodiment, the method for assessing the risk of microbial contamination, preferably viral, bacterial, archaeal, fungal, or protozoal, according to the present invention comprises a step of providing a sample. In one embodiment, the sample may be a biological or non-biological sample. In one embodiment, the method for assessing the risk of microbial contamination, preferably viral, bacterial, archaeal, fungal, or protozoal, according to the present invention comprises a step of performing any one of the methods for distinguishing between transcriptionally active and inert microbial nucleic acid sequences, preferably viral, bacterial, archaeal, fungal, or protozoal, in a sample according to the present invention. In one embodiment, the method for assessing the risk of microbial contamination, preferably viral, bacterial, archaeal, fungal, or protozoal, according to the present invention comprises a step of concluding that the sample is at risk of contamination if at least one matching microbial nucleic acid sequence identified, preferably viral, bacterial, archaeal, fungal, or protozoal, belongs to a live microbe, preferably a virus, bacterium, archaea, fungus, or protozoan. Brief description of the drawings Figure 1 is a set of two graphs illustrating the substitution rates and substitution indices of T nucleotides. Figure 1A: T nucleotide substitution rates expressed as the ratio of substituted T to total T. Figure 1B: Substitution indices expressed as the ratio of the substitution rate of "T to C" to the average of the substitution rates of "T to A" + "T to G". [T: TBEV; S: SMRV; C: cellular RNAs]. Figure 2 is a graph illustrating the substitution rates (in %) of T nucleotides with C, G or A, in a 4sU treated and alkylated sample, using as the matching microbial nucleic acid sequence a consensus TBEV sequence constructed from current condition data. Figure 3 is a graph illustrating the substitution rates (in %) of T nucleotides with C, G or A, in a 4sU treated and alkylated sample, using as the matching microbial nucleic acid sequence a consensus SMRV sequence constructed from current condition data. Figure 4 is a graph showing the distribution of GC content of the 662 contigs selected as candidates for the reconstruction of the LC5_ALAID_CNS reference genome. Figure 5 is a graph showing the mosaic of the PG8A genome of A. laidlawii with the 662 contigs selected from the initial assembly of reads from the experimental condition LC5. Matching contigs (direct in black, inverse in gray) are reported at their actual percentage of similarity (top) and are normalized to 10% similarity to flatten the coverage and facilitate visualization. Figure 6 is a set of seven graphs illustrating the substitution (or conversion) rates (in %) of T nucleotides with C, G, or A along the LC5_ALAID_CNS reference sequence for the different tested conditions. Only high-confidence events (20X depth) were selected for analysis. Figure 6A: CTRL5tag condition; Figure 6B: LC5 condition; Figure 6C: LC5tag condition; Figure 6D: LC5tag condition diluted 40x; Figure 6E: HC_HK5tag condition; Figure 6F: HC_HK5tag condition; Figure 6G: HC_G5tag condition Examples The foregoing aspects and features, and others, of the present invention will be further illustrated by the following examples. These examples are for illustrative purposes only and are not intended to be limiting. Example 1: Detection of tick-borne encephalitis virus (TBEV) replication in cultured Vero cells Materials and methods Material Vero cells were cultured in minimal essential medium (MEM) supplemented with 2% fetal bovine serum (FBS). The virus used for infection is tick-borne encephalitis virus (TBEV), a member of the Flaviviridae family, consisting of a ssRNA(+) genome with an average size of 10 kb. Methods Viral infection Vero cells were seeded at a rate of 400,000 cells / well in 3 wells of an MW6 plate, to reach 106 cells / well after 24 hours. Next, the cells were infected with TBEV at an MOI (multiplicity of infection) of 1 and incubated for 1 hour on ice with shaking. For one well, the medium was removed immediately after incubation and the cells were lysed with 1 mL of TRIzol and stored at -80°C until RNA extraction (Condition 1). For the other two wells (Conditions 2 and 3), the medium was removed and replaced with 2% MEM + FBS and incubated overnight at 37°C. Marking with 4sU This step was performed using the SLAMseq Kinetic Kit - Anabolic Kinetic Module (Lexogen, Cat. No. 061). The incorporation of 4-thiouridine (4sU) into the cell culture medium during cell culture allows the nucleotides from 4sU to be incorporated into the newly synthesized RNA. The medium containing 4sU 800 µM was prepared by adding 8 µL of 4sU 100 nM to 992 µL of MEM. The day after viral infection, the medium was removed and replaced with medium without 4sU in one well (Condition 2) or medium containing 4sU (800 µM) for the last well (Condition 3). Six hours later, the medium was removed and replaced with fresh medium without 4sU in Condition 2, or fresh medium containing 4sU (800 µM) in Condition 3. Three hours later, the medium was removed from the three wells and the cells were lysed with 1 mL of TRIzol and stored at -80°C until RNA extraction. RNA sampling This step was performed using the SLAMseq Kinetic Kit – Anabolic Kinetic Module (Lexogen, Cat. No. 061). RNA extraction was carried out in the dark using a chloroform:isoamyl alcohol 24:1 mixture (Sigma Aldrich, Cat. No. 25666) followed by isopropanol / ethanol precipitation. During extraction, a reducing agent (RA) was used to maintain the 4sU-treated samples under reducing conditions. The total RNA isolated contains both existing (unlabeled) and newly synthesized (labeled) RNA. Alkylation This step was performed using the SLAMseq Kinetic Kit – Anabolic Kinetic Module (Lexogen, Cat. No. 061). The total RNA extracted from Condition 3 was mixed with iodoacetamide (IAA), which modifies the 4-thiol group of nucleotides containing 4sU by adding a carboxyamidomethyl group. The RNA was then purified by ethanol precipitation before proceeding with library preparation. Library preparation The SMARTer Stranded Total RNA-Seq-Pico Input Mammalian kit (ClonTech) was used for direct library construction from 10 ng of RNA. The workflow used in this kit incorporates a patented technology (PathoQuest, Paris, France) that depletes ribosomal cDNA using probes specific to mammalian rRNA and some mitochondrial RNA. Sequencing Sequencing was performed on the NextSeq instrument (Illumina) using the NextSeq 500 / 550 high-performance v2 kit (FC-404-2002, Illumina). The sequencing was single read with a read length of 150 nucleotides and approximately 125 million reads were generated per sample. Scheme Table 1 The protocol used for the three different conditions (1, 2 and 3) is summarized below. Table 1: Protocol diagram Bioinformatics analysis - Analysis of the TBEV genome The primary objective of this study was to obtain a complete TBEV genome sequence from this isolate for use as a reference. This analysis was performed on the sample infected on day 0, without treatment with 4sU. Filtering of raw readings First, raw data reads were filtered to select relevant, high-quality reads. The raw data were then sorted to suppress or remove duplicates, low-quality reads, and homopolymers (using proprietary software). Sequences introduced during Illumina® library preparation (adapters, primers) were removed using Skewer (Jiang et al., 2014. BMC Bioinformatics. 15: 182). Finally, endogenous primate reads (from Vero cells) aligned with the human genome (reference GRCh37 / hg19) or reads aligned with bacterial rRNA were discarded. Local alignments were performed using BWA (Li et al., 2009. Bioinformatics.25 (14): 1754-60). The human genome was downloaded from the UCSC Genome Browser (2002. Genome Res.12 (6): 996-1006). The bacterial rRNA database was downloaded from the EMBL-EBI ENA rRNA database, with an additional internal process of cleaning and pooling sequences. These filtered readings were considered sequences of interest. De novo assembly The remaining relevant reads were then assembled into longer sequences called contigs. This de novo assembly step was performed using CLC cell assembly solution (Qiagen). Identification of agnostic viruses The resulting contigs and unassembled reads (singletons) were aligned using BLAST alignment (Altschul et al., 1990. J Mol Biol. 215(3):403–10) against viral and exhaustive databases. Contigs and singletons were first aligned against a viral nucleotide database. Matches with an e-value less than 10⁻³ were aligned against an exhaustive nucleotide database. If their best match was still a viral taxonomy, the matches were reported. The comprehensive viral and nucleotide databases were downloaded in November 2017 from the EMBL-EBI STD nucleotide sequence database. Proprietary software was developed to remove duplication and low-confidence sequences (due to being too short, multiple taxonomies, low-quality associated keywords, etc.). Contigs without any matching viral nucleotides were successively aligned in a similar manner with exhaustive viral protein databases to check for more distant viral matches. Comprehensive viral protein databases were downloaded in November 2017 from the Uniref100 database. The Uniref100 database is no longer redundant, but a taxonomic cleaning process was performed to produce the final databases. Taxonomic assignment yielded the best match results. Contigs that remained unassigned after these two rounds of alignment were classified as unknown or non-viral species. Table 2 below shows the results of the analysis. Table 2: Identification of agnostic viruses TBEV Final Consensus Edition This process identified a contigo that encompassed the entire TBEV genome sequence. Next, all reads were realigned with CLC assembly cell solution (Qiagen) in this sequence to extract a final consensus sequence. This sequence was named "TBEV REFERENCE" for the study. Bioinformatics analysis - Study of the nucleotide substitution rate The objective was to compare the readings of the different samples to check if the substitution rate of "T for C" was significantly higher in the 4sU + alkylation sample (Condition 3). Creation of a tick-borne encephalitis virus bank The "TBEV REFERENCE" sequence was used to create a BLAST bank. To detect potential sequences with a very high "T to C" substitution rate, the reference sequence was also modified by replacing each T with a C. This sequence was named "TBEV CT REFERENCE". The "TBEV REFERENCE" and "TBEV CT REFERENCE" sequences were merged to form a single "TBEV BLAST" bank. Filtering of raw readings First, a quality filtering process was performed to remove or trim low-quality readings (proprietary software). Subsequently, the sequences introduced during the preparation of the Illumina libraries (adapters, primers) were removed with Skewer (Jiang et al., 2014. BMC Bioinformatics.15: 182). To avoid any analysis bias, duplicate readings were not removed. Blast readings filtered in the TBEV Blast bank The remaining relevant readings were then aligned using BLAST (Altschul et al., 1990. J Mol Biol. 215 (3): 403-10) on the previously designed "TBEV BLAST" bench. The maximum e value was set at 10-8. All aligned readings were considered positive for TBEV and were selected for the next stage of analysis. Mapping of selected reads across the whole TBEV genome Next, positive TBEV reads were realigned by mapping with CLC assembly cell solution (Qiagen) to the "TBEV REFERENCE" sequence. Quality control was established to ensure that at least 99% of blast-selected reads realigned positively to the reference. Table 3 below summarizes the number of sense and antisense mapped readings obtained for each condition and the resulting sequence coverage. Table 3: Mapping, orientation and coverage of readings Estimation of the substitution rate The CLC program "clc_find_variations" was used to detect each mismatch at each position in the TBEV study reference. The overall variation profile was then analyzed using a proprietary script to determine the substitution rate for each nucleotide. The proportion of substituted nucleotides was compared to the total number of aligned nucleotides. Typically, the "T to C" substitution rate is calculated using the following formula: Isolated analysis of TBEV A targeted and isolated analysis was performed on the identified TBEV reads. This analysis performs a more stringent mapping alignment of the filtered reads. This alignment provides a detailed profile of horizontal genome coverage and depth. Local alignments were performed using BWA (Li et al., 2009. Bioinformatics.25 (14): 1754-60). Since the sample libraries were prepared using the SMARTer Stranded RNA-Seq kit, the RNA strand information was preserved. Therefore, mapping alignment analysis provided information about the parent strand of each read (forward or reverse relative to the parent strand). Transcript coverage allowed conclusions to be drawn about the viral replication signature in the cell sample. Results and Conclusion The ratio of the "T to C" substitution rate calculated in the mismatches mapped to the TBEV reference genome in Condition 3 to the T to C substitution rate calculated in the mismatches mapped to the TBEV reference genome in Condition 1 is 7.86:1 (Table 4). This indicates an increase in the proportion of TBEV RNA species that have incorporated 4sU, and therefore, the neo-synthesis of viral RNA, during the 9-hour incubation of Vero cell culture in medium containing 4sU. The method exemplified in this paper therefore allows the detection of replication of the (+) ssRNA virus, TBEV, using metabolic labeling. Table 4: Substitution rate of T for C. Example 2: Detection of squirrel monkey retrovirus (SMRV) replication in cultured Vero cells. After the identification of the agnostic virus in Example 1, the best-match results showed contigs assigned to squirrel monkey retrovirus. This virus is known to be endogenous and fully integrated in some monkey species. In particular, the Vero cells used in this study have been described as harboring a variety of endogenous simian D-type retrovirus sequences, particularly SMRV sequences (Sakuma et al., 2018. Sci Rep.8 (1): 644). Based on this knowledge and in view of the results shown in Table 2 above, the same bioinformatics procedure was carried out to identify a matching SMRV sequence and to study the nucleotide substitution rate in this matching sequence. Table 5 below summarizes the number of mapped sense and antisense readings obtained for each condition and the resulting sequence coverage. Table 5: Mapping, orientation and coverage of readings. The ratio of the "T for C" substitution rate calculated on the mismatches mapped to the SMRV reference genome in Condition 3 over the T for C substitution rate calculated on the mismatches mapped to the SMRV reference genome in Condition 1 is equal to 41.86:1 (Table 6). This indicates an increase in the proportion of SMRV RNA species that have incorporated 4sU, hence the neo-synthesis of viral RNA during the 9 hours of incubation of the Vero cell culture in medium containing 4sU. The method exemplified in this paper therefore allows the detection of replication of the (+) ssRNA-RT virus, SMRV, using metabolic labeling. Table 6: Substitution rate of T for C Example 3: Materials and methods Cells and viruses A vial of Vero cells (ATCC-CCL-81, lot no. 62488537, Molsheim, France) was frozen in pass 3 and then thawed in a BSL-3 laboratory and the cells were cultured on MEM supplemented with 10% FBS. The cells were used in pass 18. A second vial of Vero cells (lot no. 70005907) was purchased from the same source and used directly for the PCR assay. Tick-borne encephalitis virus (TBEV) is a member of the Flaviviridae family, which consists of an ssRNA(+) genome. The Hypr strain (Wallner et al., 1996. J. Gen Virol. 77 (Pt5): 1035-42) was kindly provided by Sarah Moutailler, ANSES, Maisons-Alfort, France). Vero cell TBEV infection Vero cells were seeded at a rate of 400,000 cells / well in 3 wells of an MW6 plate to reach 10⁶ cells / well after 24 hours. The cells were then infected with the Hypr strain of TBEV at an infection multiplicity of 1 and incubated for 1 hour on ice with shaking. The medium was removed from a well immediately after incubation and the cells were lysed with 1 mL of Trizol and stored at -80°C until RNA extraction ("D0-without 4SU" condition). For the other two wells (Conditions 2, 3 and 4), the medium was removed and replaced with MEM + FBS 10% and incubated overnight at 37°C. 4sU labeling and RNA extraction The addition of 4-thiouridine (4sU) to the cell culture medium allows 4sU nucleotides to be incorporated into newly synthesized RNA. Reverse transcription of 4sU shows a certain percentage of erroneous incorporation resulting in a T>C transition in cDNA, which can be identified by sequencing (Herzog et al., 2017. Nat. Methods 14 (12): 1198-1204). The medium containing 800 µM 4sU was prepared by adding 8 µL of 100 nM 4sU to 992 µL of MEM. The day after viral infection, the medium was removed and replaced with medium without 4sU in one well (Condition "D1-without 4sU") or medium containing 4sU (800 µM) for the other well ("Conditions "D1-with 4sU"). Six hours later, the medium was removed and replaced with fresh medium without 4sU in condition "D1-without 4sU", or with fresh medium containing 4sU (800 µM) in condition "D1-with 4sU". Three hours later, the medium was removed from the three wells and the cells were lysed with 1 mL of Trizol and stored at -80°C until RNA extraction. RNA extraction was performed in the dark using a chloroform:isoamyl alcohol 24:1 mixture (Sigma Aldrich, Cat. No. 25666, Saint Louis, USA) followed by isopropanol / ethanol precipitation. During extraction, a reducing agent was used to maintain the 4sU-treated sample under reducing conditions. Alkylation was performed using the SLAMseq Kinetic Kit – Anabolic Kinetic Module (Lexogen, Cat. No. 061, Vienna, Austria) for only part of the "D1-with 4sU" condition. The total extracted RNA was mixed with iodoacetamide (IAA), which modifies the 4-thiol group of 4sU-containing nucleotides by adding a carboxyamidomethyl group, leading to the "D1-with 4sU + alkylation" condition. This alkylation amplifies the frequency of erroneous T>C incorporations during reverse transcription. The other part was labeled "D1-with 4sU without alkylation". Next, the RNA was purified using ethanol precipitation before proceeding with library preparation. Library preparation and sequencing The SMARTer Stranded Total RNA-Seq-Pico Input Mammalian kit (ClonTech, Mountain View, USA) was used for direct library construction from 10 ng of RNA. The workflow used with this kit incorporates a patented technology (PathoQuest, Paris, France) that depletes ribosomal cDNA using probes specific to mammalian rRNA and some mitochondrial RNA. Sequencing was performed on the NextSeq instrument (Illumina, San Diego, USA) using the NextSeq 500 / 550 high-throughput v2 kit (FC-404-2002, Illumina). Single-read sequencing with a 150-nucleotide read length yielded approximately 125 million reads per sample. Agnostic bioinformatics analysis The raw data readings were filtered to select relevant, high-quality readings. The raw data was then sorted to suppress or remove duplicates, low-quality readings, and homopolymers (using PathoQuest's proprietary software). The sequences introduced during the preparation of the Illumina libraries (adapters, primers) were removed with Skewer (Jiang et al., 2014. BMC Bioinformatics.15: 182). Primate reads (from Vero cells) aligned with the human genome (reference GRCh37 / hg19) or reads aligned with bacterial rRNA were excluded. Local alignments were performed using BWA (Li et al., 2009. Bioinformatics. 25 (14): 1754-60). The human genome was downloaded from the UCSC Genome Browser (Kent et al., 2002. Genome Res. 12 (6): 996-1006). The bacterial rRNA database was initially downloaded from the EMBL-EBI ENA rRNA database (ebi.ac.uk / pub / databases / ena / rRNA / release), followed by an additional internal process of cleaning and pooling sequences. These filtered reads were considered as sequences of interest and were assembled into longer sequences called "contiges" using the CLC cell assembly solution (Qiagen Hilden, Germany). The resulting contiges and unassembled reads (singletons) were aligned using BLAST alignment (Altschul et al., 1990. J Mol Biol. 215 (3): 403-10) in viral and exhaustive databases. Contigs and singletons were first aligned in a viral nucleotide database. Matches with a value e less than 10-3 were aligned in an exhaustive nucleotide database. If the best match was still a viral taxonomy, the matches were reported. The viral and exhaustive nucleotide databases were downloaded in November 2017 from the EMBL-EBI STD nucleotide sequence database. Proprietary software (PathoQuest, Paris, France) was developed to remove duplication and low-confidence sequences (e.g., sequences that were too short, multiple taxonomies, or associated with low-quality keywords). Contigs without any viral nucleotide matches were successively aligned in a similar manner in viral and exhaustive protein databases to check for more distant viral matches. The exhaustive and viral protein databases were downloaded in November 2017 from the Uniref100 database (https: / / www.uniprot.org). Although the Uniref100 database is no longer redundant, a taxonomic cleaning process was used to produce the final databases.Taxonomic assignment reported the best matching results with unassigned contigs after these two rounds of alignment that were classified as unknown or non-viral species. The previous process identified a contig that spanned the entire TBEV genome sequence (see "Results"). All reads were then realigned using a CLC cell assembly solution (Qiagen, Hilden, Germany) to this sequence to extract a final consensus sequence. Data recovered from the "D0-without 4sU" condition allowed the identification of contigs covering the entire TBEV and SMRV genomes, and the resulting sequences were named "TBEV REFERENCE" and "SMRV REFERENCE," respectively. Estimation of the substitution ratio of T>C To detect viral sequences with a very high "T to C" substitution rate, each reference sequence was also modified by replacing each T with a C. These sequences were named "TBEV TC REFERENCE" and "SMRV TC REFERENCE". The "TBEV REFERENCE" and "TBEV TC REFERENCE" and the "SMRV REFERENCE" and "SMRV TC REFERENCE" were merged to form two banks named "TBEV BLAST" and "SMRV BLAST". The set of high-quality, filtered reads was then aligned using BLAST with these pre-designed "BLAST" banks. The maximum e-value was set to 10⁻⁸. Only aligned reads were selected for the next stage of the analysis. The CLC program "clc_find_variations" was used to detect each mismatch at each position in the TBEV study reference. The overall variation profile was then analyzed using a proprietary script (PathoQuest, Paris, France) to determine the substitution rate for each nucleotide. The proportion of substituted nucleotides was compared to the total number of aligned nucleotides. For example, the substitution rate of "T to C" was calculated using the following formula: The substitution rates for each time point were normalized with the following substitution index: As a quality control measure for labeling, the mean substitution rate of a set of exons was checked using unlabeled cells as a reference. Exons from the following human genes described by Eisenberg and Levanon (2013. Trends Genet. 29 (10): 569-74) (RefSeq accession number) were used: C1ºrf43 (NM_015449), CHMP2A (NM_014453), EMC7 (NM_020154), GPI (NM_000175). These human exons were used to identify their equivalent in the genome of Cholorcebus sabeus, from which Vero cells are derived. The complete Cholorcebus sabeus assembly (Accession Number GCF_000409795.2) was obtained from the NCBI assembly database (https: / / www.ncbi.nlm.nih.gov / assembly / ). The selected human exons were mapped onto the C. sabeus assembly using minimap2 (Li, 2018. Bioinformatics. 34 (18): 3094-3100), and the resulting .bam file was converted to a .bed file using the bamtobed ​​module from BEDTools (Quinlan & Hall, 2010. Bioinformatics. 26 (6): 841-2). Only matches with a mapping quality greater than 30 (41 exons) were retained, and the corresponding sequences were extracted from the C. sabeus assembly using the getfasta module from BEDTools and indexed for further analysis. Tagging was considered satisfactory if the substitution index was greater than 10. Isolated analysis A specific and isolated analysis was performed on the identified TBEV reads. This analysis was based on a stricter mapping alignment of the filtered reads, with the alignment providing a detailed horizontal genome coverage and depth profile. Local alignments were performed using BWA. Since the sample libraries were prepared using the SMARTer Stranded RNA-Seq kit, RNA strand information was also preserved. As a result, a mapping alignment analysis could provide information about the parent strand of each read (forward or reverse relative to the parent strand). Results Identification of adventitious viruses by agnostic RNA-Seq in Vero cells Vero cells were first exposed to a high dose of TBEV at +4°C (D0). At this temperature, only viral binding to cell receptors occurs, blocking viral entry. This experimental environment thus mimics the drag of a non-replicating virus. RNAs were extracted and sequenced as a marker of infection by either DNA or RNA viruses. The results of the agnostic analysis and the mapping of the reads against the two main viral matches identified by the agnostic analysis (TBEV and SMRV) are shown in Table 7 and Table 8, respectively. Table 7: Number (% negative sense / total reads) of reads in the TBEV and SMRV genomes and horizontal genome coverage (% of genome). Reads were mapped to the TBV and SMRV genomes found using the agnostic procedure Table 8: Agnostic analysis—Number of readings after each stage of the filtration process, results of de novo assembly and blast analysis The main viral species detected in D0 was, as expected, TBEV, but also, unexpectedly, SMRV (Table 7). More than 160,000 TBEV reads out of a total of approximately 150 million raw reads (Table 8) were identified, covering the entire genome. Vero cells were then transferred at 37°C to allow viral entry and subsequently incubated for one day before collection. The number of reads increased considerably, from 5.2 million to 6.4 million recorded TBEV reads. In addition, between 1.6 and 1.8 million SMRV-H mapping reads (an SMRV isolated from a human lymphoid cell line (Oda et al., 1988. Virology. 167(2):468–76)) were also identified, regardless of the collection day. This meant that the cells expressed SMRV transcripts without any relation to the experimental TBEV infection. A number of other matches were also identified (Table 8). The main additional match was baboon endogenous virus, a known endogenous virus of Vero cells (Ma et al., 2011. J Virol. 85(13):6579–88). A few hundred mapping reads of endogenous human retroviruses were also recorded. In our experience, this finding is common in primate / human cell lines. Some BVDV reads, normally associated with the use of gamma-irradiated bovine serum, were also found. A few (<50) reads targeting different herpesviruses were also identified and considered background noise. Differentiation of cellular infection versus the carryover of inert sequences Since the primary objective was to mimic the challenging conditions for differentiating between cellular infection and trawling while testing HTS's ability to detect early cell infection, the results of cells exposed to high doses of TBEV and stored at +4°C to inhibit viral replication were compared with those of cells infected with the same dose of virus 24 hours post-infection. The former mimicked inactivated cells containing either virus or free nucleic acids, while the latter mimicked infected cells immediately prior to storage. Because TBEV is a positive-sense ssRNA virus, negative-sense RNA was used as a marker of viral replication.The three conditions tested in D1 (without 4uS; with 4sU + alkylation; with 4sU without alkylation) showed that 0.32 to 0.36% of the reads were negative-sense compared to 0.27% in D0, a very small but highly significant difference (chi-square test, p < 0.0001). This type of comparative analysis is not relevant for chronic cell infection by SMRV, a retrovirus for which transcription uses a DNA provirus as a matrix and leads mainly to positive-sense but also negative-sense RNAs (Manghera et al., 2017. Virol J.14 (1): 9). The rate of "T to C" substitution of TBEV was then examined after metabolic labeling by 4sU of newly synthesized RNAs (Table 9 and Figure 1). Table 9: Nucleotide substitution rate T and substitution index In D1, in the absence of metabolic labeling, the T-to-C ratio was very low (0.13%) and similar to that of T-to-A or T-to-G (0.04–0.13%), resulting in a calculated background substitution index of 1.68. Similar results were obtained in D0, indicating good reproducibility of the background substitution. In stark contrast, the T-to-C substitution rate for labeled and alkylated TBEV RNAs in D1 was much higher (0.79%), resulting in a substitution index of 6.4, a 3.8-fold increase compared to the background. The substitution index in D1 for labeled and alkylated SMRV cells was 24, 16, 10, and 7-fold above the background. Comparisons between metabolically labeled and unlabeled RNAs would require two culture conditions. Consequently, the TBEV and SMRV substitution indices obtained on day 1 were also compared for the 4sU-labeled culture, with and without RNA alkylation. This requires only one culture condition, followed by RNA extraction and alkylation, or no treatment. The low level of substitutions in the RNA of unalkylated 4sU-labeled cells did not affect the detection of potential viral matches by blast analysis (Table 8). As shown in Table 9 and Figure 1B, the substitution index of unalkylated 4sU-labeled RNAs remained low and close to that of the unlabeled condition (1.71 and 2.27 for TBEV and SMRV, respectively, increasing to 4.0 and 10.6 times, respectively, in the alkylated condition).This suggests that non-alkylated RNAs extracted from the same cell culture can be used to establish the reference viral consensus bank used to calculate substitution rates. Therefore, these results show that after 4sU labeling of the cells, RNA-Seq was able to specifically identify newly synthesized viral RNAs with a high signal-to-noise ratio. Finally, the relationship between the TC substitution rate in 4sU-labeled alkylated cells and the average TA and TG substitutions observed in the same cells was also compared for TBEV (Figure 2) and SMRV (Figure 3). These relationships are presented in Table 10. A substitution ratio greater than 1 is indicative of active transcription in the sample. Therefore, these results clearly show that the method of the invention is capable of distinguishing and detecting live TBEV and SMRV by comparing the substitution rates of different nucleotides under a single condition (D1-with 4sU + alkylation). Table 10: TC substitution ratios versus average TA / TG substitutions Example 4: Materials and methods Cells and molecules A549 cells (ATCC_CCL-185) were cultured in Dulbecco-DMEM modified Eagle medium to approximately 70% confluence in a 6-well plate before contamination. Acholeplasma laidlawii is the representative of the molecute family selected to infect A549 cells. Acholeplasma laidlawii infection of A549 cells At approximately 70% confluence, the culture medium for A549 cells was changed to Earle-MEM medium supplemented with 7% fetal bovine serum and 1% L-glutamine without antibiotics. The cells were infected with various infectious doses of Acholeplasma laidlawii on day 0 (Table 11). On day 5, 4-thiouridine (4sU) (800 µM) was added to the culture medium 9, 6, and 3 hours before supernatant collection. Two mL of culture medium were extracted after 5 days of incubation at 37°C and clarified by centrifugation at 200 g for 5 minutes. One milliliter of clarified supernatant was centrifuged at 15,000–20,000 g for 10 minutes, and 900 µL of supernatant was extracted and the pellet homogenized in the remaining 100 µL of supernatant. The samples were then frozen before nucleic acid extraction. The addition of 4-thiouridine (4sU) to the cell culture medium allows 4sU nucleotides to be incorporated into newly synthesized RNA. Reverse transcription of 4sU shows a certain percentage of erroneous incorporation resulting in a T>C transition in cDNA, which can be identified by sequencing (Herzog et al., 2017. Nat. Methods 14 (12): 1198-1204). Table 11: Description of test elements CTRL5Tag is a control sample, not infected with Acholeplasma laidlawii and tagged with 4SU on day 5. LC5 is a sample infected with a low concentration of Acholeplasma laidlawii on day 5. LC5Tag is a sample infected with a low concentration of Acholeplasma laidlawii and labeled with 4SU on day 5. HC_HK5tag is a sample infected with a high concentration of Acholeplasma laidlawii heat-eliminated prior to infection and labeled with 4SU on day 5. HC_G5tag is a sample infected with a high dose of Acholeplasma laidlawii treated with gentamicin before infection and labeled with 4 SU on day 5. RNA extraction RNA extraction was performed in the dark using a chloroform:isoamyl alcohol 24:1 mixture (Sigma Aldrich, Cat. No. 25666, Saint Louis, USA) followed by isopropanol / ethanol precipitation. During extraction, a reducing agent was used to maintain the 4sU-treated sample under reducing conditions. Alkylation was performed using the SLAMseq Kinetic Kit—Anabolic Kinetic Module (Lexogen, Cat. No. 061, Vienna, Austria) for only part of the "D1-with 4sU" condition. The extracted total RNA was mixed with iodoacetamide (IAA), which modifies the 4-thiol group of 4sU-containing nucleotides by adding a carboxyamidomethyl group, leading to the "D1-with 4sU + alkylation" condition. This alkylation amplifies the frequency of erroneous T>C incorporations during reverse transcription. The other part was labeled "D1-with 4sU without alkylation". Next, the RNA was purified by ethanol precipitation before proceeding with library preparation. Library preparation and sequencing The SMARTer Stranded Total RNA-Seq-Pico Input Mammalian kit (ClonTech, Mountain View, USA) was used for direct library construction from 10 ng of RNA. Depletion of bacterial ribosomal RNA (16S and 23S) was performed on total RNA using the RiboMinus Bacterial Transcriptome Analysis Kit (ThermoFisher). Ribosomal cDNA depletion was also performed using specific probes for mammalian rRNA and some mitochondrial RNA (included in the SMARTer Stranded Total RNA-Seq kit) before library preparation, following the manufacturer's recommendations (ClonTech). Sequencing was performed on the NextSeq instrument (Illumina, San Diego, USA) using the NextSeq medium-output flow cell (FC-404-1001, Illumina). Single-read sequencing with a read length of 150 nucleotides generated approximately 125 million reads per sample. Bioinformatics analysis was agnostic. The raw data readings were filtered to select relevant, high-quality readings. The raw data was then sorted to suppress or remove duplicates, low-quality readings, and homopolymers (using PathoQuest's proprietary software). The sequences introduced during the preparation of the Illumina libraries (adapters, primers) were removed with Skewer (Jiang et al., 2014. BMC Bioinformatics.15: 182). The filtered reads from the LC5 condition were initially considered sequences of interest. Since this condition is highly likely to contain a high content of unlabeled sequences from the organism of interest, this will allow for the reconstruction of the target organism's (Acholeplasma laidlawii) genome. Therefore, the LC5 reads were assembled into longer sequences called "contiges" using Megahit (Li et al., 2015. Bioinformatics. 31 (10): 1674-1676). The resulting contiges were then remapped using minimap2 (Li, 2018. Bioinformatics. 34 (18): 3094-3100) onto the genome of the PG8A strain of Acholeplasma laidlawii (RefSeq accession number CP000896.1). Next, the positive matches were mosaicked into the genome of the PG8A strain of Acholeplasma laidlawii using Mummer 3 (Kurtz et al., 2004. Genome Biol.5 (2): R12) in order to: 1. confirm the identity of the potentially detected contigs as A. laidlawii, 2. Ensure the integrity of the newly constructed sequence. Once the identity of the contigs was assessed and the mosaic validated, the contigs were grouped into a .fasta file to serve as a reference genome (hereafter referred to as LC5_ALAID_CNS) for further analysis. Estimation of the substitution ratio of T>C To detect A. laidlawii sequences with a very high number of TC substitutions, the set of high-quality filtered reads was remapped to LC5_ALAID_CNS using minimap2 in non-multimap mode (Li, 2018. Bioinformatics. 34 (18): 3094–3100). The pileup module of the htsbox software (https: / / github.com / lh3 / htsbox) was then used to detect all mismatches (with a base quality score of at least 30) at each position in the LC5_ALAID_CNS sequence. Global variation profiles were then analyzed using a proprietary script (PathoQuest, Paris, France) to define the substitution rates for each nucleotide. The proportion of substituted nucleotides was compared to the total number of aligned nucleotides. For example, the TC substitution rate was calculated using the following formula: The substitution rates for each time point were normalized with the following substitution index: Results Sequencing throughput The performance of the sequencing experiments is reported in Table 12. For almost all conditions, more than 10 million single-ended reads were produced. For each condition, more than 90% of the reads were retained after the filtering stage, indicating that the sequencing experiments were of good quality and therefore suitable for further analysis. Table 12: Sequencing performance for all experimental conditions Reconstruction of the reference genome The LC5 read assembly process generated a set of 877 contigs (cumulative length 1,374,213; minimum length = 201; average length = 1,566.9; maximum length = 15,043). Remapping onto the PG8A genome sequence of A. laidlawii (CP000896.1) allowed the unambiguous selection of 662 contigs (cumulative length 1,287,020; minimum length = 301; average length = 1,944.1; maximum length = 15,043) as candidates for LC5_ALAID_CNS reconstruction. As a first check, the statistics and distribution of GC content were investigated to detect possible admixture of organisms within the contig set (Figure 4). As shown in Figure 4, the distribution of GC content is unimodal, suggesting a low probability of the presence of contigs representative of various organisms in the contig set. Furthermore, the mean GC content of this set is not significantly different from the expected value (32.01% vs. 31.93% for the A. laidlawii PG8A strain). To ensure that we could reconstruct the complete genome (or at least a significant part) of a close relative of the PG8A strain of A. laidlawii, the latter was "coated" with the contigs selected from the initial assembly of reads from the experimental condition LC5. The results are presented in Figure 5. As shown in Figure 5, the set of 662 contigs almost completely covers the PG8A strain of A. laidlawii with high similarity (greater than 99% in all cases; data not shown), strongly suggesting that the reconstructed LC5_ALAID_CNS is a very close relative of the PG8A strain of A. laidlawii. In conclusion, we were able to: 1. Select a clean set of contigs corresponding to laidlawii, and 2. to coat the entire genome of a close relative (PG8A strain of A. laidlawii). This process therefore validates our reference sequence LC5_ALAID_CNS for further analysis. Substitution indices and rates Poorly coated positions can introduce bias into rate estimates, as they are given the same weight as well-coated positions. In fact, if a position is coated only 3 times and one of those instances is a TC substitution, the TC substitution rate at this position would be 33%, regardless of whether it could be a genuine substitution or a sequencing / assembly error. Therefore, to avoid overestimating substitution rates and, consequently, substitution indices, we first perform the analysis by selecting all detected events (i.e., coated at least once (1X)) and then by selecting events coated at least 20 times (i.e., 20X), the latter being considered high-confidence events. The substitution rates and indices are reported in Table 13. Overall, we show in this dissertation that the TC transition rate is always higher than the TA and TG transversion rates, which is expected since classical mutation patterns favor transitions over transversions. Furthermore, TC substitution rates are significantly higher for the LC5tag and 40x diluted LC5tag conditions compared to all other conditions (including the high-load inactivated sample (HC_HK5Tag)), regardless of the event selection level. Additionally, the inclusion of the low-coverage position in this analysis had little impact on the results, as the observed rates were not significantly different at the 1X and 20X thresholds, although the latter would limit background noise. The same trend is observed for the substitution indices. Table 13: Substitution rates and substitution index (SI) for each experimental condition for all detected events (1X threshold) and for high confidence events (20X threshold). In conclusion, the reported results showed that the experiments in which enrichment with A. laidlawii and labeled with 4sU were expected were detected as such. Positional analysis We have reported an overall increase in substitution rates and substitution indices. To investigate whether these increases are the result of critical substitution points, we performed a positional analysis that evaluates substitution rates along the reference sequence LC5_ALAID_CNS (Figures 6A-G). We observed the presence of A. laidlawii reads in the CTRL5tag experimental condition, as some peaks were visible, even though this condition had not been contaminated with Acholeplasma laidlawii (Figure 6A). Most read rates reached 100%, suggesting that these substitutions are indeed real SNPs. This observation suggested either experimental contamination or cross-index contamination during the sequencing phase when multiplexing samples (so-called index skipping). However, since it involved a fairly limited number of positions, it did not compromise the analysis. Figure 6B shows a fairly low background of substitutions for the LC5 condition with all genome positions well covered (i.e., no gaps in coverage). No true dominant substitutions are observed, which is consistent with the global-scale analysis. In contrast, for the LC5tag and 40-fold diluted LC5tag conditions, large TC peaks could be distinguished emerging from the background, indicating successful tagging and thus active transcription in A. laidlawii (Figures 6C and 6D). Figures 6E and 6F show the results of 4sU labeling under experimental conditions where A. laidlawii cells have been heat-killed (HC_HK5tag and 4°HC_HK5tag). In both cases, we observed a lower number of peaks (and especially TC peaks) compared to the LC5tag and 40X_LC5tag conditions, confirming the lower amount of RNA extracted due to a low number of live bacterial cells remaining in the medium after heating. Likewise, gentamicin treatment had the same effect (HC_G5 condition; Figure 6G), but it was apparently much more moderate compared to the effect of heat under the HC_HK5tag experimental conditions (Figures 6E and 6F). However, the 4sU labeling was still visible and confirms the overall analyses with a moderate substitution index (Table 13). Example 5 Materials and methods Cells and molecules A549 cells (ATCC_CCL-185) are cultured in Dulbecco-DMEM modified Eagle medium to approximately 70% confluence in a 6-well plate prior to contamination. Acholeplasma sp or Mycoplasma sp infection of A549 cells At approximately 70% confluence, the A549 cell culture medium is changed to Earle-MEM medium supplemented with 7% fetal bovine serum and 1% L-glutamine without antibiotics. The cells will be infected with several infectious doses of Acholeplasma sp. or Mycoplasma sp. Several conditions are tested, including: CTRL5Tag: control sample, not infected with Acholeplasma sp or Mycoplasma sp and labeled with 4-SU on day 5; LC5: sample infected with a low concentration of Acholeplasma sp or Mycoplasma sp; LC5Tag: sample infected with a low concentration of Acholeplasma sp or Mycoplasma sp and labeled with 4-SU on day 5; HC_HK5tag: sample infected with a high concentration of Acholeplasma sp or Mycoplasma sp heat-eliminated prior to infection and labeled with 4-SU on day 5; HC_G5tag: sample infected with a high dose of Acholeplasma sp or Mycoplasma sp treated with gentamicin before infection and labeled with 4-SU on day 5. On day 5, 4-thiouridine (4sU) (800 µM) is added to the culture medium 9, 6, and 3 hours before cell collection. The culture medium is removed after 5 days of incubation at 37°C, the cells are precipitated, and frozen before RNA extraction. The addition of 4-thiouridine (4sU) to the cell culture medium allows 4sU nucleotides to be incorporated into newly synthesized RNA. Reverse transcription of 4sU shows a certain percentage of erroneous incorporation resulting in a T>C transition in cDNA, which can be identified by sequencing (Herzog et al., 2017. Nat. Methods 14 (12): 1198-1204). RNA extraction RNA extraction is performed in the dark using a chloroform:isoamyl alcohol 24:1 mixture (Sigma Aldrich, Cat. No. 25666, Saint Louis, USA) followed by isopropanol / ethanol precipitation. During extraction, a reducing agent is used to maintain the 4sU-treated sample under reducing conditions. Alkylation is performed using the SLAMseq Kinetic Kit—Anabolic Kinetic Module (Lexogen, Cat. No. 061, Vienna, Austria) for only part of the "D1-with 4sU" condition. The extracted total RNA is mixed with iodoacetamide (IAA), which modifies the 4-thiol group of the 4sU-containing nucleotides by adding a carboxyamidomethyl group, leading to the "D1-with 4sU + alkylation" condition. This alkylation increases the frequency of erroneous incorporations of T>C during reverse transcription. Next, the RNA is purified using ethanol precipitation before proceeding with library preparation. Library preparation and sequencing The SMARTer Stranded Total RNA-Seq-Pico Input Mammalian kit (ClonTech, Mountain View, USA) is used for direct library construction from 10 ng of RNA. Bacterial ribosomal RNA (16S and 23S) depletion is performed on the total RNA using the RiboMinus Bacterial Transcriptome Analysis Kit (ThermoFisher). Ribosomal cDNA depletion is also performed using probes specific for mammalian rRNA and some mitochondrial RNA (included in the SMARTer Stranded Total RNA-Seq kit, prior to library preparation according to the manufacturer's recommendations (ClonTech)). Sequencing is performed on an Illumina instrument (Illumina, San Diego, USA) using the NextSeq 500 / 550 High-Throughset v2 Kit (FC-404-2002, Illumina). The sequencing has one end paired with a read length of 150 nucleotides, generating approximately 100 million reads per sample. Agnostic bioinformatics analysis Raw data readings are filtered to select relevant, high-quality readings. The raw data is then sorted to suppress or remove duplicates, low-quality readings, and homopolymers (using PathoQuest's proprietary software). The sequences introduced during the preparation of the Illumina libraries (adapters, primers) are removed with Skewer (Jiang et al., 2014. BMC Bioinformatics.15: 182). Filtered reads from negative control conditions (unlabeled, inactivated, or both) are initially considered sequences of interest. Since these conditions are highly likely to contain a high sequence load from the organism of interest, this allows for the reconstruction of the target organism's genome (Acholeplasma sp. or Mycoplasma sp.). Therefore, these reads are assembled into longer sequences called "contiges" using Megahit (Li et al., 2015. Bioinformatics. 31(10): 1674–1676). The resulting contiges are then remapped using minimap2 (Li, 2018. Bioinformatics. 34(18): 3094–3100) onto the genome of the PG8A strain of Acholeplasma sp. or Mycoplasma sp. (RefSeq Accession Number CP000896.1). Positive matches are then mosaicked into the gene of the PG8A strain of Acholeplasma sp or Mycoplasma sp using Mummer 3 (Kurtz et al., 2004. Genome Biol.5 (2): R12) in order to: 1. confirm the identity of the potentially detected contigs as Acholeplasma sp or Mycoplasma sp, and 2. Ensure the integrity of the newly constructed sequence (hereinafter referred to as ALAID_CNS). Estimation of the T>C substitution ratio To detect Acholeplasma sp. or Mycoplasma sp. sequences with a very high number of TC substitutions, the quality-filtered read set is remapped to ALAID_CNS using minimap2 in non-multimap mode (Li, 2018. Bioinformatics. 34 (18): 3094–3100). The pileup module of the htsbox software (https: / / github.com / lh3 / htsbox) is then used to detect all mismatches (with a baseline quality score of at least 30) at each position in the ALAID_CNS sequence. Global variation profiles are then analyzed using a proprietary script (PathoQuest, Paris, France) to define the substitution rates for each nucleotide. The proportion of substituted nucleotides is compared to the total number of aligned nucleotides. For example, the TC substitution rate is calculated using the following formula: The substitution rates for each time point are normalized with the following substitution index:

Claims

1. An in vitro method for distinguishing between live and dead microbes in a sample, comprising distinguishing between transcriptionally active and inert microbial nucleic acid sequences in the sample, wherein the method comprises the steps of: (a) sequencing a set of RNAs extracted from the sample, wherein the set of RNAs is obtained by culturing the sample in the presence of an RNA labeling agent and further subjecting the extracted RNAs to conditions that promote nucleotide substitution, thereby obtaining a set of sequence reads; (b) identifying a matching microbial nucleic acid sequence by: (i) optionally, filtering the set of sequence reads, (ii) optionally, assembling the sequence reads into contigs, (iii) aligning the sequence reads or contigs thereof in a database comprising microbial nucleic acid sequences,(iv) identifying at least one matched microbial nucleic acid sequence mapped against at least one sequence read or contig, and (c) determining the number and / or rate of substituted nucleotides in the set of sequence reads or contigs that have been mapped against at least one matched microbial nucleic acid sequence in step (b)(iv) compared to the control sequence; and (d) concluding that the at least one matched microbial nucleic acid sequence belongs to a living microbe if, in the set of sequence reads or contigs that have been mapped against at least one matched microbial nucleic acid sequence in step (b)(iv), the number and / or rate of substituted nucleotides in the set of sequence reads is greater than the number and / or rate of randomly substituted nucleotides in the control sequence,wherein the control sequence is the same matching microbial nucleic acid sequence found in the closest microbial strain identified in nucleic acid sequence databases; and wherein the nucleotide substitution comprises chemically modifying the RNAs; and furthermore, the reverse transcription of said chemically modified RNAs.

2. The in vitro method according to claim 1, wherein the RNA labeling agent is a thiol-labeled RNA precursor.

3. The in vitro method according to claim 2, wherein the thiol-labeled RNA precursor is selected from the group comprising 4-thiouridine, 2-thiouridine, 2,4-dithiouridine, 2-thio-4-deoxyuridine, 5-carbetoxy-2-thiouridine, 5-carboxy-2-thiouridine, 5-(n-propyl)-2-thiouridine, 6-methyl-2-thiouridine, and 6-(n-propyl)-2-thiouridine.thus obtaining thiouridine-labeled RNAs; preferably the thiol-labeled RNA precursor is 4-thiouridine.

4. The in vitro method according to claim 2 or claim 3, wherein the thiol-labeled RNA precursor is 4-thiouridine.

5. The in vitro method according to any one of claims 1 to 4, wherein the conditions promoting nucleotide substitution comprise chemically modifying the RNAs, preferably by alkylation, oxidative nucleophilic aromatic substitution, or osmium-mediated transformation; more preferably by alkylation; and further reverse transcription of said chemically modified RNAs.

6. The in vitro method according to any one of claims 1 to 5, wherein the conditions promoting nucleotide substitution comprise alkylation using an alkylating agent selected from the group comprising iodoacetamide, iodoacetic acid,n-ethylmaleimide and 4-vinylpyridine; preferably the alkylating agent is iodoacetamide.

7. The method according to any one of claims 1 to 6, wherein the RNA sequencing step comprises: (i) reverse transcribing the RNAs, thereby obtaining a cDNA library, (ii) optionally, amplifying said cDNA library, and (iii) sequencing said cDNA library, preferably by next-generation sequencing (NGS), deep sequencing, or targeted sequencing of customized sequences.

8. The in vitro method according to claim 7, wherein the RNAs undergo adenine (A) to guanosine (G) substitutions in the first strand synthesis and thymidine (T) to cytidine (C) substitutions in the second strand synthesis following reverse transcription when the sample was cultured in the presence of an RNA labeling agent.preferably a thiol-labeled RNA precursor and / or when the labeled RNAs were further subjected to conditions that promote nucleotide substitution.

9. The in vitro method according to any one of claims 1 to 8,wherein at least one matching microbial nucleic acid sequence belongs to a living microbe if: - the number and / or rate of TC substitutions in second-strand synthesis at sequence reads mapped against at least one matching microbial nucleic acid sequence in the sequence read set is greater than the number and / or rate of TC substitutions in second-strand synthesis at the control sequence; and / or - the number and / or rate of TC substitutions in second-strand synthesis at sequence reads mapped against at least one matching microbial nucleic acid sequence in the sequence read set is greater than the number and / or rate of TG substitutions in second-strand synthesis at the same sequence reads.

10. The in vitro method according to any one of claims 1 to 9,wherein the number and / or rate of thymidine (T) to cytidine (C) substitutions is greater than the number and / or average rate of substitution of all other nucleotides at the same sequence reads or contigs, preferably T to adenine (A) and T to guanosine (G) substitutions at the same sequence reads or contigs.

11. The in vitro method according to any one of claims 1 to 10, wherein the microbe is selected from the group comprising viruses, bacteria, archaea, fungi, and protozoa.

12. An in vitro method for diagnosing a microbial infection in a subject, comprising: (a) providing a sample previously obtained from the subject, (b) performing the in vitro method according to any one of claims 1 to 11 on said sample,and (c) diagnosing that the subject has a microbial infection if at least one identified matching microbial nucleic acid sequence belongs to a living microbe.

13. A method for assessing the risk of microbial contamination in a non-biological sample, comprising: (a) providing a non-biological sample, (b) performing the in vitro method according to any one of claims 1 to 11 on said non-biological sample, and (c) concluding that the non-biological sample is at risk of contamination if at least one identified matching microbial nucleic acid sequence belongs to a living microbe.

14. The method for assessing the risk of microbial contamination according to claim 13, wherein the non-biological sample is selected from the group consisting of an environmental sample, a food sample, and a preservation medium.

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