Non-model species transcriptome sequencing result analysis method based on high-throughput technology

Abstract

The invention discloses a non-model species transcriptome sequencing result analysis method based on a high-throughput technology. The non-model species transcriptome sequencing result analysis method specifically comprises the following steps: (1) performing quality control on high-throughput sequencing data by using Fastp software; (2) carrying out De novo assembly on the transcriptome by using Trinity software; (3) removing redundancy of the assembled sequence by using CD-HIT software; (4) carrying out integrity evaluation on the assembled non-redundant transcript by using BUSCO software; and (5) carrying out bioinformatics analysis on an assembly result, wherein the bioinformatics analysis comprises gene function annotation, CDS prediction, SSR analysis and transcription factor identification. According to the method, a comprehensive and perfect process analysis method is formed through the steps, and effective reference is provided for analysis of non-model species transcriptome sequencing results.

Description

Technical Field

[0001] The present invention relates to a sequencing analysis method, and in particular to a method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology. Background Art

[0002] With the development of sequencing technology, high-throughput sequencing technology has become an effective way to study transcriptomes, especially for the growth and development of non-model plants without reference, gene discovery, secondary metabolite synthesis and metabolic regulation, etc. There are many analytical methods in the field of bioinformatics, but these methods are often scattered and complicated, and the analysis software in the field of bioinformatics is complicated. Incompatibility issues are common among various analysis software versions, which leads to increased time consumption and extremely low efficiency.

[0003] In order to meet the growing demand for bioinformatics analysis among scholars, a Chinese patent with publication number CN110047560A discloses an automated prokaryotic transcriptome analysis method based on second-generation sequencing. This method is mainly used for gene expression quantification and differential expression analysis of prokaryotes, but does not focus on the transcriptome assembly of non-model organisms and the software version used in the analysis process.

[0004] How to design an accurate, reliable and efficient method for analyzing transcriptome sequencing results of non-model species using high-throughput technology is an urgent problem to be solved in the field of bioinformatics. Summary of the invention

[0005] Purpose of the invention: In view of the deficiencies of existing technologies and actual needs, the present invention provides a method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, in order to achieve efficient analysis of transcriptome sequencing results of non-model species.

[0006] Technical solution: A method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, including the following steps:

[0007] S1: Use Fastp software to quality control high-throughput sequencing data to obtain clean reads;

[0008] S2: Use Trinity software to perform de novo assembly of the transcriptome of the clean reads obtained in step S1;

[0009] S3: Use CD-HIT software to remove redundancy from the sequence assembled in step S2 to obtain a non-redundant transcript sequence;

[0010] S4: Use BUSCO software to evaluate the integrity of the non-redundant transcript sequence obtained in step S3;

[0011] S5: Bioinformatics analysis of assembled non-redundant transcript sequences, including gene function annotation, CDS prediction, SSR analysis, and transcription factor identification.

[0012] The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, step S1 specifically comprises:

[0013] S1.1 Download and install Fastp Version=0.2.3.4 software;

[0014] S1.2 Use the Fastp software installed in step S1.1 to filter low-quality sequences, and then analyze the sequence length distribution and the GC content of each site to determine the quality of quality control.

[0015] The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, the method for filtering low-quality sequences in step S1.2 comprises:

[0016] S1.2.1: Remove reads containing adapters;

[0017] S1.2.2: Remove reads containing N ratio greater than 10%;

[0018] S1.2.3 remove reads containing all A bases;

[0019] S1.2.4: Remove low-quality reads.

[0020] In the method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, the low-quality reads described in step S1.2.4 are bases with a quality value Q≤20 accounting for more than 50% of the entire read.

[0021] The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, step S2 specifically comprises:

[0022] S2.1: Download and install TrinityVersion=2.8.5 software;

[0023] S2.2: Use the Trinity software installed in step S2.1 to perform de novo transcriptome assembly on the clean reads obtained in step S1.

[0024] The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, step S3 specifically comprises:

[0025] S3.1: Download and install CD-HIT Version=4.6.2 software;

[0026] S3.2: Use the CD-HIT software installed in step S3.1 to remove redundancy from the sequence assembled in step S2 to obtain non-redundant transcript sequences.

[0027] The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, step S4 specifically comprises:

[0028] S4.1: Download and install BUSCO Version=5.6.0 software;

[0029] S4.2: Use the BUSCO software installed in step S5.1 to evaluate the integrity of the non-redundant transcript sequences obtained in step S3.

[0030] The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, step S5 specifically comprises:

[0031] S5.1: Use Blastx Version=2.6.5 software to align the non-redundant transcript sequence obtained in step S3 to the protein databases NR, SwissProt, KEGG and COG / KOG (evalue<0.00001), thereby obtaining the annotation information of the Unigenes in each database, and summarizing the annotation information of each database;

[0032] S5.2: Use TransDecoder Version=5.5.0 software to align the results of step S5.1 and predict the coding region sequence and amino acid sequence of Unigene;

[0033] S5.3: Use MISA software to search the non-redundant transcript sequences obtained in S3 and perform SSR prediction analysis;

[0034] S5.4: Primer3 software was used to design primers for the examined SSR loci, and 13 pairs of primers with different SSR repeat types were randomly selected from the transcriptome data for the reliability evaluation of the primers;

[0035] S5.5: Use HMMER Version=3.3.2 software to perform hmmscan comparison between the non-redundant transcript sequence obtained in step S3 and the Transcription factors (TF) database to obtain the transcription factor prediction results of the transcript sequence.

[0036] The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology is applied in gene function annotation, CDS prediction, SSR analysis or transcription factor identification of plants.

[0037] The non-model species transcriptome sequencing result analysis method based on high-throughput technology is applied to gene function annotation, CDS prediction, SSR analysis, and transcription factor identification of Clematis calyx.

[0038] Beneficial effects: Compared with the prior art, the method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology provided by the present invention has the following beneficial effects: (1) Mainstream bioinformatics analysis software is used to improve the efficiency of data analysis and processing. (2) Clear analysis steps, analysis software, software versions and related operation scripts are provided for the analysis of transcriptome sequencing data of non-model species, which provides a reference for software version incompatibility during bioinformatics analysis and avoids repeated experiments due to solving software version incompatibility problems. (3) The present invention is designed to integrate relevant bioinformatics analysis software and corresponding software versions based on the Conda tool, and combines programming languages ​​such as Shell, Python and Perl to construct a method for analyzing transcriptome sequencing results of non-model species for high-throughput technology, which improves the process level of data analysis and has practical significance and good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the above and other advantages of the present invention will become more clear;

[0040] Figure 1 It is a flowchart of the method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology in a specific embodiment of the present invention;

[0041] Figure 2 It is a statistical result graph of non-redundant transcript sequences of samples;

[0042] Figure 3 It is a graph evaluating the completeness of the non-redundant transcript sequences of the sample;

[0043] Figure 4 This is a statistical screenshot of the non-redundant transcript sequence annotation results of the sample;

[0044] Figure 5 It is the length distribution diagram of the CDS prediction results of the non-redundant transcript sequence of the sample;

[0045] Figure 6 It is a statistical graph of repeated types in the SSR prediction results of the sample;

[0046] Figure 7 These are different types of statistical graphs of the transcription factor prediction results of the samples. DETAILED DESCRIPTION

[0047] In order to further explain the technical means and effects of the present invention, the present invention is further described below in combination with implementation cases and drawings. It should be understood that the specific implementation methods described here are only used to explain the present invention rather than to limit the present invention.

[0048] The specific technical conditions or conditions not specified in the examples shall be carried out according to the techniques or conditions described in the literature in the art.

[0049] Although there are many analysis methods for high-throughput transcriptome sequencing, these methods are often scattered and complicated, and the analysis software in the field of bioinformatics is complicated. Incompatibility issues of various analysis software versions are common, which leads to increased time consumption and extremely low efficiency. Compared with previous studies, the present invention integrates relevant bioinformatics analysis software and corresponding software versions based on the Conda tool, and combines programming languages ​​such as Shell, Python and Perl to construct a non-model species transcriptome sequencing result analysis method for high-throughput technology, which is suitable for a variety of non-model species biological research. The analysis diagram is shown in the figure. Figure 1 shown.

[0050] The specific implementation case of the present invention takes the non-model species Clematis calyx as the research object, and verifies the non-model species transcriptome sequencing result analysis method based on high-throughput technology of the present invention based on the transcriptome sequencing data of 9 samples of Clematis calyx (the sequence has been published to NCBI: PRJNA1108023).

[0051] This experimental design uses biological replication. The coding part only shows the operation process of one sample, and other samples can refer to the operation.

[0052] The main analysis process is as follows:

[0053] Step S1: Use Fastp software to perform quality control on high-throughput sequencing data to obtain clean reads;

[0054] In this embodiment, Fastp Version=0.2.3.4 software was downloaded and installed, and the installed Fastp software was used to remove reads containing adapters, remove reads containing N ratio greater than 10%, remove reads containing all A bases, and remove low-quality reads (the number of bases with quality value Q≤20 accounts for more than 50% of the entire read) to filter low-quality sequences, and finally analyze the sequence length distribution and the GC content of each site to determine the quality of quality control;

[0055] conda install fastp=0.2.3.4

[0056] fastp-i / data / Purple-1_1.fq.gz-I / data / Purple-1_2.fq.gz-o / data / fastp_Purple-11.fq.gz-O / data / fastp_Purple-1-2.fq.gz-n 10-q 20-u 50-j / data / fastp_Purple-1.json-h / data / fastp_Purple-1.html

[0057] Step S2: De novo assembly of transcriptomes of the clean reads obtained in step S1 using Trinity software;

[0058] In this embodiment, Trinity Version=2.8.5 software is downloaded and installed, and the clean reads obtained in step S1 are subjected to de novo assembly of the transcriptome using the installed Trinity software;

[0059] conda install trinity=2.8.5

[0060] Trinity--left / data / fastp_Purple-1_1.fq.gz--right / data / fastp_Purple-1_2.fq.gz--seqTypefq--max_memory 50G--SS_lib_type FR--CPU 8--normalize_reads--full_cleanup--output Purple--bflyCalculateCPU&>trinity_denovo_puprle.log

[0061] Step S3 uses CD-HIT software to remove redundancy from the sequence assembled in step S2 to obtain a non-redundant transcript sequence;

[0062] In this embodiment, CD-HIT Version = 4.6.2 software was downloaded and installed, and the installed CD-HIT software was used to remove redundancy from the sequence assembled in step S2 to obtain 375,294 non-redundant transcript sequences. The statistical results of the non-redundant transcript sequences are as follows: Figure 2 As shown;

[0063] conda install cd -hit=4.6.2

[0064] cd-hit-est-i Purple.Trinity.fasta-o Unigene.fasta-c 0.95-n 10-d 0-M60000-T20

[0065] TrinityStats.pl / data / Unigene.fasta> / data / Unigenes.fasta.stats

[0066] Step S4 uses BUSCO software to evaluate the integrity of the non-redundant transcript sequence assembled in step S3;

[0067] In this embodiment, BUSCO Version=5.6.0 software was downloaded and installed, and the installed BUSCO software was used to perform integrity assessment on the non-redundant transcript sequence obtained after the assembly in step S3. The integrity assessment diagram of the non-redundant transcript sequence of the sample is shown in FIG. Figure 3 The results showed that 1 219 (84.65%) BUSCO groups were completely aligned (including 1 181 single copies and 38 multiple copies), 95 (6.60%) were partially aligned, and 126 (8.75%) were not aligned. This indicates that the sequence quality of the Clematis calyx transcriptome assembly is high and can be used for subsequent analysis.

[0068] conda install busco=5.6.0

[0069] busco-i / data / Unigenes.fasta-o / data / busco-1 / data / arthropoda_odb-mtran-c 1 6-f--offline

[0070] Step S5 performs bioinformatics analysis on the assembled non-redundant transcript sequences, including gene function annotation, CDS prediction, SSR analysis, and transcription factor identification.

[0071] In this embodiment, the specific analysis process of step S5 is as follows:

[0072] (1) The 375,294 non-redundant transcript sequences obtained in step S4 were aligned to the protein databases NR, SwissProt, KEGG and COG / KOG (evalue < 0.00001) using Blastx Version = 2.6.5 software. A total of 32,270 transcripts were successfully annotated to the Nr, SwissProt, COG / KOG and KEGG protein databases, accounting for 85.99% of the total transcripts. The annotation information of each database was summarized. The statistical screenshot of the non-redundant transcript sequence annotation results is shown in the figure. Figure 4 As shown;

[0073] (2) Using TransDecoder Version=5.5.0 software, the coding region sequence and amino acid sequence of Unigene were predicted based on the alignment results of step S5.1. Figure 5 As shown in the figure, the length of the CDS prediction results of the non-redundant transcript sequences of the samples ranged from 102 to 7836 bp, with an average of 1406 bp. The results showed that the sequencing depth was good and the quality of the assembled transcripts was high.

[0074] (3) The non-redundant transcript sequences obtained by S4 were searched using MISA software for SSR prediction analysis. The repeat type statistics in the SSR prediction results of the samples are shown in the figure below. Figure 6 As shown. By analyzing the frequency of occurrence of various types of repeat motifs, it was found that among all repeat motif types, dinucleotide repeat motifs were mainly AG / CT, accounting for 37.78%; among trinucleotide repeat motifs, the largest number was AAG / CTT, accounting for 10.58%, followed by ATC / ATG, accounting for 5.41%; among tetranucleotide repeat motifs, the largest number was AAAT / TAAA, accounting for 1.16%. The results show that the dominant repeat motifs of Clematis calyx transcriptome SSR are mainly AG / CT and AAG / CTT, which may be related to the preference of codons.

[0075] (4) Primer3 software was used to design primers for the 6927 SSR loci detected, and 5986 pairs of primers were finally designed, with a success rate of 86.42%. Fifteen pairs of primers with different SSR repeat types were randomly selected from the transcriptome data for primer reliability evaluation. The 15 pairs of primers were amplified by a PCR instrument, and it was found that the bands amplified by 13 pairs of SSR primers showed polymorphism, and the predicted size of the amplified product was between 145 and 277 bp (Table 1). The amplification success rate was 86.67%, indicating that the developed Clematis calyx SSR primer design has strong usability.

[0076] Table 1

[0077]

[0078] (5) Using HMMER Version=3.3.2 software, the non-redundant transcript sequence obtained in step S4 was compared with the Transcription factors (TF) database by hmmscan to obtain the transcription factor prediction results of the transcript sequence. The statistical graphs of the transcription factor prediction results of different types of samples are shown in the figure below. Figure 7As shown. The results showed that a total of 739 transcription factor sequences were predicted and distributed into 41 gene families. Among them, the bHLH family had the largest number of members, a total of 82, followed by ERF, a total of 76. Studies have shown that NAC transcription factors are expressed in plants under abiotic stresses such as drought and high salt. The results of this study showed that 38 transcription factor sequences were distributed into the NAC family, which can provide basic data for further research on abiotic stress expression of Clematis calyx.

Claims

1. A method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology, characterized in that: The following steps are involved: S1: Use Fastp software to quality control high-throughput sequencing data to obtain clean reads; S2: Use Trinity software to perform de novo assembly of the transcriptome of the clean reads obtained in step S1; S3: Use CD-HIT software to remove redundancy from the sequence assembled in step S2 to obtain a non-redundant transcript sequence; S4: Use BUSCO software to evaluate the integrity of the non-redundant transcript sequence obtained in step S3; S5: Bioinformatics analysis of assembled non-redundant transcript sequences, including gene function annotation, CDS prediction, SSR analysis, and transcription factor identification.

2. The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology according to claim 1, characterized in that: Step S1 specifically includes: S1.1 Download and install Fastp Version=0.2.3.4 software; S1.2 Use the Fastp software installed in step S1.1 to filter low-quality sequences, and then analyze the sequence length distribution and the GC content of each site to determine the quality of quality control.

3. The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology according to claim 1, characterized in that: The method for filtering low-quality sequences described in step S1.2 includes: S1.2.1: Remove reads containing adapters; S1.2.2: Remove reads containing N ratio greater than 10%; S1.2.3 remove reads containing all A bases; S1.2.4: Remove low-quality reads.

4. The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology according to claim 1, characterized in that: The low-quality reads described in step S1.2.4 are those in which the number of bases with a quality value Q≤20 accounts for more than 50% of the entire read.

5. The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology according to claim 1, characterized in that: Step S2 specifically includes: S2.1: Download and install Trinity Version=2.8.5 software; S2.2: Use the Trinity software installed in step S2.1 to perform Denovo assembly of the transcriptome on the clean reads obtained in step S1.

6. The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology according to claim 1, characterized in that: Step S3 specifically includes: S3.1: Download and install CD-HIT Version=4.6.2 software; S3.2: Use the CD-HIT software installed in step S3.1 to remove redundancy from the sequence assembled in step S2 to obtain non-redundant transcript sequences.

7. The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology according to claim 1, characterized in that: Step S4 specifically includes: S4.1: Download and install BUSCO Version=5.6.0 software; S4.2: Use the BUSCO software installed in step S5.1 to evaluate the integrity of the non-redundant transcript sequences obtained in step S3.

8. The method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology according to claim 1, characterized in that: Step S5 specifically includes: S5.1: Use Blastx Version=2.6.5 software to align the non-redundant transcript sequence obtained in step S3 to the protein databases NR, SwissProt, KEGG and COG / KOG, so as to obtain the annotation information of the Unigenes in each database, and summarize the annotation information of each database; S5.2: Use TransDecoder Version=5.5.0 software to align the results of step S5.1 and predict the coding region sequence and amino acid sequence of Unigene; S5.3: Use MISA software to search the non-redundant transcript sequences obtained in S3 and perform SSR prediction analysis; S5.4: Primer3 software was used to design primers for the examined SSR loci, and 13 pairs of primers with different SSR repeat types were randomly selected from the transcriptome data for the reliability evaluation of the primers; S5.5: Use HMMER Version=3.3.2 software to perform hmmscan comparison on the non-redundant transcript sequence obtained in step S3 and the Transcription factors database to obtain the transcription factor prediction results of the transcript sequence.

9. Application of the method for analyzing transcriptome sequencing results of non-model species based on high-throughput technology as described in any one of claims 1 to 8 in gene function annotation, CDS prediction, SSR analysis or transcription factor identification of plants.

10. Application of the non-model species transcriptome sequencing result analysis method based on high-throughput technology according to any one of claims 1 to 8 in gene function annotation, CDS prediction, SSR analysis, and transcription factor identification of Clematis calyx.

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