A computational method for stoichiometric genome-based analysis

Abstract

The application discloses a kind of chemical stoichiometry genome carries out the calculation method of basic analysis. The chemical stoichiometry genome carries out the calculation method of basic analysis provided by the application comprehensively uses Perl script language programming, R script language programming and R language visualization, based on species genome data, the average content of element, the content distribution of base and element of the genome of species, the analysis and visualization display of the correlation of element content and GC value are carried out to the screening and function enrichment of extreme gene. Experiments prove that the chemical stoichiometry genome carries out the calculation method of basic analysis provided by the application is more systematic, good effect, fast, easy to realize automation and flow.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology and relates to a computational method for chemometric genomics analysis. Background Technology

[0002] In recent years, with the development of science and technology, gene sequencing methods have been gradually optimized, research in the field of genomics has become increasingly refined and developed, and genomic data has been continuously accumulated, promoting the growth and expansion of this field. Based on genomic data, by calculating the element usage preferences in genes or genomes, we can not only understand the evolutionary laws of element usage preferences, but also use this as an assessment of the direct impact of resource constraints on the genome. If a certain element (such as nitrogen or phosphorus) is insufficient in the environment, natural selection may prompt organisms to reduce their use of these limiting elements, thereby affecting gene synthesis and limiting the growth and reproduction of organisms. Chemostometric genomics refers to the computational biology method of calculating the stoichiometric characteristics of a genome or DNA sequence, including the composition and content of elements (carbon, hydrogen, oxygen, nitrogen) and monomers (nucleotides). Chemostometric genomics is an emerging interdisciplinary field that includes chemometrics, ecology, evolutionary biology, genomics, and bioinformatics, providing a theoretical foundation for the study of molecular evolution and ecosystem interactions, and also providing a comprehensive perspective for data mining in the post-genomic era. Currently, the computational methods of chemostometric genomics involve complex calculations, statistical theories, and analytical methods, and lack visualization applications. This poses a significant challenge for non-professional researchers. Furthermore, the downloaded genome files require extensive preprocessing to be converted into input files with a fixed format, which greatly limits the analysis by non-bioinformatics professionals and those with relatively weak computer skills, ultimately hindering research in the field of biochemical composition. Summary of the Invention

[0003] The purpose of this invention is to provide a computational method for chemometric genomics analysis.

[0004] The calculation method for chemostometric genomics fundamental analysis provided by this invention may specifically include the following steps:

[0005] (1) Record the FASTA or FASTQ format files of the gene or genome sequence of the species to be tested as genomic data A, place them in the folder in, and create a new folder out. Perform base and element content analysis on the genomic data in the folder in, run the Perl script 1 command ("perl coun4DNA.pl in out"), and obtain the base and element content data B (output1.xls) file and the average base and element content data C (output2.xls) file in the out folder.

[0006] Furthermore, the Perl script 1 mentioned in this invention is specifically: count4DNA.pl

[0007]

[0008]

[0009]

[0010]

[0011]

[0012]

[0013] (2) Visualize the average content data C of bases and elements by running the R script 1 command (run "Rscript genomics-circular-column.R" on UNIX / Linux / MacOSX systems; or run "genomics-circular-column.R" directly in R or Rstudio on Windows) to obtain circular and column charts of the average content of bases and elements.

[0014] Furthermore, the R script 1 described in this invention is specifically: genomics-circular-bar chart.R

[0015]

[0016]

[0017] (3) Visualize the base and element content data B by running the R script 2 command (run "Rscript genomics-frequency distribution plot.R" on UNIX / Linux / MacOSX system; or run "genomics-frequency distribution plot.R" directly in R or Rstudio on Windows) to obtain the frequency distribution plot of the element content.

[0018] Furthermore, the R script 2 mentioned in this invention is specifically: genomics-frequency distribution plot.R

[0019]

[0020]

[0021] (4) Screen the extreme value gene data of the base and element content data B, run R script 3 (run on UNIX / Linux / MacOSX system: "Rscript genomics-bubble chart.R"; or run directly in R or Rstudio on Windows: "genomics-bubble chart.R") to obtain the 10 gene diagrams with the highest and lowest content of each element.

[0022] The R script 3 mentioned in this invention is specifically: genomics-bubblechart.R

[0023]

[0024]

[0025]

[0026]

[0027] (5) Perform extreme value gene function enrichment on the base and element content data B, and run R script 4 (run "Rscript genomics-go enrichment(Ecoli).R" on UNIX / Linux / MacOSX system; or run "genomics-go enrichment(Ecoli)" directly in R or Rstudio on Windows) to obtain the functional enrichment map of extreme genes.

[0028] The R script 4 mentioned in this invention is specifically genomics-go enrichment (Escherichia coli).R

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] (6) Perform correlation analysis on the base and element content data B. Run the R script 5 command (run "Rscript genomics-correlation analysis.R" on UNIX / Linux / MacOSX system; or run "genomics-correlation analysis.R" directly in R or Rstudio on Windows) to obtain the correlation analysis graph between element content and GC value.

[0036] Furthermore, the R script 5 mentioned in this invention is specifically: genomics-correlation analysis.R

[0037]

[0038] In this invention, the species to be tested in step (1) can be any species, and the genome sequence can be obtained by downloading from a publicly available genome database or by whole-genome sequencing. Specifically, the genome to be tested in this invention is the genome of *Escherichia coli*, and its data can be downloaded from the NCBI database (https: / / www.ncbi.nlm.nih.gov).

[0039] The present invention has the following advantages: First, it can quickly calculate the results of basic genomic chemometric analysis, with comprehensive and accurate parameters, good results, and fast speed; second, it is relatively systematic, efficient, and automated; third, the present invention perfectly and smoothly combines Perl language script programming with several R language script programming, realizing good connection between software and data visualization. Attached Figure Description

[0040] Figure 1 This is a flowchart of the chemometric genomics fundamental analysis of the present invention;

[0041] Figure 2 Visualizations of the average content data C of the elements in R script 1 in step 2, including pie charts and bar charts;

[0042] Figure 3 A frequency distribution plot for visualizing the base and element content data B in R script 2 of step 3;

[0043] Figure 4 The extreme value gene diagram for visualizing the base and element content data B in R script 3 of step 4;

[0044] Figure 5 The functional enrichment plots of the extreme values ​​(element content) genes of data B in R script 4 of step 5 are shown in three subplots, namely GO-BP enrichment plot, GO-CC enrichment plot, and GO-MF enrichment plot.

[0045] Figure 6 This is a visualization of the base and element content data B from R script 5 in step 6. Detailed Implementation

[0046] The present invention will now be described in more detail using Escherichia coli genome data as an example.

[0047] The flowchart of the calculation method for chemostometric genomics-based analysis provided by this invention is shown below. Figure 1 Specifically, it includes the following steps:

[0048] It can be operated on UNIX / Linux / MacOSX / Windows systems, but Perl and R software need to be installed.

[0049] (1) Describe the FASTA or FASTQ format file of the gene or genome sequence of the species to be tested as genome data A. In the Perl environment, create a folder named "in" and place genome data A into the folder "in". At the same time, create a new folder named "out". First, perform base and elemental analysis on genome data A in the folder "in" using a Perl program. Execute the Perl script 1 command ("perl coun4DNA.pl in out") to obtain the folder "out". The "out" folder contains genome base and elemental content data B (output1.xls) and average base and elemental content data C (output2.xls).

[0050] Furthermore, the Perl script 1 mentioned in this invention is specifically: count4DNA.pl

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[0057] Furthermore, the B(output1.xls) file format described in this invention is specifically as follows:

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[0059] Furthermore, the C(output2.xls) file format described in this invention is specifically as follows:

[0060]

[0061] (2) Visualize the average base and element content data C. Run the R script 1 command (on UNIX / Linux / MacOSX systems: Rscript genomics-circular-column.R; or directly in R or Rstudio on Windows: genomics-circular-column.R). R script 1 will automatically process the data C and obtain circular and column charts of the average base and element content, as shown below. Figure 2 As shown. Figure 2 The circular plot shows the average content of E. coli genome elements (C, H, O, N), and the bar chart shows the average content of E. coli genome bases (A, T, C, N).

[0062] Furthermore, the R script 1 described in this invention is specifically: genomics-circular-bar chart.R

[0063]

[0064]

[0065]

[0066] (3) To visualize the base and element content data B, run the R script 2 command (run "Rscript genomics-frequency distribution plot.R" on UNIX / Linux / MacOSX systems; or run "genomics-frequency distribution plot.R" directly in R or Rstudio on Windows). R script 2 will automatically process data B and obtain the frequency distribution plot of the element content, such as... Figure 3 As shown. Figure 3 The frequency distribution of elements in the Escherichia coli genome is shown.

[0067] Furthermore, the R script 2 mentioned in this invention is specifically: genomics-frequency distribution plot.R

[0068]

[0069]

[0070] (4) Perform extreme value gene data screening analysis on the base and element content data B. Run R script 3 (run "Rscript g enomics-bubble chart.R" on UNIX / Linux / MacOSX systems; or run "genomics-bubble chart.R" directly in R or Rstudio on Windows) to obtain the 10 gene diagrams with the highest and lowest content of each element, such as... Figure 4 As shown.

[0071] The R script 3 mentioned in this invention is specifically: genomics-bubblechart.R

[0072]

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[0075]

[0076] (5) Visualize the functional enrichment of extreme gene values ​​in the base and element content data B. Run R script 4 (on UNIX / Linux / MacOSX systems: "Rscript genomics-go enrichment(Escherichia coli).R"; or directly in Windows R or Rstudio: "genomics-go enrichment(Ecoli).R") to obtain the functional enrichment map of extreme genes, such as... Figure 5 As shown, it includes GO-BP enrichment map, GO-CC enrichment map, and GO-MF enrichment map.

[0077] The R script 4 mentioned in this invention is specifically: genomics-go enrichment (Ecoli).R

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[0080]

[0081]

[0082]

[0083]

[0084] (6) Perform correlation analysis on the base and element content data B. Run the R script 5 command (run "Rscript genomics-correlation analysis.R" on UNIX / Linux / MacOSX systems; or run "genomics-correlation analysis.R" directly in R or Rstudio on Windows) to obtain the correlation analysis graph between element content and GC value, such as... Figure 6 As shown.

[0085] Furthermore, the R script 5 mentioned in this invention is specifically: genomics-correlation analysis.R

[0086]

[0087] Using Escherichia coli genome data as an example, the inventors of this invention achieved data processing and graphical visualization for chemometric genomics basic analysis by combining Perl and R scripts. This demonstrates the accuracy, efficiency, simplicity, and novelty of the method described above.

[0088] Table 1 lists commonly used software in the instruction manual.

[0089] Perl UNIX / Linux / Windows / MacOSX The Perl Programming Language-www.perl.org R UNIX / Linux / Windows / MacOSX R: The R Project for Statistical Computing (r-project.org)

Claims

1. A computational method for chemostometric genomics-based analysis, characterized in that, Includes the following steps: 1.1 Obtain gene or genome sequence data of the species to be tested; 1.2 The gene or genome sequence data is analyzed gene-by-gene or sequence-by-sequence, and the bases in the sequence are counted to obtain the count values ​​of bases A, G, C, T and / or U in each gene or sequence, and the sum of the count values ​​is used as the corresponding sequence length parameter; 1.3 Based on the base count value, according to the atomic composition relationship corresponding to each base, the base count value is mapped to the chemical element count value, and the chemical element count value is normalized with the sequence length parameter to obtain the chemical element content parameter of each gene or sequence, wherein the chemical elements include at least carbon, hydrogen, oxygen and nitrogen; 1.4 Statistical processing is performed on the chemical element content parameters and / or the base count values ​​within the entire gene or entire sequence range of the same species to obtain the average chemical element content information and / or average base content information of the species; 1.5 Based on the average content information of chemical elements and / or the average content information of bases, the base composition characteristics and chemical element composition characteristics of genes or genomes are visualized. The visualization process includes at least: generating a ring diagram and / or a bar chart of the average content of bases and / or the average content of chemical elements, and generating a frequency distribution diagram of chemical element content parameters among multiple genes. 1.6 Based on the distribution of the chemical element content parameters among different genes, the chemical element content parameters are sorted and analyzed to screen out the target gene set whose chemical element content is in the high value range or low value range. 1.7 Perform functional enrichment analysis on the target gene set to obtain biological functional information related to chemical element usage preferences; 1.8 Perform statistical correlation analysis on the chemical element content parameters and the GC content of the gene or genome to obtain the correlation analysis results between the two.

2. The calculation method according to claim 1, characterized in that, The species to be tested are prokaryotes or eukaryotes.

3. The calculation method according to claim 1, characterized in that, The gene or genome sequence data is nucleic acid sequence data that can be analyzed to determine the base composition information, and is processed in a structured form.

4. The calculation method according to claim 1, characterized in that, The process of mapping base counts to chemical element counts is based on the atomic composition relationship of each base, and is obtained by accumulating the number of carbon, hydrogen, oxygen and nitrogen atoms.

5. The calculation method according to claim 1, characterized in that, The average chemical element content information is obtained by summarizing the chemical element content parameters of all genes or all sequences in the same species and calculating the average value.

6. The calculation method according to claim 1, characterized in that, The process of screening the target gene set includes sorting the chemical element content parameters of the same chemical element and selecting genes whose chemical element content is located in the high or low range of the statistical distribution as the target gene set.

7. The calculation method according to claim 1, characterized in that, The functional enrichment analysis includes enrichment analysis of the target gene set based on gene ontology, focusing on biological processes, cellular components, and molecular functions.

8. The calculation method according to claim 1, characterized in that, The GC content is obtained by adding the content of base C to the content of base G, and the statistical correlation analysis is used to evaluate the correlation between the GC content and the content parameter of at least one chemical element.

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