NetSeekR: a network analysis pipeline for RNA-Seq time series data

Himangi Srivastava; Drew Ferrell; George V Popescu

doi:10.1186/s12859-021-04554-1

NetSeekR: a network analysis pipeline for RNA-Seq time series data

BMC Bioinformatics. 2022 Jan 28;23(1):54. doi: 10.1186/s12859-021-04554-1.

Authors

Himangi Srivastava^#^{1

2}, Drew Ferrell^#³, George V Popescu⁴

Affiliations

¹ Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, 39762, USA.
² Institute for Genomics, Biocomputing and Bioengineering, Mississippi State University, Mississippi State, MS, 39762, USA.
³ Department of Biochemistry, Molecular Biology, Plant Pathology and Entomology, Mississippi State University, Mississippi State, MS, 39762, USA.
⁴ Institute for Genomics, Biocomputing and Bioengineering, Mississippi State University, Mississippi State, MS, 39762, USA. popescu@igbb.msstate.edu.

^# Contributed equally.

Abstract

Background: Recent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline.

Results: We describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods.

Conclusion: Our methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data.

Keywords: Bioinformatics pipeline; Complex network analysis; Correlation gene expression analysis; Differential gene expression analysis; RNA-Seq data; Regulatory network analysis.

MeSH terms

Computational Biology*
High-Throughput Nucleotide Sequencing*
RNA-Seq
Sequence Analysis, RNA
Time Factors

Grants and funding

SRI-249170/Mississippi Agricultural and Forestry Experiment Station, Mississippi State University