期刊
BMC BIOINFORMATICS
卷 23, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s12859-021-04554-1
关键词
RNA-Seq data; Differential gene expression analysis; Correlation gene expression analysis; Regulatory network analysis; Complex network analysis; Bioinformatics pipeline
类别
资金
- National Science Foundation [NSF-MCB 1714157]
- MAFES Strategic Research Initiative project at Mississippi State University [SRI-249170]
NetSeekR is a network analysis R package that enables analysis of time series RNA-Seq data, correlation and regulatory network inference, and summarization of comparative genomics study results using network analysis methods. The pipeline integrates multiple genomic analysis tools and allows for hypothesis building, functional analysis, and genomics discovery from large-scale NGS data.
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.
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