From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data

RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge...

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Bibliographic Details
Published in:PloS one 2015-04, Vol.10 (4), p.e0125000
Main Authors: Li, Jilong, Hou, Jie, Sun, Lin, Wilkins, Jordan Maximillian, Lu, Yuan, Niederhuth, Chad E, Merideth, Benjamin Ryan, Mawhinney, Thomas P, Mossine, Valeri V, Greenlief, C Michael, Walker, John C, Folk, William R, Hannink, Mark, Lubahn, Dennis B, Birchler, James A, Cheng, Jianlin
Format: Article
Language:English
Subjects:
Algorithms
Animals
Arabidopsis - genetics
Arabidopsis thaliana
Biochemistry
Bioinformatics
Computational biology
Computational Biology - methods
Computer science
Data analysis
Data Mining
Data processing
Databases, Genetic
Datasets
Drosophila melanogaster - genetics
Fruit flies
Functional groups
Gene expression
Gene Expression Profiling
Gene mapping
Gene Regulatory Networks
Gene sequencing
Genes
Genome
Genomes
Genomics
Humans
Internet
Medical research
Mice
Proteins
Protocol (computers)
Ribonucleic acid
RNA
Sequence Analysis, RNA - methods
Software
Software packages
Statistics as Topic
Values
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Summary:RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner--a multi-level bioinformatics protocol and pipeline--has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0125000