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Adjustment of RNA‐Seq data for the effect of highly abundant transcripts: a case study in milk production (622.4)
Author(s) -
Beck Kristen,
Turco Gina,
Bradnam Keith,
Rijnkels Monique,
NommsenRivers Laurie,
Korf Ian,
Lemay Danielle
Publication year - 2014
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.28.1_supplement.622.4
Subject(s) - transcriptome , gene , rna seq , biology , gene expression , gene expression profiling , computational biology , transcription (linguistics) , genetics , linguistics , philosophy
During lactation, profound changes occur in the mammary gland and thousands of genes undergo differential regulation. RNA‐seq data analysis reveals a tiny minority of milk genes account for the vast majority of total gene expression. This striking imbalance in transcript abundances poses a significant problem for data analysis, e.g. low abundance genes may be incorrectly identified as downregulated. To tackle this problem, we developed a ‘Dilution Adjustment Model’ which more accurately classifies changes in levels of low abundance transcripts between transcriptomes at two developmental stages (baseline and mature lactation). Applying this model to human and bovine milk data led us to reclassify 2,155 human (971 bovine) genes as ‘upregulated’ instead of ‘not differentially expressed’ and 2,524 human (1,732 bovine) as ‘unregulated’ instead of ‘downregulated’. Changes in gene classification were supported by analysis of Gene Ontology and TFBS enrichment profiles. Investigation of ChIP‐Seq data showed genes reclassified as ‘upregulated’ exhibit signs of active transcription and reclassified ‘unregulated’ genes match marks of others in this set. This work leads to a better understanding of transcription mechanisms in milk production. Application of this model to other biological systems is relevant where transcripts from a minority of genes dominate transcriptome composition. Grant Funding Source : K.B was supported by Grant Number T32‐GM008799 from NIGMS‐NIH