期刊
BIOINFORMATICS
卷 24, 期 24, 页码 2887-2893出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btn571
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资金
- NCRR NIH HHS [UL1 RR024979] Funding Source: Medline
- NHGRI NIH HHS [R01 HG004634, R01 HG003903, R01HG003903, R01HG004634] Funding Source: Medline
- NIGMS NIH HHS [U54GM62119, U54 GM062119] Funding Source: Medline
Motivation: Microarray designs have become increasingly proberich, enabling targeting of specific features, such as individual exons or single nucleotide polymorphisms. These arrays have the potential to achieve quantitative high-throughput estimates of transcript abundances, but currently these estimates are affected by biases due to cross-hybridization, in which probes hybridize to off-target transcripts. Results: To study cross-hybridization, we map Affymetrix exon array probes to a set of annotated mRNA transcripts, allowing a small number of mismatches or insertion/deletions between the two sequences. Based on a systematic study of the degree to which probes with a given match type to a transcript are affected by cross-hybridization, we developed a strategy to correct for cross-hybridization biases of gene-level expression estimates. Comparison with Solexa ultra high-throughput sequencing data demonstrates that correction for cross-hybridization leads to a significant improvement of gene expression estimates. Availability: We provide mappings between human and mouse exon array probes and off-target transcripts and provide software extending the GeneBASE program for generating gene-level expression estimates including the cross-hybridization correction http://biogibbs.stanford.edu/similar to kkapur/GeneBase/.
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