Sequence biases in large scale gene expression profiling data

被引：44

作者：

Siddiqui, Asim S. ^{[1
]}

Delaney, Allen D. ^{[1
]}

Schnerch, Angelique ^{[1
]}

Griffith, Obi L. ^{[1
]}

Jones, Steven J. M. ^{[1
]}

Marra, Marco A. ^{[1
]}

机构：

[1] British Columbia Canc Agcy, British Columbia Canc Res Ctr, Canadas Michael Smith Genome Sci Ctr, Vancouver, BC V5Z 4S6, Canada

来源：

NUCLEIC ACIDS RESEARCH | 2006年 / 34卷 / 12期

基金：

加拿大健康研究院;

关键词：

D O I：

10.1093/nar/gkl404

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

We present the results of a simple, statistical assay that measures the G+C content sensitivity bias of gene expression experiments without the requirement of a duplicate experiment. We analyse five gene expression profiling methods: Affymetrix GeneChip, Long Serial Analysis of Gene Expression (LongSAGE), LongSAGELite, 'Classic' Massively Parallel Signature Sequencing (MPSS) and 'Signature' MPSS. We demonstrate the methods have systematic and random errors leading to a different G+C content sensitivity. The relationship between this experimental error and the G+C content of the probe set or tag that identifies each gene influences whether the gene is detected and, if detected, the level of gene expression measured. LongSAGE has the least bias, while Signature MPSS shows a strong bias to G+C rich tags and Affymetrix data show different bias depending on the data processing method (MAS 5.0, RMA or GC-RMA). The bias in the Affymetrix data primarily impacts genes expressed at lower levels. Despite the larger sampling of the MPSS library, SAGE identifies significantly more genes (60% more RefSeq genes in a single comparison).

引用

页数：9

共 33 条

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