Bias detection and correction in RNA-Sequencing data
<p>Abstract</p> <p>Background</p> <p>High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low backgrou...
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| Format: | Article |
| Language: | English |
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BMC
2011-07-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/12/290 |
| _version_ | 1818380910740897792 |
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| author | Zhao Hongyu Chung Lisa M Zheng Wei |
| author_facet | Zhao Hongyu Chung Lisa M Zheng Wei |
| author_sort | Zhao Hongyu |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates.</p> <p>Results</p> <p>In this paper, by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes, we showed that commonly used estimates of gene expression levels from RNA-Seq data, such as reads per kilobase of gene length per million reads (RPKM), are biased in terms of gene length, GC content and dinucleotide frequencies. We directly examined the biases at the gene-level, and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously. Compared to previously proposed base level correction methods, our method reduces bias in gene-level expression estimates more effectively.</p> <p>Conclusions</p> <p>Our method identifies and corrects different sources of biases in gene-level expression measures from RNA-Seq data, and provides more accurate estimates of gene expression levels from RNA-Seq. This method should prove useful in meta-analysis of gene expression levels using different platforms or experimental protocols.</p> |
| first_indexed | 2024-12-14T02:26:11Z |
| format | Article |
| id | doaj.art-c24ddf1f952941f4a4db217d7400ca00 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-14T02:26:11Z |
| publishDate | 2011-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c24ddf1f952941f4a4db217d7400ca002022-12-21T23:20:23ZengBMCBMC Bioinformatics1471-21052011-07-0112129010.1186/1471-2105-12-290Bias detection and correction in RNA-Sequencing dataZhao HongyuChung Lisa MZheng Wei<p>Abstract</p> <p>Background</p> <p>High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high resolution, low background, and ability to identify novel transcripts. Moreover, for genes with multiple isoforms, expression of each isoform may be estimated from RNA-Seq data. Despite these advantages, recent work revealed that base level read counts from RNA-Seq data may not be randomly distributed and can be affected by local nucleotide composition. It was not clear though how the base level read count bias may affect gene level expression estimates.</p> <p>Results</p> <p>In this paper, by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes, we showed that commonly used estimates of gene expression levels from RNA-Seq data, such as reads per kilobase of gene length per million reads (RPKM), are biased in terms of gene length, GC content and dinucleotide frequencies. We directly examined the biases at the gene-level, and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously. Compared to previously proposed base level correction methods, our method reduces bias in gene-level expression estimates more effectively.</p> <p>Conclusions</p> <p>Our method identifies and corrects different sources of biases in gene-level expression measures from RNA-Seq data, and provides more accurate estimates of gene expression levels from RNA-Seq. This method should prove useful in meta-analysis of gene expression levels using different platforms or experimental protocols.</p>http://www.biomedcentral.com/1471-2105/12/290 |
| spellingShingle | Zhao Hongyu Chung Lisa M Zheng Wei Bias detection and correction in RNA-Sequencing data BMC Bioinformatics |
| title | Bias detection and correction in RNA-Sequencing data |
| title_full | Bias detection and correction in RNA-Sequencing data |
| title_fullStr | Bias detection and correction in RNA-Sequencing data |
| title_full_unstemmed | Bias detection and correction in RNA-Sequencing data |
| title_short | Bias detection and correction in RNA-Sequencing data |
| title_sort | bias detection and correction in rna sequencing data |
| url | http://www.biomedcentral.com/1471-2105/12/290 |
| work_keys_str_mv | AT zhaohongyu biasdetectionandcorrectioninrnasequencingdata AT chunglisam biasdetectionandcorrectioninrnasequencingdata AT zhengwei biasdetectionandcorrectioninrnasequencingdata |