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IAOseq: inferring abundance of overlapping genes using RNA-seq data
BACKGROUND: Overlapping transcription constitutes a common mechanism for regulating gene expression. A major limitation of the overlapping transcription assays is the lack of high throughput expression data. RESULTS: We developed a new tool (IAOseq) that is based on reads distributions along the tra...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331702/ https://www.ncbi.nlm.nih.gov/pubmed/25707673 http://dx.doi.org/10.1186/1471-2105-16-S1-S3 |
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author | Sun, Hong Yang, Shuang Tun, Liangliang Li, Yixue |
author_facet | Sun, Hong Yang, Shuang Tun, Liangliang Li, Yixue |
author_sort | Sun, Hong |
collection | PubMed |
description | BACKGROUND: Overlapping transcription constitutes a common mechanism for regulating gene expression. A major limitation of the overlapping transcription assays is the lack of high throughput expression data. RESULTS: We developed a new tool (IAOseq) that is based on reads distributions along the transcribed regions to identify the expression levels of overlapping genes from standard RNA-seq data. Compared with five commonly used quantification methods, IAOseq showed better performance in the estimation accuracy of overlapping transcription levels. For the same strand overlapping transcription, currently existing high-throughput methods are rarely available to distinguish which strand was present in the original mRNA template. The IAOseq results showed that the commonly used methods gave an average of 1.6 fold overestimation of the expression levels of same strand overlapping genes. CONCLUSIONS: This work provides a useful tool for mining overlapping transcription levels from standard RNA-seq libraries. IAOseq could be used to help us understand the complex regulatory mechanism mediated by overlapping transcripts. IAOseq is freely available at http://lifecenter.sgst.cn/main/en/IAO_seq.jsp. |
format | Online Article Text |
id | pubmed-4331702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-43317022015-03-19 IAOseq: inferring abundance of overlapping genes using RNA-seq data Sun, Hong Yang, Shuang Tun, Liangliang Li, Yixue BMC Bioinformatics Proceedings BACKGROUND: Overlapping transcription constitutes a common mechanism for regulating gene expression. A major limitation of the overlapping transcription assays is the lack of high throughput expression data. RESULTS: We developed a new tool (IAOseq) that is based on reads distributions along the transcribed regions to identify the expression levels of overlapping genes from standard RNA-seq data. Compared with five commonly used quantification methods, IAOseq showed better performance in the estimation accuracy of overlapping transcription levels. For the same strand overlapping transcription, currently existing high-throughput methods are rarely available to distinguish which strand was present in the original mRNA template. The IAOseq results showed that the commonly used methods gave an average of 1.6 fold overestimation of the expression levels of same strand overlapping genes. CONCLUSIONS: This work provides a useful tool for mining overlapping transcription levels from standard RNA-seq libraries. IAOseq could be used to help us understand the complex regulatory mechanism mediated by overlapping transcripts. IAOseq is freely available at http://lifecenter.sgst.cn/main/en/IAO_seq.jsp. BioMed Central 2015-01-21 /pmc/articles/PMC4331702/ /pubmed/25707673 http://dx.doi.org/10.1186/1471-2105-16-S1-S3 Text en Copyright © 2015 Sun et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Proceedings Sun, Hong Yang, Shuang Tun, Liangliang Li, Yixue IAOseq: inferring abundance of overlapping genes using RNA-seq data |
title | IAOseq: inferring abundance of overlapping genes using RNA-seq data |
title_full | IAOseq: inferring abundance of overlapping genes using RNA-seq data |
title_fullStr | IAOseq: inferring abundance of overlapping genes using RNA-seq data |
title_full_unstemmed | IAOseq: inferring abundance of overlapping genes using RNA-seq data |
title_short | IAOseq: inferring abundance of overlapping genes using RNA-seq data |
title_sort | iaoseq: inferring abundance of overlapping genes using rna-seq data |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331702/ https://www.ncbi.nlm.nih.gov/pubmed/25707673 http://dx.doi.org/10.1186/1471-2105-16-S1-S3 |
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