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An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping

BACKGROUND: There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequent...

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Autores principales: Sasidharan, Rajkumar, Agarwal, Ashish, Rozowsky, Joel, Gerstein, Mark
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2764720/
https://www.ncbi.nlm.nih.gov/pubmed/19630981
http://dx.doi.org/10.1186/1756-0500-2-150
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author Sasidharan, Rajkumar
Agarwal, Ashish
Rozowsky, Joel
Gerstein, Mark
author_facet Sasidharan, Rajkumar
Agarwal, Ashish
Rozowsky, Joel
Gerstein, Mark
author_sort Sasidharan, Rajkumar
collection PubMed
description BACKGROUND: There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. FINDINGS: This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. CONCLUSION: Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure.
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spelling pubmed-27647202009-10-21 An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping Sasidharan, Rajkumar Agarwal, Ashish Rozowsky, Joel Gerstein, Mark BMC Res Notes Short Report BACKGROUND: There are two main technologies for transcriptome profiling, namely, tiling microarrays and high-throughput sequencing. Recently there has been a tremendous amount of excitement about the latter because of the advent of next-generation sequencing technologies and its promises. Consequently, the question of the moment is how these two technologies compare. Here we attempt to develop an approach to do a fair comparison of transcripts identified from tiling microarray and MPSS sequencing data. FINDINGS: This comparison is a challenging task because the sequencing data is discrete while the tiling array data is continuous. We use the published rice and Arabidopsis datasets which provide currently best matched sets of arrays and sequencing experiments using a slightly earlier generation of sequencing, the MPSS tag sequencing technology. After scoring the arrays consistently in both the organisms, a first pass comparison reveals a surprisingly small overlap in transcripts of 22% and 66% respectively, in rice and Arabidopsis. However, when we do the analysis in detail, we find that this is an underestimate. In particular, when we map the probe intensities onto the sequencing tags and then look at their intensity distribution, we see that they are very similar to exons. Furthermore, restricting our comparison to only protein-coding gene loci revealed a very good overlap between the two technologies. CONCLUSION: Our approach to compare genome tiling microarray and MPSS sequencing data suggests that there is actually a reasonable overlap in transcripts identified by the two technologies. This overlap is distorted by the scoring and thresholding in the tiling array scoring procedure. BioMed Central 2009-07-24 /pmc/articles/PMC2764720/ /pubmed/19630981 http://dx.doi.org/10.1186/1756-0500-2-150 Text en Copyright © 2009 Sasidharan et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Short Report
Sasidharan, Rajkumar
Agarwal, Ashish
Rozowsky, Joel
Gerstein, Mark
An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping
title An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping
title_full An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping
title_fullStr An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping
title_full_unstemmed An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping
title_short An approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping
title_sort approach to comparing tiling array and high throughput sequencing technologies for genomic transcript mapping
topic Short Report
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2764720/
https://www.ncbi.nlm.nih.gov/pubmed/19630981
http://dx.doi.org/10.1186/1756-0500-2-150
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