Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability

Rapidly growing public gene expression databases contain a wealth of data for building an unprecedentedly detailed picture of human biology and disease. This data comes from many diverse measurement platforms that make integrating it all difficult. Although RNA-sequencing (RNA-seq) is attracting the...

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Main Authors:	Uziela, Karolis, Honkela, Antti
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429080/

id	pubmed-4429080
recordtype	oai_dc
spelling	pubmed-44290802015-05-21 Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability Uziela, Karolis Honkela, Antti Research Article Rapidly growing public gene expression databases contain a wealth of data for building an unprecedentedly detailed picture of human biology and disease. This data comes from many diverse measurement platforms that make integrating it all difficult. Although RNA-sequencing (RNA-seq) is attracting the most attention, at present, the rate of new microarray studies submitted to public databases far exceeds the rate of new RNA-seq studies. There is clearly a need for methods that make it easier to combine data from different technologies. In this paper, we propose a new method for processing RNA-seq data that yields gene expression estimates that are much more similar to corresponding estimates from microarray data, hence greatly improving cross-platform comparability. The method we call PREBS is based on estimating the expression from RNA-seq reads overlapping the microarray probe regions, and processing these estimates with standard microarray summarisation algorithms. Using paired microarray and RNA-seq samples from TCGA LAML data set we show that PREBS expression estimates derived from RNA-seq are more similar to microarray-based expression estimates than those from other RNA-seq processing methods. In an experiment to retrieve paired microarray samples from a database using an RNA-seq query sample, gene signatures defined based on PREBS expression estimates were found to be much more accurate than those from other methods. PREBS also allows new ways of using RNA-seq data, such as expression estimation for microarray probe sets. An implementation of the proposed method is available in the Bioconductor package “prebs.” Public Library of Science 2015-05-12 /pmc/articles/PMC4429080/ /pubmed/25966034 http://dx.doi.org/10.1371/journal.pone.0126545 Text en © 2015 Uziela, Honkela http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Uziela, Karolis Honkela, Antti
spellingShingle	Uziela, Karolis Honkela, Antti Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability
author_facet	Uziela, Karolis Honkela, Antti
author_sort	Uziela, Karolis
title	Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability
title_short	Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability
title_full	Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability
title_fullStr	Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability
title_full_unstemmed	Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability
title_sort	probe region expression estimation for rna-seq data for improved microarray comparability
description	Rapidly growing public gene expression databases contain a wealth of data for building an unprecedentedly detailed picture of human biology and disease. This data comes from many diverse measurement platforms that make integrating it all difficult. Although RNA-sequencing (RNA-seq) is attracting the most attention, at present, the rate of new microarray studies submitted to public databases far exceeds the rate of new RNA-seq studies. There is clearly a need for methods that make it easier to combine data from different technologies. In this paper, we propose a new method for processing RNA-seq data that yields gene expression estimates that are much more similar to corresponding estimates from microarray data, hence greatly improving cross-platform comparability. The method we call PREBS is based on estimating the expression from RNA-seq reads overlapping the microarray probe regions, and processing these estimates with standard microarray summarisation algorithms. Using paired microarray and RNA-seq samples from TCGA LAML data set we show that PREBS expression estimates derived from RNA-seq are more similar to microarray-based expression estimates than those from other RNA-seq processing methods. In an experiment to retrieve paired microarray samples from a database using an RNA-seq query sample, gene signatures defined based on PREBS expression estimates were found to be much more accurate than those from other methods. PREBS also allows new ways of using RNA-seq data, such as expression estimation for microarray probe sets. An implementation of the proposed method is available in the Bioconductor package “prebs.”
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429080/
_version_	1613222511169765376

Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability

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