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SHEAR: sample heterogeneity estimation and assembly by reference

BACKGROUND: Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately,...

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Autores principales: Landman, Sean R, Hwang, Tae Hyun, Silverstein, Kevin AT, Li, Yingming, Dehm, Scott M, Steinbach, Michael, Kumar, Vipin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4007568/
https://www.ncbi.nlm.nih.gov/pubmed/24476358
http://dx.doi.org/10.1186/1471-2164-15-84
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author Landman, Sean R
Hwang, Tae Hyun
Silverstein, Kevin AT
Li, Yingming
Dehm, Scott M
Steinbach, Michael
Kumar, Vipin
author_facet Landman, Sean R
Hwang, Tae Hyun
Silverstein, Kevin AT
Li, Yingming
Dehm, Scott M
Steinbach, Michael
Kumar, Vipin
author_sort Landman, Sean R
collection PubMed
description BACKGROUND: Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis. RESULTS: By making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications. CONCLUSION: SHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-84) contains supplementary material, which is available to authorized users.
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spelling pubmed-40075682014-05-19 SHEAR: sample heterogeneity estimation and assembly by reference Landman, Sean R Hwang, Tae Hyun Silverstein, Kevin AT Li, Yingming Dehm, Scott M Steinbach, Michael Kumar, Vipin BMC Genomics Software BACKGROUND: Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis. RESULTS: By making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications. CONCLUSION: SHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-84) contains supplementary material, which is available to authorized users. BioMed Central 2014-01-29 /pmc/articles/PMC4007568/ /pubmed/24476358 http://dx.doi.org/10.1186/1471-2164-15-84 Text en © Landman et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. 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 Software
Landman, Sean R
Hwang, Tae Hyun
Silverstein, Kevin AT
Li, Yingming
Dehm, Scott M
Steinbach, Michael
Kumar, Vipin
SHEAR: sample heterogeneity estimation and assembly by reference
title SHEAR: sample heterogeneity estimation and assembly by reference
title_full SHEAR: sample heterogeneity estimation and assembly by reference
title_fullStr SHEAR: sample heterogeneity estimation and assembly by reference
title_full_unstemmed SHEAR: sample heterogeneity estimation and assembly by reference
title_short SHEAR: sample heterogeneity estimation and assembly by reference
title_sort shear: sample heterogeneity estimation and assembly by reference
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4007568/
https://www.ncbi.nlm.nih.gov/pubmed/24476358
http://dx.doi.org/10.1186/1471-2164-15-84
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AT liyingming shearsampleheterogeneityestimationandassemblybyreference
AT dehmscottm shearsampleheterogeneityestimationandassemblybyreference
AT steinbachmichael shearsampleheterogeneityestimationandassemblybyreference
AT kumarvipin shearsampleheterogeneityestimationandassemblybyreference