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Systematic Assessment of RNA-Seq Quantification Tools Using Simulated Sequence Data

Authors:

Raghu Chandramohan,

Po-Yen Wu,

John H. Phan,

May D. WangAuthors Info & Claims

BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

Pages 623 - 632

https://doi.org/10.1145/2506583.2506648

Published: 22 September 2013 Publication History

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Abstract

RNA-sequencing (RNA-seq) technology has emerged as the preferred method for quantification of gene and isoform expression. Numerous RNA-seq quantification tools have been proposed and developed, bringing us closer to developing expression-based diagnostic tests based on this technology. However, because of the rapidly evolving technologies and algorithms, it is essential to establish a systematic method for evaluating the quality of RNA-seq quantification. We investigate how different RNA-seq experimental designs (i.e., variations in sequencing depth and read length) affect various quantification algorithms (i.e., HTSeq, Cufflinks, and MISO). Using simulated data, we evaluate the quantification tools based on four metrics, namely: (1) total number of usable fragments for quantification, (2) detection of genes and isoforms, (3) correlation, and (4) accuracy of expression quantification with respect to the ground truth. Results show that Cufflinks is able to use the largest number of fragments for quantification, leading to better detection of genes and isoforms. However, HTSeq produces more accurate expression estimates. Moreover, each quantification algorithm is affected differently by varying sequencing depth and read length, suggesting that the selection of quantification algorithms should be application-dependent.

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Nguyen TJung HRotllant GHurwood DMather PVentura T(2018)Guidelines for RNA-seq projects: applications and opportunities in non-model decapod crustacean speciesHydrobiologia10.1007/s10750-018-3682-0825:1(5-27)Online publication date: 3-Jul-2018
https://doi.org/10.1007/s10750-018-3682-0
Yang CWu PTong LPhan JWang M(2015)The impact of RNA-seq aligners on gene expression estimationProceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/2808719.2808767(462-471)Online publication date: 9-Sep-2015
https://dl.acm.org/doi/10.1145/2808719.2808767

Index Terms

Systematic Assessment of RNA-Seq Quantification Tools Using Simulated Sequence Data
1. Applied computing
  1. Life and medical sciences

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BCB'13: Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

September 2013

987 pages

ISBN:9781450324342

DOI:10.1145/2506583

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 22 September 2013

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September 22 - 25, 2013

Wshington DC, USA

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Nguyen TJung HRotllant GHurwood DMather PVentura T(2018)Guidelines for RNA-seq projects: applications and opportunities in non-model decapod crustacean speciesHydrobiologia10.1007/s10750-018-3682-0825:1(5-27)Online publication date: 3-Jul-2018
https://doi.org/10.1007/s10750-018-3682-0
Yang CWu PTong LPhan JWang M(2015)The impact of RNA-seq aligners on gene expression estimationProceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/2808719.2808767(462-471)Online publication date: 9-Sep-2015
https://dl.acm.org/doi/10.1145/2808719.2808767

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A robust method for transcript quantification with RNA-seq data

Strand specific RNA-seq data for higher specificity

Towards Reliable Isoform Quantification Using RNA-Seq Data

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A robust method for transcript quantification with RNA-seq data

Strand specific RNA-seq data for higher specificity

Towards Reliable Isoform Quantification Using RNA-Seq Data

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