SimBA: A methodology and tools for evaluating the performance of RNA-Seq bioinformatic pipelines

被引:6
|
作者
Audoux, Jerome [1 ,2 ]
Salson, Mikael [3 ]
Grosset, Christophe F. [4 ]
Beaumeunier, Sacha [1 ,2 ]
Holder, Jean-Marc [1 ,2 ]
Commes, Therese [1 ,2 ]
Philippe, Nicolas [1 ,2 ]
机构
[1] CHR Montpellier, Hop St Eloi, IRMB, SeqOne, 80 Ave Augustin Fliche, F-34295 Montpellier, France
[2] Inst Computat Biol, 860 Rue St Priest, F-34095 Montpellier 5, France
[3] Univ Lille, CNRS, CRIStAL Ctr Rech Informat Signal & Automat Lille, INRIA,Cent Lille,UMR 9189, F-59000 Lille, France
[4] Univ Bordeaux, INSERM, BMGIC, U1035, F-33076 Bordeaux, France
来源
BMC BIOINFORMATICS | 2017年 / 18卷
关键词
RNA-Seq; Transcriptomics; Benchmark; Pipeline optimization; FRAMEWORK; ALIGNMENT; DISCOVERY; BENCHMARK;
D O I
10.1186/s12859-017-1831-5
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: The evolution of next-generation sequencing (NGS) technologies has led to increased focus on RNA-Seq. Many bioinformatic tools have been developed for RNA-Seq analysis, each with unique performance characteristics and configuration parameters. Users face an increasingly complex task in understanding which bioinformatic tools are best for their specific needs and how they should be configured. In order to provide some answers to these questions, we investigate the performance of leading bioinformatic tools designed for RNA-Seq analysis and propose a methodology for systematic evaluation and comparison of performance to help users make well informed choices. Results: To evaluate RNA-Seq pipelines, we developed a suite of two benchmarking tools. SimCT generates simulated datasets that get as close as possible to specific real biological conditions accompanied by the list of genomic incidents and mutations that have been inserted. BenchCT then compares the output of any bioinformatics pipeline that has been run against a SimCT dataset with the simulated genomic and transcriptional variations it contains to give an accurate performance evaluation in addressing specific biological question. We used these tools to simulate a real-world genomic medicine question s involving the comparison of healthy and cancerous cells. Results revealed that performance in addressing a particular biological context varied significantly depending on the choice of tools and settings used. We also found that by combining the output of certain pipelines, substantial performance improvements could be achieved. Conclusion: Our research emphasizes the importance of selecting and configuring bioinformatic tools for the specific biological question being investigated to obtain optimal results. Pipeline designers, developers and users should include benchmarking in the context of their biological question as part of their design and quality control process. Our SimBA suite of benchmarking tools provides a reliable basis for comparing the performance of RNA-Seq bioinformatics pipelines in addressing a specific biological question. We would like to see the creation of a reference corpus of data-sets that would allow accurate comparison between benchmarks performed by different groups and the publication of more benchmarks based on this public corpus. SimBA software and data-set are available at http://cractools.gforge.inria.fr/softwares/simba/.
引用
收藏
页数:14
相关论文
共 50 条
  • [31] A comprehensive benchmarking of differential splicing tools for RNA-seq analysis at the event level
    Jiang, Minghao
    Zhang, Shiyan
    Yin, Hongxin
    Zhuo, Zhiyi
    Meng, Guoyu
    BRIEFINGS IN BIOINFORMATICS, 2023, 24 (03)
  • [32] Evaluating whole transcriptome amplification for gene profiling experiments using RNA-Seq
    Sheena L Faherty
    C Ryan Campbell
    Peter A Larsen
    Anne D Yoder
    BMC Biotechnology, 15
  • [33] Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
    Christian H. Holland
    Jovan Tanevski
    Javier Perales-Patón
    Jan Gleixner
    Manu P. Kumar
    Elisabetta Mereu
    Brian A. Joughin
    Oliver Stegle
    Douglas A. Lauffenburger
    Holger Heyn
    Bence Szalai
    Julio Saez-Rodriguez
    Genome Biology, 21
  • [34] Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
    Holland, Christian H.
    Tanevski, Jovan
    Perales-Paton, Javier
    Gleixner, Jan
    Kumar, Manu P.
    Mereu, Elisabetta
    Joughin, Brian A.
    Stegle, Oliver
    Lauffenburger, Douglas A.
    Heyn, Holger
    Szalai, Bence
    Saez-Rodriguez, Julio
    GENOME BIOLOGY, 2020, 21 (01)
  • [35] FastqPuri: high-performance preprocessing of RNA-seq data
    Perez-Rubio, Paula
    Lottaz, Claudio
    Engelmann, Julia C.
    BMC BIOINFORMATICS, 2019, 20 (1)
  • [36] Evaluating whole transcriptome amplification for gene profiling experiments using RNA-Seq
    Faherty, Sheena L.
    Campbell, C. Ryan
    Larsen, Peter A.
    Yoder, Anne D.
    BMC BIOTECHNOLOGY, 2015, 15
  • [37] Comparing HISAT and STAR-based pipelines for RNA-Seq Data Analysis: a real experience
    Bianchi, Andrea
    Di Marco, Antinisca
    Pellegrini, Cristina
    2023 IEEE 36TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS, 2023, : 218 - 224
  • [38] Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline
    Rahmatallah, Yasir
    Emmert-Streib, Frank
    Glazko, Galina
    BRIEFINGS IN BIOINFORMATICS, 2016, 17 (03) : 393 - 407
  • [39] cdev: a ground-truth based measure to evaluate RNA-seq normalization performance
    Tran, Diem-Trang
    Might, Matthew
    PEERJ, 2021, 9
  • [40] BOAssembler: A Bayesian Optimization Framework to Improve RNA-Seq Assembly Performance
    Mao, Shunfu
    Jiang, Yihan
    Mathew, Edwin Basil
    Kannan, Sreeram
    ALGORITHMS FOR COMPUTATIONAL BIOLOGY (ALCOB 2020), 2020, 12099 : 188 - 197