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      Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series

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      Author(s): 1 , * , 2 , 3 , 2 , *
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Motivation: The widespread adoption of RNA-seq to quantitatively measure gene expression has increased the scope of sequencing experimental designs to include time-course experiments. maSigPro is an R package specifically suited for the analysis of time-course gene expression data, which was developed originally for microarrays and hence was limited in its application to count data.

          Results: We have updated maSigPro to support RNA-seq time series analysis by introducing generalized linear models in the algorithm to support the modeling of count data while maintaining the traditional functionalities of the package. We show a good performance of the maSigPro-GLM method in several simulated time-course scenarios and in a real experimental dataset.

          Availability and implementation: The package is freely available under the LGPL license from the Bioconductor Web site ( http://bioconductor.org).

          Contact: mj.nueda@ 123456ua.es or aconesa@ 123456cipf.es

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          Streaming fragment assignment for real-time analysis of sequencing experiments

          Adam Roberts, Lior Pachter (2012)
          We present eXpress, a software package for highly efficient probabilistic assignment of ambiguously mapping sequenced fragments. eXpress uses a streaming algorithm with linear run time and constant memory use. It can determine abundances of sequenced molecules in real time, and can be applied to ChIP-seq, metagenomics and other large-scale sequencing data. We demonstrate its use on RNA-seq data, showing greater efficiency than other quantification methods.
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            RNA-seq differential expression studies: more sequence or more replication?

            Yuwen Liu, Jie Zhou, Kevin P. White (2014)
            RNA-seq is replacing microarrays as the primary tool for gene expression studies. Many RNA-seq studies have used insufficient biological replicates, resulting in low statistical power and inefficient use of sequencing resources. We show the explicit trade-off between more biological replicates and deeper sequencing in increasing power to detect differentially expressed (DE) genes. In the human cell line MCF7, adding more sequencing depth after 10 M reads gives diminishing returns on power to detect DE genes, whereas adding biological replicates improves power significantly regardless of sequencing depth. We also propose a cost-effectiveness metric for guiding the design of large-scale RNA-seq DE studies. Our analysis showed that sequencing less reads and performing more biological replication is an effective strategy to increase power and accuracy in large-scale differential expression RNA-seq studies, and provided new insights into efficient experiment design of RNA-seq studies. The code used in this paper is provided on: http://home.uchicago.edu/∼jiezhou/replication/. The expression data is deposited in the Gene Expression Omnibus under the accession ID GSE51403.
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              maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments.

              Ana Conesa, María José Nueda, Alberto Ferrer … (2006)
              Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 September 2014
                Publication date (Electronic): 03 June 2014
                Publication date PMC-release: 03 June 2014
                Volume: 30
                Issue: 18
                Pages: 2598-2602
                Affiliations
                1Statistics and Operational Research Department, University of Alicante, 03690, Alicante, Spain, 2Genomics of Gene Expression Laboratory, Prince Felipe Research Centre, 46012 Valencia, Spain and 3Applied Statistics, Operational Research and Quality Department, Polytechnic University of Valencia, 46020 Valencia, Spain
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Ivo Hofacker

                Article
                Publisher ID: btu333
                DOI: 10.1093/bioinformatics/btu333
                PMC ID: 4155246
                PubMed ID: 24894503
                SO-VID: 8c45aeed-15e0-4560-99cf-0e7f29231fb9
                Copyright © © The Author 2014. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 16 August 2013
                Date revision received : 7 May 2014
                Date accepted : 8 May 2014
                Page count
                Pages: 5
                Categories
                Subject: Original Papers
                Subject: Gene Expression

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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