Transcriptome analysis

Section edited by Adam Olshen

This section incorporates all aspects of transcriptomic analysis including but not limited to: methods and applications for the analysis of microarray and RNA-seq data.

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  1. Research Article

    Identification of long non-coding transcripts with feature selection: a comparative study

    The unveiling of long non-coding RNAs as important gene regulators in many biological contexts has increased the demand for efficient and robust computational methods to identify novel long non-coding RNAs fro...

    Giovanna M. M. Ventola, Teresa M. R. Noviello, Salvatore D’Aniello, Antonietta Spagnuolo, Michele Ceccarelli and Luigi Cerulo

    BMC Bioinformatics 2017 18:187

    Published on: 23 March 2017

  2. Software

    QuickMIRSeq: a pipeline for quick and accurate quantification of both known miRNAs and isomiRs by jointly processing multiple samples from microRNA sequencing

    Genome-wide miRNA expression data can be used to study miRNA dysregulation comprehensively. Although many open-source tools for microRNA (miRNA)-seq data analyses are available, challenges remain in accurate m...

    Shanrong Zhao, William Gordon, Sarah Du, Chi Zhang, Wen He, Li Xi, Sachin Mathur, Michael Agostino, Theresa Paradis, David von Schack, Michael Vincent and Baohong Zhang

    BMC Bioinformatics 2017 18:180

    Published on: 20 March 2017

  3. Methodology Article

    Two-way learning with one-way supervision for gene expression data

    A family of parsimonious Gaussian mixture models for the biclustering of gene expression data is introduced. Biclustering is accommodated by adopting a mixture of factor analyzers model with a binary, row-stoc...

    Monica H. T. Wong, David M. Mutch and Paul D. McNicholas

    BMC Bioinformatics 2017 18:150

    Published on: 4 March 2017

  4. Methodology article

    Avoiding the pitfalls of gene set enrichment analysis with SetRank

    The purpose of gene set enrichment analysis (GSEA) is to find general trends in the huge lists of genes or proteins generated by many functional genomics techniques and bioinformatics analyses.

    Cedric Simillion, Robin Liechti, Heidi E.L. Lischer, Vassilios Ioannidis and Rémy Bruggmann

    BMC Bioinformatics 2017 18:151

    Published on: 4 March 2017

  5. Research article

    Differential correlation analysis of glioblastoma reveals immune ceRNA interactions predictive of patient survival

    Recent studies illuminated a novel role of microRNA (miRNA) in the competing endogenous RNA (ceRNA) interaction: two genes (ceRNAs) can achieve coexpression by competing for a pool of common targeting miRNAs. ...

    Yu-Chiao Chiu, Li-Ju Wang, Tzu-Pin Lu, Tzu-Hung Hsiao, Eric Y. Chuang and Yidong Chen

    BMC Bioinformatics 2017 18:132

    Published on: 28 February 2017

  6. Methodology Article

    MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms

    Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integr...

    Florian Rohart, Aida Eslami, Nicholas Matigian, Stéphanie Bougeard and Kim-Anh Lê Cao

    BMC Bioinformatics 2017 18:128

    Published on: 27 February 2017

  7. Software

    Manananggal - a novel viewer for alternative splicing events

    Alternative splicing is an important cellular mechanism that can be analyzed by RNA sequencing. However, identification of splicing events in an automated fashion is error-prone. Thus, further validation is re...

    Matthias Barann, Ralf Zimmer and Fabian Birzele

    BMC Bioinformatics 2017 18:120

    Published on: 21 February 2017

  8. Software

    Orthograph: a versatile tool for mapping coding nucleotide sequences to clusters of orthologous genes

    Orthology characterizes genes of different organisms that arose from a single ancestral gene via speciation, in contrast to paralogy, which is assigned to genes that arose via gene duplication. An accurate ort...

    Malte Petersen, Karen Meusemann, Alexander Donath, Daniel Dowling, Shanlin Liu, Ralph S. Peters, Lars Podsiadlowski, Alexandros Vasilikopoulos, Xin Zhou, Bernhard Misof and Oliver Niehuis

    BMC Bioinformatics 2017 18:111

    Published on: 16 February 2017

  9. Research article

    The Drosophila Gene Expression Tool (DGET) for expression analyses

    Next-generation sequencing technologies have greatly increased our ability to identify gene expression levels, including at specific developmental stages and in specific tissues. Gene expression data can help ...

    Yanhui Hu, Aram Comjean, Norbert Perrimon and Stephanie E. Mohr

    BMC Bioinformatics 2017 18:98

    Published on: 10 February 2017

  10. Methodology article

    Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis

    Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) r...

    Seung Hoan Choi, Adam T. Labadorf, Richard H. Myers, Kathryn L. Lunetta, Josée Dupuis and Anita L. DeStefano

    BMC Bioinformatics 2017 18:91

    Published on: 6 February 2017

  11. Software

    BicPAMS: software for biological data analysis with pattern-based biclustering

    Biclustering has been largely applied for the unsupervised analysis of biological data, being recognised today as a key technique to discover putative modules in both expression data (subsets of genes correlat...

    Rui Henriques, Francisco L. Ferreira and Sara C. Madeira

    BMC Bioinformatics 2017 18:82

    Published on: 2 February 2017

    The Erratum to this article has been published in BMC Bioinformatics 2017 18:162

  12. Methodology Article

    TTCA: an R package for the identification of differentially expressed genes in time course microarray data

    The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quic...

    Marco Albrecht, Damian Stichel, Benedikt Müller, Ruth Merkle, Carsten Sticht, Norbert Gretz, Ursula Klingmüller, Kai Breuhahn and Franziska Matthäus

    BMC Bioinformatics 2017 18:33

    Published on: 14 January 2017

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