Skip to content


BMC Bioinformatics

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.

Previous Page Page 3 of 21 Next Page
  1. Content type: Methodology Article

    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...

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

    Citation: BMC Bioinformatics 2017 18:128

    Published on:

  2. Content type: Software

    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...

    Authors: Matthias Barann, Ralf Zimmer and Fabian Birzele

    Citation: BMC Bioinformatics 2017 18:120

    Published on:

  3. Content type: Software

    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...

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

    Citation: BMC Bioinformatics 2017 18:111

    Published on:

  4. Content type: Research article

    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 ...

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

    Citation: BMC Bioinformatics 2017 18:98

    Published on:

  5. Content type: Methodology article

    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...

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

    Citation: BMC Bioinformatics 2017 18:91

    Published on:

  6. Content type: Software

    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...

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

    Citation: BMC Bioinformatics 2017 18:82

    Published on:

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

  7. Content type: Methodology article

    The Friedman rank sum test is a widely-used nonparametric method in computational biology. In addition to examining the overall null hypothesis of no significant difference among any of the rank sums, it is ty...

    Authors: Rob Eisinga, Tom Heskes, Ben Pelzer and Manfred Te Grotenhuis

    Citation: BMC Bioinformatics 2017 18:68

    Published on:

  8. Content type: Research article

    RNA-Seq has supplanted microarrays as the preferred method of transcriptome-wide identification of differentially expressed genes. However, RNA-Seq analysis is still rapidly evolving, with a large number of to...

    Authors: Claire R. Williams, Alyssa Baccarella, Jay Z. Parrish and Charles C. Kim

    Citation: BMC Bioinformatics 2017 18:38

    Published on:

  9. Content type: Methodology Article

    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...

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

    Citation: BMC Bioinformatics 2017 18:33

    Published on:

  10. Content type: Methodology article

    Performing statistical tests is an important step in analyzing genome-wide datasets for detecting genomic features differentially expressed between conditions. Each type of statistical test has its own advanta...

    Authors: Yuanzhe Bei and Pengyu Hong

    Citation: BMC Bioinformatics 2016 17:541

    Published on:

  11. Content type: Research Article

    Competitive gene set analysis is a standard exploratory tool for gene expression data. Permutation-based competitive gene set analysis methods are preferable to parametric ones because the latter make strong s...

    Authors: Pashupati P. Mishra, Alan Medlar, Liisa Holm and Petri Törönen

    Citation: BMC Bioinformatics 2016 17:526

    Published on:

  12. Content type: Methodology article

    Next-generation sequencing (NGS) technologies are arguably the most revolutionary technical development to join the list of tools available to molecular biologists since PCR. For researchers working with nonco...

    Authors: Nicolas Cerveau and Daniel J. Jackson

    Citation: BMC Bioinformatics 2016 17:525

    Published on:

  13. Content type: Database

    Increased emphasis on reproducibility of published research in the last few years has led to the large-scale archiving of sequencing data. While this data can, in theory, be used to reproduce results in papers...

    Authors: Harold Pimentel, Pascal Sturmfels, Nicolas Bray, Páll Melsted and Lior Pachter

    Citation: BMC Bioinformatics 2016 17:490

    Published on:

  14. Content type: Software

    Active protein translation can be assessed and measured using ribosome profiling sequencing strategies. Prevailing analytical approaches applied to this technology make use of sequence fragment length profilin...

    Authors: Sang Y. Chun, Caitlin M. Rodriguez, Peter K. Todd and Ryan E. Mills

    Citation: BMC Bioinformatics 2016 17:482

    Published on:

Previous Page Page 3 of 21 Next Page

2016 Journal Metrics

  • Citation Impact
    2.448 - 2-year Impact Factor
    3.450 - 5-year Impact Factor
    0.946 - Source Normalized Impact per Paper (SNIP)
    1.467 - SCImago Journal Rank (SJR)

    1405 Usage Factor

    Social Media Impact
    816 mentions