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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. Content type: Methodology article

    To ensure cancer patients are stratified towards treatments that are optimally beneficial, it is a priority to define robust molecular subtypes using clustering methods applied to high-dimensional biological d...

    Authors: Katherine Eason, Gift Nyamundanda and Anguraj Sadanandam

    Citation: BMC Bioinformatics 2018 19:182

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  2. Content type: Software

    Complex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing....

    Authors: Samuel T. Westreich, Michelle L. Treiber, David A. Mills, Ian Korf and Danielle G. Lemay

    Citation: BMC Bioinformatics 2018 19:175

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  3. Content type: Research article

    Learning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads ...

    Authors: Marta B. Lopes, André Veríssimo, Eunice Carrasquinha, Sandra Casimiro, Niko Beerenwinkel and Susana Vinga

    Citation: BMC Bioinformatics 2018 19:168

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  4. Content type: Software

    Metabolomics has the promise to transform the area of personalized medicine with the rapid development of high throughput technology for untargeted analysis of metabolites. Open access, easy to use, analytic t...

    Authors: Alexander S. Kirpich, Miguel Ibarra, Oleksandr Moskalenko, Justin M. Fear, Joseph Gerken, Xinlei Mi, Ali Ashrafi, Alison M. Morse and Lauren M. McIntyre

    Citation: BMC Bioinformatics 2018 19:151

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  5. Content type: Software

    RNA sequencing has become a ubiquitous technology used throughout life sciences as an effective method of measuring RNA abundance quantitatively in tissues and cells. The increase in use of RNA-seq technology ...

    Authors: MacIntosh Cornwell, Mahesh Vangala, Len Taing, Zachary Herbert, Johannes Köster, Bo Li, Hanfei Sun, Taiwen Li, Jian Zhang, Xintao Qiu, Matthew Pun, Rinath Jeselsohn, Myles Brown, X. Shirley Liu and Henry W. Long

    Citation: BMC Bioinformatics 2018 19:135

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  6. Content type: Software

    In-depth study of the intron retention levels of transcripts provide insights on the mechanisms regulating pre-mRNA splicing efficiency. Additionally, detailed analysis of retained introns can link these intro...

    Authors: Ali Oghabian, Dario Greco and Mikko J. Frilander

    Citation: BMC Bioinformatics 2018 19:130

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  7. Content type: Software

    The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the disco...

    Authors: Pedro S. T. Russo, Gustavo R. Ferreira, Lucas E. Cardozo, Matheus C. Bürger, Raul Arias-Carrasco, Sandra R. Maruyama, Thiago D. C. Hirata, Diógenes S. Lima, Fernando M. Passos, Kiyoshi F. Fukutani, Melissa Lever, João S. Silva, Vinicius Maracaja-Coutinho and Helder I. Nakaya

    Citation: BMC Bioinformatics 2018 19:56

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  8. Content type: Software

    Many R packages have been developed for transcriptome analysis but their use often requires familiarity with R and integrating results of different packages requires scripts to wrangle the datatypes. Furthermo...

    Authors: Qin Zhu, Stephen A. Fisher, Hannah Dueck, Sarah Middleton, Mugdha Khaladkar and Junhyong Kim

    Citation: BMC Bioinformatics 2018 19:6

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  9. Content type: Research Article

    Gene expression connectivity mapping has gained much popularity in recent years with a number of successful applications in biomedical research testifying its utility and promise. A major application of connec...

    Authors: Gayathri Thillaiyampalam, Fabio Liberante, Liam Murray, Chris Cardwell, Ken Mills and Shu-Dong Zhang

    Citation: BMC Bioinformatics 2017 18:581

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  10. Content type: Methodology Article

    DNA methylation is an important tissue-specific epigenetic event that influences transcriptional regulation of gene expression. Differentially methylated CpG sites may act as mediators between genetic variatio...

    Authors: Chaitanya R. Acharya, Kouros Owzar and Andrew S. Allen

    Citation: BMC Bioinformatics 2017 18:455

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  11. Content type: Research Article

    Predicting disease-associated genes is helpful for understanding the molecular mechanisms during the disease progression. Since the pathological mechanisms of neurodegenerative diseases are very complex, tradi...

    Authors: Xue Jiang, Han Zhang, Feng Duan and Xiongwen Quan

    Citation: BMC Bioinformatics 2017 18:447

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  12. Content type: Software

    Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most ...

    Authors: Joseph N. Paulson, Cho-Yi Chen, Camila M. Lopes-Ramos, Marieke L. Kuijjer, John Platig, Abhijeet R. Sonawane, Maud Fagny, Kimberly Glass and John Quackenbush

    Citation: BMC Bioinformatics 2017 18:437

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  13. Content type: Software

    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 characteris...

    Authors: Jérôme Audoux, Mikaël Salson, Christophe F. Grosset, Sacha Beaumeunier, Jean-Marc Holder, Thérèse Commes and Nicolas Philippe

    Citation: BMC Bioinformatics 2017 18:428

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  14. Content type: Software

    RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previou...

    Authors: Matthias Zytnicki

    Citation: BMC Bioinformatics 2017 18:411

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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
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