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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.

Page 3 of 22
  1. Content type: Research Article

    There exist many methods for describing the complex relation between changes of gene expression in molecular pathways or gene ontologies under different experimental conditions. Among them, Gene Set Enrichment...

    Authors: Joanna Zyla, Michal Marczyk, January Weiner and Joanna Polanska

    Citation: BMC Bioinformatics 2017 18:256

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  2. Content type: Methodology Article

    Exponentially increasing numbers of NGS-based epigenomic datasets in public repositories like GEO constitute an enormous source of information that is invaluable for integrative and comparative studies of gene...

    Authors: Mohamed-Ashick M. Saleem, Marco-Antonio Mendoza-Parra, Pierre-Etienne Cholley, Matthias Blum and Hinrich Gronemeyer

    Citation: BMC Bioinformatics 2017 18:259

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  3. Content type: Software

    A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined fo...

    Authors: Liang Sun, Yongnan Zhu, A. S. M. Ashique Mahmood, Catalina O. Tudor, Jia Ren, K. Vijay-Shanker, Jian Chen and Carl J. Schmidt

    Citation: BMC Bioinformatics 2017 18:237

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  4. Content type: Methodology Article

    Sample size calculation and power estimation are essential components of experimental designs in biomedical research. It is very challenging to estimate power for RNA-Seq differential expression under complex ...

    Authors: Lianbo Yu, Soledad Fernandez and Guy Brock

    Citation: BMC Bioinformatics 2017 18:234

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  5. Content type: Methodology Article

    Reconstructing transcript models from RNA-sequencing (RNA-seq) data and establishing these as independent transcriptional units can be a challenging task. Current state-of-the-art tools for long non-coding RNA...

    Authors: Francisco Avila Cobos, Jasper Anckaert, Pieter-Jan Volders, Celine Everaert, Dries Rombaut, Jo Vandesompele, Katleen De Preter and Pieter Mestdagh

    Citation: BMC Bioinformatics 2017 18:231

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  6. Content type: Research Article

    MicroRNAs always function cooperatively in their regulation of gene expression. Dysfunctions of these co-functional microRNAs can play significant roles in disease development. We are interested in those multi...

    Authors: Hui Peng, Chaowang Lan, Yi Zheng, Gyorgy Hutvagner, Dacheng Tao and Jinyan Li

    Citation: BMC Bioinformatics 2017 18:193

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  7. Content type: Research Article

    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...

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

    Citation: BMC Bioinformatics 2017 18:187

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  8. Content type: Software

    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...

    Authors: 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

    Citation: BMC Bioinformatics 2017 18:180

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  9. Content type: Research article

    Phenotypic studies in Triticeae have shown that low temperature-induced protective mechanisms are developmentally regulated and involve dynamic acclimation processes. Understanding these mechanisms is importan...

    Authors: Alain B. Tchagang, François Fauteux, Dan Tulpan and Youlian Pan

    Citation: BMC Bioinformatics 2017 18:174

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  10. Content type: Methodology article

    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.

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

    Citation: BMC Bioinformatics 2017 18:151

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  11. Content type: Methodology Article

    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...

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

    Citation: BMC Bioinformatics 2017 18:150

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  12. Content type: Research article

    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. ...

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

    Citation: BMC Bioinformatics 2017 18:132

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  13. 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

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  14. 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

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  15. 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

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  16. 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

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  17. 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

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  18. 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

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

Page 3 of 22

2016 Journal Metrics

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    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)

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