Skip to main content

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 4 of 10

  1. Post-transcriptional regulation is a complex mechanism that plays a central role in defining multiple cellular identities starting from a common genome. Modifications in the length of 3’UTRs have been found to...

    Authors: Elena Grassi, Elisa Mariella, Antonio Lembo, Ivan Molineris and Paolo Provero

    Citation: BMC Bioinformatics 2016 17:423

    Content type: Software

    Published on:

  2. Although metatranscriptomics—the study of diverse microbial population activity based on RNA-seq data—is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Cu...

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

    Citation: BMC Bioinformatics 2016 17:399

    Content type: Methodology article

    Published on:

  3. Over the last ten years, there has been explosive development in methods for measuring gene expression. These methods can identify thousands of genes altered between conditions, but understanding these dataset...

    Authors: David Angeles-Albores, Raymond Y. N. Lee, Juancarlos Chan and Paul W. Sternberg

    Citation: BMC Bioinformatics 2016 17:366

    Content type: Methodology Article

    Published on:

  4. Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used t...

    Authors: Alan Hodgkinson, Jean-Christophe Grenier, Elias Gbeha and Philip Awadalla

    Citation: BMC Bioinformatics 2016 17:364

    Content type: Methodology article

    Published on:

  5. Single-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality redu...

    Authors: David A. duVerle, Sohiya Yotsukura, Seitaro Nomura, Hiroyuki Aburatani and Koji Tsuda

    Citation: BMC Bioinformatics 2016 17:363

    Content type: Software

    Published on:

  6. Batch effects are a persistent and pervasive form of measurement noise which undermine the scientific utility of high-throughput genomic datasets. At their most benign, they reduce the power of statistical tes...

    Authors: Yalchin Oytam, Fariborz Sobhanmanesh, Konsta Duesing, Joshua C. Bowden, Megan Osmond-McLeod and Jason Ross

    Citation: BMC Bioinformatics 2016 17:332

    Content type: Methodology article

    Published on:

  7. Accurate identification of differentially expressed (DE) genes in time course RNA-Seq data is crucial for understanding the dynamics of transcriptional regulatory network. However, most of the available method...

    Authors: Xiaoxiao Sun, David Dalpiaz, Di Wu, Jun S. Liu, Wenxuan Zhong and Ping Ma

    Citation: BMC Bioinformatics 2016 17:324

    Content type: Methodology Article

    Published on:

  8. The interplay among genetic, environment and epigenetic variation is not fully understood. Advances in high-throughput genotyping methods, high-density DNA methylation detection and well-characterized sample c...

    Authors: Hong Pan, Joanna D. Holbrook, Neerja Karnani and Chee Keong Kwoh

    Citation: BMC Bioinformatics 2016 17:299

    Content type: Software

    Published on:

  9. Major Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) Class I molecules bind to peptide fragments of proteins degraded inside the cell and display them on the cell surface. We are interested ...

    Authors: Ankur Dhanik, Jessica R. Kirshner, Douglas MacDonald, Gavin Thurston, Hsin C. Lin, Andrew J. Murphy and Wen Zhang

    Citation: BMC Bioinformatics 2016 17:286

    Content type: Research Article

    Published on:

  10. It is now clearly evident that cancer outcome and response to therapy is guided by diverse immune-cell activity in tumors. Presently, a key challenge is to comprehensively identify networks of distinct immune-...

    Authors: Trevor Clancy and Eivind Hovig

    Citation: BMC Bioinformatics 2016 17:263

    Content type: Methodology article

    Published on:

  11. Recent interest in reference-free deconvolution of DNA methylation data has led to several supervised methods, but these methods do not easily permit the interpretation of underlying cell types.

    Authors: E. Andres Houseman, Molly L. Kile, David C. Christiani, Tan A. Ince, Karl T. Kelsey and Carmen J. Marsit

    Citation: BMC Bioinformatics 2016 17:259

    Content type: Methodology article

    Published on:

  12. In order to better understand complex diseases, it is important to understand how genetic variation in the regulatory regions affects gene expression. Genetic variants found in these regulatory regions have be...

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

    Citation: BMC Bioinformatics 2016 17:257

    Content type: Methodology Article

    Published on:

  13. The inference of gene regulatory networks (GRNs) from transcriptional expression profiles is challenging, predominantly due to its underdetermined nature. One important consequence of underdetermination is the...

    Authors: S.M. Minhaz Ud-Dean, Sandra Heise, Steffen Klamt and Rudiyanto Gunawan

    Citation: BMC Bioinformatics 2016 17:252

    Content type: Methodology article

    Published on:

  14. Integrative analysis of multi-omics data is becoming increasingly important to unravel functional mechanisms of complex diseases. However, the currently available multi-omics datasets inevitably suffer from mi...

    Authors: Dongdong Lin, Jigang Zhang, Jingyao Li, Chao Xu, Hong-Wen Deng and Yu-Ping Wang

    Citation: BMC Bioinformatics 2016 17:247

    Content type: Methodology article

    Published on:

  15. An exponential growth of high-throughput biological information and data has occurred in the past decade, supported by technologies, such as microarrays and RNA-Seq. Most data generated using such methods are ...

    Authors: Arnav Kapur, Kshitij Marwah and Gil Alterovitz

    Citation: BMC Bioinformatics 2016 17:243

    Content type: Research Article

    Published on:

  16. Single-cell technologies make it possible to quantify the comprehensive states of individual cells, and have the power to shed light on cellular differentiation in particular. Although several methods have bee...

    Authors: Hirotaka Matsumoto and Hisanori Kiryu

    Citation: BMC Bioinformatics 2016 17:232

    Content type: Methodology Article

    Published on:

  17. Discovery of microRNAs (miRNAs) relies on predictive models for characteristic features from miRNA precursors (pre-miRNAs). The short length of miRNA genes and the lack of pronounced sequence features complica...

    Authors: Ivani de O. N. Lopes, Alexander Schliep and André P. de L. F. de Carvalho

    Citation: BMC Bioinformatics 2016 17:224

    Content type: Research Article

    Published on:

  18. Gene expression connectivity mapping has gained much popularity recently with a number of successful applications in biomedical research testifying its utility and promise. Previously methodological research i...

    Authors: Qing Wen, Chang-Sik Kim, Peter W. Hamilton and Shu-Dong Zhang

    Citation: BMC Bioinformatics 2016 17:211

    Content type: Methodology Article

    Published on:

  19. Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying pote...

    Authors: Paul G. O’Reilly, Qing Wen, Peter Bankhead, Philip D. Dunne, Darragh G. McArt, Suzanne McPherson, Peter W. Hamilton, Ken I. Mills and Shu-Dong Zhang

    Citation: BMC Bioinformatics 2016 17:198

    Content type: Software

    Published on:

  20. RNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small nu...

    Authors: Ran Bi and Peng Liu

    Citation: BMC Bioinformatics 2016 17:146

    Content type: Methodology Article

    Published on:

  21. Several techniques have been tailored to the quantification of microRNA expression, including hybridization arrays, quantitative PCR (qPCR), and high-throughput sequencing. Each of these has certain strengths ...

    Authors: Matthew N. McCall, Alexander S. Baras, Alexander Crits-Christoph, Roxann Ingersoll, Melissa A. McAlexander, Kenneth W. Witwer and Marc K. Halushka

    Citation: BMC Bioinformatics 2016 17:138

    Content type: Research Article

    Published on:

  22. Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA meth...

    Authors: Devin C. Koestler, Meaghan J. Jones, Joseph Usset, Brock C. Christensen, Rondi A. Butler, Michael S. Kobor, John K. Wiencke and Karl T. Kelsey

    Citation: BMC Bioinformatics 2016 17:120

    Content type: Methodology Article

    Published on:

  23. To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be...

    Authors: Meng-Yun Wu, Xiao-Fei Zhang, Dao-Qing Dai, Le Ou-Yang, Yuan Zhu and Hong Yan

    Citation: BMC Bioinformatics 2016 17:108

    Content type: Research Article

    Published on:

  24. Often researchers are interested in comparing multiple experimental groups (e.g. tumor size) with a reference group (e.g. normal tissue) on the basis of thousands of features (e.g. genes) and determine if a di...

    Authors: Anjana Grandhi, Wenge Guo and Shyamal D. Peddada

    Citation: BMC Bioinformatics 2016 17:104

    Content type: Research Article

    Published on:

  25. High-throughput RNA-Sequencing (RNA-Seq) has become the preferred technique for studying gene expression differences between biological samples and for discovering novel isoforms, though the techniques to anal...

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

    Citation: BMC Bioinformatics 2016 17:103

    Content type: Research article

    Published on:

  26. Accurate detection of homologous relationships of biological sequences (DNA or amino acid) amongst organisms is an important and often difficult task that is essential to various evolutionary studies, ranging ...

    Authors: M. Stanley Fujimoto, Anton Suvorov, Nicholas O. Jensen, Mark J. Clement and Seth M. Bybee

    Citation: BMC Bioinformatics 2016 17:101

    Content type: Methodology article

    Published on:

  27. The regulation of gene expression in eukaryotic cells is a complex process that involves epigenetic modifications and the interaction of DNA with multiple transcription factors. This process can be studied wit...

    Authors: Marcin Piechota, Michal Korostynski, Joanna Ficek, Andrzej Tomski and Ryszard Przewlocki

    Citation: BMC Bioinformatics 2016 17:85

    Content type: Software

    Published on:

  28. Repositioning approved drug and small molecules in novel therapeutic areas is of key interest to the pharmaceutical industry. A number of promising computational techniques have been developed to aid in reposi...

    Authors: Adam S. Brown, Sek Won Kong, Isaac S. Kohane and Chirag J. Patel

    Citation: BMC Bioinformatics 2016 17:78

    Content type: Methodology article

    Published on:

  29. It has been shown that a random-effects framework can be used to test the association between a gene’s expression level and the number of DNA copies of a set of genes. This gene-set modelling framework was lat...

    Authors: Renée X. Menezes, Leila Mohammadi, Jelle J. Goeman and Judith M. Boer

    Citation: BMC Bioinformatics 2016 17:77

    Content type: Methodology Article

    Published on:

  30. Many tools exist in the analysis of bacterial RNA sequencing (RNA-seq) transcriptional profiling experiments to identify differentially expressed genes between experimental conditions. Generally, the workflow ...

    Authors: Benjamin K. Johnson, Matthew B. Scholz, Tracy K. Teal and Robert B. Abramovitch

    Citation: BMC Bioinformatics 2016 17:66

    Content type: Software

    Published on:

  31. Stored biological samples with pathology information and medical records are invaluable resources for translational medical research. However, RNAs extracted from the archived clinical tissues are often substa...

    Authors: Liguo Wang, Jinfu Nie, Hugues Sicotte, Ying Li, Jeanette E. Eckel-Passow, Surendra Dasari, Peter T. Vedell, Poulami Barman, Liewei Wang, Richard Weinshiboum, Jin Jen, Haojie Huang, Manish Kohli and Jean-Pierre A. Kocher

    Citation: BMC Bioinformatics 2016 17:58

    Content type: Methodology article

    Published on:

  32. One of our goals for the echinoderm tree of life project (http://​echinotol.​org) is to identify orthologs suitable for phylogenetic analysis from next-g...

    Authors: Daniel A. Janies, Zach Witter, Gregorio V. Linchangco, David W. Foltz, Allison K. Miller, Alexander M. Kerr, Jeremy Jay, Robert W. Reid and Gregory A. Wray

    Citation: BMC Bioinformatics 2016 17:48

    Content type: Database

    Published on:

  33. Consider the problem of designing a panel of complex biomarkers to predict a patient’s health or disease state when one can pair his or her current test sample, called a target sample, with the patient’s previ...

    Authors: Tzu-Yu Liu, Thomas Burke, Lawrence P. Park, Christopher W. Woods, Aimee K. Zaas, Geoffrey S. Ginsburg and Alfred O. Hero

    Citation: BMC Bioinformatics 2016 17:47

    Content type: Methodology Article

    Published on:

  34. Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Numerous methods have been published that either ignore (set-b...

    Authors: Ludwig Geistlinger, Gergely Csaba and Ralf Zimmer

    Citation: BMC Bioinformatics 2016 17:45

    Content type: Software

    Published on:

  35. Technical advances in Next Generation Sequencing (NGS) provide a means to acquire deeper insights into cellular functions. The lack of standardized and automated methodologies poses a challenge for the analysi...

    Authors: Markus Wolfien, Christian Rimmbach, Ulf Schmitz, Julia Jeannine Jung, Stefan Krebs, Gustav Steinhoff, Robert David and Olaf Wolkenhauer

    Citation: BMC Bioinformatics 2016 17:21

    Content type: Software

    Published on:

  36. The Pacific oyster, Crassostrea gigas, is one of the most important aquaculture shellfish resources worldwide. Important efforts have been undertaken towards a better knowledge of its genome and transcriptome, wh...

    Authors: Guillaume Riviere, Christophe Klopp, Nabihoudine Ibouniyamine, Arnaud Huvet, Pierre Boudry and Pascal Favrel

    Citation: BMC Bioinformatics 2015 16:401

    Content type: Database

    Published on:

  37. Genomic data production is at its highest level and continues to increase, making available novel primary data and existing public data to researchers for exploration. Here we explore the consequences of “batc...

    Authors: Andrew E. Jaffe, Thomas Hyde, Joel Kleinman, Daniel R. Weinbergern, Joshua G. Chenoweth, Ronald D. McKay, Jeffrey T. Leek and Carlo Colantuoni

    Citation: BMC Bioinformatics 2015 16:372

    Content type: Research article

    Published on:

    The Erratum to this article has been published in BMC Bioinformatics 2016 17:302

2019 Journal Metrics

  • Citation Impact
    3.242 - 2-year Impact Factor
    3.213 - 5-year Impact Factor
    1.156 - Source Normalized Impact per Paper (SNIP)
    1.626 - SCImago Journal Rank (SJR)

    Usage 
    4,058,323 downloads

    Social Media Impact
    6067 mentions