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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 10

  1. 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

    Content type: Software

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  2. 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

    Content type: Software

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  3. 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

    Content type: Research article

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  4. 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

    Content type: Methodology article

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  5. 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

    Content type: Software

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

  6. 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

    Content type: Methodology article

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  7. 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

    Content type: Research article

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  8. 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

    Content type: Methodology Article

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  9. 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

    Content type: Methodology article

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  10. 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

    Content type: Research Article

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  11. 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

    Content type: Methodology article

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  12. 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

    Content type: Database

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  13. 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

    Content type: Software

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  14. Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expres...

    Authors: Gregory W. Gundersen, Kathleen M. Jagodnik, Holly Woodland, Nicholas F. Fernandez, Kevin Sani, Anders B. Dohlman, Peter Man-Un Ung, Caroline D. Monteiro, Avner Schlessinger and Avi Ma’ayan

    Citation: BMC Bioinformatics 2016 17:461

    Content type: Software

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  15. Binding of transcription factors to transcription factor binding sites (TFBSs) is key to the mediation of transcriptional regulation. Information on experimentally validated functional TFBSs is limited and con...

    Authors: Narayan Jayaram, Daniel Usvyat and Andrew C. R. Martin

    Citation: BMC Bioinformatics 2016 17:547

    Content type: Research Article

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  16. 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

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  17. 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

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  18. 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

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  19. 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

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  20. 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

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  21. 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

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  22. 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

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  23. 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

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  24. 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

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  25. 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

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  26. 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

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  27. 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

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  28. 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

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  29. 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

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  30. 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

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  31. 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

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  32. 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:

  33. 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

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2018 Journal Metrics

  • Citation Impact
    2.511 - 2-year Impact Factor
    2.970 - 5-year Impact Factor
    0.855 - Source Normalized Impact per Paper (SNIP)
    1.374 - SCImago Journal Rank (SJR)

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    Social Media Impact
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