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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. 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

    Content type: Methodology article

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  2. 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

    Content type: Software

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  3. 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

    Content type: Research article

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  4. 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

    Content type: Software

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  5. 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

    Content type: Software

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  6. 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

    Content type: Software

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  7. 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

    Content type: Software

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  8. 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

    Content type: Software

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  9. 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

    Content type: Research Article

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  10. 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

    Content type: Methodology Article

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  11. 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

    Content type: Research Article

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  12. 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

    Content type: Software

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  13. 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

    Content type: Software

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  14. 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

    Content type: Software

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  15. A group of miRNAs can regulate a biological process by targeting genes involved in the process. The unbiased miRNA functional enrichment analysis is the most precise in silico approach to predict the biological p...

    Authors: Konstantinos Zagganas, Thanasis Vergoulis, Maria D. Paraskevopoulou, Ioannis S. Vlachos, Spiros Skiadopoulos and Theodore Dalamagas

    Citation: BMC Bioinformatics 2017 18:399

    Content type: Software

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  16. As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to...

    Authors: Lian Liu, Shao-Wu Zhang, Yufei Huang and Jia Meng

    Citation: BMC Bioinformatics 2017 18:387

    Content type: Methodology Article

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  17. Large-scale accumulation of omics data poses a pressing challenge of integrative analysis of multiple data sets in bioinformatics. An open question of such integrative analysis is how to pinpoint consistent bu...

    Authors: Xin-Ping Xie, Yu-Feng Xie and Hong-Qiang Wang

    Citation: BMC Bioinformatics 2017 18:375

    Content type: Research Article

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  18. MicroRNAs carry out post-transcriptional gene regulation in animals by binding to the 3' untranslated regions of mRNAs, causing their degradation or translational repression. MicroRNAs influence many biologica...

    Authors: Daniel Amsel, Andreas Vilcinskas and André Billion

    Citation: BMC Bioinformatics 2017 18:359

    Content type: Research Article

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  19. Personalizing treatment regimes based on gene expression profiles of individual tumors will facilitate management of cancer. Although many methods have been developed to identify pathways perturbed in tumors, ...

    Authors: Michael I. Klein, David F. Stern and Hongyu Zhao

    Citation: BMC Bioinformatics 2017 18:317

    Content type: Research Article

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  20. Given the development of high-throughput experimental techniques, an increasing number of whole genome transcription profiling time series data sets, with good temporal resolution, are becoming available to re...

    Authors: Giorgos Minas, Hiroshi Momiji, Dafyd J. Jenkins, Maria J. Costa, David A. Rand and Bärbel Finkenstädt

    Citation: BMC Bioinformatics 2017 18:316

    Content type: Software

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  21. Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation...

    Authors: Xiting Yan, Anqi Liang, Jose Gomez, Lauren Cohn, Hongyu Zhao and Geoffrey L. Chupp

    Citation: BMC Bioinformatics 2017 18:309

    Content type: Methodology Article

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  22. The availability of fast alignment-free algorithms has greatly reduced the computational burden of RNA-seq processing, especially for relatively poorly assembled genomes. Using these approaches, previous RNA-s...

    Authors: Stephen J. Bush, Mary E. B. McCulloch, Kim M. Summers, David A. Hume and Emily L. Clark

    Citation: BMC Bioinformatics 2017 18:301

    Content type: Methodology Article

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  23. Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typic...

    Authors: Atif Khan, Dejan Katanic and Juilee Thakar

    Citation: BMC Bioinformatics 2017 18:295

    Content type: Research Article

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  24. Quantitative molecular biology remains a challenge for researchers due to inconsistent approaches for control of errors in the final results. Due to several factors that can influence the final result, quantit...

    Authors: Špela Baebler, Miha Svalina, Marko Petek, Katja Stare, Ana Rotter, Maruša Pompe-Novak and Kristina Gruden

    Citation: BMC Bioinformatics 2017 18:276

    Content type: Software

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  25. Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes....

    Authors: Marijke Van Moerbeke, Adetayo Kasim, Willem Talloen, Joke Reumers, Hinrick W. H. Göhlmann and Ziv Shkedy

    Citation: BMC Bioinformatics 2017 18:273

    Content type: Methodology Article

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  26. 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

    Content type: Methodology Article

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  27. 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

    Content type: Research Article

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  28. 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

    Content type: Software

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  29. 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

    Content type: Methodology Article

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  30. 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

    Content type: Methodology Article

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  31. 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

    Content type: Research Article

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  32. 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

    Content type: Research Article

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  33. 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

    Content type: Software

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  34. 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

    Content type: Research article

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  35. 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

    Content type: Methodology article

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  36. 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

    Content type: Methodology Article

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  37. 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

    Content type: Research article

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  38. 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

    Content type: Methodology Article

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  39. 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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  40. 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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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)

    Usage 
    4,129,368 downloads

    Social Media Impact
    4446 mentions

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