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 1 of 11

  1. RNA sequencing allows the study of both gene expression changes and transcribed mutations, providing a highly effective way to gain insight into cancer biology. When planning the sequencing of a large cohort o...

    Authors: Anna Quaglieri, Christoffer Flensburg, Terence P. Speed and Ian J. Majewski

    Citation: BMC Bioinformatics 2020 21:553

    Content type: Methodology article

    Published on:

  2. Human skeletal muscle responds to weight-bearing exercise with significant inter-individual differences. Investigation of transcriptome responses could improve our understanding of this variation. However, thi...

    Authors: Yusuf Khan, Daniel Hammarström, Bent R. Rønnestad, Stian Ellefsen and Rafi Ahmad

    Citation: BMC Bioinformatics 2020 21:548

    Content type: Research article

    Published on:

  3. Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions fai...

    Authors: Valeriia Sherina, Helene R. McMurray, Winslow Powers, Harmut Land, Tanzy M. T. Love and Matthew N. McCall

    Citation: BMC Bioinformatics 2020 21:545

    Content type: Methodology article

    Published on:

  4. Nutrigenomics aims at understanding the interaction between nutrition and gene information. Due to the complex interactions of nutrients and genes, their relationship exhibits non-linearity. One of the most ef...

    Authors: Xiangnan Xu, Samantha M. Solon-Biet, Alistair Senior, David Raubenheimer, Stephen J. Simpson, Luigi Fontana, Samuel Mueller and Jean Y. H. Yang

    Citation: BMC Bioinformatics 2020 21:530

    Content type: Methodology article

    Published on:

  5. The pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. P...

    Authors: Shaoke Lou, Tianxiao Li, Daniel Spakowicz, Xiting Yan, Geoffrey Lowell Chupp and Mark Gerstein

    Citation: BMC Bioinformatics 2020 21:457

    Content type: Research article

    Published on:

  6. Bayesian factorization methods, including Coordinated Gene Activity in Pattern Sets (CoGAPS), are emerging as powerful analysis tools for single cell data. However, these methods have greater computational cos...

    Authors: Thomas D. Sherman, Tiger Gao and Elana J. Fertig

    Citation: BMC Bioinformatics 2020 21:453

    Content type: Software

    Published on:

  7. As the barriers to incorporating RNA sequencing (RNA-Seq) into biomedical studies continue to decrease, the complexity and size of RNA-Seq experiments are rapidly growing. Paired, longitudinal, and other corre...

    Authors: Brian E. Vestal, Camille M. Moore, Elizabeth Wynn, Laura Saba, Tasha Fingerlin and Katerina Kechris

    Citation: BMC Bioinformatics 2020 21:375

    Content type: Methodology article

    Published on:

  8. Systematic technical effects—also called batch effects—are a considerable challenge when analyzing DNA methylation (DNAm) microarray data, because they can lead to false results when confounded with the variab...

    Authors: Tristan Zindler, Helge Frieling, Alexandra Neyazi, Stefan Bleich and Eva Friedel

    Citation: BMC Bioinformatics 2020 21:271

    Content type: Research article

    Published on:

  9. Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole b...

    Authors: Raúl Aguirre-Gamboa, Niek de Klein, Jennifer di Tommaso, Annique Claringbould, Monique GP van der Wijst, Dylan de Vries, Harm Brugge, Roy Oelen, Urmo Võsa, Maria M. Zorro, Xiaojin Chu, Olivier B. Bakker, Zuzanna Borek, Isis Ricaño-Ponce, Patrick Deelen, Cheng-Jiang Xu…

    Citation: BMC Bioinformatics 2020 21:243

    Content type: Methodology article

    Published on:

  10. Single-cell RNA sequencing (scRNA-seq) provides an effective tool to investigate the transcriptomic characteristics at the single-cell resolution. Due to the low amounts of transcripts in single cells and the ...

    Authors: Yang Qi, Yang Guo, Huixin Jiao and Xuequn Shang

    Citation: BMC Bioinformatics 2020 21:240

    Content type: Methodology Article

    Published on:

  11. Mounting evidence suggests several diseases and biological processes target transcription termination to misregulate gene expression. Disruption of transcription termination leads to readthrough transcription ...

    Authors: Samuel J. Roth, Sven Heinz and Christopher Benner

    Citation: BMC Bioinformatics 2020 21:214

    Content type: Software

    Published on:

  12. With the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compar...

    Authors: David Gerard

    Citation: BMC Bioinformatics 2020 21:206

    Content type: Methodology Article

    Published on:

  13. Circular RNAs (circRNAs) are a newly appreciated class of non-coding RNA molecules. Numerous tools have been developed for the detection of circRNAs, however computational tools to perform downstream functiona...

    Authors: Simona Aufiero, Yolan J. Reckman, Anke J. Tijsen, Yigal M. Pinto and Esther E. Creemers

    Citation: BMC Bioinformatics 2020 21:164

    Content type: Software

    Published on:

  14. Microarray technology provides the expression level of many genes. Nowadays, an important issue is to select a small number of informative differentially expressed genes that provide biological knowledge and m...

    Authors: José María Martínez-Otzeta, Itziar Irigoien, Basilio Sierra and Concepción Arenas

    Citation: BMC Bioinformatics 2020 21:135

    Content type: Software

    Published on:

  15. With the cost of DNA sequencing decreasing, increasing amounts of RNA-Seq data are being generated giving novel insight into gene expression and regulation. Prior to analysis of gene expression, the RNA-Seq da...

    Authors: Xiaokang Zhang and Inge Jonassen

    Citation: BMC Bioinformatics 2020 21:110

    Content type: Software

    Published on:

  16. To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been in...

    Authors: Celine Everaert, Pieter-Jan Volders, Annelien Morlion, Olivier Thas and Pieter Mestdagh

    Citation: BMC Bioinformatics 2020 21:58

    Content type: Methodology article

    Published on:

  17. Quality Control in any high-throughput sequencing technology is a critical step, which if overlooked can compromise an experiment and the resulting conclusions. A number of methods exist to identify biases dur...

    Authors: Gaurav Kumar, Adam Ertel, George Feldman, Joan Kupper and Paolo Fortina

    Citation: BMC Bioinformatics 2020 21:56

    Content type: Software

    Published on:

  18. High-throughput transcriptomics has matured into a very well established and widely utilised research tool over the last two decades. Clinical datasets generated on a range of different platforms continue to b...

    Authors: Arran K. Turnbull, Cigdem Selli, Carlos Martinez-Perez, Anu Fernando, Lorna Renshaw, Jane Keys, Jonine D. Figueroa, Xiaping He, Maki Tanioka, Alison F. Munro, Lee Murphy, Angie Fawkes, Richard Clark, Audrey Coutts, Charles M. Perou, Lisa A. Carey…

    Citation: BMC Bioinformatics 2020 21:30

    Content type: Research article

    Published on:

  19. Cell-type heterogeneity of tumors is a key factor in tumor progression and response to chemotherapy. Tumor cell-type heterogeneity, defined as the proportion of the various cell-types in a tumor, can be inferr...

    Authors: Clémentine Decamps, Florian Privé, Raphael Bacher, Daniel Jost, Arthur Waguet, Eugene Andres Houseman, Eugene Lurie, Pavlo Lutsik, Aleksandar Milosavljevic, Michael Scherer, Michael G. B. Blum and Magali Richard

    Citation: BMC Bioinformatics 2020 21:16

    Content type: Methodology article

    Published on:

    The Protocol to this article has been published in Nature Protocols 2020 15:369

  20. Reactivation of the telomerase reverse transcriptase gene TERT is a central feature for unlimited proliferation of the majority of cancers. However, the underlying regulatory processes are only partly understood.

    Authors: Alexandra M. Poos, Theresa Kordaß, Amol Kolte, Volker Ast, Marcus Oswald, Karsten Rippe and Rainer König

    Citation: BMC Bioinformatics 2019 20:737

    Content type: Methodology article

    Published on:

  21. Although microarray studies have greatly contributed to recent genetic advances, lack of replication has been a continuing concern in this area. Complex study designs have the potential to address this concern...

    Authors: Elham Khodayari Moez, Morteza Hajihosseini, Jeffrey L. Andrews and Irina Dinu

    Citation: BMC Bioinformatics 2019 20:650

    Content type: Methodology article

    Published on:

  22. High-throughput gene expression profiles have allowed discovery of potential biomarkers enabling early diagnosis, prognosis and developing individualized treatment. However, it remains a challenge to identify ...

    Authors: Ling Zhang, Ishwor Thapa, Christian Haas and Dhundy Bastola

    Citation: BMC Bioinformatics 2019 20:601

    Content type: Methodology Article

    Published on:

  23. Tea is the oldest and among the world’s most popular non-alcoholic beverages, which has important economic, health and cultural values. Tea is commonly produced from the leaves of tea plants (Camellia sinensis), ...

    Authors: Fang-Dong Li, Wei Tong, En-Hua Xia and Chao-Ling Wei

    Citation: BMC Bioinformatics 2019 20:553

    Content type: Research article

    Published on:

  24. Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representa...

    Authors: Marcelo P. Segura-Lepe, Hector C. Keun and Timothy M. D. Ebbels

    Citation: BMC Bioinformatics 2019 20:543

    Content type: Research article

    Published on:

  25. High-throughput sequencing experiments, which can determine allele origins, have been used to assess genome-wide allele-specific expression. Despite the amount of data generated from high-throughput experiment...

    Authors: Jing Xie, Tieming Ji, Marco A. R. Ferreira, Yahan Li, Bhaumik N. Patel and Rocio M. Rivera

    Citation: BMC Bioinformatics 2019 20:530

    Content type: Methodology Article

    Published on:

  26. 5′-end sequencing assays, and Cap Analysis of Gene Expression (CAGE) in particular, have been instrumental in studying transcriptional regulation. 5′-end methods provide genome-wide maps of transcription start...

    Authors: Malte Thodberg, Axel Thieffry, Kristoffer Vitting-Seerup, Robin Andersson and Albin Sandelin

    Citation: BMC Bioinformatics 2019 20:487

    Content type: Software

    Published on:

  27. High-throughput gene expression technologies provide complex datasets reflecting mechanisms perturbed in an experiment, typically in a treatment versus control design. Analysis of these information-rich data c...

    Authors: Florian Martin, Sylvain Gubian, Marja Talikka, Julia Hoeng and Manuel C. Peitsch

    Citation: BMC Bioinformatics 2019 20:451

    Content type: Software

    Published on:

  28. The epidermal growth factor receptor (EGFR) is a major regulator of proliferation in tumor cells. Elevated expression levels of EGFR are associated with prognosis and clinical outcomes of patients in a variety...

    Authors: Claus Weinholdt, Henri Wichmann, Johanna Kotrba, David H. Ardell, Matthias Kappler, Alexander W. Eckert, Dirk Vordermark and Ivo Grosse

    Citation: BMC Bioinformatics 2019 20:434

    Content type: Research article

    Published on:

  29. Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantificat...

    Authors: Mohamed K Gunady, Stephen M Mount and Héctor Corrada Bravo

    Citation: BMC Bioinformatics 2019 20:421

    Content type: Methodology article

    Published on:

  30. Standard RNAseq methods using bulk RNA and recent single-cell RNAseq methods use DNA barcodes to identify samples and cells, and the barcoded cDNAs are pooled into a library pool before high throughput sequenc...

    Authors: Shintaro Katayama, Tiina Skoog, Cilla Söderhäll, Elisabet Einarsdottir, Kaarel Krjutškov and Juha Kere

    Citation: BMC Bioinformatics 2019 20:418

    Content type: Software

    Published on:

  31. High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to ...

    Authors: Anjali Silva, Steven J. Rothstein, Paul D. McNicholas and Sanjeena Subedi

    Citation: BMC Bioinformatics 2019 20:394

    Content type: Methodology article

    Published on:

  32. MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In ...

    Authors: Emmy Borgmästars, Hendrik Arnold de Weerd, Zelmina Lubovac-Pilav and Malin Sund

    Citation: BMC Bioinformatics 2019 20:393

    Content type: Research article

    Published on:

  • Editorial Board
  • Sign up for article alerts and news from this journal
  • As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times.  Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time.

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