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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 1 of 11

  1. Gene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping ...

    Authors: Sagnik Banerjee, Priyanka Bhandary, Margaret Woodhouse, Taner Z. Sen, Roger P. Wise and Carson M. Andorf

    Citation: BMC Bioinformatics 2021 22:205

    Content type: Software

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  2. Despite the importance of alternative poly-adenylation and 3′ UTR length for a variety of biological phenomena, there are limited means of detecting UTR changes from standard transcriptomic data.

    Authors: Stefan Gerber, Gerhard Schratt and Pierre-Luc Germain

    Citation: BMC Bioinformatics 2021 22:189

    Content type: Methodology article

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  3. Clustering is a crucial step in the analysis of single-cell data. Clusters identified in an unsupervised manner are typically annotated to cell types based on differentially expressed genes. In contrast, super...

    Authors: Bobby Ranjan, Florian Schmidt, Wenjie Sun, Jinyu Park, Mohammad Amin Honardoost, Joanna Tan, Nirmala Arul Rayan and Shyam Prabhakar

    Citation: BMC Bioinformatics 2021 22:186

    Content type: Methodology article

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  4. Spliced leader (SL) trans-splicing replaces the 5′ end of pre-mRNAs with the spliced leader, an exon derived from a specialised non-coding RNA originating from elsewhere in the genome. This process is essential f...

    Authors: Marius A. Wenzel, Berndt Müller and Jonathan Pettitt

    Citation: BMC Bioinformatics 2021 22:140

    Content type: Software

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  5. Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made us...

    Authors: Arika Fukushima, Masahiro Sugimoto, Satoru Hiwa and Tomoyuki Hiroyasu

    Citation: BMC Bioinformatics 2021 22:132

    Content type: Methodology article

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  6. Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this rega...

    Authors: Heeju Noh, Ziyi Hua, Panagiotis Chrysinas, Jason E. Shoemaker and Rudiyanto Gunawan

    Citation: BMC Bioinformatics 2021 22:108

    Content type: Methodology article

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  7. Single-cell RNA-seq (scRNA-seq) enables the profiling of genome-wide gene expression at the single-cell level and in so doing facilitates insight into and information about cellular heterogeneity within a tiss...

    Authors: Matthew N. Bernstein, Zijian Ni, Michael Collins, Mark E. Burkard, Christina Kendziorski and Ron Stewart

    Citation: BMC Bioinformatics 2021 22:83

    Content type: Software

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  8. Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shar...

    Authors: Ryan B. Patterson-Cross, Ariel J. Levine and Vilas Menon

    Citation: BMC Bioinformatics 2021 22:39

    Content type: Methodology article

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  9. Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computa...

    Authors: Dipan Shaw, Hao Chen, Minzhu Xie and Tao Jiang

    Citation: BMC Bioinformatics 2021 22:24

    Content type: Methodology article

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  10. In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enri...

    Authors: Mike Fang, Brian Richardson, Cheryl M. Cameron, Jean-Eudes Dazard and Mark J. Cameron

    Citation: BMC Bioinformatics 2021 22:22

    Content type: Methodology article

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  11. Circular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an import...

    Authors: Shuwei Yin, Xiao Tian, Jingjing Zhang, Peisen Sun and Guanglin Li

    Citation: BMC Bioinformatics 2021 22:10

    Content type: Software

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  12. Gene fusion events are significant sources of somatic variation across adult and pediatric cancers and are some of the most clinically-effective therapeutic targets, yet low consensus of RNA-Seq fusion predict...

    Authors: Krutika S. Gaonkar, Federico Marini, Komal S. Rathi, Payal Jain, Yuankun Zhu, Nicholas A. Chimicles, Miguel A. Brown, Ammar S. Naqvi, Bo Zhang, Phillip B. Storm, John M. Maris, Pichai Raman, Adam C. Resnick, Konstantin Strauch, Jaclyn N. Taroni and Jo Lynne Rokita

    Citation: BMC Bioinformatics 2020 21:577

    Content type: Software

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  13. 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

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  14. 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

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  15. 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

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  16. 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

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  17. 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

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  18. 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

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  19. 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

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  20. 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

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  21. 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

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  22. 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

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  23. 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

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  24. 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

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  25. 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:

  26. 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

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  27. 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:

  28. 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

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Annual Journal Metrics

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    44 days to first decision for all manuscripts
    163 days from submission to acceptance
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    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)

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