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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. Quantification of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specificities. Bioinformatic tools to assess the different cell populat...

    Authors: Clémentine Decamps, Alexis Arnaud, Florent Petitprez, Mira Ayadi, Aurélia Baurès, Lucile Armenoult, Sergio Escalera, Isabelle Guyon, Rémy Nicolle, Richard Tomasini, Aurélien de Reyniès, Jérôme Cros, Yuna Blum and Magali Richard

    Citation: BMC Bioinformatics 2021 22:473

    Content type: Software

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  2. To avoid false-positive findings and detect cell-type specific associations in methylation and transcription investigations with bulk samples, it is critical to know the proportions of the major cell-types.

    Authors: Edwin J. C. G. van den Oord, Lin Y. Xie, Charles J. Tran, Min Zhao and Karolina A. Aberg

    Citation: BMC Bioinformatics 2021 22:462

    Content type: Methodology article

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  3. Once bulk RNA-seq data has been processed, i.e. aligned and then expression and differential tables generated, there remains the essential process where the biology is explored, visualized and interpreted. Wit...

    Authors: John J. Cole, Bekir A. Faydaci, David McGuinness, Robin Shaw, Rose A. Maciewicz, Neil A. Robertson and Carl S. Goodyear

    Citation: BMC Bioinformatics 2021 22:411

    Content type: Software

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  4. Multiple studies rely on ChIP-seq experiments to assess the effect of gene modulation and drug treatments on protein binding and chromatin structure. However, most methods commonly used for the normalization o...

    Authors: Lélia Polit, Gwenneg Kerdivel, Sebastian Gregoricchio, Michela Esposito, Christel Guillouf and Valentina Boeva

    Citation: BMC Bioinformatics 2021 22:407

    Content type: Software

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  5. Integrating data from different sources is a recurring question in computational biology. Much effort has been devoted to the integration of data sets of the same type, typically multiple numerical data tables...

    Authors: Audrey Hulot, Denis Laloë and Florence Jaffrézic

    Citation: BMC Bioinformatics 2021 22:392

    Content type: Methodology article

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  6. A range of spatially resolved transcriptomic methods has recently emerged as a way to spatially characterize the molecular and cellular diversity of a tissue. As a consequence, an increasing number of computat...

    Authors: Sergio Marco Salas, Daniel Gyllborg, Christoffer Mattsson Langseth and Mats Nilsson

    Citation: BMC Bioinformatics 2021 22:391

    Content type: Software

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  7. Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene ...

    Authors: Verônica R. de Melo Costa, Julianus Pfeuffer, Annita Louloupi, Ulf A. V. Ørom and Rosario M. Piro

    Citation: BMC Bioinformatics 2021 22:368

    Content type: Software

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  8. Analyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in thi...

    Authors: Tianyu Wang, Jun Bai and Sheida Nabavi

    Citation: BMC Bioinformatics 2021 22:364

    Content type: Methodology article

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  9. With the rapid growth in the use of high-throughput methods for characterizing translation and the continued expansion of multi-omics, there is a need for back-end functions and streamlined tools for processin...

    Authors: Håkon Tjeldnes, Kornel Labun, Yamila Torres Cleuren, Katarzyna Chyżyńska, Michał Świrski and Eivind Valen

    Citation: BMC Bioinformatics 2021 22:336

    Content type: Software

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  10. While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a f...

    Authors: Gulden Olgun, Afshan Nabi and Oznur Tastan

    Citation: BMC Bioinformatics 2021 22:294

    Content type: Software

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    The Correction to this article has been published in BMC Bioinformatics 2021 22:393

  11. Full-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA...

    Authors: Dimitra Sarantopoulou, Thomas G. Brooks, Soumyashant Nayak, Antonijo Mrčela, Nicholas F. Lahens and Gregory R. Grant

    Citation: BMC Bioinformatics 2021 22:266

    Content type: Review

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  12. RNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform ...

    Authors: Nicholas J. Eagles, Emily E. Burke, Jacob Leonard, Brianna K. Barry, Joshua M. Stolz, Louise Huuki, BaDoi N. Phan, Violeta Larios Serrato, Everardo Gutiérrez-Millán, Israel Aguilar-Ordoñez, Andrew E. Jaffe and Leonardo Collado-Torres

    Citation: BMC Bioinformatics 2021 22:224

    Content type: Software

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    The Correction to this article has been published in BMC Bioinformatics 2021 22:381

  13. Next generation sequencing has allowed the discovery of miRNA isoforms, termed isomiRs. Some isomiRs are derived from imprecise processing of pre-miRNA precursors, leading to length variants. Additional variab...

    Authors: Jose Francisco Sanchez Herrero, Raquel Pluvinet, Antonio Luna de Haro and Lauro Sumoy

    Citation: BMC Bioinformatics 2021 22:215

    Content type: Research article

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  14. 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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  15. 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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  16. 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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  17. 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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  18. 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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  19. 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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  20. 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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  21. 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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  22. 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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  23. 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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  24. 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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  25. 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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  26. 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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  27. 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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  28. 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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  29. 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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  30. 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:

  31. 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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  32. 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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  33. 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:

Annual Journal Metrics

  • Speed
    70 days to first decision for reviewed manuscripts only
    44 days to first decision for all manuscripts
    163 days from submission to acceptance
    36 days from acceptance to publication

    Citation Impact
    3.169 - 2-year Impact Factor
    3.629 - 5-year Impact Factor
    1.276 - Source Normalized Impact per Paper (SNIP)
    1.567 - SCImago Journal Rank (SJR)

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