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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 10

  1. 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

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  2. 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

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  3. 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

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  4. 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

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  5. 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

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  6. 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

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  7. 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

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  8. 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

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  9. 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

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  10. 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

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  11. The QuantiGene® Plex 2.0 platform (ThermoFisher Scientific) combines bDNA with the Luminex/xMAP magnetic bead capturing technology to assess differential gene expression in a compound exposure setting. This te...

    Authors: Bie Verbist, Eva Adriaensen, Vikki Keersmaekers, Dea Putri, Marjolein Crabbe, Maarten Derks, Rytis Bagdziunas, Griet Laenen and Hans De Wolf

    Citation: BMC Bioinformatics 2019 20:378

    Content type: Methodology article

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  12. Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. ...

    Authors: Sergii Domanskyi, Anthony Szedlak, Nathaniel T Hawkins, Jiayin Wang, Giovanni Paternostro and Carlo Piermarocchi

    Citation: BMC Bioinformatics 2019 20:369

    Content type: Methodology article

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  13. A microRNA (miRNA) sponge is an RNA molecule with multiple tandem miRNA response elements that can sequester miRNAs from their target mRNAs. Despite growing appreciation of the importance of miRNA sponges, our...

    Authors: Junpeng Zhang, Lin Liu, Taosheng Xu, Yong Xie, Chunwen Zhao, Jiuyong Li and Thuc Duy Le

    Citation: BMC Bioinformatics 2019 20:235

    Content type: Software

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  14. The rapid increase in High-throughput sequencing of RNA (RNA-seq) has led to tremendous improvements in the detection and reconstruction of both expressed coding and non-coding RNA transcripts. Yet, the comple...

    Authors: Thomas Gatter and Peter F Stadler

    Citation: BMC Bioinformatics 2019 20:190

    Content type: Methodology article

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  15. For many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regression t-test for weighted linear mixed-effects regression (LMER) is a legit...

    Authors: Yun Zhang, Gautam Bandyopadhyay, David J. Topham, Ann R. Falsey and Xing Qiu

    Citation: BMC Bioinformatics 2019 20:185

    Content type: Methodology article

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  16. The design of an experiment influences both what a researcher can measure, as well as how much confidence can be placed in the results. As such, it is vitally important that experimental design decisions do no...

    Authors: Daphne Ezer and Joseph Keir

    Citation: BMC Bioinformatics 2019 20:166

    Content type: Methodology article

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  17. RNA-Seq technology is routinely used to characterize the transcriptome, and to detect gene expression differences among cell types, genotypes and conditions. Advances in short-read sequencing instruments such ...

    Authors: Refael Kohen, Jonathan Barlev, Gil Hornung, Gil Stelzer, Ester Feldmesser, Kiril Kogan, Marilyn Safran and Dena Leshkowitz

    Citation: BMC Bioinformatics 2019 20:154

    Content type: Software

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  18. Gene duplications are a major source of raw material for evolution and a likely contributor to the diversity of life on earth. Duplicate genes (i.e., homeologs, in the case of a whole genome duplication) may r...

    Authors: Ronald D. Smith, Taliesin J. Kinser, Gregory D. Conradi Smith and Joshua R. Puzey

    Citation: BMC Bioinformatics 2019 20:149

    Content type: Methodology article

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  19. Gene Set Enrichment Analysis (GSEA) is a powerful tool to identify enriched functional categories of informative biomarkers. Canonical GSEA takes one-dimensional feature scores derived from the data of one pla...

    Authors: Khong-Loon Tiong and Chen-Hsiang Yeang

    Citation: BMC Bioinformatics 2019 20:145

    Content type: Methodology article

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  20. Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimen...

    Authors: Christine Winter, Robin Kosch, Martin Ludlow, Albert D. M. E. Osterhaus and Klaus Jung

    Citation: BMC Bioinformatics 2019 20:144

    Content type: Methodology article

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  21. microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanis...

    Authors: Vu VH Pham, Junpeng Zhang, Lin Liu, Buu Truong, Taosheng Xu, Trung T. Nguyen, Jiuyong Li and Thuc D. Le

    Citation: BMC Bioinformatics 2019 20:143

    Content type: Research article

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  22. A large fraction of human and mouse autosomal genes are subject to random monoallelic expression (MAE), an epigenetic mechanism characterized by allele-specific gene expression that varies between clonal cell ...

    Authors: Svetlana Vinogradova, Sachit D. Saksena, Henry N. Ward, Sébastien Vigneau and Alexander A. Gimelbrant

    Citation: BMC Bioinformatics 2019 20:106

    Content type: Software

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  23. Prioritisation of gene ontology terms from differential gene expression analyses in a two-dimensional format remains a challenge with exponentially growing data volumes. Typically, gene ontology terms are repr...

    Authors: Irina Kuznetsova, Artur Lugmayr, Stefan J. Siira, Oliver Rackham and Aleksandra Filipovska

    Citation: BMC Bioinformatics 2019 20:84

    Content type: Software

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  24. Life science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questi...

    Authors: Joshua R. Williams, Ruoting Yang, John L. Clifford, Daniel Watson, Ross Campbell, Derese Getnet, Raina Kumar, Rasha Hammamieh and Marti Jett

    Citation: BMC Bioinformatics 2019 20:81

    Content type: Software

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  25. Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs...

    Authors: Qiu Xiao, Jiawei Luo, Cheng Liang, Jie Cai, Guanghui Li and Buwen Cao

    Citation: BMC Bioinformatics 2019 20:67

    Content type: Research article

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  26. Harmonization techniques make different gene expression profiles and their sets compatible and ready for comparisons. Here we present a new bioinformatic tool termed Shambhala for harmonization of multiple hum...

    Authors: Nicolas Borisov, Irina Shabalina, Victor Tkachev, Maxim Sorokin, Andrew Garazha, Andrey Pulin, Ilya I. Eremin and Anton Buzdin

    Citation: BMC Bioinformatics 2019 20:66

    Content type: Research article

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  27. Exploring cellular responses to stimuli using extensive gene expression profiles has become a routine procedure performed on a daily basis. Raw and processed data from these studies are available on public dat...

    Authors: Marco Moretto, Paolo Sonego, Ana B. Villaseñor-Altamirano and Kristof Engelen

    Citation: BMC Bioinformatics 2019 20:54

    Content type: Software

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  28. Non-co-linear (NCL) transcripts consist of exonic sequences that are topologically inconsistent with the reference genome in an intragenic fashion (circular or intragenic trans-spliced RNAs) or in an intergenic f...

    Authors: Chia-Ying Chen and Trees-Juen Chuang

    Citation: BMC Bioinformatics 2019 20:3

    Content type: Software

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  29. Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed eff...

    Authors: S. M. Salleh, G. Mazzoni, P. Løvendahl and H. N. Kadarmideen

    Citation: BMC Bioinformatics 2018 19:513

    Content type: Research article

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  30. Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput ‘omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric c...

    Authors: Qian Li, Janelle R. Noel-MacDonnell, Devin C. Koestler, Ellen L. Goode and Brooke L. Fridley

    Citation: BMC Bioinformatics 2018 19:474

    Content type: Methodology article

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  31. The knowledge of miRNAs regulating the expression of sets of mRNAs has led to novel insights into numerous and diverse cellular mechanisms. While a single miRNA may regulate many genes, one gene can be regulat...

    Authors: Luqman Hakim Abdul Hadi, Quy Xiao Xuan Lin, Tri Tran Minh, Marie Loh, Hong Kiat Ng, Agus Salim, Richie Soong and Touati Benoukraf

    Citation: BMC Bioinformatics 2018 19:299

    Content type: Software

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  32. Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary “library size” by the sequencing depth of the assay, a...

    Authors: Thomas P. Quinn, Tamsyn M. Crowley and Mark F. Richardson

    Citation: BMC Bioinformatics 2018 19:274

    Content type: Research article

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  33. Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. How...

    Authors: Yuqing Zhang, David F. Jenkins, Solaiappan Manimaran and W. Evan Johnson

    Citation: BMC Bioinformatics 2018 19:262

    Content type: Methodology article

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  34. The single cell RNA sequencing (scRNA-seq) technique begin a new era by allowing the observation of gene expression at the single cell level. However, there is also a large amount of technical and biological n...

    Authors: Wuming Gong, Il-Youp Kwak, Pruthvi Pota, Naoko Koyano-Nakagawa and Daniel J. Garry

    Citation: BMC Bioinformatics 2018 19:220

    Content type: Methodology article

    Published on:

  35. The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-speci...

    Authors: Ankit Jambusaria, Jeff Klomp, Zhigang Hong, Shahin Rafii, Yang Dai, Asrar B. Malik and Jalees Rehman

    Citation: BMC Bioinformatics 2018 19:217

    Content type: Research article

    Published on:

  36. 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

    Published on:

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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