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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 6 of 10

  1. A challenge in gene expression studies is the reliable identification of differentially expressed genes. In many high-throughput studies, genes are accepted as differentially expressed only if they satisfy sim...

    Authors: Evelien Vaes, Mona Khan and Peter Mombaerts

    Citation: BMC Bioinformatics 2014 15:39

    Content type: Methodology article

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  2. Given the estimate that 30% of our genes are controlled by microRNAs, it is essential that we understand the precise relationship between microRNAs and their targets. OncomiRs are microRNAs (miRNAs) that have ...

    Authors: Rimpi Khurana, Vinod Kumar Verma, Abdul Rawoof, Shrish Tiwari, Rekha A Nair, Ganesh Mahidhara, Mohammed M Idris, Alan R Clarke and Lekha Dinesh Kumar

    Citation: BMC Bioinformatics 2014 15:15

    Content type: Database

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  3. The Kruskal-Wallis test is a popular non-parametric statistical test for identifying expression quantitative trait loci (eQTLs) from genome-wide data due to its robustness against variations in the underlying ...

    Authors: Jianlong Qi, Hassan Foroughi Asl, Johan Björkegren and Tom Michoel

    Citation: BMC Bioinformatics 2014 15:11

    Content type: Software

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  4. Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon. At the same time, gene selecti...

    Authors: Miron Bartosz Kursa

    Citation: BMC Bioinformatics 2014 15:8

    Content type: Research article

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  5. RNA-seq is now widely used to quantitatively assess gene expression, expression differences and isoform switching, and promises to deliver results for the entire transcriptome. However, whether the transcripti...

    Authors: Hubert Rehrauer, Lennart Opitz, Ge Tan, Lina Sieverling and Ralph Schlapbach

    Citation: BMC Bioinformatics 2013 14:370

    Content type: Research article

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  6. As high-throughput genomic technologies become accurate and affordable, an increasing number of data sets have been accumulated in the public domain and genomic information integration and meta-analysis have b...

    Authors: Lun-Ching Chang, Hui-Min Lin, Etienne Sibille and George C Tseng

    Citation: BMC Bioinformatics 2013 14:368

    Content type: Research article

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  7. Significance analysis plays a major role in identifying and ranking genes, transcription factor binding sites, DNA methylation regions, and other high-throughput features associated with illness. We propose a ...

    Authors: Andrew E Jaffe, John D Storey, Hongkai Ji and Jeffrey T Leek

    Citation: BMC Bioinformatics 2013 14:360

    Content type: Methodology article

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  8. The production of multiple transcript isoforms from one gene is a major source of transcriptome complexity. RNA-Seq experiments, in which transcripts are converted to cDNA and sequenced, allow the resolution a...

    Authors: David Sturgill, John H Malone, Xia Sun, Harold E Smith, Leonard Rabinow, Marie-Laure Samson and Brian Oliver

    Citation: BMC Bioinformatics 2013 14:320

    Content type: Methodology article

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  9. The development of new therapies for orphan genetic diseases represents an extremely important medical and social challenge. Drug repositioning, i.e. finding new indications for approved drugs, could be one of...

    Authors: Ivan Molineris, Ugo Ala, Paolo Provero and Ferdinando Di Cunto

    Citation: BMC Bioinformatics 2013 14:288

    Content type: Research article

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  10. Analysis of global gene expression by DNA microarrays is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully understand the und...

    Authors: Juliane Charlotte Thøgersen, Morten Mørup, Søren Damkiær, Søren Molin and Lars Jelsbak

    Citation: BMC Bioinformatics 2013 14:279

    Content type: Research article

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  11. Chromatin immunoprecipitation coupled with hybridization to a tiling array (ChIP-chip) is a cost-effective and routinely used method to identify protein-DNA interactions or chromatin/histone modifications. The...

    Authors: Caroline Bérard, Michael Seifert, Tristan Mary-Huard and Marie-Laure Martin-Magniette

    Citation: BMC Bioinformatics 2013 14:271

    Content type: Software

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  12. New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resultin...

    Authors: Terrence F Meehan, Nicole A Vasilevsky, Christopher J Mungall, David S Dougall, Melissa A Haendel, Judith A Blake and Alexander D Diehl

    Citation: BMC Bioinformatics 2013 14:263

    Content type: Methodology article

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  13. Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes...

    Authors: Jonathan A Gelfond, Joseph G Ibrahim, Mayetri Gupta, Ming-Hui Chen and Jannine D Cody

    Citation: BMC Bioinformatics 2013 14:258

    Content type: Methodology article

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  14. High-throughput RNA sequencing (RNA-seq) offers unprecedented power to capture the real dynamics of gene expression. Experimental designs with extensive biological replication present a unique opportunity to e...

    Authors: Mikel Esnaola, Pedro Puig, David Gonzalez, Robert Castelo and Juan R Gonzalez

    Citation: BMC Bioinformatics 2013 14:254

    Content type: Methodology article

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  15. Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks suc...

    Authors: James Hensman, Neil D Lawrence and Magnus Rattray

    Citation: BMC Bioinformatics 2013 14:252

    Content type: Research article

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  16. In addition to probe sequence characteristics, noise in hybridization array data is thought to be influenced by competitive hybridization between probes tiled at high densities. Empirical evaluation of competi...

    Authors: Cali E Willet, Laura Bunbury-Cruickshank, Diane van Rooy, Georgina Child, Mohammad R Shariflou, Peter C Thomson and Claire M Wade

    Citation: BMC Bioinformatics 2013 14:231

    Content type: Research article

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  17. Frequent pattern mining analysis applied on microarray dataset appears to be a promising strategy for identifying relationships between gene expression levels. Unfortunately, too many itemsets (co-expressed ge...

    Authors: Yu-Cheng Liu, Chun-Pei Cheng and Vincent S Tseng

    Citation: BMC Bioinformatics 2013 14:230

    Content type: Methodology article

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  18. The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool t...

    Authors: Márcia M Almeida-de-Macedo, Nick Ransom, Yaping Feng, Jonathan Hurst and Eve Syrkin Wurtele

    Citation: BMC Bioinformatics 2013 14:214

    Content type: Research article

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  19. Gene set analysis (GSA) methods test the association of sets of genes with a phenotype in gene expression microarray studies. Many GSA methods have been proposed, especially methods for use with a binary pheno...

    Authors: Irina Dinu, Xiaoming Wang, Linda E Kelemen, Shabnam Vatanpour and Saumyadipta Pyne

    Citation: BMC Bioinformatics 2013 14:212

    Content type: Methodology article

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  20. Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repe...

    Authors: Yen-Tsung Huang and Xihong Lin

    Citation: BMC Bioinformatics 2013 14:210

    Content type: Methodology article

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  21. Microarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regulariza...

    Authors: Yong Liang, Cheng Liu, Xin-Ze Luan, Kwong-Sak Leung, Tak-Ming Chan, Zong-Ben Xu and Hai Zhang

    Citation: BMC Bioinformatics 2013 14:198

    Content type: Research article

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  22. Interpretation of gene expression microarray data in the light of external information on both columns and rows (experimental variables and gene annotations) facilitates the extraction of pertinent information...

    Authors: Florent Baty, Jochen Rüdiger, Nicola Miglino, Lukas Kern, Peter Borger and Martin Brutsche

    Citation: BMC Bioinformatics 2013 14:178

    Content type: Methodology article

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  23. ImmunoPrecipitation (IP) efficiencies may vary largely between different antibodies and between repeated experiments with the same antibody. These differences have a large impact on the quality of ChIP-seq dat...

    Authors: Yanchun Bao, Veronica Vinciotti, Ernst Wit and Peter AC ’t Hoen

    Citation: BMC Bioinformatics 2013 14:169

    Content type: Research article

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  24. Many models have been proposed to detect copy number alterations in chromosomal copy number profiles, but it is usually not obvious to decide which is most effective for a given data set. Furthermore, most met...

    Authors: Toby Dylan Hocking, Gudrun Schleiermacher, Isabelle Janoueix-Lerosey, Valentina Boeva, Julie Cappo, Olivier Delattre, Francis Bach and Jean-Philippe Vert

    Citation: BMC Bioinformatics 2013 14:164

    Content type: Research article

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  25. Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significan...

    Authors: Eric A Welsh, Steven A Eschrich, Anders E Berglund and David A Fenstermacher

    Citation: BMC Bioinformatics 2013 14:153

    Content type: Methodology article

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  26. The use of tissue microarrays (TMA) and advances in digital scanning microscopy has enabled the collection of thousands of tissue images. There is a need for software tools to annotate, query and share this da...

    Authors: Lorna Morris, Andrew Tsui, Charles Crichton, Steve Harris, Peter H Maccallum, William J Howat, Jim Davies, James D Brenton and Carlos Caldas

    Citation: BMC Bioinformatics 2013 14:147

    Content type: Software

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  27. The transcriptomes of several cyanobacterial strains have been shown to exhibit diurnal oscillation patterns reflecting the diurnal phototrophic lifestyle of the organisms. The analysis of such genome-wide tra...

    Authors: Robert Lehmann, Rainer Machné, Jens Georg, Manuela Benary, Ilka M Axmann and Ralf Steuer

    Citation: BMC Bioinformatics 2013 14:133

    Content type: Methodology article

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  28. RNA-Seq technology measures the transcript abundance by generating sequence reads and counting their frequencies across different biological conditions. To identify differentially expressed genes between two c...

    Authors: Lisa M Chung, John P Ferguson, Wei Zheng, Feng Qian, Vincent Bruno, Ruth R Montgomery and Hongyu Zhao

    Citation: BMC Bioinformatics 2013 14:110

    Content type: Methodology article

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  29. Classification using class-imbalanced data is biased in favor of the majority class. The bias is even larger for high-dimensional data, where the number of variables greatly exceeds the number of samples. The ...

    Authors: Rok Blagus and Lara Lusa

    Citation: BMC Bioinformatics 2013 14:106

    Content type: Research article

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  30. DNA microarrays are used for discovery of genes expressed differentially between various biological conditions. In microarray experiments the number of analyzed samples is often much lower than the number of g...

    Authors: Michal Marczyk, Roman Jaksik, Andrzej Polanski and Joanna Polanska

    Citation: BMC Bioinformatics 2013 14:101

    Content type: Methodology article

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  31. This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expr...

    Authors: Cécile Bazot, Nicolas Dobigeon, Jean-Yves Tourneret, Aimee K Zaas, Geoffrey S Ginsburg and Alfred O Hero III

    Citation: BMC Bioinformatics 2013 14:99

    Content type: Research article

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  32. Cellular heterogeneity is present in almost all gene expression profiles. However, transcriptome analysis of tissue specimens often ignores the cellular heterogeneity present in these samples. Standard deconvo...

    Authors: Yi Zhong, Ying-Wooi Wan, Kaifang Pang, Lionel ML Chow and Zhandong Liu

    Citation: BMC Bioinformatics 2013 14:89

    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)

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