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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 7 of 10

  1. 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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  2. 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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  3. 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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  4. 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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  5. 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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  6. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. 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

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  15. Public databases such as the NCBI Gene Expression Omnibus contain extensive and exponentially increasing amounts of high-throughput data that can be applied to molecular phenotype characterization. Collectivel...

    Authors: John C Earls, James A Eddy, Cory C Funk, Younhee Ko, Andrew T Magis and Nathan D Price

    Citation: BMC Bioinformatics 2013 14:78

    Content type: Software

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  16. Genome-wide tiling array experiments are increasingly used for the analysis of DNA methylation. Because DNA methylation patterns are tissue and cell type specific, the detection of differentially methylated re...

    Authors: Jerry Guintivano, Michal Arad, Kellie LK Tamashiro, Todd D Gould and Zachary A Kaminsky

    Citation: BMC Bioinformatics 2013 14:76

    Content type: Software

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  17. Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no on...

    Authors: Erik Kristiansson, Tobias Österlund, Lina Gunnarsson, Gabriella Arne, D G Joakim Larsson and Olle Nerman

    Citation: BMC Bioinformatics 2013 14:70

    Content type: Methodology article

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  18. Gene clustering algorithms are massively used by biologists when analysing omics data. Classical gene clustering strategies are based on the use of expression data only, directly as in Heatmaps, or indirectly ...

    Authors: Marie Verbanck, Sébastien Lê and Jérôme Pagès

    Citation: BMC Bioinformatics 2013 14:42

    Content type: Methodology article

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  19. Gene fusions are the result of chromosomal aberrations and encode chimeric RNA (fusion transcripts) that play an important role in cancer genesis. Recent advances in high throughput transcriptome sequencing ha...

    Authors: Andrew E Bruno, Jeffrey C Miecznikowski, Maochun Qin, Jianmin Wang and Song Liu

    Citation: BMC Bioinformatics 2013 14:13

    Content type: Software

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  20. Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis ...

    Authors: Sonja Hänzelmann, Robert Castelo and Justin Guinney

    Citation: BMC Bioinformatics 2013 14:7

    Content type: Software

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  21. One of the fundamental problems in time course gene expression data analysis is to identify genes associated with a biological process or a particular stimulus of interest, like a treatment or virus infection....

    Authors: Shuang Wu and Hulin Wu

    Citation: BMC Bioinformatics 2013 14:6

    Content type: Methodology article

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  22. With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no...

    Authors: Jonatan Taminau, Stijn Meganck, Cosmin Lazar, David Steenhoff, Alain Coletta, Colin Molter, Robin Duque, Virginie de Schaetzen, David Y Weiss Solís, Hugues Bersini and Ann Nowé

    Citation: BMC Bioinformatics 2012 13:335

    Content type: Software

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  23. Study on long non-coding RNAs (lncRNAs) has been promoted by high-throughput RNA sequencing (RNA-Seq). However, it is still not trivial to identify lncRNAs from the RNA-Seq data and it remains a challenge to u...

    Authors: Lei Sun, Zhihua Zhang, Timothy L Bailey, Andrew C Perkins, Michael R Tallack, Zhao Xu and Hui Liu

    Citation: BMC Bioinformatics 2012 13:331

    Content type: Research article

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  24. In studies of case-parent trios, we define copy number variants (CNVs) in the offspring that differ from the parental copy numbers as de novo and of interest for their potential functional role in disease. Amo...

    Authors: Robert B Scharpf, Terri H Beaty, Holger Schwender, Samuel G Younkin, Alan F Scott and Ingo Ruczinski

    Citation: BMC Bioinformatics 2012 13:330

    Content type: Methodology article

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  25. Illumina BeadArray technology includes non specific negative control features that allow a precise estimation of the background noise. As an alternative to the background subtraction proposed in BeadStudio whi...

    Authors: Sandra Plancade, Yves Rozenholc and Eiliv Lund

    Citation: BMC Bioinformatics 2012 13:329

    Content type: Research article

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  26. Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is h...

    Authors: Oliver P Günther, Virginia Chen, Gabriela Cohen Freue, Robert F Balshaw, Scott J Tebbutt, Zsuzsanna Hollander, Mandeep Takhar, W Robert McMaster, Bruce M McManus, Paul A Keown and Raymond T Ng

    Citation: BMC Bioinformatics 2012 13:326

    Content type: Methodology article

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  27. High throughput ’omics’ experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We cons...

    Authors: Benoit Liquet, Kim-Anh Lê Cao, Hakim Hocini and Rodolphe Thiébaut

    Citation: BMC Bioinformatics 2012 13:325

    Content type: Methodology article

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  28. MicroRNAs (miRNAs) are important regulators of gene expression encoded by a variety of organisms, including viruses. Although the function of most of the viral miRNAs is currently unknown, there is evidence th...

    Authors: Isana Veksler-Lublinsky, Yonat Shemer-Avni, Eti Meiri, Zvi Bentwich, Klara Kedem and Michal Ziv-Ukelson

    Citation: BMC Bioinformatics 2012 13:322

    Content type: Research article

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  29. MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including ne...

    Authors: Susan C Tilton, Tamara L Tal, Sheena M Scroggins, Jill A Franzosa, Elena S Peterson, Robert L Tanguay and Katrina M Waters

    Citation: BMC Bioinformatics 2012 13:311

    Content type: Software

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  30. Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data, currently used Fourier series approximations of periodic gene expressions have been found not...

    Authors: Kui Wang, Shu Kay Ng and Geoffrey J McLachlan

    Citation: BMC Bioinformatics 2012 13:300

    Content type: Methodology article

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  31. Early screening for cancer is arguably one of the greatest public health advances over the last fifty years. However, many cancer screening tests are invasive (digital rectal exams), expensive (mammograms, ima...

    Authors: Héctor Corrada Bravo, Vasyl Pihur, Matthew McCall, Rafael A Irizarry and Jeffrey T Leek

    Citation: BMC Bioinformatics 2012 13:272

    Content type: Research article

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  32. A feature selection method in microarray gene expression data should be independent of platform, disease and dataset size. Our hypothesis is that among the statistically significant ranked genes in a gene list...

    Authors: Argiris Sakellariou, Despina Sanoudou and George Spyrou

    Citation: BMC Bioinformatics 2012 13:270

    Content type: Methodology article

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  33. Sporadic Amyotrophic Lateral Sclerosis (sALS) is a devastating, complex disease of unknown etiology. We studied this disease with microarray technology to capture as much biological complexity as possible. The...

    Authors: Cristina Baciu, Kevin J Thompson, Jean-Luc Mougeot, Benjamin R Brooks and Jennifer W Weller

    Citation: BMC Bioinformatics 2012 13:244

    Content type: Research article

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  34. Existing statistical methods for tiling array transcriptome data either focus on transcript discovery in one biological or experimental condition or on the detection of differential expression between two cond...

    Authors: Kristof De Beuf, Peter Pipelers, Megan Andriankaja, Olivier Thas, Dirk Inzé, Ciprian Crainiceanu and Lieven Clement

    Citation: BMC Bioinformatics 2012 13:234

    Content type: Software

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  35. Relative expression algorithms such as the top-scoring pair (TSP) and the top-scoring triplet (TST) have several strengths that distinguish them from other classification methods, including resistance to overf...

    Authors: Andrew T Magis and Nathan D Price

    Citation: BMC Bioinformatics 2012 13:227

    Content type: Methodology article

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  36. High-density oligonucleotide microarray is an appropriate technology for genomic analysis, and is particulary useful in the generation of transcriptional maps, ChIP-on-chip studies and re-sequencing of the gen...

    Authors: Víctor Segura, Alejandro Toledo-Arana, Maite Uzqueda, Iñigo Lasa and Arrate Muñoz-Barrutia

    Citation: BMC Bioinformatics 2012 13:222

    Content type: Research article

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    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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