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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.

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  1. The detection of genomic copy number alterations (CNA) in cancer based on SNP arrays requires methods that take into account tumour specific factors such as normal cell contamination and tumour heterogeneity. ...

    Authors: David Mosén-Ansorena, Ana María Aransay and Naiara Rodríguez-Ezpeleta

    Citation: BMC Bioinformatics 2012 13:192

    Content type: Research article

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  2. Based on available biological information, genomic data can often be partitioned into pre-defined sets (e.g. pathways) and subsets within sets. Biologists are often interested in determining whether some pre-d...

    Authors: Wenge Guo, Mingan Yang, Chuanhua Xing and Shyamal D Peddada

    Citation: BMC Bioinformatics 2012 13:177

    Content type: Methodology article

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  3. The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-mer choices might result in the...

    Authors: Berat Z Haznedaroglu, Darryl Reeves, Hamid Rismani-Yazdi and Jordan Peccia

    Citation: BMC Bioinformatics 2012 13:170

    Content type: Research article

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  4. Identity by descent (IBD) has played a fundamental role in the discovery of genetic loci underlying human diseases. Both pedigree-based and population-based linkage analyses rely on estimating recent IBD, and ...

    Authors: Shu-Yi Su, Jay Kasberger, Sergio Baranzini, William Byerley, Wilson Liao, Jorge Oksenberg, Elliott Sherr and Eric Jorgenson

    Citation: BMC Bioinformatics 2012 13:121

    Content type: Research article

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  5. A recent large-scale analysis of Gene Expression Omnibus (GEO) data found frequent evidence for spatial defects in a substantial fraction of Affymetrix microarrays in the GEO. Nevertheless, in contrast to qual...

    Authors: Tobias Petri, Evi Berchtold, Ralf Zimmer and Caroline C Friedel

    Citation: BMC Bioinformatics 2012 13:114

    Content type: Research article

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  6. Detailed and systematic understanding of the biological effects of millions of available compounds on living cells is a significant challenge. As most compounds impact multiple targets and pathways, traditiona...

    Authors: Suleiman A Khan, Ali Faisal, John Patrick Mpindi, Juuso A Parkkinen, Tuomo Kalliokoski, Antti Poso, Olli P Kallioniemi, Krister Wennerberg and Samuel Kaski

    Citation: BMC Bioinformatics 2012 13:112

    Content type: Research article

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  7. Microarray data analysis has been the subject of extensive and ongoing pipeline development due to its complexity, the availability of several options at each analysis step, and the development of new analysis...

    Authors: Thomas Stropp, Timothy McPhillips, Bertram Ludäscher and Mark Bieda

    Citation: BMC Bioinformatics 2012 13:102

    Content type: Software

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  8. For gene expression or gene association studies with a large number of hypotheses the number of measurements per marker in a conventional single-stage design is often low due to limited resources. Two-stage de...

    Authors: Sonja Zehetmayer and Martin Posch

    Citation: BMC Bioinformatics 2012 13:81

    Content type: Methodology article

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  9. Detecting candidate markers in transcriptomic studies often encounters difficulties in complex diseases, particularly when overall signals are weak and sample size is small. Covariates including demographic, c...

    Authors: Xingbin Wang, Yan Lin, Chi Song, Etienne Sibille and George C Tseng

    Citation: BMC Bioinformatics 2012 13:52

    Content type: Research article

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  10. Expression profiling provides new insights into regulatory and metabolic processes and in particular into pathogenic mechanisms associated with diseases. Besides genes, non-coding transcripts as microRNAs (miR...

    Authors: Cedric Laczny, Petra Leidinger, Jan Haas, Nicole Ludwig, Christina Backes, Andreas Gerasch, Michael Kaufmann, Britta Vogel, Hugo A Katus, Benjamin Meder, Cord Stähler, Eckart Meese, Hans-Peter Lenhof and Andreas Keller

    Citation: BMC Bioinformatics 2012 13:36

    Content type: Software

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  11. A key question when analyzing high throughput data is whether the information provided by the measured biological entities (gene, metabolite expression for example) is related to the experimental conditions, o...

    Authors: Fangzhou Yao, Jeff Coquery and Kim-Anh Lê Cao

    Citation: BMC Bioinformatics 2012 13:24

    Content type: Research article

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  12. Whole exome capture sequencing allows researchers to cost-effectively sequence the coding regions of the genome. Although the exome capture sequencing methods have become routine and well established, there is...

    Authors: Danny Challis, Jin Yu, Uday S Evani, Andrew R Jackson, Sameer Paithankar, Cristian Coarfa, Aleksandar Milosavljevic, Richard A Gibbs and Fuli Yu

    Citation: BMC Bioinformatics 2012 13:8

    Content type: Software

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  13. Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect ...

    Authors: Davide Risso, Katja Schwartz, Gavin Sherlock and Sandrine Dudoit

    Citation: BMC Bioinformatics 2011 12:480

    Content type: Research article

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  14. The nCounter analysis system (NanoString Technologies, Seattle, WA) is a technology that enables the digital quantification of multiplexed target RNA molecules using color-coded molecular barcodes and single-m...

    Authors: Christopher D Brumbaugh, Hyunsung J Kim, Mario Giovacchini and Nader Pourmand

    Citation: BMC Bioinformatics 2011 12:479

    Content type: Software

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  15. Interpretation of gene expression microarrays requires a mapping from probe set to gene. On many Affymetrix gene expression microarrays, a given gene may be detected by multiple probe sets, which may deliver i...

    Authors: Qiyuan Li, Nicolai J Birkbak, Balazs Gyorffy, Zoltan Szallasi and Aron C Eklund

    Citation: BMC Bioinformatics 2011 12:474

    Content type: Methodology article

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  16. The rapid accumulation of molecular sequence data, driven by novel wet-lab sequencing technologies, poses new challenges for large-scale maximum likelihood-based phylogenetic analyses on trees with more than 3...

    Authors: Fernando Izquierdo-Carrasco, Stephen A Smith and Alexandros Stamatakis

    Citation: BMC Bioinformatics 2011 12:470

    Content type: Research article

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  17. Our goal was to examine how various aspects of a gene signature influence the success of developing multi-gene prediction models. We inserted gene signatures into three real data sets by altering the expressio...

    Authors: Kenneth R Hess, Caimiao Wei, Yuan Qi, Takayuki Iwamoto, W Fraser Symmans and Lajos Pusztai

    Citation: BMC Bioinformatics 2011 12:463

    Content type: Research article

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  18. Enrichment testing assesses the overall evidence of differential expression behavior of the elements within a defined set. When we have measured many molecular aspects, e.g. gene expression, metabolites, prote...

    Authors: Laila M Poisson, Jeremy M Taylor and Debashis Ghosh

    Citation: BMC Bioinformatics 2011 12:459

    Content type: Research article

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  19. Genome-wide experiments are routinely conducted to measure gene expression, DNA-protein interactions and epigenetic status. Structured metadata for these experiments is imperative for a complete understanding ...

    Authors: Amit U Sinha, Emily Merrill, Scott A Armstrong, Tim W Clark and Sudeshna Das

    Citation: BMC Bioinformatics 2011 12:452

    Content type: Software

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  20. High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular tech...

    Authors: Katrijn Van Deun, Tom F Wilderjans, Robert A van den Berg, Anestis Antoniadis and Iven Van Mechelen

    Citation: BMC Bioinformatics 2011 12:448

    Content type: Methodology article

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  21. The biological phenotype of a cell, such as a characteristic visual image or behavior, reflects activities derived from the expression of collections of genes. As such, an ability to measure the expression of ...

    Authors: Jeffrey T Chang, Michael L Gatza, Joseph E Lucas, William T Barry, Peyton Vaughn and Joseph R Nevins

    Citation: BMC Bioinformatics 2011 12:443

    Content type: Software

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  22. To utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant...

    Authors: Lam C Tsoi, Tingting Qin, Elizabeth H Slate and W Jim Zheng

    Citation: BMC Bioinformatics 2011 12:438

    Content type: Methodology article

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  23. In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns w...

    Authors: John H Morris, Leonard Apeltsin, Aaron M Newman, Jan Baumbach, Tobias Wittkop, Gang Su, Gary D Bader and Thomas E Ferrin

    Citation: BMC Bioinformatics 2011 12:436

    Content type: Software

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  24. Many analyses of gene expression data involve hypothesis tests of an interaction term between two fixed effects, typically tested using a residual variance. In expression studies, the issue of variance heteros...

    Authors: Jie Yang, George Casella and Lauren M McIntyre

    Citation: BMC Bioinformatics 2011 12:427

    Content type: Research article

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  25. In experimental data analysis, bioinformatics researchers increasingly rely on tools that enable the composition and reuse of scientific workflows. The utility of current bioinformatics workflow environments c...

    Authors: Vid Podpečan, Nada Lavrač, Igor Mozetič, Petra Kralj Novak, Igor Trajkovski, Laura Langohr, Kimmo Kulovesi, Hannu Toivonen, Marko Petek, Helena Motaln and Kristina Gruden

    Citation: BMC Bioinformatics 2011 12:416

    Content type: Methodology article

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  26. The standard approach for preprocessing spotted microarray data is to subtract the local background intensity from the spot foreground intensity, to perform a log2 transformation and to normalize the data with...

    Authors: Jérôme Ambroise, Bertrand Bearzatto, Annie Robert, Bernadette Govaerts, Benoît Macq and Jean-Luc Gala

    Citation: BMC Bioinformatics 2011 12:413

    Content type: Research article

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  27. Post-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites. Time series experiments have become increasingly common, necess...

    Authors: Emma J Cooke, Richard S Savage, Paul DW Kirk, Robert Darkins and David L Wild

    Citation: BMC Bioinformatics 2011 12:399

    Content type: Methodology article

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  28. Although numerous methods of using microarray data analysis for cancer classification have been proposed, most utilize many genes to achieve accurate classification. This can hamper interpretability of the mod...

    Authors: Xiaosheng Wang and Richard Simon

    Citation: BMC Bioinformatics 2011 12:391

    Content type: Research article

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  29. Identifying similarities between patterns of differential gene expression provides an opportunity to identify similarities between the experimental and biological conditions that give rise to these gene expres...

    Authors: Adam C Gower, Avrum Spira and Marc E Lenburg

    Citation: BMC Bioinformatics 2011 12:381

    Content type: Methodology article

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  30. Many gene-set analysis methods have been previously proposed and compared through simulation studies and analysis of real datasets for binary phenotypes. We focused on the survival phenotype and compared the p...

    Authors: Seungyeoun Lee, Jinheum Kim and Sunho Lee

    Citation: BMC Bioinformatics 2011 12:377

    Content type: Methodology article

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  31. The widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is ...

    Authors: Ping Shi, Surajit Ray, Qifu Zhu and Mark A Kon

    Citation: BMC Bioinformatics 2011 12:375

    Content type: Research article

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  32. A novel method of microarray preprocessing - Frozen Robust Multi-array Analysis (fRMA) - has recently been developed. This algorithm allows the user to preprocess arrays individually while retaining the advant...

    Authors: Matthew N McCall and Rafael A Irizarry

    Citation: BMC Bioinformatics 2011 12:369

    Content type: Software

    Published on:

  33. Immunosignaturing is a new peptide microarray based technology for profiling of humoral immune responses. Despite new challenges, immunosignaturing gives us the opportunity to explore new and fundamentally dif...

    Authors: Justin R Brown, Phillip Stafford, Stephen A Johnston and Valentin Dinu

    Citation: BMC Bioinformatics 2011 12:349

    Content type: Methodology article

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