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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 8 of 10

  1. 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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  2. 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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  3. 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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  4. 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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  5. 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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  6. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. 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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  15. 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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  16. 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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  17. 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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  18. 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

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  19. 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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  20. Comparing biological time series data across different conditions, or different specimens, is a common but still challenging task. Algorithms aligning two time series represent a valuable tool for such compari...

    Authors: Yuan Yuan, Yi-Ping Phoebe Chen, Shengyu Ni, Augix Guohua Xu, Lin Tang, Martin Vingron, Mehmet Somel and Philipp Khaitovich

    Citation: BMC Bioinformatics 2011 12:347

    Content type: Research article

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  21. Modern high-throughput measurement technologies such as DNA microarrays and next generation sequencers produce extensive datasets. With large datasets the emphasis has been moving from traditional statistical ...

    Authors: Aleksi Kallio, Niko Vuokko, Markus Ojala, Niina Haiminen and Heikki Mannila

    Citation: BMC Bioinformatics 2011 12:330

    Content type: Methodology article

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  22. Peptide microarrays offer an enormous potential as a screening tool for peptidomics experiments and have recently seen an increased field of application ranging from immunological studies to systems biology. B...

    Authors: Bernhard Y Renard, Martin Löwer, Yvonne Kühne, Ulf Reimer, Andrée Rothermel, Özlem Türeci, John C Castle and Ugur Sahin

    Citation: BMC Bioinformatics 2011 12:324

    Content type: Methodology article

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  23. Genomic and other high dimensional analyses often require one to summarize multiple related variables by a single representative. This task is also variously referred to as collapsing, combining, reducing, or ...

    Authors: Jeremy A Miller, Chaochao Cai, Peter Langfelder, Daniel H Geschwind, Sunil M Kurian, Daniel R Salomon and Steve Horvath

    Citation: BMC Bioinformatics 2011 12:322

    Content type: Methodology article

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  24. Differential coexpression analysis (DCEA) is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based...

    Authors: Hui Yu, Bao-Hong Liu, Zhi-Qiang Ye, Chun Li, Yi-Xue Li and Yuan-Yuan Li

    Citation: BMC Bioinformatics 2011 12:315

    Content type: Methodology article

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  25. Current biosensors are designed to target and react to specific nucleic acid sequences or structural epitopes. These 'target-specific' platforms require creation of new physical capture reagents when new organ...

    Authors: Mojdeh Mohtashemi, David K Walburger, Matthew W Peterson, Felicia N Sutton, Haley B Skaer and James C Diggans

    Citation: BMC Bioinformatics 2011 12:314

    Content type: Research article

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  26. Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge...

    Authors: Henry Wirth, Markus Löffler, Martin von Bergen and Hans Binder

    Citation: BMC Bioinformatics 2011 12:306

    Content type: Research article

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  27. MicroRNAs (miRNAs) are small non-coding RNA molecules that are ~22-nt-long sequences capable of suppressing protein synthesis. Previous research has suggested that miRNAs regulate 30% or more of the human prot...

    Authors: Justin Bo-Kai Hsu, Chih-Min Chiu, Sheng-Da Hsu, Wei-Yun Huang, Chia-Hung Chien, Tzong-Yi Lee and Hsien-Da Huang

    Citation: BMC Bioinformatics 2011 12:300

    Content type: Software

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  28. High throughput sequencing technology provides us unprecedented opportunities to study transcriptome dynamics. Compared to microarray-based gene expression profiling, RNA-Seq has many advantages, such as high ...

    Authors: Wei Zheng, Lisa M Chung and Hongyu Zhao

    Citation: BMC Bioinformatics 2011 12:290

    Content type: Research article

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  29. DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes.

    Authors: Jeff Skinner, Yuri Kotliarov, Sudhir Varma, Karina L Mine, Anatoly Yambartsev, Richard Simon, Yentram Huyen and Andrey Morgun

    Citation: BMC Bioinformatics 2011 12:286

    Content type: Software

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  30. Gene set analysis (GSA) has become a successful tool to interpret gene expression profiles in terms of biological functions, molecular pathways, or genomic locations. GSA performs statistical tests for indepen...

    Authors: Ke Zhang, Haiyan Wang, Arne C Bathke, Solomon W Harrar, Hans-Peter Piepho and Youping Deng

    Citation: BMC Bioinformatics 2011 12:273

    Content type: Methodology article

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  31. Current approaches for identifying transcriptional regulatory elements are mainly via the combination of two properties, the evolutionary conservation and the overrepresentation of functional elements in the p...

    Authors: Changqing Zhang, Jin Wang, Xu Hua, Jinggui Fang, Huaiqiu Zhu and Xiang Gao

    Citation: BMC Bioinformatics 2011 12:262

    Content type: Methodology article

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  32. Genome-wide transcriptional profiling of patient blood samples offers a powerful tool to investigate underlying disease mechanisms and personalized treatment decisions. Most studies are based on analysis of to...

    Authors: Christopher R Bolen, Mohamed Uduman and Steven H Kleinstein

    Citation: BMC Bioinformatics 2011 12:258

    Content type: Methodology article

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  33. Normalization of target gene expression, measured by real-time quantitative PCR (qPCR), is a requirement for reducing experimental bias and thereby improving data quality. The currently used normalization appr...

    Authors: Lars-Henrik Heckmann, Peter B Sørensen, Paul Henning Krogh and Jesper G Sørensen

    Citation: BMC Bioinformatics 2011 12:250

    Content type: Research article

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  34. Normalization of gene expression data has been studied for many years and various strategies have been formulated to deal with various types of data. Most normalization algorithms rely on the assumption that t...

    Authors: Wen-Ping Hsieh, Tzu-Ming Chu, Yu-Min Lin and Russell D Wolfinger

    Citation: BMC Bioinformatics 2011 12:222

    Content type: Methodology article

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  35. Copy number data are routinely being extracted from genome-wide association study chips using a variety of software. We empirically evaluated and compared four freely-available software packages designed for A...

    Authors: Jeanette E Eckel-Passow, Elizabeth J Atkinson, Sooraj Maharjan, Sharon LR Kardia and Mariza de Andrade

    Citation: BMC Bioinformatics 2011 12:220

    Content type: Research 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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