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

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  18. Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper we propose a beta-mixture model to describ...

    Authors: Kirsti Laurila, Bodil Oster, Claus L Andersen, Philippe Lamy, Torben Orntoft, Olli Yli-Harja and Carsten Wiuf

    Citation: BMC Bioinformatics 2011 12:215

    Content type: Research article

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  19. Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relati...

    Authors: Roger S Day, Kevin K McDade, Uma R Chandran, Alex Lisovich, Thomas P Conrads, Brian L Hood, VS Kumar Kolli, David Kirchner, Traci Litzi and G Larry Maxwell

    Citation: BMC Bioinformatics 2011 12:213

    Content type: Methodology article

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  20. A key objective in many microarray association studies is the identification of individual genes associated with clinical outcome. It is often of additional interest to identify sets of genes, known a priori t...

    Authors: Insuk Sohn, Kouros Owzar, Johan Lim, Stephen L George, Stephanie Mackey Cushman and Sin-Ho Jung

    Citation: BMC Bioinformatics 2011 12:209

    Content type: Methodology article

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  21. The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) genes from the study and determining which...

    Authors: Alfredo A Kalaitzis and Neil D Lawrence

    Citation: BMC Bioinformatics 2011 12:180

    Content type: Research article

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  22. A major challenge in genomic research is identifying significant biological processes and generating new hypotheses from large gene sets. Gene sets often consist of multiple separate biological pathways, contr...

    Authors: Mitja I Kurki, Jussi Paananen, Markus Storvik, Seppo Ylä-Herttuala, Juha E Jääskeläinen, Mikael von und zu Fraunberg, Garry Wong and Petri Pehkonen

    Citation: BMC Bioinformatics 2011 12:171

    Content type: Methodology article

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  23. Gene expression is regulated at both the DNA sequence level and through modification of chromatin. However, the effect of chromatin on tissue/cell-type specific gene regulation (TCSR) is largely unknown. In th...

    Authors: Zhihua Zhang and Michael Q Zhang

    Citation: BMC Bioinformatics 2011 12:155

    Content type: Research article

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  24. Chromatin immunoprecipitation (ChIP), coupled with massively parallel short-read sequencing (seq) is used to probe chromatin dynamics. Although there are many algorithms to call peaks from ChIP-seq datasets, m...

    Authors: Xin Feng, Robert Grossman and Lincoln Stein

    Citation: BMC Bioinformatics 2011 12:139

    Content type: Software

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  25. Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and pre...

    Authors: Natalia Becker, Grischa Toedt, Peter Lichter and Axel Benner

    Citation: BMC Bioinformatics 2011 12:138

    Content type: Methodology article

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  26. Microarray technology has become a widely used tool in the biological sciences. Over the past decade, the number of users has grown exponentially, and with the number of applications and secondary data analyse...

    Authors: Matthew N McCall, Peter N Murakami, Margus Lukk, Wolfgang Huber and Rafael A Irizarry

    Citation: BMC Bioinformatics 2011 12:137

    Content type: Methodology article

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  27. Probes on a microarray represent a frozen view of a genome and are quickly outdated when new sequencing studies extend our knowledge, resulting in significant measurement error when analyzing any microarray ex...

    Authors: Christopher C Overall, D Andrew Carr, Ehsan S Tabari, Kevin J Thompson and Jennifer W Weller

    Citation: BMC Bioinformatics 2011 12:136

    Content type: Software

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  28. Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing ...

    Authors: Luis Rueda and Iman Rezaeian

    Citation: BMC Bioinformatics 2011 12:113

    Content type: Research article

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  29. OmniLog™ phenotype microarrays (PMs) have the capability to measure and compare the growth responses of biological samples upon exposure to hundreds of growth conditions such as different metabolites and antib...

    Authors: Wenling E Chang, Keri Sarver, Brandon W Higgs, Timothy D Read, Nichole ME Nolan, Carol E Chapman, Kimberly A Bishop-Lilly and Shanmuga Sozhamannan

    Citation: BMC Bioinformatics 2011 12:109

    Content type: Database

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  30. In the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focused on the identification of significant dif...

    Authors: Paolo Martini, Davide Risso, Gabriele Sales, Chiara Romualdi, Gerolamo Lanfranchi and Stefano Cagnin

    Citation: BMC Bioinformatics 2011 12:92

    Content type: Methodology article

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  31. Advances in biotechnology offer a fast growing variety of high-throughput data for screening molecular activities of genomic, transcriptional, post-transcriptional and translational observations. However, to d...

    Authors: Raffaele Fronza, Michele Tramonti, William R Atchley and Christine Nardini

    Citation: BMC Bioinformatics 2011 12:86

    Content type: Research article

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  32. Many different microarray experiments are publicly available today. It is natural to ask whether different experiments for the same phenotypic conditions can be combined using meta-analysis, in order to increa...

    Authors: Fan Shi, Gad Abraham, Christopher Leckie, Izhak Haviv and Adam Kowalczyk

    Citation: BMC Bioinformatics 2011 12:84

    Content type: Methodology article

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  33. Gene set enrichment testing has helped bridge the gap from an individual gene to a systems biology interpretation of microarray data. Although gene sets are defined a priori based on biological knowledge, curr...

    Authors: Jun Ma, Maureen A Sartor and HV Jagadish

    Citation: BMC Bioinformatics 2011 12:81

    Content type: Research article

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  34. Cross-platform analysis of gene express data requires multiple, intricate processes at different layers with various platforms. However, existing tools handle only a single platform and are not flexible enough...

    Authors: Jihoon Kim, Kiltesh Patel, Hyunchul Jung, Winston P Kuo and Lucila Ohno-Machado

    Citation: BMC Bioinformatics 2011 12:75

    Content type: Software

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  35. With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research ...

    Authors: Peter Sykacek, David P Kreil, Lisa A Meadows, Richard P Auburn, Bettina Fischer, Steven Russell and Gos Micklem

    Citation: BMC Bioinformatics 2011 12:73

    Content type: Methodology article

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  36. Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene expression based biomarkers in breast cancer...

    Authors: Samir B Amin, Parantu K Shah, Aimin Yan, Sophia Adamia, Stéphane Minvielle, Hervé Avet-Loiseau, Nikhil C Munshi and Cheng Li

    Citation: BMC Bioinformatics 2011 12:72

    Content type: Software

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  37. The creation of a complete genome-wide map of transcription factor binding sites is essential for understanding gene regulatory networks in vivo. However, current prediction methods generally rely on statistical ...

    Authors: Jonathon T Hill, Keith R Anderson, Teresa L Mastracci, Klaus H Kaestner and Lori Sussel

    Citation: BMC Bioinformatics 2011 12:62

    Content type: Research article

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  38. Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. ...

    Authors: Elizabeth Tapia, Leonardo Ornella, Pilar Bulacio and Laura Angelone

    Citation: BMC Bioinformatics 2011 12:59

    Content type: Methodology article

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  39. Alternative splicing (AS) is a process which generates several distinct mRNA isoforms from the same gene by splicing different portions out of the precursor transcript. Due to the (patho-)physiological importa...

    Authors: Johannes Eichner, Georg Zeller, Sascha Laubinger and Gunnar Rätsch

    Citation: BMC Bioinformatics 2011 12:55

    Content type: Research article

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