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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. Tumor cells are considered to have an aberrant cell state, and some evidence indicates different development states appearing in the tumorigenesis. Embryonic development and stem cell differentiation are order...

    Authors: Bo Zhang, Beibei Chen, Tao Wu, Zhenyu Xuan, Xiaopeng Zhu and Runsheng Chen

    Citation: BMC Bioinformatics 2011 12:53

    Content type: Methodology article

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  2. The Gene Ontology (GO) Consortium organizes genes into hierarchical categories based on biological process, molecular function and subcellular localization. Tools such as GoMiner can leverage GO to perform ont...

    Authors: Barry R Zeeberg, Hongfang Liu, Ari B Kahn, Martin Ehler, Vinodh N Rajapakse, Robert F Bonner, Jacob D Brown, Brian P Brooks, Vladimir L Larionov, William Reinhold, John N Weinstein and Yves G Pommier

    Citation: BMC Bioinformatics 2011 12:52

    Content type: Software

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  3. DNA microarrays have become a nearly ubiquitous tool for the study of human disease, and nowhere is this more true than in cancer. With hundreds of studies and thousands of expression profiles representing the...

    Authors: Fenglong Liu, Joseph A White, Corina Antonescu, Daniel Gusenleitner and John Quackenbush

    Citation: BMC Bioinformatics 2011 12:46

    Content type: Database

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  4. The interest in non-coding RNAs (ncRNAs) constantly rose during the past few years because of the wide spectrum of biological processes in which they are involved. This led to the discovery of numerous ncRNA g...

    Authors: Alexander Herbig and Kay Nieselt

    Citation: BMC Bioinformatics 2011 12:40

    Content type: Research article

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  5. Microarray technology allows the analysis of genomic aberrations at an ever increasing resolution, making functional interpretation of these vast amounts of data the main bottleneck in routine implementation o...

    Authors: Geert Vandeweyer, Edwin Reyniers, Wim Wuyts, Liesbeth Rooms and R Frank Kooy

    Citation: BMC Bioinformatics 2011 12:4

    Content type: Software

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  6. High complexity is considered a hallmark of living systems. Here we investigate the complexity of temporal gene expression patterns using the concept of Permutation Entropy (PE) first introduced in dynamical s...

    Authors: Xiaoliang Sun, Yong Zou, Victoria Nikiforova, Jürgen Kurths and Dirk Walther

    Citation: BMC Bioinformatics 2010 11:607

    Content type: Research article

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  7. With the expansion of public repositories such as the Gene Expression Omnibus (GEO), we are rapidly cataloging cellular transcriptional responses to diverse experimental conditions. Methods that query these re...

    Authors: Jesse M Engreitz, Alexander A Morgan, Joel T Dudley, Rong Chen, Rahul Thathoo, Russ B Altman and Atul J Butte

    Citation: BMC Bioinformatics 2010 11:603

    Content type: Research article

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  8. Expression levels for 47294 transcripts in lymphoblastoid cell lines from all 270 HapMap phase II individuals, and genotypes (both HapMap phase II and III) of 3.96 million single nucleotide polymorphisms (SNPs...

    Authors: Kristian Holm, Espen Melum, Andre Franke and Tom H Karlsen

    Citation: BMC Bioinformatics 2010 11:600

    Content type: Software

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  9. High-throughput profiling of DNA methylation status of CpG islands is crucial to understand the epigenetic regulation of genes. The microarray-based Infinium methylation assay by Illumina is one platform for l...

    Authors: Pan Du, Xiao Zhang, Chiang-Ching Huang, Nadereh Jafari, Warren A Kibbe, Lifang Hou and Simon M Lin

    Citation: BMC Bioinformatics 2010 11:587

    Content type: Research article

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  10. External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes...

    Authors: Andreas Kowarsch, Florian Blöchl, Sebastian Bohl, Maria Saile, Norbert Gretz, Ursula Klingmüller and Fabian J Theis

    Citation: BMC Bioinformatics 2010 11:585

    Content type: Research article

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  11. Exon arrays provide a way to measure the expression of different isoforms of genes in an organism. Most of the procedures to deal with these arrays are focused on gene expression or on exon expression. Althoug...

    Authors: Miguel A Anton, Ander Aramburu and Angel Rubio

    Citation: BMC Bioinformatics 2010 11:578

    Content type: Research article

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  12. The data from DNA microarrays are increasingly being used in order to understand effects of different conditions, exposures or diseases on the modulation of the expression of various genes in a biological syst...

    Authors: Reuben Thomas, Luis de la Torre, Xiaoqing Chang and Sanjay Mehrotra

    Citation: BMC Bioinformatics 2010 11:576

    Content type: Research article

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  13. Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays hav...

    Authors: Zhijin Wu, Bethany D Jenkins, Tatiana A Rynearson, Sonya T Dyhrman, Mak A Saito, Melissa Mercier and LeAnn P Whitney

    Citation: BMC Bioinformatics 2010 11:564

    Content type: Research article

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  14. Recent advances in antibody microarray technology have made it possible to measure the expression of hundreds of proteins simultaneously in a competitive dual-colour approach similar to dual-colour gene expres...

    Authors: Martin Sill, Christoph Schröder, Jörg D Hoheisel, Axel Benner and Manuela Zucknick

    Citation: BMC Bioinformatics 2010 11:556

    Content type: Methodology article

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  15. In a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gat...

    Authors: Greg Finak, Juan-Manuel Perez, Andrew Weng and Raphael Gottardo

    Citation: BMC Bioinformatics 2010 11:546

    Content type: Methodology article

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  16. Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science ...

    Authors: Eric Bareke, Michael Pierre, Anthoula Gaigneaux, Bertrand De Meulder, Sophie Depiereux, Naji Habra and Eric Depiereux

    Citation: BMC Bioinformatics 2010 11:528

    Content type: Database

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  17. The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main ...

    Authors: Rok Blagus and Lara Lusa

    Citation: BMC Bioinformatics 2010 11:523

    Content type: Research article

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  18. Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been descri...

    Authors: Fabrice Berger, Bertrand De Meulder, Anthoula Gaigneaux, Sophie Depiereux, Eric Bareke, Michael Pierre, Benoît De Hertogh, Mauro Delorenzi and Eric Depiereux

    Citation: BMC Bioinformatics 2010 11:510

    Content type: Research article

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  19. Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes....

    Authors: Eva Freyhult, Mattias Landfors, Jenny Önskog, Torgeir R Hvidsten and Patrik Rydén

    Citation: BMC Bioinformatics 2010 11:503

    Content type: Research article

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  20. Microorganisms display vast diversity, and each one has its own set of genes, cell components and metabolic reactions. To assess their huge unexploited metabolic potential in different ecosystems, we need high...

    Authors: Sébastien Terrat, Eric Peyretaillade, Olivier Gonçalves, Eric Dugat-Bony, Fabrice Gravelat, Anne Moné, Corinne Biderre-Petit, Delphine Boucher, Julien Troquet and Pierre Peyret

    Citation: BMC Bioinformatics 2010 11:478

    Content type: Research article

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  21. In the study of cancer genomics, gene expression microarrays, which measure thousands of genes in a single assay, provide abundant information for the investigation of interesting genes or biological pathways....

    Authors: Fan Shi, Christopher Leckie, Geoff MacIntyre, Izhak Haviv, Alex Boussioutas and Adam Kowalczyk

    Citation: BMC Bioinformatics 2010 11:477

    Content type: Research article

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  22. In the last decade, a large amount of microarray gene expression data has been accumulated in public repositories. Integrating and analyzing high-throughput gene expression data have become key activities for ...

    Authors: Ming Zhang, Yudong Zhang, Li Liu, Lijuan Yu, Shirley Tsang, Jing Tan, Wenhua Yao, Manjit S Kang, Yongqiang An and Xingming Fan

    Citation: BMC Bioinformatics 2010 11:433

    Content type: Software

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  23. Identification of transcription factors (TFs) involved in a biological process is the first step towards a better understanding of the underlying regulatory mechanisms. However, due to the involvement of a lar...

    Authors: Xiaoqi Cui, Tong Wang, Huann-Sheng Chen, Victor Busov and Hairong Wei

    Citation: BMC Bioinformatics 2010 11:425

    Content type: Methodology article

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  24. Over the past decade, gene expression microarray studies have greatly expanded our knowledge of genetic mechanisms of human diseases. Meta-analysis of substantial amounts of accumulated data, by integrating va...

    Authors: Wei-Chung Cheng, Min-Lung Tsai, Cheng-Wei Chang, Ching-Lung Huang, Chaang-Ray Chen, Wun-Yi Shu, Yun-Shien Lee, Tzu-Hao Wang, Ji-Hong Hong, Chia-Yang Li and Ian C Hsu

    Citation: BMC Bioinformatics 2010 11:421

    Content type: Database

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  25. The correlation between the expression levels of transcription factors and their target genes can be used to infer interactions within animal regulatory networks, but current methods are limited in their abili...

    Authors: Anil Aswani, Soile VE Keränen, James Brown, Charless C Fowlkes, David W Knowles, Mark D Biggin, Peter Bickel and Claire J Tomlin

    Citation: BMC Bioinformatics 2010 11:413

    Content type: Research article

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  26. Meta-analysis methods exist for combining multiple microarray datasets. However, there are a wide range of issues associated with microarray meta-analysis and a limited ability to compare the performance of di...

    Authors: Anna Campain and Yee Hwa Yang

    Citation: BMC Bioinformatics 2010 11:408

    Content type: Research article

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  27. In a time-course microarray experiment, the expression level for each gene is observed across a number of time-points in order to characterize the temporal trajectories of the gene-expression profiles. For man...

    Authors: Insuk Sohn, Kouros Owzar, Stephen L George, Sujong Kim and Sin-Ho Jung

    Citation: BMC Bioinformatics 2010 11:391

    Content type: Methodology article

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  28. Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (BMC Bioinformatics, 2005, 6, Art. No. S11 Suppl. 2.) reported usage of a Full Moon BioSystems slide f...

    Authors: Alexander E Pozhitkov

    Citation: BMC Bioinformatics 2010 11:361

    Content type: Correspondence

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  29. Comparative analysis of gene expression profiling of multiple biological categories, such as different species of organisms or different kinds of tissue, promises to enhance the fundamental understanding of th...

    Authors: Wensheng Zhang, Andrea Edwards, Wei Fan, Dongxiao Zhu and Kun Zhang

    Citation: BMC Bioinformatics 2010 11:338

    Content type: Methodology article

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  30. Typically, pooling of mRNA samples in microarray experiments implies mixing mRNA from several biological-replicate samples before hybridization onto a microarray chip. Here we describe an alternative smart poo...

    Authors: Raghunandan M Kainkaryam, Angela Bruex, Anna C Gilbert, John Schiefelbein and Peter J Woolf

    Citation: BMC Bioinformatics 2010 11:299

    Content type: Research article

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  31. Different microarray studies have compiled gene lists for predicting outcomes of a range of treatments and diseases. These have produced gene lists that have little overlap, indicating that the results from an...

    Authors: Gad Abraham, Adam Kowalczyk, Sherene Loi, Izhak Haviv and Justin Zobel

    Citation: BMC Bioinformatics 2010 11:277

    Content type: Research article

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2019 Journal Metrics

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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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