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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. 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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  2. 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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  3. 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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  4. 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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  5. 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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  6. 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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  7. 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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  8. 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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  9. 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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  10. 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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  11. 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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  12. 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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  13. 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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  14. 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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  15. 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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  16. 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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  17. 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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  18. 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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  19. 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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  20. 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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  21. 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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  22. 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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  23. 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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  24. 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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  25. 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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  26. 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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  27. 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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  28. 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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  29. 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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  30. 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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  31. 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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  32. 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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  33. 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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  34. 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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  35. 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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  36. 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

    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)

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