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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 5 of 10

  1. Ribosome profiling (ribo-seq) provides experimental data on the density of elongating or initiating ribosomes at the whole transcriptome level that can be potentially used for estimating absolute levels of tra...

    Authors: Audrey M Michel, Dmitry E Andreev and Pavel V Baranov

    Citation: BMC Bioinformatics 2014 15:380

    Content type: Methodology article

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  2. The rank product method is a powerful statistical technique for identifying differentially expressed molecules in replicated experiments. A critical issue in molecule selection is accurate calculation of the p-va...

    Authors: Tom Heskes, Rob Eisinga and Rainer Breitling

    Citation: BMC Bioinformatics 2014 15:367

    Content type: Methodology article

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  3. Tools for high throughput sequencing and de novo assembly make the analysis of transcriptomes (i.e. the suite of genes expressed in a tissue) feasible for almost any organism. Yet a challenge for biologists is th...

    Authors: Daniel I Speiser, M Sabrina Pankey, Alexander K Zaharoff, Barbara A Battelle, Heather D Bracken-Grissom, Jesse W Breinholt, Seth M Bybee, Thomas W Cronin, Anders Garm, Annie R Lindgren, Nipam H Patel, Megan L Porter, Meredith E Protas, Ajna S Rivera, Jeanne M Serb, Kirk S Zigler…

    Citation: BMC Bioinformatics 2014 15:350

    Content type: Software

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  4. High-throughput molecular profiling data has been used to improve clinical decision making by stratifying subjects based on their molecular profiles. Unsupervised clustering algorithms can be used for stratifi...

    Authors: Shicai Wang, Ioannis Pandis, David Johnson, Ibrahim Emam, Florian Guitton, Axel Oehmichen and Yike Guo

    Citation: BMC Bioinformatics 2014 15:351

    Content type: Research article

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  5. Handling the vast amount of gene expression data generated by genome-wide transcriptional profiling techniques is a challenging task, demanding an informed combination of pre-processing, filtering and analysis...

    Authors: Maysson Ibrahim, Sabah Jassim, Michael Anthony Cawthorne and Kenneth Langlands

    Citation: BMC Bioinformatics 2014 15:358

    Content type: Software

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  6. The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to di...

    Authors: Elisabetta Fersini, Enza Messina and Francesco Archetti

    Citation: BMC Bioinformatics 2014 15:353

    Content type: Methodology Article

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  7. As time series experiments in higher eukaryotes usually obtain data from different individuals collected at the different time points, a time series sample itself is not equivalent to a true biological replica...

    Authors: Wencke Walter, Bernd Striberny, Emmanuel Gaquerel, Ian T Baldwin, Sang-Gyu Kim and Ines Heiland

    Citation: BMC Bioinformatics 2014 15:352

    Content type: Methodology article

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  8. To determine which changes in the host cell genome are crucial for cervical carcinogenesis, a longitudinal in vitro model system of HPV-transformed keratinocytes was profiled in a genome-wide manner. Four cell li...

    Authors: Viktorian Miok, Saskia M Wilting, Mark A van de Wiel, Annelieke Jaspers, Paula I van Noort, Ruud H Brakenhoff, Peter JF Snijders, Renske DM Steenbergen and Wessel N van Wieringen

    Citation: BMC Bioinformatics 2014 15:327

    Content type: Methodology article

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  9. The scale and complexity of genomic data lend themselves to analysis using sophisticated mathematical techniques to yield information that can generate new hypotheses and so guide further experimental investig...

    Authors: Basel Abu-Jamous, Rui Fa, David J Roberts and Asoke K Nandi

    Citation: BMC Bioinformatics 2014 15:322

    Content type: Research article

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  10. DNA methylation is a widely studied epigenetic phenomenon; alterations in methylation patterns influence human phenotypes and risk of disease. As part of the Atherosclerosis Risk in Communities (ARIC) study, t...

    Authors: Maitreyee Bose, Chong Wu, James S Pankow, Ellen W Demerath, Jan Bressler, Myriam Fornage, Megan L Grove, Thomas H Mosley, Chindo Hicks, Kari North, Wen Hong Kao, Yu Zhang, Eric Boerwinkle and Weihua Guan

    Citation: BMC Bioinformatics 2014 15:312

    Content type: Research article

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  11. A number of statistical models has been proposed for studying the association between gene expression and copy number data in integrated analysis. The next step is to compare association patterns between diffe...

    Authors: Nimisha Chaturvedi, Jelle J Goeman, Judith M Boer, Wessel N van Wieringen and Renée X de Menezes

    Citation: BMC Bioinformatics 2014 15:236

    Content type: Research article

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  12. Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There i...

    Authors: Krishna R Kalari, Asha A Nair, Jaysheel D Bhavsar, Daniel R O’Brien, Jaime I Davila, Matthew A Bockol, Jinfu Nie, Xiaojia Tang, Saurabh Baheti, Jay B Doughty, Sumit Middha, Hugues Sicotte, Aubrey E Thompson, Yan W Asmann and Jean-Pierre A Kocher

    Citation: BMC Bioinformatics 2014 15:224

    Content type: Software

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  13. Transcriptome sequencing is a powerful tool for measuring gene expression, but as well as some other technologies, various artifacts and biases affect the quantification. In order to correct some of them, seve...

    Authors: Cyril Filloux, Meersseman Cédric, Philippe Romain, Forestier Lionel, Klopp Christophe, Rocha Dominique, Maftah Abderrahman and Petit Daniel

    Citation: BMC Bioinformatics 2014 15:188

    Content type: Research article

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  14. Human disease often arises as a consequence of alterations in a set of associated genes rather than alterations to a set of unassociated individual genes. Most previous microarray-based meta-analyses identifie...

    Authors: Chun-Pei Cheng, Christopher DeBoever, Kelly A Frazer, Yu-Cheng Liu and Vincent S Tseng

    Citation: BMC Bioinformatics 2014 15:173

    Content type: Methodology article

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  15. The reproducibility of transcriptomic biomarkers across datasets remains poor, limiting clinical application. We and others have suggested that this is in-part caused by differential error-structure between da...

    Authors: Natalie S Fox, Maud HW Starmans, Syed Haider, Philippe Lambin and Paul C Boutros

    Citation: BMC Bioinformatics 2014 15:170

    Content type: Research article

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  16. To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple...

    Authors: Chen Meng, Bernhard Kuster, Aedín C Culhane and Amin Moghaddas Gholami

    Citation: BMC Bioinformatics 2014 15:162

    Content type: Methodology article

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  17. SNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Exi...

    Authors: Ruijie Liu, Zhiyin Dai, Meredith Yeager, Rafael A Irizarry and Matthew E Ritchie

    Citation: BMC Bioinformatics 2014 15:158

    Content type: Methodology article

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  18. Cap analysis of gene expression (CAGE) is a sequencing based technology to capture the 5’ ends of RNAs in a biological sample. After mapping, a CAGE peak on the genome indicates the position of an active trans...

    Authors: Akira Hasegawa, Carsten Daub, Piero Carninci, Yoshihide Hayashizaki and Timo Lassmann

    Citation: BMC Bioinformatics 2014 15:144

    Content type: Software

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  19. DNA microarrays are potentially powerful technology for improving diagnostic classification, treatment selection, and prognostic assessment. The use of this technology to predict cancer outcome has a history o...

    Authors: Minta Thomas, Kris De Brabanter and Bart De Moor

    Citation: BMC Bioinformatics 2014 15:137

    Content type: Methodology article

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  20. Computational discovery of microRNAs (miRNA) is based on pre-determined sets of features from miRNA precursors (pre-miRNA). Some feature sets are composed of sequence-structure patterns commonly found in pre-m...

    Authors: Ivani de ON Lopes, Alexander Schliep and André CP de LF de Carvalho

    Citation: BMC Bioinformatics 2014 15:124

    Content type: Research article

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  21. Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of ran...

    Authors: Mark A van de Wiel, Maarten Neerincx, Tineke E Buffart, Daoud Sie and Henk MW Verheul

    Citation: BMC Bioinformatics 2014 15:116

    Content type: Software

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  22. It is of great importance to identify molecular processes and pathways that are involved in disease etiology. Although there has been an extensive use of various high-throughput methods for this task, pathogen...

    Authors: Ståle Nygård, Trond Reitan, Trevor Clancy, Vegard Nygaard, Johannes Bjørnstad, Biljana Skrbic, Theis Tønnessen, Geir Christensen and Eivind Hovig

    Citation: BMC Bioinformatics 2014 15:115

    Content type: Methodology article

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  23. The aim of connectivity mapping is to match drugs using drug-treatment gene expression profiles from multiple cell lines. This can be viewed as an information retrieval task, with the goal of finding the most ...

    Authors: Juuso A Parkkinen and Samuel Kaski

    Citation: BMC Bioinformatics 2014 15:113

    Content type: Methodology article

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  24. High-throughput sequencing is now regularly used for studies of the transcriptome (RNA-seq), particularly for comparisons among experimental conditions. For the time being, a limited number of biological repli...

    Authors: Andrea Rau, Guillemette Marot and Florence Jaffrézic

    Citation: BMC Bioinformatics 2014 15:91

    Content type: Methodology article

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  25. Differential RNA sequencing (dRNA-seq) is a high-throughput screening technique designed to examine the architecture of bacterial operons in general and the precise position of transcription start sites (TSS) ...

    Authors: Fabian Amman, Michael T Wolfinger, Ronny Lorenz, Ivo L Hofacker, Peter F Stadler and Sven Findeiß

    Citation: BMC Bioinformatics 2014 15:89

    Content type: Software

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  26. The circadian clock is a critical regulator of biological functions controlling behavioral, physiological and biochemical processes. Because the liver is the primary regulator of metabolites within the mammali...

    Authors: Tung T Nguyen, John SA Mattick, Qian Yang, Mehmet A Orman, Marianthi G Ierapetritou, Francois Berthiaume and Ioannis P Androulakis

    Citation: BMC Bioinformatics 2014 15:83

    Content type: Research article

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  27. RNA-seq data is currently underutilized, in part because it is difficult to predict the functional impact of alternate transcription events. Recent software improvements in full-length transcript deconvolution...

    Authors: Kristoffer Vitting-Seerup, Bo Torben Porse, Albin Sandelin and Johannes Waage

    Citation: BMC Bioinformatics 2014 15:81

    Content type: Software

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  28. Identifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Anal...

    Authors: Neil R Clark, Kevin S Hu, Axel S Feldmann, Yan Kou, Edward Y Chen, Qiaonan Duan and Avi Ma’ayan

    Citation: BMC Bioinformatics 2014 15:79

    Content type: Research article

    Published on:

  29. MicroRNAs (miRNAs) are short (19-23 nucleotides) non-coding RNAs that bind to sites in the 3’untranslated regions (3’UTR) of a targeted messenger RNA (mRNA). Binding leads to degradation of the transcript or b...

    Authors: Mehmet Deveci, Ümit V Çatalyürek and Amanda Ewart Toland

    Citation: BMC Bioinformatics 2014 15:73

    Content type: Software

    Published on:

  30. Molecular data, e.g. arising from microarray technology, is often used for predicting survival probabilities of patients. For multivariate risk prediction models on such high-dimensional data, there are establ...

    Authors: Murat Sariyar, Isabell Hoffmann and Harald Binder

    Citation: BMC Bioinformatics 2014 15:58

    Content type: Research article

    Published on:

  31. In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use var...

    Authors: Kun-Huang Chen, Kung-Jeng Wang, Min-Lung Tsai, Kung-Min Wang, Angelia Melani Adrian, Wei-Chung Cheng, Tzu-Sen Yang, Nai-Chia Teng, Kuo-Pin Tan and Ku-Shang Chang

    Citation: BMC Bioinformatics 2014 15:49

    Content type: Methodology article

    Published on:

  32. A challenge in gene expression studies is the reliable identification of differentially expressed genes. In many high-throughput studies, genes are accepted as differentially expressed only if they satisfy sim...

    Authors: Evelien Vaes, Mona Khan and Peter Mombaerts

    Citation: BMC Bioinformatics 2014 15:39

    Content type: Methodology article

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  33. Cancer subtype information is critically important for understanding tumor heterogeneity. Existing methods to identify cancer subtypes have primarily focused on utilizing generic clustering algorithms (such as...

    Authors: Yiyi Liu, Quanquan Gu, Jack P Hou, Jiawei Han and Jian Ma

    Citation: BMC Bioinformatics 2014 15:37

    Content type: Methodology article

    Published on:

  34. Given the estimate that 30% of our genes are controlled by microRNAs, it is essential that we understand the precise relationship between microRNAs and their targets. OncomiRs are microRNAs (miRNAs) that have ...

    Authors: Rimpi Khurana, Vinod Kumar Verma, Abdul Rawoof, Shrish Tiwari, Rekha A Nair, Ganesh Mahidhara, Mohammed M Idris, Alan R Clarke and Lekha Dinesh Kumar

    Citation: BMC Bioinformatics 2014 15:15

    Content type: Database

    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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