Skip to main content

Advertisement

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 10 of 10

  1. 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

    Published on:

  2. 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

    Published on:

  3. 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

    Published on:

  4. 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

    Published on:

  5. 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

    Published on:

  6. 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

    Published on:

  7. 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

    Published on:

  8. 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

    Published on:

  9. 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

    Published on:

  10. 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

    Published on:

  11. 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

    Published on:

  12. 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

    Published on:

  13. 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

    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

Advertisement