52
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Comparing Bioinformatic Gene Expression Profiling Methods: Microarray and RNA-Seq

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Understanding the control of gene expression is critical for our understanding of the relationship between genotype and phenotype. The need for reliable assessment of transcript abundance in biological samples has driven scientists to develop novel technologies such as DNA microarray and RNA-Seq to meet this demand. This review focuses on comparing the two most useful methods for whole transcriptome gene expression profiling. Microarrays are reliable and more cost effective than RNA-Seq for gene expression profiling in model organisms. RNA-Seq will eventually be used more routinely than microarray, but right now the techniques can be complementary to each other. Microarrays will not become obsolete but might be relegated to only a few uses. RNA-Seq clearly has a bright future in bioinformatic data collection.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: found
          • Article: not found

          Transcriptome Sequencing to Detect Gene Fusions in Cancer

          Recurrent gene fusions, typically associated with hematological malignancies and rare bone and soft tissue tumors1, have been recently described in common solid tumors2–9. Here we employ an integrative analysis of high-throughput long and short read transcriptome sequencing of cancer cells to discover novel gene fusions. As a proof of concept we successfully utilized integrative transcriptome sequencing to “re-discover” the BCR-ABL1 10 gene fusion in a chronic myelogenous leukemia cell line and the TMPRSS2-ERG 2,3 gene fusion in a prostate cancer cell line and tissues. Additionally, we nominated, and experimentally validated, novel gene fusions resulting in chimeric transcripts in cancer cell lines and tumors. Taken together, this study establishes a robust pipeline for the discovery of novel gene chimeras using high throughput sequencing, opening up an important class of cancer-related mutations for comprehensive characterization.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Transcript length bias in RNA-seq data confounds systems biology

            Background Several recent studies have demonstrated the effectiveness of deep sequencing for transcriptome analysis (RNA-seq) in mammals. As RNA-seq becomes more affordable, whole genome transcriptional profiling is likely to become the platform of choice for species with good genomic sequences. As yet, a rigorous analysis methodology has not been developed and we are still in the stages of exploring the features of the data. Results We investigated the effect of transcript length bias in RNA-seq data using three different published data sets. For standard analyses using aggregated tag counts for each gene, the ability to call differentially expressed genes between samples is strongly associated with the length of the transcript. Conclusion Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology. This has implications for the ranking of differentially expressed genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses. Reviewers This article was reviewed by Rohan Williams (nominated by Gavin Huttley), Nicole Cloonan (nominated by Mark Ragan) and James Bullard (nominated by Sandrine Dudoit).
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              RNA-seq: from technology to biology

              Next-generation sequencing technologies are now being exploited not only to analyse static genomes, but also dynamic transcriptomes in an approach termed RNA-seq. Although these powerful and rapidly evolving technologies have only been available for a couple of years, they are already making substantial contributions to our understanding of genome expression and regulation. Here, we briefly describe technical issues accompanying RNA-seq data generation and analysis, highlighting differences to array-based approaches. We then review recent biological insight gained from applying RNA-seq and related approaches to deeply sample transcriptomes in different cell types or physiological conditions. These approaches are providing fascinating information about transcriptional and post-transcriptional gene regulation, and they are also giving unique insight into the richness of transcript structures and processing on a global scale and at unprecedented resolution.
                Bookmark

                Author and article information

                Journal
                Med Sci Monit Basic Res
                Medical Science Monitor Basic Research
                Medical Science Monitor Basic Research
                International Scientific Literature, Inc.
                2325-4394
                2325-4416
                2014
                23 August 2014
                : 20
                : 138-141
                Affiliations
                [1 ]Neuroscience Research Institute, State University of New York, College at Old Westbury, Old Westbury, NY, U.S.A.
                [2 ]Center for Molecular and Cognitive Neuroscience, 1 st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
                [3 ]Department of Biology and Medical Genetics, 2 nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
                Author notes
                Corresponding Author: Kirk J. Mantione, e-mail: kmantione@ 123456sunynri.org
                [A]

                Study Design

                [B]

                Data Collection

                [C]

                Statistical Analysis

                [D]

                Data Interpretation

                [E]

                Manuscript Preparation

                [F]

                Literature Search

                [G]

                Funds Collection

                Article
                892101
                10.12659/MSMBR.892101
                4152252
                25149683
                385f85d3-603f-4ff1-89fd-076bf2cadfa7
                © Med Sci Monit, 2014

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License

                History
                : 27 July 2014
                : 27 July 2014
                Categories
                Molecular Biology

                gene expression profiling,high-throughput nucleotide sequencing,microarray analysis

                Comments

                Comment on this article