For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
56
views
44
references
 Top references
cited by
71
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
1 collections
    16
    shares
      250
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Using high-density DNA methylation arrays to profile copy number alterations

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          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

          The integration of genomic and epigenomic data is an increasingly popular approach for studying the complex mechanisms driving cancer development. We have developed a method for evaluating both methylation and copy number from high-density DNA methylation arrays. Comparing copy number data from Infinium HumanMethylation450 BeadChips and SNP arrays, we demonstrate that Infinium arrays detect copy number alterations with the sensitivity of SNP platforms. These results show that high-density methylation arrays provide a robust and economic platform for detecting copy number and methylation changes in a single experiment. Our method is available in the ChAMP Bioconductor package: http://www.bioconductor.org/packages/2.13/bioc/html/ChAMP.html.

          Related collections

          Most cited references44

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          The Sequence Alignment/Map format and SAMtools

          Heng Li, Bob Handsaker, Alec Wysoker … (2009)
          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
          Article 0 comments Cited 15200 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found
            Is Open Access

            Comprehensive molecular portraits of human breast tumors

            Nikolaus Schultz (2013)
            Summary We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.
            Article 0 comments Cited 2187 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Adjusting batch effects in microarray expression data using empirical Bayes methods.

              W Evan Johnson, Cheng Li, Ariel Rabinovic (2007)
              Non-biological experimental variation or "batch effects" are commonly observed across multiple batches of microarray experiments, often rendering the task of combining data from these batches difficult. The ability to combine microarray data sets is advantageous to researchers to increase statistical power to detect biological phenomena from studies where logistical considerations restrict sample size or in studies that require the sequential hybridization of arrays. In general, it is inappropriate to combine data sets without adjusting for batch effects. Methods have been proposed to filter batch effects from data, but these are often complicated and require large batch sizes ( > 25) to implement. Because the majority of microarray studies are conducted using much smaller sample sizes, existing methods are not sufficient. We propose parametric and non-parametric empirical Bayes frameworks for adjusting data for batch effects that is robust to outliers in small sample sizes and performs comparable to existing methods for large samples. We illustrate our methods using two example data sets and show that our methods are justifiable, easy to apply, and useful in practice. Software for our method is freely available at: http://biosun1.harvard.edu/complab/batch/.
              Article 0 comments Cited 1859 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                Andrew Feber
                Paul Guilhamon
                Matthias Lechner
                Tim Fenton
                Gareth A Wilson
                Christina Thirlwell
                Tiffany J Morris
                Adrienne M Flanagan
                Andrew E Teschendorff
                John D Kelly
                Stephan Beck
                Journal
                Journal ID (nlm-ta): Genome Biol
                Journal ID (iso-abbrev): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2014
                Publication date (Electronic): 3 February 2014
                Volume: 15
                Issue: 2
                Page: R30
                Affiliations
                [1 ]UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK
                [2 ]Royal National Orthopaedic Hospital, Stanmore, Brockly Hill, Middlesex HA7 4LP, UK
                [3 ]Division of Surgery and Interventional Science, UCL Medical School, University College London, London WC1E 6BT, UK
                Article
                Publisher ID: gb-2014-15-2-r30
                DOI: 10.1186/gb-2014-15-2-r30
                PMC ID: 4054098
                PubMed ID: 24490765
                SO-VID: dc662a6f-083b-4b27-9434-49f2e291ff73
                Copyright © Copyright © 2014 Feber et al.; licensee BioMed Central Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 3 July 2013
                Date accepted : 3 February 2014
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

                Comments

                Comment on this article

                scite_

                Similar content250

                See all similar

                Cited by67

                See all cited by

                Most referenced authors1,741

                See all reference authors