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Mitochondrial Genome Sequence Analysis
Genomic microarrays have yielded an unprecedented amount of biological information in the research setting since their introduction, providing tens to hundreds of thousands of signals for each biological sample. This technology has been applied to RNA expression, whole-genome sequence resolution, SNP identification, epigenetics, and other emerging areas of investigation.
While there are large differences in how different microarrays are constructed and their resulting physical behavior, identifying true signals in the presence of multiple sources of variability is a considerable challenge, given the inherent noise and large number of features on each array. For example, an error rate of just 1% when applied to an array of 100,000 features will yield 1,000 errors for each sample, resulting in potentially erroneous results when cross-comparing samples.
Array manufacturers have traditionally provided “out-of-the-box” algorithms for quality control and resolution that can be individually applied to any one array and achieve good results. However, it has been recently shown that distribution-based estimates of both signal and noise based upon a multi-sample experiment are superior in terms of accuracy and reproducibility {RMA citation, sequence citation}.
The Center for Biomedical Informatics’ (CBMi) genome analysis group, in collaboration with the Division of Genetics at The Children’s Hospital of Philadelphia and the University of Pennsylvania, has investigated a distributional approach to the Affymetrix MitoChip v2.0 array, a platform designed to report the entire sequence of mitochondrial DNA. The results of our analysis have revealed the potential for improved results in three areas of particular concern for mitochondrial sequence analysis using this platform:
- The detection of large deletions
- The identification of heteroplasmic loci.
- Metrics to assess data quality that can lead to more accurate calls
While this investigation was focused on a non-standard single data set, improvements were shown in each of the above areas to suggest that distribution-based estimates of signal and noise can be applied to the MitoCHIP v2.0 array. These results move forward the possibility of improved detection of mitochondrial DNA variations, which is an important milestone for accurately diagnosing disorders of energy metabolism, both in research and clinical settings.
Read the full study here.
Mitochondrial Genome Sequence Analysis: A Custom Bioinformatics Pipeline Substantially Improves Affymetrix MitoChip v2.0 Call Rate and Accuracy.
H. Michael Xie1, Juan C. Perin1, Theodore G. Schurr10, Matthew C. Dulik10, Sergey I. Zhadanov10, Joseph A. Baur8, Michael P. King11, Emily Place4,5, Colleen Clarke5, Matthew Grauer1, Jonathan Schug9, Avni Santani2, Anthony Albano3, Cecilia Kim3, Hakon Hakonarson3,6, Xiaowu Gai1*, Marni J. Falk4,5,7*
BMC Bioinformatics 2011, 12:402
Focus - CNV Workshop
CBMi has developed a suite of software tools and resources (CNV Workshop) for automated, genome-wide CNV detection from a variety of SNP array platforms and have made the suite and its code available under an open source license.
CNV Workshop includes three major components: detection, annotation, and presentation of structural variants from genome array data. Predicted CNVs are captured in a MySQL database that supports cohort-based projects and incorporates a secure user authentication layer and user/admin roles. Results are easily queried, sorted, filtered, and visualized via a web-based presentation layer that includes a GBrowse-based graphical representation of CNV content and relevant public data. To our knowledge, CNV Workshop has been successfully utilized for assessment of genomic variation in healthy individuals and disease cohorts and is an ideal platform for coordinating multiple associated projects.
Access the CNV workshop download page (free download from sourceforge).
Access the BMC Bioinformatics manuscript (free download).
Publications
The most recent publications by members of the CBMi NGS/ Bioinformatics group:
Genome-wide analysis of interferon regulatory factor I binding in primary human monocytes.
Shi L, Perin JC, Leipzig J, Zhang Z, Sullivan KE.
Source: The Division of Allergy Immunology, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania School of Medicine, 3615 Civic Center Blvd., Philadelphia, PA 19104, USA.
PMID: 21803131
Download the original Genome Analysis file here.
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About this Newsletter
Genome Analysis (Vol. 3.0. October, 2011) shares the latest news and developments from the Center for Biomedical Informatics(CBMi) in next generation sequencing (NGS) and bioinformatics including current project reports, upcoming events and staff news. To subscribe, email
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New CBMi Website
CBMi is pleased to announce the launch of our new website . You can view the latest projects and publications from CBMi staff. Learn about new educational events or view past presentations in the online learning library and Vimeo channel.
The site is easy to navigate with informative Discovery Bits throughout about innovative programs and resources from CBMi.
You can also read our new blog, Informatics 360o and connect with the CBMi team for your data reporting, application development and genome analysis projects.
Penn Genome Frontiers Institute - 10 Year Anniversary Symposium
Celebrating its 10-year anniversary, the Penn Genome Frontiers Institute is hosting a half-day genomics symposium on Wed., Nov. 9, 1-5 PM at the University of Pennsylvania’s Houston Hall in Bodek Lounge (3417 Spruce Street, Philadelphia). Symposium participants are invited to meet the speakers at a reception following the talks. Click here to read more .
 Download Genome Wowser @ iTunes
Genome Wowser provides an iPad-enabled view of the human genome. The app provides a functional presentation of the popular UCSC Genome Browser that is intuitive, highly portable, and allows a “Google Maps”-like navigation experience. Users can view genomic annotation tracks, zoom in, out, and across a chromosome, search for genomic elements, and download displayed data of interest. Download here
Meet with our experts
Members from our team are available to meet with you every Tuesday and Thursday in Abramson Research Center Room 710B. Learn about our capabilities and how we can support your research here at CHOP.
Meet the Team
Ariella Sasson
Associate Bioinformatics Scientist
 Ariella joined CBMi’s genome analysis and bioinformatics group in 2010 after receiving her PhD in computational biology and molecular biophysics from Rutgers University. Her primary research has focused on the optimization of analysis strategies for next generation sequence data.
Prior to her doctoral work, Ariella participated in a variety of analytics units in the fields of actuarial risk modeling and software agent-mediated discovery. Ariella also holds a bachelor’s in mathematics from Rutgers and was a Department of Energy Computational Science graduate fellow.
Contact
For consultations, please contact Juan Perin.
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