Schug, Jonathan
Email Address
ORCID
Disciplines
Search Results
Now showing 1 - 2 of 2
Publication Modeling and Analyzing Biomolecular Networks(2002-01-01) Alur, Rajeev; Kumar, Vijay; Mintz, Max; Pappas, George J.; Rubin, Harvey; Schug, Jonathan; Belta, CalinThe authors argue for the need to model and analyze biological networks at molecular and cellular levels. They propose a computational toolbox for biologists. Central to their approach is the paradigm of hybrid models in which discrete events are combined with continuous differential equations to capture switching behavior.Publication Mitochondrial Genome Sequence Analysis: A Custom Bioinformatics Pipeline Substantially Improves Affymetrix MitoChip v2.0 Call Rate and Accuracy(2011-10-19) Schurr, Theodore G; Dulik, Matthew C; Xie, Hongbo M; Perin, Juan C; Baur, Joseph A; Zhadanov, Sergey I; King, Michael P; Schug, Jonathan; Place, Emily; Clarke, Colleen; Grauer, Michael; Santani, Avni; Albano, Anthony; Kim, Cecilia; Procaccio, Vincent; Hakonarson, Hakon; Gai, Xiaowu; Falk, Marni JBackground Mitochondrial genome sequence analysis is critical to the diagnostic evaluation of mitochondrial disease. Existing methodologies differ widely in throughput, complexity, cost efficiency, and sensitivity of heteroplasmy detection. Affymetrix MitoChip v2.0, which uses a sequencing-by-genotyping technology, allows potentially accurate and high-throughput sequencing of the entire human mitochondrial genome to be completed in a cost-effective fashion. However, the relatively low call rate achieved using existing software tools has limited the wide adoption of this platform for either clinical or research applications. Here, we report the design and development of a custom bioinformatics software pipeline that achieves a much improved call rate and accuracy for the Affymetrix MitoChip v2.0 platform. We used this custom pipeline to analyze MitoChip v2.0 data from 24 DNA samples representing a broad range of tissue types (18 whole blood, 3 skeletal muscle, 3 cell lines), mutations (a 5.8 kilobase pair deletion and 6 known heteroplasmic mutations), and haplogroup origins. All results were compared to those obtained by at least one other mitochondrial DNA sequence analysis method, including Sanger sequencing, denaturing HPLC-based heteroduplex analysis, and/or the Illumina Genome Analyzer II next generation sequencing platform. Results An average call rate of 99.75% was achieved across all samples with our custom pipeline. Comparison of calls for 15 samples characterized previously by Sanger sequencing revealed a total of 29 discordant calls, which translates to an estimated 0.012% for the base call error rate. We successfully identified 4 known heteroplasmic mutations and 24 other potential heteroplasmic mutations across 20 samples that passed quality control. Conclusions Affymetrix MitoChip v2.0 analysis using our optimized MitoChip Filtering Protocol (MFP) bioinformatics pipeline now offers the high sensitivity and accuracy needed for reliable, high-throughput and cost-efficient whole mitochondrial genome sequencing. This approach provides a viable alternative of potential utility for both clinical diagnostic and research applications to traditional Sanger and other emerging sequencing technologies for whole mitochondrial genome analysis.