Vincent J. Magrini

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3ranked-venue papers

0as first author

0since 2021 · last 2015

0000-0002-2019-4872ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 3

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing

2 papers

Bioinformatics and computational biology · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Bioinformatics and computational biology › genomics › structural variation structural variant detection	0.1	1	2010	SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data · Bioinform. 2010
Bioinformatics and computational biology › sequence analysis targeted sequencing	0.1	1	2010	SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data · Bioinform. 2010
Bioinformatics and computational biology › cancer genomics › chromosomal aberration detection translocation detection	0.1	1	2010	SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data · Bioinform. 2010
Bioinformatics and computational biology › sequence analysis › sequence assembly › genome assembly de novo assembly	0.1	1	2007	Extending assembly of short DNA sequences to handle error · Bioinform. 2007
Bioinformatics and computational biology › sequence analysis › sequence assembly genome assembly	0.1	1	2007	Extending assembly of short DNA sequences to handle error · Bioinform. 2007

Methods — techniques the papers use, named apart from their topics

short-read analysis · 0.1high-depth coverage · 0.1consensus assembly · 0.1

Year	Publication	Venue	Position
2015	Genome Modeling System: A Knowledge Management Platform for Genomics abstract In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms. Malachi Griffith, Obi L. Griffith, Scott M. Smith, Avinash Ramu, Matthew B. Callaway, Anthony M. Brummett, Michael J. Kiwala, Adam C. Coffman, Allison A. Regier, Benjamin J. Oberkfell, Gabriel E. Sanderson, Thomas P. Mooney, Nathaniel G. Nutter, Edward A. Belter, Feiyu Du, Robert L. Long, Travis E. Abbott, Ian T. Ferguson, David L. Morton, Mark M. Burnett, James V. Weible, Joshua B. Peck, Adam Dukes, Joshua F. McMichael, Justin T. Lolofie, Brian R. Derickson, Jasreet Hundal, Zachary L. Skidmore, Benjamin J. Ainscough, Nathan D. Dees, William S. Schierding, Cyriac Kandoth, Kyung H. Kim, Charles Lu 0002, Christopher C. Harris, Nicole Maher, Christopher A. Maher, Vincent J. Magrini, Benjamin S. Abbott, Ken Chen 0001, Eric Clark, Indraniel Das, Xian Fan, Amy E. Hawkins, Todd G. Hepler, Todd Wylie, Shawn M. Leonard, William E. Schroeder, Xiaoqi Shi, Lynn K. Carmichael, Matthew R. Weil, Richard W. Wohlstadter, Gary Stiehr, Michael D. McLellan, Craig S. Pohl, Christopher A. Miller 0002, Daniel C. Koboldt, Jason R. Walker, James M. Eldred, David E. Larson, David J. Dooling, Elaine R. Mardis, Richard K. Wilson	PLoS Comput. Biol.	38
2010	SLOPE: a quick and accurate method for locating non-SNP structural variation from targeted next-generation sequence data abstract MOTIVATION: Targeted 'deep' sequencing of specific genes or regions is of great interest in clinical cancer diagnostics where some sequence variants, particularly translocations and indels, have known prognostic or diagnostic significance. In this setting, it is unnecessary to sequence an entire genome, and target capture methods can be applied to limit sequencing to important regions, thereby reducing costs and the time required to complete testing. Existing 'next-gen' sequencing analysis packages are optimized for efficiency in whole-genome studies and are unable to benefit from the particular structure of targeted sequence data. RESULTS: We developed SLOPE to detect structural variants from targeted short-DNA reads. We use both real and simulated data to demonstrate SLOPE's ability to rapidly detect insertion/deletion events of various sizes as well as translocations and viral integration sites with high sensitivity and low false discovery rate. AVAILABILITY: Binary code available at http://www-genepi.med.utah.edu/suppl/SLOPE/index.html Haley J. Abel, Eric J. Duncavage, Nils Becker, Jon R. Armstrong, Vincent J. Magrini, John D. Pfeifer	Bioinform.	5
2007	Extending assembly of short DNA sequences to handle error abstract UNLABELLED: Inexpensive de novo genome sequencing, particularly in organisms with small genomes, is now possible using several new sequencing technologies. Some of these technologies such as that from Illumina's Solexa Sequencing, produce high genomic coverage by generating a very large number of small reads ( approximately 30 bp). While prior work shows that partial assembly can be performed by k-mer extension in error-free reads, this algorithm is unsuccessful with the sequencing error rates found in practice. We present VCAKE (Verified Consensus Assembly by K-mer Extension), a modification of simple k-mer extension that overcomes error by using high depth coverage. Though it is a simple modification of a previous approach, we show significant improvements in assembly results on simulated and experimental datasets that include error. AVAILABILITY: http://152.2.15.114/~labweb/VCAKE William R. Jeck, Josephine A. Reinhardt, David A. Baltrus, Matthew T. Hickenbotham, Vincent J. Magrini, Elaine R. Mardis, Jeffery L. Dangl, Corbin D. Jones	Bioinform.	5