Journal ArticleDOI
MutationTaster evaluates disease-causing potential of sequence alterations
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TLDR
MutationTaster allows the efficient filtering of NGS data for alterations with high disease-causing potential and provides Perl scripts that can process data from all major platforms (Roche 454, Illumina Genome Analyzer and ABI SOLiD).Abstract:
(simple_aae) or at alterations causing complex changes in the amino acid sequence (complex_aae). To train the classifier, we generated a dataset with all available and suitable common polymorphisms and known diseasecausing mutations extracted from common databases and the literature. We cross-validated the classifier five times including all three prediction models and obtained an overall accuracy of 91.1 ± 0.1%. We also compared MutationTaster with similar applications (Panther3, Pmut4, PolyPhen and PolyPhen-2 (ref. 5) and ‘screening for non-acceptable polymorphisms’ (SNAP)6) and analyzed the identical 1,000 disease-linked mutations and 1,000 polymorphisms with all programs. For this comparison, we used only alterations causing single amino acid exchanges. MutationTaster performed best in terms of accuracy and speed (Table 1). A description of all training and validation procedures and detailed statistics are available in Supplementary Methods. MutationTaster can be used via an intuitive web interface to analyze single mutations as well as in batch mode. To streamline and to standardize the analysis of NGS data, we provide Perl scripts that can process data from all major platforms (Roche 454, Illumina Genome Analyzer and ABI SOLiD). MutationTaster hence allows the efficient filtering of NGS data for alterations with high disease-causing potential (see Supplementary Methods for an example). Present limitations of the software comprise its inability to analyze insertion-deletions greater than 12 base pairs and alterations spanning an intron-exon border. Also, analysis of non-exonic alterations is restricted to Kozak consensus sequence, splice sites and poly(A) signal. We will add tests for other sequence motifs in the near future. MutationTaster is available at http://www.mutationtaster.org/.read more
Citations
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Journal ArticleDOI
Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.
Sue Richards,Nazneen Aziz,Nazneen Aziz,Sherri J. Bale,David P. Bick,Soma Das,Julie M. Gastier-Foster,Wayne W. Grody,Madhuri Hegde,Elaine Lyon,Elaine B. Spector,Karl V. Voelkerding,Heidi L. Rehm +12 more
TL;DR: Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends thatclinical molecular genetic testing should be performed in a Clinical Laboratory Improvement Amendments–approved laboratory, with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or the equivalent.
Journal ArticleDOI
MutationTaster2: mutation prediction for the deep-sequencing age
TL;DR: This method takes advantage of the high hybridization efficiency of FISH and the fact that base-pair resolution is usually not needed to uniquely identify a transcript, and will enable the transcriptome to be directly imaged at single-cell resolution in complex samples such as brain tissue.
Journal ArticleDOI
Evolution and functional impact of rare coding variation from deep sequencing of human exomes
Jacob A. Tennessen,Abigail W. Bigham,Timothy D. O’Connor,Wenqing Fu,Eimear E. Kenny,Simon Gravel,Sean McGee,Ron Do,Ron Do,Xiaoming Liu,Goo Jun,Hyun Min Kang,Daniel M. Jordan,Suzanne M. Leal,Stacey Gabriel,Mark J. Rieder,Gonçalo R. Abecasis,David Altshuler,Deborah A. Nickerson,Eric Boerwinkle,Eric Boerwinkle,Shamil R. Sunyaev,Shamil R. Sunyaev,Carlos Bustamante,Michael J. Bamshad,Joshua M. Akey +25 more
TL;DR: The findings suggest that most human variation is rare, not shared between populations, and that rare variants are likely to play a role in human health, and show that large sample sizes will be required to associate rare variants with complex traits.
Journal ArticleDOI
REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
Nilah M. Ioannidis,Joseph H. Rothstein,Joseph H. Rothstein,Vikas Pejaver,Sumit Middha,Shannon K. McDonnell,Saurabh Baheti,Anthony M. Musolf,Qing Li,Emily R. Holzinger,Danielle M. Karyadi,Lisa A. Cannon-Albright,Craig C. Teerlink,Janet L. Stanford,William B. Isaacs,Jianfeng Xu,Kathleen A. Cooney,Kathleen A. Cooney,Ethan M. Lange,Johanna Schleutker,John D. Carpten,Isaac J. Powell,Olivier Cussenot,Geraldine Cancel-Tassin,Graham G. Giles,Graham G. Giles,Robert J. MacInnis,Robert J. MacInnis,Christiane Maier,Chih-Lin Hsieh,Fredrik Wiklund,William J. Catalona,William D. Foulkes,Diptasri Mandal,Rosalind A. Eeles,Zsofia Kote-Jarai,Carlos Bustamante,Daniel J. Schaid,Trevor Hastie,Elaine A. Ostrander,Joan E. Bailey-Wilson,Predrag Radivojac,Stephen N. Thibodeau,Alice S. Whittemore,Weiva Sieh,Weiva Sieh +45 more
TL;DR: This work developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, LRT, GERP, SiPhy, phyloP, and phastCons.
Journal ArticleDOI
The genetic landscape of high-risk neuroblastoma
Trevor J. Pugh,Olena Morozova,Edward F. Attiyeh,Edward F. Attiyeh,Shahab Asgharzadeh,Shahab Asgharzadeh,Jun S. Wei,Daniel Auclair,Scott L. Carter,Kristian Cibulskis,Megan Hanna,Megan Hanna,Adam Kiezun,Jaegil Kim,Michael S. Lawrence,Lee Lichenstein,Aaron McKenna,Chandra Sekhar Pedamallu,Chandra Sekhar Pedamallu,Alex H. Ramos,Alex H. Ramos,Erica Shefler,Andrey Sivachenko,Carrie Sougnez,Chip Stewart,Adrian Ally,Inanc Birol,Readman Chiu,Richard Corbett,Martin Hirst,Shaun D. Jackman,Baljit Kamoh,Alireza Hadj Khodabakshi,Martin Krzywinski,Allan Lo,Richard A. Moore,Karen Mungall,Jenny Q. Qian,Angela Tam,Nina Thiessen,Yongjun Zhao,Kristina A. Cole,Kristina A. Cole,Maura Diamond,Maura Diamond,Sharon J. Diskin,Sharon J. Diskin,Yael P. Mosse,Yael P. Mosse,Andrew Wood,Andrew Wood,Lingyun Ji,Lingyun Ji,Richard Sposto,Richard Sposto,Thomas C. Badgett,Wendy B. London,Yvonne Moyer,Yvonne Moyer,Julie M. Gastier-Foster,Julie M. Gastier-Foster,Malcolm A. Smith,Jaime M. Guidry Auvil,Daniela S. Gerhard,Michael D. Hogarty,Michael D. Hogarty,Steven J.M. Jones,Eric S. Lander,Stacey Gabriel,Gad Getz,Robert C. Seeger,Robert C. Seeger,Javed Khan,Marco A. Marra,Matthew Meyerson,Matthew Meyerson,John M. Maris +76 more
TL;DR: The authors reported a low median exonic mutation frequency of 0.60 per Mb (0.48 nonsilent) and notably few recurrently mutated genes in high-risk neuroblastoma.
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