Ka-Chun Wong1, Zhaolei Zhang2. 1. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8. 2. Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4 The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario, Canada M5S 3E1 and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada M5S 1A8.
Abstract
MOTIVATION: The recent advances in genome sequencing have revealed an abundance of non-synonymous polymorphisms among human individuals; subsequently, it is of immense interest and importance to predict whether such substitutions are functional neutral or have deleterious effects. The accuracy of such prediction algorithms depends on the quality of the multiple-sequence alignment, which is used to infer how an amino acid substitution is tolerated at a given position. Because of the scarcity of orthologous protein sequences in the past, the existing prediction algorithms all include sequences of protein paralogs in the alignment, which can dilute the conservation signal and affect prediction accuracy. However, we believe that, with the sequencing of a large number of mammalian genomes, it is now feasible to include only protein orthologs in the alignment and improve the prediction performance. RESULTS: We have developed a novel prediction algorithm, named SNPdryad, which only includes protein orthologs in building a multiple sequence alignment. Among many other innovations, SNPdryad uses different conservation scoring schemes and uses Random Forest as a classifier. We have tested SNPdryad on several datasets. We found that SNPdryad consistently outperformed other methods in several performance metrics, which is attributed to the exclusion of paralogous sequence. We have run SNPdryad on the complete human proteome, generating prediction scores for all the possible amino acid substitutions. AVAILABILITY AND IMPLEMENTATION: The algorithm and the prediction results can be accessed from the Web site: http://snps.ccbr.utoronto.ca:8080/SNPdryad/ CONTACT: Zhaolei.Zhang@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.
MOTIVATION: The recent advances in genome sequencing have revealed an abundance of non-synonymous polymorphisms among human individuals; subsequently, it is of immense interest and importance to predict whether such substitutions are functional neutral or have deleterious effects. The accuracy of such prediction algorithms depends on the quality of the multiple-sequence alignment, which is used to infer how an amino acid substitution is tolerated at a given position. Because of the scarcity of orthologous protein sequences in the past, the existing prediction algorithms all include sequences of protein paralogs in the alignment, which can dilute the conservation signal and affect prediction accuracy. However, we believe that, with the sequencing of a large number of mammalian genomes, it is now feasible to include only protein orthologs in the alignment and improve the prediction performance. RESULTS: We have developed a novel prediction algorithm, named SNPdryad, which only includes protein orthologs in building a multiple sequence alignment. Among many other innovations, SNPdryad uses different conservation scoring schemes and uses Random Forest as a classifier. We have tested SNPdryad on several datasets. We found that SNPdryad consistently outperformed other methods in several performance metrics, which is attributed to the exclusion of paralogous sequence. We have run SNPdryad on the complete human proteome, generating prediction scores for all the possible amino acid substitutions. AVAILABILITY AND IMPLEMENTATION: The algorithm and the prediction results can be accessed from the Web site: http://snps.ccbr.utoronto.ca:8080/SNPdryad/ CONTACT: Zhaolei.Zhang@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Haiquan Li; Nima Pouladi; Ikbel Achour; Vincent Gardeux; Jianrong Li; Qike Li; Hao Helen Zhang; Fernando D Martinez; Joe G N 'Skip' Garcia; Yves A Lussier Journal: J Biomed Inform Date: 2015-10-30 Impact factor: 6.317
Authors: Ya-Chun Chen; Michaela Auer-Grumbach; Shinya Matsukawa; Manuela Zitzelsberger; Andreas C Themistocleous; Tim M Strom; Chrysanthi Samara; Adrian W Moore; Lily Ting-Yin Cho; Gareth T Young; Caecilia Weiss; Maria Schabhüttl; Rolf Stucka; Annina B Schmid; Yesim Parman; Luitgard Graul-Neumann; Wolfram Heinritz; Eberhard Passarge; Rosemarie M Watson; Jens Michael Hertz; Ute Moog; Manuela Baumgartner; Enza Maria Valente; Diego Pereira; Carlos M Restrepo; Istvan Katona; Marina Dusl; Claudia Stendel; Thomas Wieland; Fay Stafford; Frank Reimann; Katja von Au; Christian Finke; Patrick J Willems; Michael S Nahorski; Samiha S Shaikh; Ofélia P Carvalho; Adeline K Nicholas; Gulshan Karbani; Maeve A McAleer; Maria Roberta Cilio; John C McHugh; Sinead M Murphy; Alan D Irvine; Uffe Birk Jensen; Reinhard Windhager; Joachim Weis; Carsten Bergmann; Bernd Rautenstrauss; Jonathan Baets; Peter De Jonghe; Mary M Reilly; Regina Kropatsch; Ingo Kurth; Roman Chrast; Tatsuo Michiue; David L H Bennett; C Geoffrey Woods; Jan Senderek Journal: Nat Genet Date: 2015-05-25 Impact factor: 38.330
Authors: Melissa Milan; Silvia Benvenuti; Alice Maria Balderacchi; Anna Rita Virzì; Alessandra Gentile; Rebecca Senetta; Paola Cassoni; Paolo Maria Comoglio; Giulia Maria Stella Journal: ERJ Open Res Date: 2018-03-06