A
Ayushi Hegde
Publications - 3
Citations - 589
Ayushi Hegde is an academic researcher. The author has contributed to research in topics: splice & Sequence logo. The author has an hindex of 1, co-authored 3 publications receiving 374 citations.
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Journal ArticleDOI
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources
Sebastian Köhler,Leigh C. Carmody,Nicole Vasilevsky,Julius O.B. Jacobsen,Daniel Danis,Jean-Philippe F. Gourdine,Michael A. Gargano,Nomi L. Harris,Nicolas Matentzoglu,Julie A. McMurry,David Osumi-Sutherland,Valentina Cipriani,James P. Balhoff,Tom Conlin,Hannah Blau,Gareth Baynam,Richard Palmer,Dylan Gratian,Hugh Dawkins,Michael M. Segal,Anna Jansen,Ahmed Muaz,Willie H. Chang,Jenna R.E. Bergerson,Stanley J. F. Laulederkind,Zafer Yüksel,Sergi Beltran,Alexandra F. Freeman,Panagiotis I. Sergouniotis,Daniel Durkin,Andrea L. Storm,Marc Hanauer,Michael Brudno,Susan M. Bello,Murat Sincan,Kayli Rageth,Matthew T. Wheeler,Renske Oegema,Halima Lourghi,Maria G. Della Rocca,Rachel Thompson,Francisco Castellanos,James R. Priest,Charlotte Cunningham-Rundles,Ayushi Hegde,Ruth C. Lovering,Catherine Hajek,Annie Olry,Luigi D. Notarangelo,Morgan Similuk,Xingmin Aaron Zhang,David Gómez-Andrés,Hanns Lochmüller,Hélène Dollfus,Sergio Rosenzweig,Shruti Marwaha,Ana Rath,Kathleen E. Sullivan,Cynthia L. Smith,Joshua D. Milner,Dorothée Leroux,Cornelius F. Boerkoel,Amy D. Klion,Melody C. Carter,Tudor Groza,Damian Smedley,Melissa A. Haendel,Melissa A. Haendel,Christopher J. Mungall,Peter N. Robinson +69 more
TL;DR: The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data and plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data.
Journal ArticleDOI
Interpretable prioritization of splice variants in diagnostic next-generation sequencing.
Daniel Danis,Julius O.B. Jacobsen,Leigh C. Carmody,Michael A. Gargano,Julie A. McMurry,Ayushi Hegde,Melissa A. Haendel,Giorgio Valentini,Damian Smedley,Peter N. Robinson +9 more
TL;DR: The Super Quick Information-content Random-forest Learning of Splice variants (SQUIRLS) algorithm as mentioned in this paper generates a small set of interpretable features for machine learning by calculating the information-content of wild type and variant sequences of canonical and cryptic splice sites, assessing changes in candidate splicing regulatory sequences, and incorporating characteristics of the sequence such as exon length, disruptions of the AG exclusion zone, and conservation.
Posted ContentDOI
Interpretable prioritization of splice variants in diagnostic next-generation sequencing
Daniel Danis,Julius O.B. Jacobsen,Leigh C. Carmody,Michael A. Gargano,Julie A. McMurry,Ayushi Hegde,Melissa A. Haendel,Giorgio Valentini,Damian Smedley,Peter N. Robinson +9 more
TL;DR: The Super Quick Information-content Random-forest Learning of Splice variants (SQUIRLS) algorithm as discussed by the authors generates a small set of interpretable features for machine learning by calculating the information-content (IC) of wildtype and variant sequences of canonical and cryptic splice sites, assessing changes in candidate splicing regulatory sequences, and incorporating characteristics of the sequence such as exon length, disruptions of the AG exclusion zone, and conservation.