The Human Phenotype Ontology project:linking molecular biology and disease through phenotype data

The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online

De nouvelles méthodes pour l'alignement des séquences biologiques

L'alignement de séquences biologiques est une technique fondamentale en bioinformatique, et consiste à identifier des séries de caractères similaires (conservés) qui apparaissent dans le même ordre dans les deux séquences, et à inférer un ensemble de modifications (substitutions, insertions et suppressions) impliquées dans la transformation d'une séquence en l'autre. Cette technique permet de déduire, sur la base de la similarité de séquence, si deux ou plusieurs séquences biologiques sont potentiellement homologues, donc si elles partagent un ancêtre commun, permettant ainsi de mieux comprendre l'évolution des séquences. Cette thèse aborde les problèmes de comparaison de séquences dans deux cadres différents: la détection d'homologies et le séquençage à haut débit. L'objectif de ce travail est de développer des méthodes d'alignement qui peuvent apporter des solutions aux deux problèmes suivants: i) la détection d'homologies cachées entre des protéines par comparaison de séquences protéiques, lorsque la source de leur divergence sont les mutations qui changent le cadre de lecture, et ii) le mapping de reads SOLiD (séquences de di-nucléotides chevauchantes codés par des couleurs) sur un génome de référence. Dans les deux cas, la même idée générale est appliquée: comparer implicitement les séquences d'ADN pour la détection de changements qui se produisent à ce niveau, en manipulant, en pratique, d'autres représentations (séquences de protéines, séquences de codes di-nucléotides) qui fournissent des informations supplémentaires et qui aident à améliorer la recherche de similarités. Le but est de concevoir et d'appliquer des méthodes exactes et heuristiques d'alignement, ainsi que des systemes de scores, adaptés à ces scénarios.Biological sequence alignment is a fundamental technique in bioinformatics, and consists of identifying series of similar (conserved) characters that appear in the same order in both sequences, and eventually deducing a set of modifications (substitutions, insertions and deletions) involved in the transformation of one sequence into the other. This technique allows one to infer, based on sequence similarity, if two or more biological sequences are potentially homologous, i.e. if they share a common ancestor, thus enabling the understanding of sequence evolution.This thesis addresses sequence comparison problems in two different contexts: homology detection and high throughput DNA sequencing. The goal of this work is to develop sensitive alignment methods that provide solutions to the following two problems: i) the detection of hidden protein homologies by protein sequence comparison, when the source of the divergence are frameshift mutations, and ii) mapping short SOLiD reads (sequences of overlapping di-nucleotides encoded as colors) to a reference genome. In both cases, the same general idea is applied: to implicitly compare DNA sequences for detecting changes occurring at this level, while manipulating, in practice, other representations (protein sequences, sequences of di-nucleotide codes) that provide additional information and thus help to improve the similarity search. The aim is to design and implement exact and heuristic alignment methods, along with scoring schemes, adapted to these scenarios.LILLE1-Bib. Electronique (590099901) / SudocSudocFranceF

Importance of Model Features.

<p>(a) Histogram of CNV lengths (on log scale) for harmful and benign CNVs within our dataset shows that harmful CNVs are more likely to be longer, and hence likely affect more genes and gene functions. (b-d) Precision (b), recall (c) and f-measure (d) for predicting harmful versus benign CNVs relative to the number of closest neighbors considered within the gene interaction network. Both precision (b) and f-measure (d) improve as we expand the number of neighbors considered, but stabilize or slightly descend after 10 neighbors. We also see an improvement from utilizing the patient phenotypes uniform model in precision and accuracy as we add the ranking as a source for weighing our features.</p

Prioritizing Clinically Relevant Copy Number Variation from Genetic Interactions and Gene Function Data

<div><p>It is becoming increasingly necessary to develop computerized methods for identifying the few disease-causing variants from hundreds discovered in each individual patient. This problem is especially relevant for Copy Number Variants (CNVs), which can be cheaply interrogated via low-cost hybridization arrays commonly used in clinical practice. We present a method to predict the disease relevance of CNVs that combines functional context and clinical phenotype to discover clinically harmful CNVs (and likely causative genes) in patients with a variety of phenotypes. We compare several feature and gene weighing systems for classifying both genes and CNVs. We combined the best performing methodologies and parameters on over 2,500 Agilent CGH 180k Microarray CNVs derived from 140 patients. Our method achieved an F-score of 91.59%, with 87.08% precision and 97.00% recall. Our methods are freely available at <a href="https://github.com/compbio-UofT/cnv-prioritization" target="_blank">https://github.com/compbio-UofT/cnv-prioritization</a>. Our dataset is included with the supplementary information.</p></div

Precision, recall and f-measure for CNVs when combining the three following features length, DGV and gene.

<p>Length is the CNV length. DGV is a measure of the CNV’s frequency in the Database of Genomic Variants. Gene is the feature derived from the previous machine learning step in this method.</p

The overall structure of the two layer classifier, with the output of hte Gene Classifier being one of the inputs to the CNV classifier.

<p>The overall structure of the two layer classifier, with the output of hte Gene Classifier being one of the inputs to the CNV classifier.</p

Databases, ontologies and known associations used to identify CNV-phenotype correlations.

<p>Our approach integrates 3 types of information: 1) CNVs an their non-exhaustive frequency in healthy individuals, 2) genes and gene interactions, with their respective functions (each gene within a CNV is weighted by its likelihood of contributing to the phenotypes; via semantic similarity within the GO ontology), and 3) phenotypic descriptions and relationships between them as specified by HPO, with their non-exhaustive associations to disease genes (via OMIM). For an individuals variants and known HPO phenotypes, genes affected by these variants are highlighted within the gene interaction network, while the phenotypes are emphasized in the phenotype ontology layer.</p

Using phenotypic similarity to improve rare disease identification in PhenomeCentral

<p>Presentation given by Orion Buske at Genome Informatics 2014 in Cambridge, UK. Covers the current performance of patient matching and gene prioritization algorithms in PhenomeCentral.</p