Biology subjects

Moghul, I.

Publications and source records attributed to Moghul, I..

5 recordsLinked to original sources

bioRxiv2018

Phenogenon: Gene to Phenotype Associations for Rare Genetic Diseases

As genome sequencing is increasingly applied to molecular diagnosis of rare Mendelian disorders, large number of patients with diverse phenotypes have their genomic and phenotypic data pooled together to uncover new genotype - phenotype relations. We introduce Phenogenon, a method that combines: the power of Human Phenotype Ontology for describing patient phenotypes, gnomAD for estimating rare variant population frequency, and CADD for variant pathogenicity prediction. By using a divide and conquer approach, we demonstrate here that Phenogenon is able to uncover true gene to phenotype relations, such as \"ABCA4 - Macular dystrophy\" and \"SCN1A - Seizures\". Additionally, it accurately infers mode of inheritance, such as a recessive mode of inheritance in the case of the \"ABCA4 - Macular dystrophy\" relationship and a dominant mode of inheritance with the \"SCN1A - Seizures\" relationship. We also found that CADD has more power to detect early-onset rare genetic diseases than late-onset diseases. In this study, we ran Phenogenon against a diverse cohort of 3288 patients. Among the top 13 gene-phenotype relations, seven were previously known. We also highlight four potentially novel gene - phenotype relations such as \"SIPA1L3 - Abnormal electroretinogram\".

bioinformatics↗

bioRxiv2018

PGP-UK: a research and citizen science hybrid project in support of personalized medicine

Molecular analyses such as whole-genome sequencing have become routine and are expected to be transformational for future healthcare and lifestyle decisions. Population-wide implementation of such analyses is, however, not without challenges, and multiple studies are ongoing to identify what these are and explore how they can be addressed. Defined as a research project, the Personal Genome Project UK (PGP-UK) is part of the global PGP network and focuses on open data sharing and citizen science to advance and accelerate personalized genomics and medicine. Here we report our findings on using an open consent recruitment protocol, active participant involvement, open access release of personal genome, methylome and transcriptome data and associated analyses, including 47 new variants predicted to affect gene function and innovative reports based on the analysis of genetic and epigenetic variants. For this pilot study, we recruited ten participants willing to actively engage as citizen scientists with the project. In addition, we introduce Genome Donation as a novel mechanism for openly sharing previously restricted data and discuss the first three donations received. Lastly, we present GenoME, a free, open-source educational app suitable for the lay public to allow exploration of personal genomes. Our findings demonstrate that citizen science-based approaches like PGP-UK have an important role to play in the public awareness, acceptance and implementation of genomics and personalized medicine.

genomics↗

bioRxiv2017

Pheno4J: A Gene To Phenotype Graph Database

SummaryEfficient storage and querying of large amounts of genetic and phenotypic data is crucial to contemporary clinical genetic research. This introduces computational challenges for classical relational databases, due to the sparsity and sheer volume of the data. Our Java based solution loads annotated genetic variants and well phenotyped patients into a graph database to allow fast efficient storage and querying of large volumes of structured genetic and phenotypic data. This abstracts technical problems away and lets researchers focus on the science rather than the implementation. We have also developed an accompanying webserver with end-points to facilitate querying of the database.\n\nAvailability and ImplementationThe Java code and python code is available at https://github.com/phenopolis/pheno4i\n\nContactn.pontikos@ucl.ac.uk

bioinformatics↗

bioRxiv2017

Ophiuroid Phylotranscriptomics Enables Discovery Of Novel Echinoderm Representatives Of Bilaterian Neuropeptide Families And Reconstruction Of Neuropeptide Precursor Evolution Over ~270 Million Years

BackgroundNeuropeptides are a diverse class of intercellular signaling molecules that mediate neuronal regulation of many physiological and behavioural processes, including feeding, reproduction and locomotion. Recent advances in genome/transcriptome sequencing are enabling identification of neuropeptide precursor proteins in species from a growing variety of animal taxa, providing new insights into the evolution of neuropeptide signaling. Here we report a phylo-transcriptomic analysis of neuropeptide precursors in over fifty species of brittle stars (Class Ophiuroidea; Phylum Echinodermata).\n\nResultsDetailed analysis of transcriptome sequence data from three brittle star species, Ophionotus victoriae, Amphiura filiformis and Ophiopsila aranea, enabled the first comprehensive identification of neuropeptide precursors in ophiuroids. Representatives of over thirty bilaterian neuropeptide precursor families were identified, some of which occur as paralogs (e.g. thyrotropin-releasing hormone, corticotropin-releasing hormone, cholecystokinin, somatostatin and pedal peptide). Furthermore, homologs of endothelin/CCHamide, eclosion hormone, neuropeptide-F/Y and nucleobinin/nesfatin were discovered here in a deuterostome/echinoderm for the first time. The majority of ophiuroid neuropeptide precursors contain a single copy of a neuropeptide, but several precursors comprise multiple copies of identical or non-identical, but structurally-related, neuropeptides. Here we performed an unprecedented investigation of the evolution of neuropeptide copy-number over a period of ~270 million years by analysing sequence data from over fifty ophiuroid species, with reference to a robust phylogeny. Interestingly, the number of neuropeptide copies in the majority of precursors was constant across all the species examined, but examples of clade-specific losses/gains of neuropeptides were also observed.\n\nConclusionsWe report here the most comprehensive analysis to date of neuropeptide precursors in the phylum Echinodermata, with novel representatives of several bilaterian neuropeptide families discovered for the first time in echinoderms. Furthermore, analysis of precursor proteins comprising multiple copies of identical or related neuropeptides across ~270 million years of ophiuroid evolution indicates that the composition of neuropeptide \"cocktails\" is functionally important, but with plasticity over long evolutionary time scales.

neuroscience↗

bioRxiv2017

GeoDiver: Differential Gene Expression Analysis & Gene-Set Analysis For GEO Datasets

SummaryGeoDiver is an online web application for performing Differential Gene Expression Analysis (DGEA) and Generally Applicable Gene-set Enrichment Analysis (GAGE) on gene expression datasets from the publicly available Gene Expression Omnibus (GEO). The output produced includes numerous high quality interactive graphics, allowing users to easily explore and examine complex datasets instantly. Furthermore, the results produced can be reviewed at a later date and shared with collaborators.\n\nAvailabilityGeoDiver is freely available online at http://www.geodiver.co.uk. The source code is available on Github: https://github.com/GeoDiver/GeoDiver and a docker image is available for easy installation.

bioinformatics↗