bioRxiv Science⌕ Search

Biology subjects

Cooper, D. N.

Publications and source records attributed to Cooper, D. N..

25 records · Page 2Linked to original sources
bioRxiv2019

Genetic analysis of the STIM1 gene in chronic pancreatitis

Chronic pancreatitis is a complex disease that involves many factors, both genetic and environmental. Over the past two decades, molecular genetic analysis of five genes that are highly expressed in human pancreatic acinar cells, namely PRSS1, PRSS2, SPINK1, CTRC and CTRB1/CTRB2, has established that a trypsin-dependent pathway plays a key role in the etiology of chronic pancreatitis. Since Ca2+ deregulation can lead to intracellular trypsin activation in experimental acute pancreatitis, we analyzed STIM1 (encoding stromal interaction molecule-1, the main regulator of Ca2+ homeostasis in pancreatic acinar cells) as a candidate modifier gene in French, German and Chinese patients with chronic pancreatitis. The French and German subjects were analyzed by Sanger sequencing whereas the Chinese subjects were analyzed by targeted next-generation sequencing confirmed by Sanger sequencing. A total of 37 rare coding variants (35 missense and 2 nonsense) were identified, which were enriched in patients as compared with controls [2.28% (47/2,057) vs. 0.99% (33/3,322); odds ratio = 2.33, P = 0.0001]. This is the first large case-control study to demonstrate a putative association of rare STIM1 coding variants with chronic pancreatitis. Functional analysis will be required to clarify whether or not the rare STIM1 variants detected predispose to pancreatitis.

genetics↗
bioRxiv2019

RegSNPs-Intron: A Computational Framework For Prioritizing Intronic Single Nucleotide Variants in Human Genetic Disease

A large number of single nucleotide variants (SNVs) in the human genome are known to be responsible for inherited disease. An even larger number of SNVs, particularly those located in introns, have yet to be investigated for their pathogenic potential. Using known pathogenic and neutral intronic SNVs (iSNVs), we developed the regSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure and evolutionary conservation features. regSNPs-intron showed high accuracy in computing disease-causing probabilities of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we validated regSNPs-intron predictions by measuring the impact of iSNVs on splicing outcome. Together, regSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis. regSNPs-intron is available at https://regsnps-intron.ccbb.iupui.edu.

bioinformatics↗
bioRxiv2018

First estimation of the scale of canonical 5’ splice site GT>GC mutations generating wild-type transcripts and their medical genetic implications

It has long been known that canonical 5 splice site (5SS) GT>GC mutations may be compatible with normal splicing. However, to date, the true scale of canonical 5SS GT>GC mutations generating wild-type transcripts, both in the context of the frequency of such mutations and the level of wild-type transcripts generated from the mutation alleles, remain unknown. Herein, combining data derived from a meta-analysis of 45 informative disease-causing 5SS GT>GC mutations (from 42 genes) and a cell culture-based full-length gene splicing assay of 103 5SS GT>GC mutations (from 30 genes), we estimate that [~]15-18% of the canonical GT 5SSs are capable of generating between 1 and 84% normal transcripts as a consequence of the substitution of GT by GC. We further demonstrate that the canonical 5SSs whose substitutions of GT by GC generated normal transcripts show stronger complementarity to the 5 end of U1 snRNA than those sites whose substitutions of GT by GC did not lead to the generation of normal transcripts. We also observed a correlation between the generation of wild-type transcripts and a milder than expected clinical phenotype but found that none of the available splicing prediction tools were able to accurately predict the functional impact of 5SS GT>GC mutations. Our findings imply that 5SS GT>GC mutations may not invariably cause human disease but should also help to improve our understanding of the evolutionary processes that accompanied GT>GC subtype switching of U2-type introns in mammals.

genetics↗
bioRxiv2018

AVADA Enables Automated Genetic Variant Curation Directly from the Full Text Literature

PurposeThe primary literature on human genetic diseases includes descriptions of pathogenic variants that are essential for clinical diagnosis. Variant databases such as ClinVar and HGMD collect pathogenic variants by manual curation. We aimed to automatically construct a freely accessible database of pathogenic variants directly from full-text articles about genetic disease.\n\nMethodsAVADA (Automatically curated VAriant DAtabase) is a novel machine learning tool that uses natural language processing to automatically identify pathogenic variants and genes in full text of primary literature and converts them to genomic coordinates for rapid downstream use.\n\nResultsAVADA automatically curated almost 60% of pathogenic variants deposited in HGMD, a 4.4-fold improvement over the current state of the art in automated variant extraction. AVADA also contains more than 60,000 pathogenic variants that are in HGMD, but not in ClinVar. In a cohort of 245 diagnosed patients, AVADA correctly annotated 38 previously described diagnostic variants, compared to 43 using HGMD, 20 using ClinVar and only 13 (wholly subsumed by AVADA and ClinVars) using the best automated abstracts-only based approach.\n\nConclusionAVADA is the first machine learning tool that automatically curates a variants database directly from full text literature. AVADA is available upon publication at http://bejerano.stanford.edu/AVADA.

genetics↗
bioRxiv2018

S-CAP extends clinical-grade pathogenicity prediction to genetic variants that affect RNA splicing

There are over 15,000 known variants that cause human inherited disease by disrupting RNA splicing. While several in silico methods such as CADD, EIGEN and LINSIGHT are commonly used to predict the pathogenicity of noncoding variants, we introduce S-CAP, a tool developed specially for splicing which is better able to effectively distinguish pathogenic splicing-relevant variants from benign variants. S-CAP is a novel splicing pathogenicity predictor that reduces the number of splicing-relevant variants of uncertain significance in patient exomes by 41%, a nearly 3-fold improvement over existing noncoding pathogenicity measures while correctly classifying known pathogenic splicing-relevant variants with a clinical-grade 95% sensitivity.

genomics↗
bioRxiv2017

AMELIE accelerates Mendelian patient diagnosis directly from the primary literature

The diagnosis of Mendelian disorders requires labor-intensive literature research. Our software system AMELIE (Automatic Mendelian Literature Evaluation) greatly automates this process. AMELIE parses hundreds of thousands of full text articles to find an underlying diagnosis to explain a patients phenotypes given the patients exome. AMELIE prioritizes patient candidate genes for their likelihood of causing the patients phenotypes. Diagnosis of singleton patients (without relatives exomes) is the most time-consuming scenario. AMELIEs gene ranking method was tested on 215 singleton Mendelian patients with a clinical diagnosis. AMELIE ranked the causal gene among the top 2 in the majority (63%) of cases. Examining AMELIEs top 10 genes, amounting to 8% of 124 candidate genes with rare functional variants per patient, results in diagnosis for 95% of cases. Strikingly, training only on gene pathogenicity knowledge from 2011 leads to identical performance compared to training on current data. An accompanying analysis web portal has launched at AMELIE.stanford.edu.

genetics↗
bioRxiv2017

MutPred2: inferring the molecular and phenotypic impact of amino acid variants

We introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. While its prioritization performance is state-of-the-art, a novel and distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited diseases have the potential to significantly accelerate the discovery of clinically actionable variants.\n\nAvailability: http://mutpred.mutdb.org/

bioinformatics↗