TRANSCRIPT ANNOTATION PRIORITIZATION AND SCREENING SYSTEM (TrAPSS) FOR MUTATION SCREENING

2007 ◽  
Vol 05 (06) ◽  
pp. 1155-1172 ◽  
Author(s):  
BRIAN M. O'LEARY ◽  
STEVEN G. DAVIS ◽  
MICHAEL F. SMITH ◽  
BARTLEY BROWN ◽  
MATHEW B. KEMP ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Information Management ◽  
Sequence Data ◽  
Mutation Screening ◽  
Disease Genes ◽  
Screening System ◽  
Genomic Context ◽  
Significant Information ◽  
Algorithmic Technique ◽  
Protein Functional Domains

When searching for disease-causing mutations with polymerase chain reaction (PCR)-based methods, candidate genes are usually screened in their entirety, exon by exon. Genomic resources (i.e. www.ncbi.nih.gov, www.ensembl.org, and genome.ucsc.edu) largely support this paradigm for mutation screening by making it easy to view and access sequence data associated with genes in their genomic context. However, the administrative burden of conducting mutation screening in potentially hundreds of genes and thousands of exons in thousands of patients is significant, even with the use of public genome resources. For example, the manual design of oligonucleotide primers for all exons of the 10 Leber's congenital amaurosis (LCA) genes (149 exons) represents a significant information management challenge. The Transcript Annotation Prioritization and Screening System (TrAPSS) is designed to accelerate mutation screening by (1) providing a gene-based local cache of candidate disease genes in a genomic context, (2) automating tasks associated with optimizing candidate disease gene screening and information management, and (3) providing the implementation of an algorithmic technique to utilize large amounts of heterogeneous genome annotation (e.g. conserved protein functional domains) so as to prioritize candidate genes.

PheGee@Home

2012 ◽  
pp. 1885-1903
Author(s):  
Bertil Schmidt ◽  
Chen Chen ◽  
Weiguo Liu ◽  
Wayne P. Mitchell
Keyword(s):  
Comparative Genomics ◽  
Environmental Factors ◽  
Candidate Genes ◽  
Sequence Data ◽  
Computer Architectures ◽  
Graphics Hardware ◽  
Microbial Genomes ◽  
Genetically Determined ◽  
Physical Manifestation ◽  
Grid Based

In this chapter we present PheGee@Home, a grid-based comparative genomics tool that nominates candidate genes responsible for a given phenotype. A phenotype is the physical manifestation of the interplay of genetic, epigenetic and environmental factors. Our tool is designed to facilitate the discovery and prioritization of candidate genes controlling or contributing to the genetically determined portion of a specified phenotype. However, in order to make reliable nominations of candidate genes from sequence data, several genome-size sequence datasets are required. This makes the approach impractical on traditional computer architectures leading to prohibitively long runtimes. Therefore, we use a computational architecture based on a desktop grid environment and commodity graphics hardware to significantly accelerate PheGee. We validate this approach by showing the deployment and evaluation on a grid testbed for the comparison of microbial genomes.

scholarly journals DeepCLIP: predicting the effect of mutations on protein–RNA binding with deep learning

2020 ◽  
Author(s):  
Alexander Gulliver Bjørnholt Grønning ◽  
Thomas Koed Doktor ◽  
Simon Jonas Larsen ◽  
Ulrika Simone Spangsberg Petersen ◽  
Lise Lolle Holm ◽  
...  
Keyword(s):  
Protein Binding ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Sequence Data ◽  
Genomic Context ◽  
Functional Changes ◽  
Lab Experiments ◽  
Sequence Variations ◽  
Wet Lab ◽  
Nuclear Shuttling

Abstract Nucleotide variants can cause functional changes by altering protein–RNA binding in various ways that are not easy to predict. This can affect processes such as splicing, nuclear shuttling, and stability of the transcript. Therefore, correct modeling of protein–RNA binding is critical when predicting the effects of sequence variations. Many RNA-binding proteins recognize a diverse set of motifs and binding is typically also dependent on the genomic context, making this task particularly challenging. Here, we present DeepCLIP, the first method for context-aware modeling and predicting protein binding to RNA nucleic acids using exclusively sequence data as input. We show that DeepCLIP outperforms existing methods for modeling RNA-protein binding. Importantly, we demonstrate that DeepCLIP predictions correlate with the functional outcomes of nucleotide variants in independent wet lab experiments. Furthermore, we show how DeepCLIP binding profiles can be used in the design of therapeutically relevant antisense oligonucleotides, and to uncover possible position-dependent regulation in a tissue-specific manner. DeepCLIP is freely available as a stand-alone application and as a webtool at http://deepclip.compbio.sdu.dk.

scholarly journals Exome sequencing study of 20 patients with high myopia

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5552 ◽  
Author(s):  
Ling Wan ◽  
Boling Deng ◽  
Zhengzheng Wu ◽  
Xiaoming Chen
Keyword(s):  
Exome Sequencing ◽  
Candidate Genes ◽  
High Myopia ◽  
Cellular Response ◽  
Gene Mutations ◽  
Enrichment Analysis ◽  
Zinc Ion ◽  
Ocular Disease ◽  
Disease Genes ◽  
Bioinformatics Analyses

Background High myopia is a common ocular disease worldwide. To expand our current understanding of the genetic basis of high myopia, we carried out a whole exome sequencing (WES) study to identify potential causal gene mutations. Methods A total of 20 individuals with high myopia were exome sequenced. A novel filtering strategy combining phenotypes and functional impact of variants was applied to identify candidate genes by multi-step bioinformatics analyses. Network and enrichment analysis were employed to examine the biological pathways involved in the candidate genes. Results In 16 out of 20 patients, we identified 20 potential pathogenic gene variants for high myopia. A total of 18 variants were located in myopia-associated chromosomal regions. In addition to the novel mutations found in five known myopia genes (ADAMTS18, CSMD1, P3H2, RPGR, and SLC39A5), we also identified pathogenic variants in seven ocular disease genes (ABCA4, CEP290, HSPG2, PCDH15, SAG, SEMA4A, and USH2A) as novel candidate genes. The biological processes associated with vision were significantly enriched in our candidate genes, including visual perception, photoreceptor cell maintenance, retinoid metabolic process, and cellular response to zinc ion starvation. Discussion Systematic mutation analysis of candidate genes was performed using WES data, functional interaction (FI) network, Gene Ontology and pathway enrichment. FI network analysis revealed important network modules and regulator linker genes (EP300, CTNNB1) potentially related to high myopia development. Our study expanded the list of candidate genes associated with high myopia, which increased the genetic screening performance and provided implications for future studies on the molecular genetics of myopia.

scholarly journals Mutation Screening of Candidate Genes in Patients with Nonsyndromic Sagittal Craniosynostosis

2016 ◽  
Vol 137 (3) ◽  
pp. 952-961 ◽  
Author(s):  
Xiaoqian Ye ◽  
Audrey Guilmatre ◽  
Boris Reva ◽  
Inga Peter ◽  
Yann Heuzé ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Mutation Screening ◽  
Sagittal Craniosynostosis

scholarly journals Mutation screening and imprinting analysis of four candidate genes for autism in the 7q32 region

2002 ◽  
Vol 7 (3) ◽  
pp. 289-301 ◽  
Author(s):  
E Bonora ◽  
◽  
E Bacchelli ◽  
E R Levy ◽  
F Blasi ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Mutation Screening

scholarly journals The Contribution of Known Familial Cardiovascular Disease Genes to Sudden Cardiac Death in Patients Undergoing Hemodialysis

2021 ◽  
pp. 1-10
Author(s):  
Tae-Hwi Schwantes-An ◽  
Matteo Vatta ◽  
Marco Abreu ◽  
Leah Wetherill ◽  
Howard J. Edenberg ◽  
...  
Keyword(s):  
Sudden Cardiac Death ◽  
Kidney Disease ◽  
Cardiac Death ◽  
Multiple Testing ◽  
Sequence Data ◽  
European Ancestry ◽  
Disease Genes ◽  
Mortality And Morbidity ◽  
Coding Regions ◽  
Burden Tests

<b><i>Introduction:</i></b> Patients with chronic kidney disease experience high rates of cardiovascular mortality and morbidity. When kidney disease progresses to the need for dialysis, sudden cardiac death (SCD) accounts for 25–35% of all cardiovascular deaths. The objective was to determine if rare genetic variants known to be associated with cardiovascular death in the general population are associated with SCD in patients undergoing hemodialysis. <b><i>Methods:</i></b> We performed a case-control study comparing 126 (37 African American [AfAn] and 89 European ancestry [EA]) SCD subjects and 107 controls (34 AfAn and 73 EA), matched for age, sex, self-reported race, dialysis duration (&#x3c;2, 2–5 and &#x3e;5 years), and the presence or absence of diabetes mellitus. To target the coding regions of genes previously reported to be associated with 15 inherited cardiac conditions (ICCs), we used the TruSight Cardio Kit (Illumina, San Diego, CA, USA) to capture the genetic regions of interest. In all, the kit targets 572-kb regions that include the protein-coding regions and 40-bp 5′ and 3′ end-flanking regions of 174 genes associated with the 15 ICCs. Using the sequence data, burden tests were conducted to identify genes with an increased number of variants among SCD cases compared to matched controls. <b><i>Results:</i></b> Eleven genes were associated with SCD, but after correction for multiple testing, none of the 174 genes were identified as having more variants in the SCD cases than the matched controls, including previously identified genes. Secondary burden tests grouping variants based on diseases and gene function did not produce statistically significant associations. <b><i>Discussion/Conclusions:</i></b> We found no associations between genes known to be associated with ICCs and SCD in our sample of patients undergoing hemodialysis. This suggests that genetic causes are unlikely to be a major pathogenic factor in SCD in hemodialysis patients, although our sample size limits definitive conclusions.

Sign in / Sign up

Export Citation Format

Share Document