scholarly journals The Ensembl Variant Effect Predictor

2016 ◽  
Author(s):  
William McLaren ◽  
Laurent Gil ◽  
Sarah E Hunt ◽  
Harpreet Singh Riat ◽  
Graham R. S. Ritchie ◽  
...  
Keyword(s):  
Web Interface ◽  
Large Collection ◽  
High Quality ◽  
Binding Motifs ◽  
Variant Effect Predictor ◽  
Genomic Variants ◽  
Coding Regions ◽  
Variation Data ◽  
Variant Effect ◽  
Program Interface

The Ensembl Variant Effect Predictor (VEP) is a powerful toolset for the analysis, annotation and prioritization of genomic variants, including in non-coding regions. The VEP accurately predicts the effects of sequence variants on transcripts, protein products, regulatory regions and binding motifs by leveraging the high quality, broad scope, and integrated nature of the Ensembl databases. In addition, it enables comparison with a large collection of existing publicly available variation data within Ensembl to provide insights into population and ancestral genetics, phenotypes and disease. The VEP is open source and free to use. It is available via a simple web interface (http://www.ensembl.org/vep), a powerful downloadable package, and both Ensembl’s Perl and REST application program interface (API) services.

scholarly journals Annotating and prioritising genomic variants using the Ensembl Variant Effect Predictor ‐ a tutorial

2021 ◽  
Author(s):  
Sarah E. Hunt ◽  
Benjamin Moore ◽  
Ridwan M. Amode ◽  
Irina M. Armean ◽  
Diana Lemos ◽  
...  
Keyword(s):  
Variant Effect Predictor ◽  
Genomic Variants ◽  
Variant Effect

scholarly journals Annotating and prioritising genomic variants using the Ensembl Variant Effect Predictor - a tutorial

2021 ◽  
Author(s):  
Sarah Hunt ◽  
Benjamin Moore ◽  
M. Amode ◽  
Irina Armean ◽  
Diana Lemos ◽  
...  
Keyword(s):  
Application Programming Interface ◽  
Access Methods ◽  
Variant Effect Predictor ◽  
Genomic Variants ◽  
Representational State Transfer ◽  
Gene Sets ◽  
Command Line Tool ◽  
Application Programming ◽  
Variant Effect ◽  
User Friendly

The Ensembl Variant Effect Predictor (VEP) is a freely available, open source tool for the annotation and filtering of genomic variants. It predicts variant molecular consequence using the Ensembl/GENCODE or RefSeq gene sets. It also reports phenotype associations from databases such as ClinVar, allele frequencies from studies including gnomAD, and predictions of deleteriousness from tools such as SIFT and CADD. Ensembl VEP includes filtering options to customise variant prioritisation. It is well supported and updated roughly quarterly to incorporate the latest gene, variant and phenotype association information. Ensembl VEP analysis can be performed using a highly configurable, extensible command-line tool, a Representational State Transfer (REST) application programming interface (API) and a user-friendly web interface. These access methods are designed to suit different levels of bioinformatics experience and meet different needs in terms of data size, visualisation and flexibility. In this tutorial, we will describe performing variant annotation using the Ensembl VEP web tool, which enables sophisticated analysis through a simple interface.

scholarly journals Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Jean-Marc Aury ◽  
Benjamin Istace
Keyword(s):  
Single Molecule ◽  
Direct Consequence ◽  
High Quality ◽  
Sequencing Errors ◽  
Coding Regions ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Oxford Nanopore ◽  
Long Read ◽  
Genome Assemblies

Abstract Single-molecule sequencing technologies have recently been commercialized by Pacific Biosciences and Oxford Nanopore with the promise of sequencing long DNA fragments (kilobases to megabases order) and then, using efficient algorithms, provide high quality assemblies in terms of contiguity and completeness of repetitive regions. However, the error rate of long-read technologies is higher than that of short-read technologies. This has a direct consequence on the base quality of genome assemblies, particularly in coding regions where sequencing errors can disrupt the coding frame of genes. In the case of diploid genomes, the consensus of a given gene can be a mixture between the two haplotypes and can lead to premature stop codons. Several methods have been developed to polish genome assemblies using short reads and generally, they inspect the nucleotide one by one, and provide a correction for each nucleotide of the input assembly. As a result, these algorithms are not able to properly process diploid genomes and they typically switch from one haplotype to another. Herein we proposed Hapo-G (Haplotype-Aware Polishing Of Genomes), a new algorithm capable of incorporating phasing information from high-quality reads (short or long-reads) to polish genome assemblies and in particular assemblies of diploid and heterozygous genomes.

Finding a Needle in a Haystack: Variant Effect Predictor (VEP) Prioritizes Disease Causative Variants from Millions of Neutral Ones

2019 ◽  
pp. 85-104
Author(s):  
Yashvant Khimsuriya ◽  
Salil Vaniyawala ◽  
Babajan Banaganapalli ◽  
Muhammadh Khan ◽  
Ramu Elango ◽  
...  
Keyword(s):  
Variant Effect Predictor ◽  
Variant Effect

scholarly journals A plugin for the Ensembl Variant Effect Predictor that uses MaxEntScan to predict variant spliceogenicity

2018 ◽  
Vol 35 (13) ◽  
pp. 2315-2317 ◽  
Author(s):  
Jannah Shamsani ◽  
Stephen H Kazakoff ◽  
Irina M Armean ◽  
Will McLaren ◽  
Michael T Parsons ◽  
...  
Keyword(s):  
Splice Site ◽  
Disease Risk ◽  
Cancer Susceptibility ◽  
Mrna Splicing ◽  
Supplementary Information ◽  
Mendelian Disease ◽  
Functional Protein ◽  
Variant Effect Predictor ◽  
Cancer Susceptibility Genes ◽  
Variant Effect

Abstract Summary Assessing the pathogenicity of genetic variants can be a complex and challenging task. Spliceogenic variants, which alter mRNA splicing, may yield mature transcripts that encode non-functional protein products, an important predictor of Mendelian disease risk. However, most variant annotation tools do not adequately assess spliceogenicity outside the native splice site and thus the disease-causing potential of variants in other intronic and exonic regions is often overlooked. Here, we present a plugin for the Ensembl Variant Effect Predictor that packages MaxEntScan and extends its functionality to provide splice site predictions using a maximum entropy model. The plugin incorporates a sliding window algorithm to predict splice site loss or gain for any variant that overlaps a transcript feature. We also demonstrate the utility of the plugin by comparing our predictions to two mRNA splicing datasets containing several cancer-susceptibility genes. Availability and implementation Source code is freely available under the Apache License, Version 2.0: https://github.com/Ensembl/VEP_plugins. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome

2019 ◽  
Vol 47 (W1) ◽  
pp. W136-W141 ◽  
Author(s):  
Emidio Capriotti ◽  
Ludovica Montanucci ◽  
Giuseppe Profiti ◽  
Ivan Rossi ◽  
Diana Giannuzzi ◽  
...  
Keyword(s):  
Matthews Correlation Coefficient ◽  
Genomic Variation ◽  
Gradient Boosting ◽  
Binary Classifier ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Coding Regions ◽  
Variation Data ◽  
Boosting Algorithm ◽  
The Impact

Abstract As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve.

scholarly journals RNA–protein interactions: disorder, moonlighting and junk contribute to eukaryotic complexity

Open Biology ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 190096 ◽  
Author(s):  
Anna Balcerak ◽  
Alicja Trebinska-Stryjewska ◽  
Ryszard Konopinski ◽  
Maciej Wakula ◽  
Ewa Anna Grzybowska
Keyword(s):  
Protein Interactions ◽  
Regulatory Networks ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Binding Motifs ◽  
Coding Regions ◽  
Regulatory Circuits ◽  
Proteins Interactions

RNA–protein interactions are crucial for most biological processes in all organisms. However, it appears that the complexity of RNA-based regulation increases with the complexity of the organism, creating additional regulatory circuits, the scope of which is only now being revealed. It is becoming apparent that previously unappreciated features, such as disordered structural regions in proteins or non-coding regions in DNA leading to higher plasticity and pliability in RNA–protein complexes, are in fact essential for complex, precise and fine-tuned regulation. This review addresses the issue of the role of RNA–protein interactions in generating eukaryotic complexity, focusing on the newly characterized disordered RNA-binding motifs, moonlighting of metabolic enzymes, RNA-binding proteins interactions with different RNA species and their participation in regulatory networks of higher order.

Barbara Marchioness of Hastings (1810-1858)—Fossil Collector and ‘Lady-Geologist’

2004 ◽  
Vol 23 (1) ◽  
pp. 75-87 ◽  
Author(s):  
Martina Kölbl-Ebert
Keyword(s):  
Nineteenth Century ◽  
Research Program ◽  
Scientific Community ◽  
British Museum ◽  
Social Position ◽  
Short Paper ◽  
Large Collection ◽  
Independent Research ◽  
High Quality ◽  
Restricted Area

During the first half of the nineteenth century a large group of non-professional scientists still existed in British geology. For these ‘gentleman-geologists' geology was more or less a private interest. Female counterparts or ‘lady-geologists'—following an independent research program, publishing their results, and presenting them to the contemporary scientific community—were quite rare. One of these remarkable exceptions was Barbara Marchioness of Hastings (1810-1858). She was married and a mother of seven children. She was a keen collector of fossils and sold a large collection of several thousand vertebrate fossils to the British Museum in 1855. Beginning in 1845, she undertook detailed stratigraphical fieldwork in the Eocene strata at Hordle and Beacon Cliff near Milford (Hampshire), where she produced a coloured, scale-drawn section of the strata. Between 1848 and 1853, she published three papers summarising this work. Hastings considered herself a serious geological worker and her contributions were of high quality. Her high social position ensured a reasonable reception among her male colleagues, allowing her to present a short paper at the meeting of the British Association for the Advancement of Science in Oxford in 1847. Nevertheless her opus is rather small compared to male contemporary geologists. Being female, she had no liberal access to the geological ‘scientific community'. Additionally, family commitments held her in Hampshire, confining her work to this restricted area.

scholarly journals Assessing computational variant effect predictors with a large prospective cohort

2021 ◽  
Author(s):  
Da Kuang ◽  
Roujia Li ◽  
Yingzhou Wu ◽  
Jochen Weile ◽  
Robert A. Hegele ◽  
...  
Keyword(s):  
Performance Measures ◽  
Genetic Variants ◽  
Prospective Cohort ◽  
Missense Variant ◽  
Uk Biobank ◽  
High Quality ◽  
Whole Exome ◽  
Unbiased Test ◽  
Variant Effect ◽  
Large Prospective Cohort

Computational predictors can help interpret pathogenicity of human genetic variants, especially for the majority of variants where no experimental data are available. However, because we lack a high-quality unbiased test set, identifying the best-performing predictors remains a challenge. To address this issue, we evaluated missense variant effect predictors using genotypes and traits from a prospective cohort. We considered 139 gene-trait combinations with rare-variant burden association based on at least one of four systematic studies using phenotypes and whole-exome sequences from ~200K UK Biobank participants. Using an evaluation set of 35,525 rare missense variants and the relevant associated traits, we assessed the correlation of participants' traits with scores derived from 20 computational variant effect predictors. We found that two predictors—VARITY and REVEL—outperformed all others according to multiple performance measures. We expect that this study will help in selecting variant effect predictors, for both research and clinical purposes, while providing an unbiased benchmarking strategy that can be applied to additional cohorts and predictors.

scholarly journals SBSA: an online service for somatic binding sequence annotation

2021 ◽  
Author(s):  
Limin Jiang ◽  
Fei Guo ◽  
Jijun Tang ◽  
Hui Yu ◽  
Scott Ness ◽  
...  
Keyword(s):  
Somatic Mutation ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Human Subjects ◽  
Specific Factor ◽  
Single Nucleotide ◽  
Binding Motifs ◽  
Genomic Variants ◽  
Full Capacity ◽  
Binding Sequence

Abstract Efficient annotation of alterations in binding sequences of molecular regulators can help identify novel candidates for mechanisms study and offer original therapeutic hypotheses. In this work, we developed Somatic Binding Sequence Annotator (SBSA) as a full-capacity online tool to annotate altered binding motifs/sequences, addressing diverse types of genomic variants and molecular regulators. The genomic variants can be somatic mutation, single nucleotide polymorphism, RNA editing, etc. The binding motifs/sequences involve transcription factors (TFs), RNA-binding proteins, miRNA seeds, miRNA-mRNA 3′-UTR binding target, or can be any custom motifs/sequences. Compared to similar tools, SBSA is the first to support miRNA seeds and miRNA-mRNA 3′-UTR binding target, and it unprecedentedly implements a personalized genome approach that accommodates joint adjacent variants. SBSA is empowered to support an indefinite species, including preloaded reference genomes for SARS-Cov-2 and 25 other common organisms. We demonstrated SBSA by annotating multi-omics data from over 30,890 human subjects. Of the millions of somatic binding sequences identified, many are with known severe biological repercussions, such as the somatic mutation in TERT promoter region which causes a gained binding sequence for E26 transformation-specific factor (ETS1). We further validated the function of this TERT mutation using experimental data in cancer cells. Availability:http://innovebioinfo.com/Annotation/SBSA/SBSA.php.

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