Browse
Submit Data
Databases
API
Help

Dataset Information

24 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap.

ABSTRACT: The interpretation of genome-wide association results is confounded by linkage disequilibrium between nearby alleles. We have developed a flexible bioinformatics query tool for single-nucleotide polymorphisms (SNPs) to identify and to annotate nearby SNPs in linkage disequilibrium (proxies) based on HapMap. By offering functionality to generate graphical plots for these data, the SNAP server will facilitate interpretation and comparison of genome-wide association study results, and the design of fine-mapping experiments (by delineating genomic regions harboring associated variants and their proxies).SNAP server is available at http://www.broad.mit.edu/mpg/snap/.

SUBMITTER: Johnson AD

PROVIDER: S-EPMC2720775 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap.

Johnson Andrew D AD Handsaker Robert E RE Pulit Sara L SL Nizzari Marcia M MM O'Donnell Christopher J CJ de Bakker Paul I W PI

Bioinformatics (Oxford, England) 20081030 24

<h4>Summary</h4>The interpretation of genome-wide association results is confounded by linkage disequilibrium between nearby alleles. We have developed a flexible bioinformatics query tool for single-nucleotide polymorphisms (SNPs) to identify and to annotate nearby SNPs in linkage disequilibrium (proxies) based on HapMap. By offering functionality to generate graphical plots for these data, the SNAP server will facilitate interpretation and comparison of genome-wide association study results, a ...[more]

PMID: 18974171

Similar Datasets

SNPsnap: a Web-based tool for identification and annotation of matched SNPs.

Project description:An important computational step following genome-wide association studies (GWAS) is to assess whether disease or trait-associated single-nucleotide polymorphisms (SNPs) enrich for particular biological annotations. SNP-based enrichment analysis needs to account for biases such as co-localization of GWAS signals to gene-dense and high linkage disequilibrium (LD) regions, and correlations of gene size, location and function. The SNPsnap Web server enables SNP-based enrichment analysis by providing matched sets of SNPs that can be used to calibrate background expectations. Specifically, SNPsnap efficiently identifies sets of randomly drawn SNPs that are matched to a set of query SNPs based on allele frequency, number of SNPs in LD, distance to nearest gene and gene density.SNPsnap server is available at http://www.broadinstitute.org/mpg/snpsnap/.joelh@broadinstitute.orgSupplementary data are available at Bioinformatics online.

| S-EPMC4308663 | biostudies-literature

yrGATE: a web-based gene-structure annotation tool for the identification and dissemination of eukaryotic genes.

Project description:Your Gene structure Annotation Tool for Eukaryotes (yrGATE) provides an Annotation Tool and Community Utilities for worldwide web-based community genome and gene annotation. Annotators can evaluate gene structure evidence derived from multiple sources to create gene structure annotations. Administrators regulate the acceptance of annotations into published gene sets. yrGATE is designed to facilitate rapid and accurate annotation of emerging genomes as well as to confirm, refine, or correct currently published annotations. yrGATE is highly portable and supports different standard input and output formats. The yrGATE software and usage cases are available at http://www.plantgdb.org/prj/yrGATE.

| S-EPMC1779557 | biostudies-literature

Genome-wide screen for universal individual identification SNPs based on the HapMap and 1000 Genomes databases.

Project description:Differences among SNP panels for individual identification in SNP-selecting and populations led to few common SNPs, compromising their universal applicability. To screen all universal SNPs, we performed a genome-wide SNP mining in multiple populations based on HapMap and 1000Genomes databases. SNPs with high minor allele frequencies (MAF) in 37 populations were selected. With MAF from ?0.35 to ?0.43, the number of selected SNPs decreased from 2769 to 0. A total of 117 SNPs with MAF ?0.39 have no linkage disequilibrium with each other in every population. For 116 of the 117 SNPs, cumulative match probability (CMP) ranged from 2.01?×?10-48 to 1.93?×?10-50 and cumulative exclusion probability (CEP) ranged from 0.9999999996653 to 0.9999999999945. In 134 tested Han samples, 110 of the 117 SNPs remained within high MAF and conformed to Hardy-Weinberg equilibrium, with CMP?=?4.70?×?10-47 and CEP?=?0.999999999862. By analyzing the same number of autosomal SNPs as in the HID-Ion AmpliSeq Identity Panel, i.e. 90 randomized out of the 110 SNPs, our panel yielded preferable CMP and CEP. Taken together, the 110-SNPs panel is advantageous for forensic test, and this study provided plenty of highly informative SNPs for compiling final universal panels.

| S-EPMC5882920 | biostudies-literature

NeuCA web server: a neural network-based cell annotation tool with web-app and GUI.

Project description:SummaryCorrectly annotating individual cell's type is an important initial step in single-cell RNA sequencing (scRNA-seq) data analysis. Here, we present NeuCA web server, a neural network-based scRNA-seq cell annotation tool with web-app portal and graphical user interface, for automatically assigning cell labels. NeuCA algorithm is accurate and exhaustive, maximizing the usage of measured cells for downstream analysis. NeuCA web server provides over 20 ready-to-use pre-trained classifiers for commonly used tissue types. As the first web-app tool with neural-network infrastructure implemented, NeuCA web will facilitate the research community in analyzing and annotating scRNA-seq data.Availability and implementationNeuCA web server is implemented with R Shiny application online at https://statbioinfo.shinyapps.io/NeuCA/.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC9004646 | biostudies-literature

Should we have blind faith in bioinformatics software? Illustrations from the SNAP web-based tool.

Project description:Bioinformatics tools have gained popularity in biology but little is known about their validity. We aimed to assess the early contribution of 415 single nucleotide polymorphisms (SNPs) associated with eight cardio-metabolic traits at the genome-wide significance level in adults in the Family Atherosclerosis Monitoring In earLY Life (FAMILY) birth cohort. We used the popular web-based tool SNAP to assess the availability of the 415 SNPs in the Illumina Cardio-Metabochip genotyped in the FAMILY study participants. We then compared the SNAP output with the Cardio-Metabochip file provided by Illumina using chromosome and chromosomal positions of SNPs from NCBI Human Genome Browser (Genome Reference Consortium Human Build 37). With the HapMap 3 release 2 reference, 201 out of 415 SNPs were reported as missing in the Cardio-Metabochip by the SNAP output. However, the Cardio-Metabochip file revealed that 152 of these 201 SNPs were in fact present in the Cardio-Metabochip array (false negative rate of 36.6%). With the more recent 1000 Genomes Project release, we found a false-negative rate of 17.6% by comparing the outputs of SNAP and the Illumina product file. We did not find any 'false positive' SNPs (SNPs specified as available in the Cardio-Metabochip by SNAP, but not by the Cardio-Metabochip Illumina file). The Cohen's Kappa coefficient, which calculates the percentage of agreement between both methods, indicated that the validity of SNAP was fair to moderate depending on the reference used (the HapMap 3 or 1000 Genomes). In conclusion, we demonstrate that the SNAP outputs for the Cardio-Metabochip are invalid. This study illustrates the importance of systematically assessing the validity of bioinformatics tools in an independent manner. We propose a series of guidelines to improve practices in the fast-moving field of bioinformatics software implementation.

| S-EPMC4351168 | biostudies-literature

How well do HapMap SNPs capture the untyped SNPs?

Project description:BackgroundThe recent advancement in human genome sequencing and genotyping has revealed millions of single nucleotide polymorphisms (SNP) which determine the variation among human beings. One of the particular important projects is The International HapMap Project which provides the catalogue of human genetic variation for disease association studies. In this paper, we analyzed the genotype data in HapMap project by using National Institute of Environmental Health Sciences Environmental Genome Project (NIEHS EGP) SNPs. We first determine whether the HapMap data are transferable to the NIEHS data. Then, we study how well the HapMap SNPs capture the untyped SNPs in the region. Finally, we provide general guidelines for determining whether the SNPs chosen from HapMap may be able to capture most of the untyped SNPs.ResultsOur analysis shows that HapMap data are not robust enough to capture the untyped variants for most of the human genes. The performance of SNPs for European and Asian samples are marginal in capturing the untyped variants, i.e. approximately 55%. Expectedly, the SNPs from HapMap YRI panel can only capture approximately 30% of the variants. Although the overall performance is low, however, the SNPs for some genes perform very well and are able to capture most of the variants along the gene. This is observed in the European and Asian panel, but not in African panel. Through observation, we concluded that in order to have a well covered SNPs reference panel, the SNPs density and the association among reference SNPs are important to estimate the robustness of the chosen SNPs.ConclusionWe have analyzed the coverage of HapMap SNPs using NIEHS EGP data. The results show that HapMap SNPs are transferable to the NIEHS SNPs. However, HapMap SNPs cannot capture some of the untyped SNPs and therefore resequencing may be needed to uncover more SNPs in the missing region.

| S-EPMC1586200 | biostudies-literature

ProFAT: a web-based tool for the functional annotation of protein sequences.

Project description:BACKGROUND: The functional annotation of proteins relies on published information concerning their close and remote homologues in sequence databases. Evidence for remote sequence similarity can be further strengthened by a similar biological background of the query sequence and identified database sequences. However, few tools exist so far, that provide a means to include functional information in sequence database searches. RESULTS: We present ProFAT, a web-based tool for the functional annotation of protein sequences based on remote sequence similarity. ProFAT combines sensitive sequence database search methods and a fold recognition algorithm with a simple text-mining approach. ProFAT extracts identified hits based on their biological background by keyword-mining of annotations, features and most importantly, literature associated with a sequence entry. A user-provided keyword list enables the user to specifically search for weak, but biologically relevant homologues of an input query. The ProFAT server has been evaluated using the complete set of proteins from three different domain families, including their weak relatives and could correctly identify between 90% and 100% of all domain family members studied in this context. ProFAT has furthermore been applied to a variety of proteins from different cellular contexts and we provide evidence on how ProFAT can help in functional prediction of proteins based on remotely conserved proteins. CONCLUSION: By employing sensitive database search programs as well as exploiting the functional information associated with database sequences, ProFAT can detect remote, but biologically relevant relationships between proteins and will assist researchers in the prediction of protein function based on remote homologies.

| S-EPMC1636073 | biostudies-literature

SPOT: a web-based tool for using biological databases to prioritize SNPs after a genome-wide association study.

Project description:SPOT (http://spot.cgsmd.isi.edu), the SNP prioritization online tool, is a web site for integrating biological databases into the prioritization of single nucleotide polymorphisms (SNPs) for further study after a genome-wide association study (GWAS). Typically, the next step after a GWAS is to genotype the top signals in an independent replication sample. Investigators will often incorporate information from biological databases so that biologically relevant SNPs, such as those in genes related to the phenotype or with potentially non-neutral effects on gene expression such as a splice sites, are given higher priority. We recently introduced the genomic information network (GIN) method for systematically implementing this kind of strategy. The SPOT web site allows users to upload a list of SNPs and GWAS P-values and returns a prioritized list of SNPs using the GIN method. Users can specify candidate genes or genomic regions with custom levels of prioritization. The results can be downloaded or viewed in the browser where users can interactively explore the details of each SNP, including graphical representations of the GIN method. For investigators interested in incorporating biological databases into a post-GWAS SNP selection strategy, the SPOT web tool is an easily implemented and flexible solution.

| S-EPMC2896195 | biostudies-literature

GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data.

Project description:Downstream analysis of genomic and transcriptomic sequence data is often executed by functional annotation that can be performed by various bioinformatics tools and biological databases. However, a full fast integrated tool is not available for such analysis. Besides, the current available software is not able to produce analytic lists of annotations and graphs to help users in evaluating the output results. Therefore, we present the Gene Ontology Functional Enrichment Annotation Tool (GO FEAT), a free web platform for functional annotation and enrichment of genomic and transcriptomic data based on sequence homology search. The analysis can be customized and visualized as per users' needs and specifications. GO FEAT is freely available at http://computationalbiology.ufpa.br/gofeat/ and its source code is hosted at https://github.com/fabriciopa/gofeat .

| S-EPMC5789007 | biostudies-literature

CRISPRTE: A web-based tool to generate single guide RNA targets transposable elements

Project description:CRISPR/Cas systems have gained prominence as powerful tools for genome engineering. Recent investigations into the crucial role of transposable elements (TEs) have stimulated research interest in manipulating TEs to elucidate their functions. Nevertheless, designing single guide RNAs (sgRNAs) that are both specific and efficient for TE manipulation presents a formidable challenge, considering the repetitive nature and high copy numbers of TEs. Although various sgRNA design tools have been developed for gene editing, an optimized sgRNA designer explicitly for TE manipulation has yet to be established. To bridge this gap, we presented CRISPR-TE, a web-based application featuring an accessible graphical user interface, available at https://www.crisprte.cn. CRISPR-TE could identify all potential sgRNAs for TEs and offers a comprehensive solution for efficient TE targeting at both the single duplicate and subfamily levels. We also demonstrated that young TEs can be targeted with higher coverage at the subfamily level. Finally, we validated the overexpression of SVAD, a human-specific TE, using dCas9-VP64 activator incorporated with three sgRNAs designed by our tool. Collectively, our findings suggest that CRISPR-TE may serve as a versatile framework for designing sgRNAs aimed at TE targeting.

2024-03-24 | GSE228266 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data