MonkeySNP: a web portal for non-human primate single nucleotide polymorphisms.
Ontology highlight
ABSTRACT: Unlabelled
MonkeySNP is a web-based resource created by the Genetic Resource and Informatics Program at the Oregon National Primate Research Center to facilitate access to non-human primate (NHP) single nucleotide polymorphisms (SNP) data. MonkeySNP is a mirror of the NCBI dbSNP database and contains additional NHP subpopulation genotype data and visual genotype displays to support SNP review and selection.Availability
http://monkeysnp.ohsu.edu/snp/Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Khouangsathiene S
PROVIDER: S-EPMC2579713 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The Human Cell Atlas (HCA) is expected to facilitate the creation of reference cell profiles, marker genes, and gene regulatory networks that will provide a deeper understanding of healthy and disease cell types from clinical biospecimens. The hematopoietic system includes dozens of distinct, transcriptionally coherent cell types, including intermediate transitional populations that have not been previously described at a molecular level. Using the first data release from the HCA bone marrow tissue project, we resolved common, rare, and potentially transitional cell populations from over 100,000 hematopoietic cells spanning 35 transcriptionally coherent groups across eight healthy donors using emerging new computational approaches. These data highlight novel mixed-lineage progenitor populations and putative trajectories governing granulocytic, monocytic, lymphoid, erythroid, megakaryocytic, and eosinophil specification. Our analyses suggest significant variation in cell-type composition and gene expression among donors, including biological processes affected by donor age. To enable broad exploration of these findings, we provide an interactive website to probe intra-cell and extra-cell population differences within and between donors and reference markers for cellular classification and cellular trajectories through associated progenitor states.
| S-EPMC6296228 | biostudies-literature
Project description:Interruptions of microsatellite sequences impact genome evolution and can alter disease manifestation. However, human polymorphism levels at interrupted microsatellites (iMSs) are not known at a genome-wide scale, and the pathways for gaining interruptions are poorly understood. Using the 1000 Genomes Phase-1 variant call set, we interrogated mono-, di-, tri-, and tetranucleotide repeats up to 10 units in length. We detected ∼26,000-40,000 iMSs within each of four human population groups (African, European, East Asian, and American). We identified population-specific iMSs within exonic regions, and discovered that known disease-associated iMSs contain alleles present at differing frequencies among the populations. By analyzing longer microsatellites in primate genomes, we demonstrate that single interruptions result in a genome-wide average two- to six-fold reduction in microsatellite mutability, as compared with perfect microsatellites. Centrally located interruptions lowered mutability dramatically, by two to three orders of magnitude. Using a biochemical approach, we tested directly whether the mutability of a specific iMS is lower because of decreased DNA polymerase strand slippage errors. Modeling the adenomatous polyposis coli tumor suppressor gene sequence, we observed that a single base substitution interruption reduced strand slippage error rates five- to 50-fold, relative to a perfect repeat, during synthesis by DNA polymerases α, β, or η. Computationally, we demonstrate that iMSs arise primarily by base substitution mutations within individual human genomes. Our biochemical survey of human DNA polymerase α, β, δ, κ, and η error rates within certain microsatellites suggests that interruptions are created most frequently by low fidelity polymerases. Our combined computational and biochemical results demonstrate that iMSs are abundant in human genomes and are sources of population-specific genetic variation that may affect genome stability. The genome-wide identification of iMSs in human populations presented here has important implications for current models describing the impact of microsatellite polymorphisms on gene expression.
| S-EPMC4102424 | biostudies-literature
Project description:UDP glucuronosyltransferases (UGTs) are an important class of Phase II enzymes involved in the metabolism and detoxification of numerous xenobiotics including therapeutic drugs and endogenous compounds (e.g. bilirubin). To date, there are 21 human UGT genes identified, and most of them contain single-nucleotide polymorphisms (SNPs). Non-synonymous SNPs (nsSNPs) of the human UGT genes may cause absent or reduced enzyme activity and polymorphisms of UGT have been found to be closely related to altered drug clearance and/or drug response, hyperbilirubinemia, Gilbert's syndrome, and Crigler-Najjar syndrome. However, it is unlikely to study the functional impact of all identified nsSNPs in humans using laboratory approach due to its giant number. We have investigated the potential for bioinformatics approach for the prediction of phenotype based on known nsSNPs. We have identified a total of 248 nsSNPs from human UGT genes. The two algorithms tools, sorting intolerant from tolerant (SIFT) and polymorphism phenotyping (PolyPhen), were used to predict the impact of these nsSNPs on protein function. SIFT classified 35.5% of the UGT nsSNPs as "deleterious"; while PolyPhen identified 46.0% of the UGT nsSNPs as "potentially damaging" and "damaging". The results from the two algorithms were highly associated. Among 63 functionally characterized nsSNPs in the UGTs, 24 showed altered enzyme expression/activities and 45 were associated with disease susceptibility. SIFT and Polyphen had a correct prediction rate of 57.1% and 66.7%, respectively. These findings demonstrate the potential use of bioinformatics techniques to predict genotype-phenotype relationships which may constitute the basis for future functional studies.
| S-EPMC2758119 | biostudies-literature
Project description:UnlabelledUnderstanding the effect of single nucleotide polymorphisms (SNPs) on the expression level of genes is an important goal. We recently published a study in which we devised a multi-SNP predictive model for gene expression in Lymphoblastoid cell lines (LCL), and showed that it can robustly predict the expression of a small number of genes in test individuals. Here, we validate the generality of our models by predicting expression profiles for genes in LCL in an independent study, and extend the pool of predictable genes for which we are able to explain more than 25% of their expression variability to 232 genes across 14 different cell types. As the number of people who obtained their SNP profiles through companies such as 23andMe is rising rapidly, we developed GenoExp, a web-based tool in which users can upload their individual SNP data and obtain predicted expression levels for the set of predictable genes across the 14 different cell types. Our tool thus allows users with biological knowledge to study the possible effects that their set of SNPs might have on these genes and predict their cell-specific expression levels relative to the population average.Availability and implementationGenoExp is freely available at http://genie.weizmann.ac.il/pubs/GenoExp/.
| S-EPMC4443679 | biostudies-literature
Project description:The melanocortin 1 receptor (MC1R) is involved in the control of melanogenesis. Polymorphisms in this gene have been associated with variation in skin and hair color and with elevated risk for the development of melanoma. Here we used 11 computational tools based on different approaches to predict the damage-associated non-synonymous single nucleotide polymorphisms (nsSNPs) in the coding region of the human MC1R gene. Among the 92 nsSNPs arranged according to the predictions 62% were classified as damaging in more than five tools. The classification was significantly correlated with the scores of two consensus programs. Alleles associated with the red hair color (RHC) phenotype and with the risk of melanoma were examined. The R variants D84E, R142H, R151C, I155T, R160W and D294H were classified as damaging by the majority of the tools while the r variants V60L, V92M and R163Q have been predicted as neutral in most of the programs The combination of the prediction tools results in 14 nsSNPs indicated as the most damaging mutations in MC1R (L48P, R67W, H70Y, P72L, S83P, R151H, S172I, L206P, T242I, G255R, P256S, C273Y, C289R and R306H); C273Y showed to be highly damaging in SIFT, Polyphen-2, MutPred, PANTHER and PROVEAN scores. The computational analysis proved capable of identifying the potentially damaging nsSNPs in MC1R, which are candidates for further laboratory studies of the functional and pharmacological significance of the alterations in the receptor and the phenotypic outcomes.
| S-EPMC4368538 | biostudies-literature
Project description:As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole-genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are 'disease agnostic' but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole-genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases. A web-based implementation of our algorithm is available at http://fathmm.biocompute.org.uk.
| S-EPMC4083756 | biostudies-literature
Project description:Single-nucleotide polymorphisms (SNPs) provide an abundant source of DNA polymorphisms in a number of eukaryotic species. Information on the frequency, nature, and distribution of SNPs in plant genomes is limited. Thus, our objectives were (1) to determine SNP frequency in coding and noncoding soybean (Glycine max L. Merr.) DNA sequence amplified from genomic DNA using PCR primers designed to complete genes, cDNAs, and random genomic sequence; (2) to characterize haplotype variation in these sequences; and (3) to provide initial estimates of linkage disequilibrium (LD) in soybean. Approximately 28.7 kbp of coding sequence, 37.9 kbp of noncoding perigenic DNA, and 9.7 kbp of random noncoding genomic DNA were sequenced in each of 25 diverse soybean genotypes. Over the >76 kbp, mean nucleotide diversity expressed as Watterson's theta was 0.00097. Nucleotide diversity was 0.00053 and 0.00111 in coding and in noncoding perigenic DNA, respectively, lower than estimates in the autogamous model species Arabidopsis thaliana. Haplotype analysis of SNP-containing fragments revealed a deficiency of haplotypes vs. the number that would be anticipated at linkage equilibrium. In 49 fragments with three or more SNPs, five haplotypes were present in one fragment while four or less were present in the remaining 48, thereby supporting the suggestion of relatively limited genetic variation in cultivated soybean. Squared allele-frequency correlations (r(2)) among haplotypes at 54 loci with two or more SNPs indicated low genome-wide LD. The low level of LD and the limited haplotype diversity suggested that the genome of any given soybean accession is a mosaic of three or four haplotypes. To facilitate SNP discovery and the development of a transcript map, subsets of four to six diverse genotypes, whose sequence analysis would permit the discovery of at least 75% of all SNPs present in the 25 genotypes as well as 90% of the common (frequency >0.10) SNPs, were identified.
| S-EPMC1462490 | biostudies-other
Project description:The single nucleotide polymorphisms (SNPs) in conserved protein regions have been thought to be strong candidates that alter protein functions. Thus, we have developed SNP@Domain, a web resource, to identify SNPs within human protein domains. We annotated SNPs from dbSNP with protein structure-based as well as sequence-based domains: (i) structure-based using SCOP and (ii) sequence-based using Pfam to avoid conflicts from two domain assignment methodologies. Users can investigate SNPs within protein domains with 2D and 3D maps. We expect this visual annotation of SNPs within protein domains will help scientists select and interpret SNPs associated with diseases. A web interface for the SNP@Domain is freely available at http://snpnavigator.net/ and from http://bioportal.net/.
| S-EPMC1538855 | biostudies-other
Project description:MicroRNAs (miRNAs) are endogenous small non-coding RNAs that repress their targets at post transcriptional level. Single Nucleotide Polymorphisms (SNPs) in miRNAs can lead to severe defects to the functions of miRNAs and might result in diseases. Although several studies have tried to identify the SNPs in human miRNA genes or only in the mature miRNAs, there are only limited endeavors to explain the distribution of SNPs in these important genes. After a genome-wide scan for SNPs in human miRNAs, we totally identified 1899 SNPs in 961 out of the 1527 reported miRNA precursors of human, which is the most complete list of SNPs in human miRNAs to date. More importantly, to explain the distributions of SNPs existed in human miRNAs, we comprehensively and systematically analyzed the identified SNPs in miRNAs from several aspects. Our results suggest that conservation, genomic context, secondary structure, and functional importance of human miRNAs affect the accumulations of SNPs in these genes. Our results also show that the number of SNPs with significantly different frequencies among various populations in the HapMap and 1000 Genome Project data are consistent with the geographical distributions of these populations. These analyses provide a better insight of SNPs in human miRNAs and the spreading of the SNPs in miRNAs in different populations.
| S-EPMC3818353 | biostudies-literature
Project description:Regulatory SNPs (rSNPs) reside primarily within the nonprotein coding genome and are thought to disturb normal patterns of gene expression by altering DNA binding of transcription factors. Nevertheless, despite the explosive rise in SNP association studies, there is little information as to the function of rSNPs in human disease. Serum response factor (SRF) is a widely expressed DNA-binding transcription factor that has variable affinity to at least 1,216 permutations of a 10 bp transcription factor binding site (TFBS) known as the CArG box. We developed a robust in silico bioinformatics screening method to evaluate sequences around RefSeq genes for conserved CArG boxes. Utilizing a predetermined phastCons threshold score, we identified 8,252 strand-specific CArGs within an 8 kb window around the transcription start site of 5,213 genes, including all previously defined SRF target genes. We then interrogated this CArG dataset for the presence of previously annotated common polymorphisms. We found a total of 118 unique CArG boxes harboring a SNP within the 10 bp CArG sequence and 1,130 CArG boxes with SNPs located just outside the CArG element. Gel shift and luciferase reporter assays validated SRF binding and functional activity of several new CArG boxes. Importantly, SNPs within or just outside the CArG box often resulted in altered SRF binding and activity. Collectively, these findings demonstrate a powerful approach to computationally define rSNPs in the human CArGome and provide a foundation for similar analyses of other TFBS. Such information may find utility in genetic association studies of human disease where little insight is known regarding the functionality of rSNPs.
| S-EPMC3180741 | biostudies-literature