共查询到20条相似文献,搜索用时 31 毫秒
1.
Background
The allele frequencies of single-nucleotide polymorphisms (SNPs) are needed to select an optimal subset of common SNPs for use in association studies. Sequence-based methods for finding SNPs with allele frequencies may need to handle thousands of sequences from the same genome location (sequences of deep coverage). 相似文献2.
Background
Recent studies have shown that the patterns of linkage disequilibrium observed in human populations have a block-like structure, and a small subset of SNPs (called tag SNPs) is sufficient to distinguish each pair of haplotype patterns in the block. In reality, some tag SNPs may be missing, and we may fail to distinguish two distinct haplotypes due to the ambiguity caused by missing data. 相似文献3.
Background
Single Nucleotide Polymorphisms (SNPs) are an increasingly important tool for genetic and biomedical research. Although current genomic databases contain information on several million SNPs and are growing at a very fast rate, the true value of a SNP in this context is a function of the quality of the annotations that characterize it. Retrieving and analyzing such data for a large number of SNPs often represents a major bottleneck in the design of large-scale association studies. 相似文献4.
Background
In population-based studies, it is generally recognized that single nucleotide polymorphism (SNP) markers are not independent. Rather, they are carried by haplotypes, groups of SNPs that tend to be coinherited. It is thus possible to choose a much smaller number of SNPs to use as indices for identifying haplotypes or haplotype blocks in genetic association studies. We refer to these characteristic SNPs as index SNPs. In order to reduce costs and work, a minimum number of index SNPs that can distinguish all SNP and haplotype patterns should be chosen. Unfortunately, this is an NP-complete problem, requiring brute force algorithms that are not feasible for large data sets. 相似文献5.
Olivier Delaneau Cédric Coulonges Pierre-Yves Boelle George Nelson Jean-Louis Spadoni Jean-François Zagury 《BMC bioinformatics》2007,8(1):205
Background
We have developed a new haplotyping program based on the combination of an iterative multiallelic EM algorithm (IEM), bootstrap resampling and a pseudo Gibbs sampler. The use of the IEM-bootstrap procedure considerably reduces the space of possible haplotype configurations to be explored, greatly reducing computation time, while the adaptation of the Gibbs sampler with a recombination model on this restricted space maintains high accuracy. On large SNP datasets (>30 SNPs), we used a segmented approach based on a specific partition-ligation strategy. We compared this software, Ishape (Iterative Segmented HAPlotyping by Em), with reference programs such as Phase, Fastphase, and PL-EM. Analogously with Phase, there are 2 versions of Ishape: Ishape1 which uses a simple coalescence model for the pseudo Gibbs sampler step, and Ishape2 which uses a recombination model instead. 相似文献6.
Helen I Field Serena A Scollen Craig Luccarini Caroline Baynes Jonathan Morrison Alison M Dunning Douglas F Easton Paul DP Pharoah 《BMC bioinformatics》2009,10(1):180
Background
In moderate-throughput SNP genotyping there was a gap in the workflow, between choosing a set of SNPs and submitting their sequences to proprietary assay design software, which was not met by existing software. Retrieval and formatting of sequences flanking each SNP, prior to assay design, becomes rate-limiting for more than about ten SNPs, especially if annotated for repetitive regions and adjacent variations. We routinely process up to 50 SNPs at once. 相似文献7.
Leah E Mechanic Brian T Luke Julie E Goodman Stephen J Chanock Curtis C Harris 《BMC bioinformatics》2008,9(1):146
Background
The risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives. 相似文献8.
Background
Simple sequence repeats (SSRs), microsatellites or polymeric sequences are common in DNA and are important biologically. From mononucleotide to trinucleotide repeats and beyond, they can be found in long (> 6 repeating units) tracts and may be characterized by quantifying the frequencies in which they are found and their tract lengths. However, most of the existing computer programs that find SSR tracts do not include these methods. 相似文献9.
Background
Human genome contains millions of common single nucleotide polymorphisms (SNPs) and these SNPs play an important role in understanding the association between genetic variations and human diseases. Many SNPs show correlated genotypes, or linkage disequilibrium (LD), thus it is not necessary to genotype all SNPs for association study. Many algorithms have been developed to find a small subset of SNPs called tag SNPs that are sufficient to infer all the other SNPs. Algorithms based on the r 2 LD statistic have gained popularity because r 2 is directly related to statistical power to detect disease associations. Most of existing r 2 based algorithms use pairwise LD. Recent studies show that multi-marker LD can help further reduce the number of tag SNPs. However, existing tag SNP selection algorithms based on multi-marker LD are both time-consuming and memory-consuming. They cannot work on chromosomes containing more than 100 k SNPs using length-3 tagging rules. 相似文献10.
Background
The performance of alignment programs is traditionally tested on sets of protein sequences, of which a reference alignment is known. Conclusions drawn from such protein benchmarks do not necessarily hold for the RNA alignment problem, as was demonstrated in the first RNA alignment benchmark published so far. For example, the twilight zone – the similarity range where alignment quality drops drastically – starts at 60 % for RNAs in comparison to 20 % for proteins. In this study we enhance the previous benchmark. 相似文献11.
Marian Suarez-Gestal Eva Perez-Pampin Manuel Calaza Juan J Gomez-Reino Antonio Gonzalez 《Arthritis research & therapy》2010,12(2):R72
Introduction
We aimed to replicate the strong associations that a recent genome wide association study (GWAS) has found between 16 single nucleotide polymorphisms (SNPs) and response to anti-tumour necrosis factor (TNF) treatment in 89 patients with rheumatoid arthritis (RA). This study is very important because, according to published simulations, associations as strong as the reported ones will mean that these SNPs could be used as predictors of response at the individual level. 相似文献12.
Background
MixtureTree v1.0 is a Linux based program (written in C++) which implements an algorithm based on mixture models for reconstructing phylogeny from binary sequence data, such as single-nucleotide polymorphisms (SNPs). In addition to the mixture algorithm with three different optimization options, the program also implements a bootstrap procedure with majority-rule consensus. 相似文献13.
Rong Chen Alex A Morgan Joel Dudley Tarangini Deshpande Li Li Keiichi Kodama Annie P Chiang Atul J Butte 《Genome biology》2009,9(12):R170
Background
Candidate single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWASs) were often selected for validation based on their functional annotation, which was inadequate and biased. We propose to use the more than 200,000 microarray studies in the Gene Expression Omnibus to systematically prioritize candidate SNPs from GWASs. 相似文献14.
Mehar S Khatkar Matthew Hobbs Markus Neuditschko Johann Sölkner Frank W Nicholas Herman W Raadsma 《BMC bioinformatics》2010,11(1):171
Background
Recent developments of high-density SNP chips across a number of species require accurate genetic maps. Despite rapid advances in genome sequence assembly and availability of a number of tools for creating genetic maps, the exact genome location for a number of SNPs from these SNP chips still remains unknown. We have developed a locus ordering procedure based on linkage disequilibrium (LODE) which provides estimation of the chromosomal positions of unaligned SNPs and scaffolds. It also provides an alternative means for verification of genetic maps. We exemplified LODE in cattle. 相似文献15.
Chenggang Yu Nela Zavaljevski Valmik Desai Seth Johnson Fred J Stevens Jaques Reifman 《BMC bioinformatics》2008,9(1):52
Background
Automated protein function prediction methods are needed to keep pace with high-throughput sequencing. With the existence of many programs and databases for inferring different protein functions, a pipeline that properly integrates these resources will benefit from the advantages of each method. However, integrated systems usually do not provide mechanisms to generate customized databases to predict particular protein functions. Here, we describe a tool termed PIPA (Pipeline for Protein Annotation) that has these capabilities. 相似文献16.
Background
There is recently great interest in haplotype block structure and haplotype tagging SNPs (htSNPs) in the human genome for its implication on htSNPs-based association mapping strategy for complex disease. Different definitions have been used to characterize the haplotype block structure in the human genome, and several different performance criteria and algorithms have been suggested on htSNPs selection. 相似文献17.
Yan A Meng Yi Yu L Adrienne Cupples Lindsay A Farrer Kathryn L Lunetta 《BMC bioinformatics》2009,10(1):78
Background
Single nucleotide polymorphisms (SNPs) may be correlated due to linkage disequilibrium (LD). Association studies look for both direct and indirect associations with disease loci. In a Random Forest (RF) analysis, correlation between a true risk SNP and SNPs in LD may lead to diminished variable importance for the true risk SNP. One approach to address this problem is to select SNPs in linkage equilibrium (LE) for analysis. Here, we explore alternative methods for dealing with SNPs in LD: change the tree-building algorithm by building each tree in an RF only with SNPs in LE, modify the importance measure (IM), and use haplotypes instead of SNPs to build a RF. 相似文献18.
Zhaoxia Yu Chad Garner Argyrios Ziogas Hoda Anton-Culver Daniel J Schaid 《BMC bioinformatics》2009,10(1):63
Background
Genome-wide association studies with single nucleotide polymorphisms (SNPs) show great promise to identify genetic determinants of complex human traits. In current analyses, genotype calling and imputation of missing genotypes are usually considered as two separated tasks. The genotypes of SNPs are first determined one at a time from allele signal intensities. Then the missing genotypes, i.e., no-calls caused by not perfectly separated signal clouds, are imputed based on the linkage disequilibrium (LD) between multiple SNPs. Although many statistical methods have been developed to improve either genotype calling or imputation of missing genotypes, treating the two steps independently can lead to loss of genetic information. 相似文献19.
Hong Yu Jialiang Huang Nan Qiao Christopher D Green Jing-Dong J Han 《BMC systems biology》2010,4(1):97
Background
Genome-wide association studies (GWAS) have found hundreds of single nucleotide polymorphisms (SNPs) associated with common diseases. However, it is largely unknown what genes linked with the SNPs actually implicate disease causality. A definitive proof for disease causality can be demonstration of disease-like phenotypes through genetic perturbation of the genes or alleles, which is obviously a daunting task for complex diseases where only mammalian models can be used. 相似文献20.