NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data.
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ABSTRACT:
SUBMITTER: Nohr AK
PROVIDER: S-EPMC8496226 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationPairwise relatedness estimation is important in many contexts such as disease mapping and population genetics. However, all existing estimation methods are based on called genotypes, which is not ideal for next-generation sequencing (NGS) data of low depth from which genotypes cannot be called with high certainty.ResultsWe present a software tool, NgsRelate, for estimating pairwise relatedness from NGS data. It provides maximum likelihood estimates that are based on genotype likelihoods instead of genotypes and thereby takes the inherent uncertainty of the genotypes into account. Using both simulated and real data, we show that NgsRelate provides markedly better estimates for low-depth NGS data than two state-of-the-art genotype-based methods.AvailabilityNgsRelate is implemented in C++ and is available under the GNU license at www.popgen.dk/software.
| S-EPMC4673978 | biostudies-literature
Project description:The genetic relatedness between individuals because of their recent common ancestry is now routinely estimated from marker genotype data in molecular ecology, evolutionary biology and conservation studies. The estimators developed for this purpose assume that marker allele freque218 in a population are known without errors. Unfortunately, however, these frequencies, upon which both the definition and the estimation of relatedness are based, are rarely known in reality. Frequently, the only data available in a relatedness analysis are a sample of multilocus genotypes from which both allele frequencies and relatedness must be deduced. Furthermore, because of various constraints, sample sizes of individuals can be quite small (say <50 individuals) in practice. This study shows, for the first time, that the widely used relatedness estimators become severely biased when they use allele frequencies calculated from an extremely small sample (say <10 individuals). The extent of bias depends on the sample size, the (unknown) population allele frequencies, the actual relatedness and the estimators. It also shows that relatedness estimators become even more biased when they use allele frequencies calculated from a sample by omitting a focal pair of individuals whose relatedness is being estimated. This study modifies two estimators to suit small samples and shows, both analytically and by analysing simulated and empirical data, that the two modified estimators are much less biased, more precise and more accurate than the original estimators. These performance advantages of the modified estimators are shown to increase with a decreasing sample size of individuals and with an increasing value of actual relatedness.
| S-EPMC5637371 | biostudies-literature
Project description:BACKGROUND:Multiple RNA samples are frequently processed together and often mixed before multiplex sequencing in the same sequencing run. While different samples can be separated post sequencing using sample barcodes, the possibility of cross contamination between biological samples from different species that have been processed or sequenced in parallel has the potential to be extremely deleterious for downstream analyses. RESULTS:We present CroCo, a software package for identifying and removing such cross contaminants from assembled transcriptomes. Using multiple, recently published sequence datasets, we show that cross contamination is consistently present at varying levels in real data. Using real and simulated data, we demonstrate that CroCo detects contaminants efficiently and correctly. Using a real example from a molecular phylogenetic dataset, we show that contaminants, if not eliminated, can have a decisive, deleterious impact on downstream comparative analyses. CONCLUSIONS:Cross contamination is pervasive in new and published datasets and, if undetected, can have serious deleterious effects on downstream analyses. CroCo is a database-independent, multi-platform tool, designed for ease of use, that efficiently and accurately detects and removes cross contamination in assembled transcriptomes to avoid these problems. We suggest that the use of CroCo should become a standard cleaning step when processing multiple samples for transcriptome sequencing.
| S-EPMC5838952 | biostudies-literature
Project description:BackgroundNext-generation sequencing is revolutionising diagnosis and treatment of rare diseases, however its application to understanding common disease aetiology is limited. Rare disease applications binarily attribute genetic change(s) at a single locus to a specific phenotype. In common diseases, where multiple genetic variants within and across genes contribute to disease, binary modelling cannot capture the burden of pathogenicity harboured by an individual across a given gene/pathway. We present GenePy, a novel gene-level scoring system for integration and analysis of next-generation sequencing data on a per-individual basis that transforms NGS data interpretation from variant-level to gene-level. This simple and flexible scoring system is intuitive and amenable to integration for machine learning, network and topological approaches, facilitating the investigation of complex phenotypes.ResultsWhole-exome sequencing data from 508 individuals were used to generate GenePy scores. For each variant a score is calculated incorporating: i) population allele frequency estimates; ii) individual zygosity, determined through standard variant calling pipelines and; iii) any user defined deleteriousness metric to inform on functional impact. GenePy then combines scores generated for all variants observed into a single gene score for each individual. We generated a matrix of ~ 14,000 GenePy scores for all individuals for each of sixteen popular deleteriousness metrics. All per-gene scores are corrected for gene length. The majority of genes generate GenePy scores < 0.01 although individuals harbouring multiple rare highly deleterious mutations can accumulate extremely high GenePy scores. In the absence of a comparator metric, we examine GenePy performance in discriminating genes known to be associated with three common, complex diseases. A Mann-Whitney U test conducted on GenePy scores for this positive control gene in cases versus controls demonstrates markedly more significant results (p = 1.37 × 10- 4) compared to the most commonly applied association tool that combines common and rare variation (p = 0.003).ConclusionsPer-gene per-individual GenePy scores are intuitive when assessing genetic variation in individual patients or comparing scores between groups. GenePy outperforms the currently accepted best practice tools for combining common and rare variation. GenePy scores are suitable for downstream data integration with transcriptomic and proteomic data that also report at the gene level.
| S-EPMC6524327 | biostudies-literature
Project description:Inference of population structure and individual ancestry is important both for population genetics and for association studies. With next generation sequencing technologies it is possible to obtain genetic data for all accessible genetic variations in the genome. Existing methods for admixture analysis rely on known genotypes. However, individual genotypes cannot be inferred from low-depth sequencing data without introducing errors. This article presents a new method for inferring an individual's ancestry that takes the uncertainty introduced in next generation sequencing data into account. This is achieved by working directly with genotype likelihoods that contain all relevant information of the unobserved genotypes. Using simulations as well as publicly available sequencing data, we demonstrate that the presented method has great accuracy even for very low-depth data. At the same time, we demonstrate that applying existing methods to genotypes called from the same data can introduce severe biases. The presented method is implemented in the NGSadmix software available at http://www.popgen.dk/software.
| S-EPMC3813857 | biostudies-other
Project description:Population structure can be described by genotypic-correlation coefficients between groups of individuals, the most basic of which are the pairwise relatedness coefficients between any two individuals. There are nine pairwise relatedness coefficients in the most general model, and we show that these can be reduced to seven coefficients for biallelic loci. Although all nine coefficients can be estimated from pedigrees, six coefficients have been beyond empirical reach. We provide a numerical optimization procedure that estimates all seven reduced coefficients from population-genomic data. Simulations show that the procedure is nearly unbiased, even at 3× coverage, and errors in five of the seven coefficients are statistically uncorrelated. The remaining two coefficients have a negative correlation of errors, but their sum provides an unbiased assessment of the overall correlation of heterozygosity between two individuals. Application of these new methods to four populations of the freshwater crustacean Daphnia pulex reveal the occurrence of half siblings in our samples, as well as a number of identical individuals that are likely obligately asexual clone mates. Statistically significant negative estimates of these pairwise relatedness coefficients, including inbreeding coefficients that were typically negative, underscore the difficulties that arise when interpreting genotypic correlations as estimations of the probability that alleles are identical by descent.
| S-EPMC5419463 | biostudies-literature
Project description:Next-generation sequencing (NGS) diagnostics continue to expand rapidly in clinical medicine. An ever-expanding menu of molecular biomarkers is deemed important for diagnostic, prognostic, and therapeutic assessment in patients. The increasing role of NGS in the clinic is driven mainly by the falling costs of sequencing. However, the data-intensive nature of NGS makes bioinformatic analysis a major challenge to many clinical laboratories. Critically needed NGS bioinformatics personnel are hard to recruit and retain in small- to mid-size clinical laboratories. Also, NGS software often lacks the scalability necessary for expanded clinical laboratory testing volumes. Commercial software solutions aim to bridge the bioinformatics barrier via turnkey informatics solutions tailored specifically for the clinical workplace. Yet, there has been no systematic assessment of these software solutions thus far. This article presents an end-to-end vendor evaluation experience of commercial NGS bioinformatics solutions. Six different commercial vendor solutions were assessed systematically. Key metrics of NGS software evaluation to aid in the robust assessment of software solutions are described. Comprehensive feedback, provided by the TriCore Reference Laboratories molecular pathology team, enabled the final vendor selection. Many key lessons were learned during the software evaluation process, which are described herein. This article aims to provide a detailed road map for small- to mid-size clinical laboratories interested in evaluating commercial bioinformatics solutions available in the marketplace.
| S-EPMC7031678 | biostudies-literature
Project description:High-throughput next-generation sequencing technologies pose increasing demands on the efficiency, accuracy and usability of data analysis software. In this article, we present ZOOM Lite, a software for efficient reads mapping and result visualization. With a kernel capable of mapping tens of millions of Illumina or AB SOLiD sequencing reads efficiently and accurately, and an intuitive graphical user interface, ZOOM Lite integrates reads mapping and result visualization into a easy to use pipeline on desktop PC. The software handles both single-end and paired-end reads, and can output both the unique mapping result or the top N mapping results for each read. Additionally, the software takes a variety of input file formats and outputs to several commonly used result formats. The software is freely available at http://bioinfor.com/zoom/lite/.
| S-EPMC2896157 | biostudies-literature
Project description:PurposeGenotyping CYP2D6 is important for precision drug therapy because the enzyme it encodes metabolizes approximately 25% of drugs, and its activity varies considerably among individuals. Genotype analysis of CYP2D6 is challenging due to its highly polymorphic nature. Over 100 haplotypes (star alleles) have been defined for CYP2D6, some involving a gene conversion with its nearby nonfunctional but highly homologous paralog CYP2D7. We present Stargazer, a new bioinformatics tool that uses next-generation sequencing (NGS) data to call star alleles for CYP2D6 ( https://stargazer.gs.washington.edu/stargazerweb/ ). Stargazer is currently being extended for other pharmacogenes.MethodsStargazer identifies star alleles from NGS data by detecting single nucleotide variants, insertion-deletion variants, and structural variants. Stargazer detects structural variation, including gene deletions, duplications, and conversions, by calculating paralog-specific copy numbers from read depths.ResultsWe applied Stargazer to the NGS data of 32 ethnically diverse HapMap trios that were genotyped by TaqMan assays, long-range polymerase chain reaction, quantitative multiplex polymerase chain reaction, high-resolution melting analysis, and/or Sanger sequencing. CYP2D6 genotyping by Stargazer was 99.0% concordant with the data obtained by these methods, and showed that 28.1% of the samples had structural variation including CYP2D6/CYP2D7 hybrids.ConclusionAccurate genotyping of pharmacogenes with NGS and subsequent allele calling with Stargazer will aid the implementation of precision drug therapy.
| S-EPMC6281872 | biostudies-literature
Project description:NGPS is a method for de-novo, full-length protein sequencing in high throughput. The method is based on cleavage of the protein at semi-random sites by microwave-assisted acid hydrolysis (MAAH), enrichment of LC-MS/MS amenable peptides from the hydrolysate by solid-phase-extraction, LC-MS/MS analysis, de-novo long peptide tag sequencing of resulting peptides and assembly of peptide tags into consensus contigs.
2017-04-03 | PXD003804 | Pride