SNPRelate - Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data
Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data.
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infrastructuregeneticsstatisticalmethodprincipalcomponentbioinformaticsgds-formatpcasimdsnpopenblascpp
12.58 score 116 stars 17 dependents 1.8k scripts 3.1k downloadsSeqArray - Data management of large-scale whole-genome sequence variant calls using GDS files
Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language.
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infrastructuredatarepresentationsequencinggeneticsbioinformaticsgds-formatsnpsnvweswgszlibcpp
12.39 score 49 stars 12 dependents 1.3k scripts 2.0k downloadsgdsfmt - R Interface to CoreArray Genomic Data Structure (GDS) Files
Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. The companion package 'gdscloud' extends gdsfmt with read-only access to GDS files stored on cloud storage services.
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infrastructuredataimportbioinformaticsgds-formatgenomicscpp
11.82 score 20 stars 34 dependents 1.1k scripts 5.1k downloadsHIBAG - HLA Genotype Imputation with Attribute Bagging
Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection.
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geneticsstatisticalmethodbioinformaticsgpuhlaimputationmhcsnpcpp
8.29 score 31 stars 70 scripts 680 downloadsgdscloud - Cloud Storage Access for GDS Files
Provides read-only access to GDS (Genomic Data Structure) files stored on cloud storage services including Amazon S3, Google Cloud Storage (GCS), and Azure Blob Storage, as well as any HTTP/HTTPS URL. It extends the 'gdsfmt' package so that cloud URLs (http://, https://, s3://, gs://, az://) can be opened transparently, without downloading the whole file first. Only the blocks that are actually read are fetched, using HTTP Range requests via libcurl together with an in-memory least-recently-used block cache, so that random access to a remote GDS file behaves like access to a local one. Credentials are resolved from the usual environment variables of each service, or set per session and per URL prefix, and they can be exported to the workers of a parallel cluster.
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infrastructuredataimportazurecloudgcsgdss3curlopensslcpp
4.65 score 110 downloadspgen2gds - Format Conversion between PLINK2 PGEN and GDS
Provides functions to convert files from the PLINK2 pgen format to SeqArray GDS, and from SeqArray GDS to the PLINK2 pgen format.
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infrastructuredataimportgeneticsgdsplinkcpp
4.40 score 3 scripts 282 downloadsGDSAnnotator - Whole-genome variant annotation data manipulation using GDS files
Fast and memory-efficient manipulation of whole-genome variant annotation data stored in Genomic Data Structure (GDS) files. The annotated GDS (aGDS) format introduced by FAVOR stores the FAVOR annotations of the variants together with the genotypes in one SeqArray GDS file. This package generalizes the format to annotation GDS files of any source, with or without genotypes. Annotation resources and annotator outputs, including FAVOR, gnomAD, Ensembl VEP and SnpEff, are converted into compact SeqArray-compatible GDS files with random access. Variants can be given as chr-pos-ref-alt strings, data frames, GRanges objects, VCF files or objects, or SeqArray GDS files. They are annotated by querying the annotation files, and the annotations can be written back into a genotype GDS file. Summary statistics of the variants and their annotations are computed in a single pass over a file and rendered as a VEP/SnpEff-like report. The package is intended for large-scale whole-genome sequencing studies.
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infrastructuredatarepresentationdataimportsequencingwholegenomegeneticsannotationvariantannotationgenomeannotationgenomicvariationgdsweswgs
3.88 score 1 stars 7 scripts