1000 genomes project
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PROVIDER: PRJEB56604 | ENA |
REPOSITORIES: ENA
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Project description:BackgroundIn silco Biology is increasingly important and is often based on public data. While the problem of contamination is well recognised in microbiology labs the corresponding problem of database corruption has received less attention.ResultsMapping 50 billion next generation DNA sequences from The Thousand Genome Project against published genomes reveals many that match one or more Mycoplasma but are not included in the reference human genome GRCh37.p5. Many of these are of low quality but NCBI BLAST searches confirm some high quality, high entropy sequences match Mycoplasma but no human sequences.ConclusionsIt appears at least 7% of 1000G samples are contaminated.
| S-EPMC4022254 | biostudies-literature
Project description:The 1000 Genomes Project was launched as one of the largest distributed data collection and analysis projects ever undertaken in biology. In addition to the primary scientific goals of creating both a deep catalog of human genetic variation and extensive methods to accurately discover and characterize variation using new sequencing technologies, the project makes all of its data publicly available. Members of the project data coordination center have developed and deployed several tools to enable widespread data access.
| S-EPMC3340611 | biostudies-literature
Project description:Human genetic variation is likely to be responsible for a substantial fraction of the variability in complex traits including drug response. Single nucleotide polymorphisms have been implicated in drug response using genome-wide association studies as well as candidate-gene approaches. A more comprehensive catalogue of human genetic variation should complement the current large-scale genotypic dataset from the International HapMap Project, which focuses on common genetic variants. The 1000 Genomes Project is an international research effort that aims to provide the most comprehensive map of human genetic variation using next-generation sequencing platforms. Owing to the lack of convenient tools, however, it is a challenge for the pharmacogenetic research community to take advantage of these data. Here, we present a new database of some pharmacogenes of particular interest to pharmacogenetic researchers. Our database provides a convenient portal for immediate utilization of the newly released 1000 Genomes Project data in pharmacogenetic studies.
| S-EPMC2935084 | biostudies-literature
Project description:BackgroundData from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotyping, phasing, and imputation accuracy data in the 1000 Genomes project. We compare the phased haplotype calls from the 1000 Genomes project to experimentally phased haplotypes for 28 of the same individuals sequenced using the 10X Genomics platform.ResultsWe observe that phasing and imputation for rare variants are unreliable, which likely reflects the limited sample size of the 1000 Genomes project data. Further, it appears that using a population specific reference panel does not improve the accuracy of imputation over using the entire 1000 Genomes data set as a reference panel. We also note that the error rates and trends depend on the choice of definition of error, and hence any error reporting needs to take these definitions into account.ConclusionsThe quality of the 1000 Genomes data needs to be considered while using this database for further studies. This work presents an analysis that can be used for these assessments.
| S-EPMC6696682 | biostudies-literature
Project description:We performed RNA-seq for four balanced translocations' EB virus transformation cell line
2017-07-01 | GSE94043 | GEO
Project description:The sequence diversity of individual human genomes has been extensively analyzed for variations and phenotypic implications for mRNA, miRNA, and long non-coding RNA genes. TRNA (tRNA) also exhibits large sequence diversity in the human genome, but tRNA gene sequence variation and potential functional implications in individual human genomes have not been investigated. Here we capitalize on the sequencing data from the 1000-genomes project to examine the diversity of tRNA genes in the human population. Previous analysis of the reference human genome indicated an unexpected large number of diverse tRNA genes beyond the necessity of translation, suggesting that some tRNA transcripts may perform non-canonical functions. We found 24 new tRNA sequences in>1% and 76 new tRNA sequences in>0.2% of all individuals, indicating that tRNA genes are also subject to evolutionary changes in the human population. Unexpectedly, two abundant new tRNA genes contain base-pair mismatches in the anticodon stem. We experimentally determined that these two new tRNAs have altered structures in vitro; however, one new tRNA is not aminoacylated but extremely stable in HeLa cells, suggesting that this new tRNA can be used for non-canonical function. Our results show that at the scale of human population, tRNA genes are more diverse than conventionally understood, and some new tRNAs may perform non-canonical, extra-translational functions that may be linked to human health and disease.
| S-EPMC3917988 | biostudies-literature
Project description:The 1000 Genomes Project produced more than 100 trillion basepairs of short read sequence from more than 2600 samples in 26 populations over a period of five years. In its final phase, the project released over 85 million genotyped and phased variants on human reference genome assembly GRCh37. An updated reference assembly, GRCh38, was released in late 2013, but there was insufficient time for the final phase of the project analysis to change to the new assembly. Although it is possible to lift the coordinates of the 1000 Genomes Project variants to the new assembly, this is a potentially error-prone process as coordinate remapping is most appropriate only for non-repetitive regions of the genome and those that did not see significant change between the two assemblies. It will also miss variants in any region that was newly added to GRCh38. Thus, to produce the highest quality variants and genotypes on GRCh38, the best strategy is to realign the reads and recall the variants based on the new alignment. As the first step of variant calling for the 1000 Genomes Project data, we have finished remapping all of the 1000 Genomes sequence reads to GRCh38 with alternative scaffold-aware BWA-MEM. The resulting alignments are available as CRAM, a reference-based sequence compression format. The data have been released on our FTP site and are also available from European Nucleotide Archive to facilitate researchers discovering variants on the primary sequences and alternative contigs of GRCh38.
| S-EPMC5522380 | biostudies-literature
Project description:Genotype imputations based on 1000 Genomes (1KG) Project data have the advantage of imputing many more SNPs than imputations based on HapMap data. It also provides an opportunity to discover associations with relatively rare variants. Recent investigations are increasingly using 1KG data for genotype imputations, but only limited evaluations of the performance of this approach are available. In this paper, we empirically evaluated imputation performance using 1KG data by comparing imputation results to those using the HapMap Phase II data that have been widely used. We used three reference panels: the CEU panel consisting of 120 haplotypes from HapMap II and 1KG data (June 2010 release) and the EUR panel consisting of 566 haplotypes also from 1KG data (August 2010 release). We used Illumina 324,607 autosomal SNPs genotyped in 501 individuals of European ancestry. Our most important finding was that both 1KG reference panels provided much higher imputation yield than the HapMap II panel. There were more than twice as many successfully imputed SNPs as there were using the HapMap II panel (6.7 million vs. 2.5 million). Our second most important finding was that accuracy using both 1KG panels was high and almost identical to accuracy using the HapMap II panel. Furthermore, after removing SNPs with MACH Rsq <0.3, accuracy for both rare and low frequency SNPs was very high and almost identical to accuracy for common SNPs. We found that imputation using the 1KG-EUR panel had advantages in successfully imputing rare, low frequency and common variants. Our findings suggest that 1KG-based imputation can increase the opportunity to discover significant associations for SNPs across the allele frequency spectrum. Because the 1KG Project is still underway, we expect that later versions will provide even better imputation performance.
| S-EPMC3322630 | biostudies-literature
Project description:The 1000 Genomes Project provides a unique source of whole genome sequencing data for studies of human population genetics and human diseases. The last release of this project includes more than 2,500 sequenced individuals from 26 populations. Although relationships among individuals have been investigated in some of the populations, inbreeding has never been studied. In this article, we estimated the genomic inbreeding coefficient of each individual and found an unexpected high level of inbreeding in 1000 Genomes data: nearly a quarter of the individuals were inbred and around 4% of them had inbreeding coefficients similar or greater than the ones expected for first-cousin offspring. Inbred individuals were found in each of the 26 populations, with some populations showing proportions of inbred individuals above 50%. We also detected 227 previously unreported pairs of close relatives (up to and including first-cousins). Thus, we propose subsets of unrelated and outbred individuals, for use by the scientific community. In addition, because admixed populations are present in the 1000 Genomes Project, we performed simulations to study the robustness of inbreeding coefficient estimates in the presence of admixture. We found that our multi-point approach (FSuite) was quite robust to admixture, unlike single-point methods (PLINK).
| S-EPMC4667178 | biostudies-literature