Dataset Information

Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods.

ABSTRACT: A high-confidence, comprehensive human variant set is critical in assessing accuracy of sequencing algorithms, which are crucial in precision medicine based on high-throughput sequencing. Although recent works have attempted to provide such a resource, they still do not encompass all major types of variants including structural variants (SVs). Thus, we leveraged the massive high-quality Sanger sequences from the HuRef genome to construct by far the most comprehensive gold set of a single individual, which was cross validated with deep Illumina sequencing, population datasets, and well-established algorithms. It was a necessary effort to completely reanalyze the HuRef genome as its previously published variants were mostly reported five years ago, suffering from compatibility, organization, and accuracy issues that prevent their direct use in benchmarking. Our extensive analysis and validation resulted in a gold set with high specificity and sensitivity. In contrast to the current gold sets of the NA12878 or HS1011 genomes, our gold set is the first that includes small variants, deletion SVs and insertion SVs up to a hundred thousand base-pairs. We demonstrate the utility of our HuRef gold set to benchmark several published SV detection tools.

SUBMITTER: Mu JC

PROVIDER: S-EPMC4585973 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods.

Mu John C JC Tootoonchi Afshar Pegah P Mohiyuddin Marghoob M Chen Xi X Li Jian J Bani Asadi Narges N Gerstein Mark B MB Wong Wing H WH Lam Hugo Y K HY

Scientific reports 20150928

A high-confidence, comprehensive human variant set is critical in assessing accuracy of sequencing algorithms, which are crucial in precision medicine based on high-throughput sequencing. Although recent works have attempted to provide such a resource, they still do not encompass all major types of variants including structural variants (SVs). Thus, we leveraged the massive high-quality Sanger sequences from the HuRef genome to construct by far the most comprehensive gold set of a single individ ...[more]

PMID: 26412485

Similar Datasets

Project description:Microbes live in complex communities that are of major importance for environmental ecology, public health, and animal physiology and pathology. Short-read metagenomic shotgun sequencing is currently the state-of-the-art technique for exploring these communities. With the aid of metagenomics, our understanding of the microbiome is moving from composition toward functionality, even down to the genetic variant level. While the exploration of single-nucleotide variation in a genome is a standard procedure in genomics, and many sophisticated tools exist to perform this task, identification of genetic variation in metagenomes remains challenging. Major factors that hamper the widespread application of variant-calling analysis include low-depth sequencing of individual genomes (which is especially significant for the microorganisms present in low abundance), the existence of large genomic variation even within the same species, the absence of comprehensive reference genomes, and the noise introduced by next-generation sequencing errors. Some bioinformatics tools, such as metaSNV or InStrain, have been created to identify genetic variants in metagenomes, but the performance of these tools has not been systematically assessed or compared with the variant callers commonly used on single or pooled genomes. In this study, we benchmark seven bioinformatic tools for genetic variant calling in metagenomics data and assess their performance. To do so, we simulated metagenomic reads to mimic human microbial composition, sequencing errors, and genetic variability. We also simulated different conditions, including low and high depth of coverage and unique or multiple strains per species. Our analysis of the simulated data shows that probabilistic method-based tools such as HaplotypeCaller and Mutect2 from the GATK toolset show the best performance. By applying these tools to longitudinal gut microbiome data from the Human Microbiome Project, we show that the genetic similarity between longitudinal samples from the same individuals is significantly greater than the similarity between samples from different individuals. Our benchmark shows that probabilistic tools can be used to call metagenomes, and we recommend the use of GATK's tools as reliable variant callers for metagenomic samples.

Dataset Information

Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods.

Publications

Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets