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Utilizing mapping targets of sequences underrepresented in the reference assembly to reduce false positive alignments.

ABSTRACT: The human reference assembly remains incomplete due to the underrepresentation of repeat-rich sequences that are found within centromeric regions and acrocentric short arms. Although these sequences are marginally represented in the assembly, they are often fully represented in whole-genome short-read datasets and contribute to inappropriate alignments and high read-depth signals that localize to a small number of assembled homologous regions. As a consequence, these regions often provide artifactual peak calls that confound hypothesis testing and large-scale genomic studies. To address this problem, we have constructed mapping targets that represent roughly 8% of the human genome generally omitted from the human reference assembly. By integrating these data into standard mapping and peak-calling pipelines we demonstrate a 10-fold reduction in signals in regions common to the blacklisted region and identify a comprehensive set of regions that exhibit mapping sensitivity with the presence of the repeat-rich targets.

SUBMITTER: Miga KH

PROVIDER: S-EPMC4787761 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Utilizing mapping targets of sequences underrepresented in the reference assembly to reduce false positive alignments.

Miga Karen H KH Eisenhart Christopher C Kent W James WJ

Nucleic acids research 20150710 20

The human reference assembly remains incomplete due to the underrepresentation of repeat-rich sequences that are found within centromeric regions and acrocentric short arms. Although these sequences are marginally represented in the assembly, they are often fully represented in whole-genome short-read datasets and contribute to inappropriate alignments and high read-depth signals that localize to a small number of assembled homologous regions. As a consequence, these regions often provide artifa ...[more]

PMID: 26163063

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Project description:Background:Cross-species whole-genome sequence alignment is a critical first step for genome comparative analyses, ranging from the detection of sequence variants to studies of chromosome evolution. Animal genomes are large and complex, and whole-genome alignment is a computationally intense process, requiring expensive high-performance computing systems due to the need to explore extensive local alignments. With hundreds of sequenced animal genomes available from multiple projects, there is an increasing demand for genome comparative analyses. Results:Here, we introduce G-Anchor, a new, fast, and efficient pipeline that uses a strictly limited but highly effective set of local sequence alignments to anchor (or map) an animal genome to another species' reference genome. G-Anchor makes novel use of a databank of highly conserved DNA sequence elements. We demonstrate how these elements may be aligned to a pair of genomes, creating anchors. These anchors enable the rapid mapping of scaffolds from a de novo assembled genome to chromosome assemblies of a reference species. Our results demonstrate that G-Anchor can successfully anchor a vertebrate genome onto a phylogenetically related reference species genome using a desktop or laptop computer within a few hours and with comparable accuracy to that achieved by a highly accurate whole-genome alignment tool such as LASTZ. G-Anchor thus makes whole-genome comparisons accessible to researchers with limited computational resources. Conclusions:G-Anchor is a ready-to-use tool for anchoring a pair of vertebrate genomes. It may be used with large genomes that contain a significant fraction of evolutionally conserved DNA sequences and that are not highly repetitive, polypoid, or excessively fragmented. G-Anchor is not a substitute for whole-genome aligning software but can be used for fast and accurate initial genome comparisons. G-Anchor is freely available and a ready-to-use tool for the pairwise comparison of two genomes.

Dataset Information

Utilizing mapping targets of sequences underrepresented in the reference assembly to reduce false positive alignments.

Publications

Utilizing mapping targets of sequences underrepresented in the reference assembly to reduce false positive alignments.

Similar Datasets

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