Dataset Information

Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions.

ABSTRACT: Genomes assembled de novo from short reads are highly fragmented relative to the finished chromosomes of Homo sapiens and key model organisms generated by the Human Genome Project. To address this problem, we need scalable, cost-effective methods to obtain assemblies with chromosome-scale contiguity. Here we show that genome-wide chromatin interaction data sets, such as those generated by Hi-C, are a rich source of long-range information for assigning, ordering and orienting genomic sequences to chromosomes, including across centromeres. To exploit this finding, we developed an algorithm that uses Hi-C data for ultra-long-range scaffolding of de novo genome assemblies. We demonstrate the approach by combining shotgun fragment and short jump mate-pair sequences with Hi-C data to generate chromosome-scale de novo assemblies of the human, mouse and Drosophila genomes, achieving--for the human genome--98% accuracy in assigning scaffolds to chromosome groups and 99% accuracy in ordering and orienting scaffolds within chromosome groups. Hi-C data can also be used to validate chromosomal translocations in cancer genomes.

SUBMITTER: Burton JN

PROVIDER: S-EPMC4117202 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions.

Burton Joshua N JN Adey Andrew A Patwardhan Rupali P RP Qiu Ruolan R Kitzman Jacob O JO Shendure Jay J

Nature biotechnology 20131103 12

Genomes assembled de novo from short reads are highly fragmented relative to the finished chromosomes of Homo sapiens and key model organisms generated by the Human Genome Project. To address this problem, we need scalable, cost-effective methods to obtain assemblies with chromosome-scale contiguity. Here we show that genome-wide chromatin interaction data sets, such as those generated by Hi-C, are a rich source of long-range information for assigning, ordering and orienting genomic sequences to ...[more]

PMID: 24185095

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Project description:The current revolution in genomics has been made possible by software tools called genome assemblers, which stitch together DNA fragments "read" by sequencing machines into complete or nearly complete genome sequences. Despite decades of research in this field and the development of dozens of genome assemblers, assessing and comparing the quality of assembled genome sequences still relies on the availability of independently determined standards, such as manually curated genome sequences, or independently produced mapping data. These "gold standards" can be expensive to produce and may only cover a small fraction of the genome, which limits their applicability to newly generated genome sequences. Here we introduce a de novo? probabilistic measure of assembly quality which allows for an objective comparison of multiple assemblies generated from the same set of reads. We define the quality of a sequence produced by an assembler as the conditional probability of observing the sequenced reads from the assembled sequence. A key property of our metric is that the true genome sequence maximizes the score, unlike other commonly used metrics.We demonstrate that our de novo? score can be computed quickly and accurately in a practical setting even for large datasets, by estimating the score from a relatively small sample of the reads. To demonstrate the benefits of our score, we measure the quality of the assemblies generated in the GAGE and Assemblathon 1 assembly "bake-offs" with our metric. Even without knowledge of the true reference sequence, our de novo? metric closely matches the reference-based evaluation metrics used in the studies and outperforms other de novo? metrics traditionally used to measure assembly quality (such as N50). Finally, we highlight the application of our score to optimize assembly parameters used in genome assemblers, which enables better assemblies to be produced, even without prior knowledge of the genome being assembled.Likelihood-based measures, such as ours proposed here, will become the new standard for de novo? assembly evaluation.

Project description:BackgroundGenomic data have become major resources to understand complex mechanisms at fine-scale temporal and spatial resolution in functional and evolutionary genetic studies, including human diseases, such as cancers. Recently, a large number of whole genomes of evolving populations of yeast (Saccharomyces cerevisiae W303 strain) were sequenced in a time-dependent manner to identify temporal evolutionary patterns. For this type of study, a chromosome-level sequence assembly of the strain or population at time zero is required to compare with the genomes derived later. However, there is no fully automated computational approach in experimental evolution studies to establish the chromosome-level genome assembly using unique features of sequencing data.Methods and resultsIn this study, we developed a new software pipeline, the integrative meta-assembly pipeline (IMAP), to build chromosome-level genome sequence assemblies by generating and combining multiple initial assemblies using three de novo assemblers from short-read sequencing data. We significantly improved the continuity and accuracy of the genome assembly using a large collection of sequencing data and hybrid assembly approaches. We validated our pipeline by generating chromosome-level assemblies of yeast strains W303 and SK1, and compared our results with assemblies built using long-read sequencing and various assembly evaluation metrics. We also constructed chromosome-level sequence assemblies of S. cerevisiae strain Sigma1278b, and three commonly used fungal strains: Aspergillus nidulans A713, Neurospora crassa 73, and Thielavia terrestris CBS 492.74, for which long-read sequencing data are not yet available. Finally, we examined the effect of IMAP parameters, such as reference and resolution, on the quality of the final assembly of the yeast strains W303 and SK1.ConclusionsWe developed a cost-effective pipeline to generate chromosome-level sequence assemblies using only short-read sequencing data. Our pipeline combines the strengths of reference-guided and meta-assembly approaches. Our pipeline is available online at http://github.com/jkimlab/IMAP including a Docker image, as well as a Perl script, to help users install the IMAP package, including several prerequisite programs. Users can use IMAP to easily build the chromosome-level assembly for the genome of their interest.

Dataset Information

Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions.

Publications

Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions.

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