10 research outputs found
Optimal Assembly for High Throughput Shotgun Sequencing
We present a framework for the design of optimal assembly algorithms for
shotgun sequencing under the criterion of complete reconstruction. We derive a
lower bound on the read length and the coverage depth required for
reconstruction in terms of the repeat statistics of the genome. Building on
earlier works, we design a de Brujin graph based assembly algorithm which can
achieve very close to the lower bound for repeat statistics of a wide range of
sequenced genomes, including the GAGE datasets. The results are based on a set
of necessary and sufficient conditions on the DNA sequence and the reads for
reconstruction. The conditions can be viewed as the shotgun sequencing analogue
of Ukkonen-Pevzner's necessary and sufficient conditions for Sequencing by
Hybridization.Comment: 26 pages, 18 figure
Telescoper: de novo assembly of highly repetitive regions.
MotivationWith advances in sequencing technology, it has become faster and cheaper to obtain short-read data from which to assemble genomes. Although there has been considerable progress in the field of genome assembly, producing high-quality de novo assemblies from short-reads remains challenging, primarily because of the complex repeat structures found in the genomes of most higher organisms. The telomeric regions of many genomes are particularly difficult to assemble, though much could be gained from the study of these regions, as their evolution has not been fully characterized and they have been linked to aging.ResultsIn this article, we tackle the problem of assembling highly repetitive regions by developing a novel algorithm that iteratively extends long paths through a series of read-overlap graphs and evaluates them based on a statistical framework. Our algorithm, Telescoper, uses short- and long-insert libraries in an integrated way throughout the assembly process. Results on real and simulated data demonstrate that our approach can effectively resolve much of the complex repeat structures found in the telomeres of yeast genomes, especially when longer long-insert libraries are used.AvailabilityTelescoper is publicly available for download at sourceforge.net/p/[email protected] informationSupplementary data are available at Bioinformatics online
SMaSH: A Benchmarking Toolkit for Human Genome Variant Calling
Motivation: Computational methods are essential to extract actionable
information from raw sequencing data, and to thus fulfill the promise of
next-generation sequencing technology. Unfortunately, computational tools
developed to call variants from human sequencing data disagree on many of their
predictions, and current methods to evaluate accuracy and computational
performance are ad-hoc and incomplete. Agreement on benchmarking variant
calling methods would stimulate development of genomic processing tools and
facilitate communication among researchers.
Results: We propose SMaSH, a benchmarking methodology for evaluating human
genome variant calling algorithms. We generate synthetic datasets, organize and
interpret a wide range of existing benchmarking data for real genomes, and
propose a set of accuracy and computational performance metrics for evaluating
variant calling methods on this benchmarking data. Moreover, we illustrate the
utility of SMaSH to evaluate the performance of some leading single nucleotide
polymorphism (SNP), indel, and structural variant calling algorithms.
Availability: We provide free and open access online to the SMaSH toolkit,
along with detailed documentation, at smash.cs.berkeley.edu
Recommended from our members
Telescoper: de novo assembly of highly repetitive regions.
MotivationWith advances in sequencing technology, it has become faster and cheaper to obtain short-read data from which to assemble genomes. Although there has been considerable progress in the field of genome assembly, producing high-quality de novo assemblies from short-reads remains challenging, primarily because of the complex repeat structures found in the genomes of most higher organisms. The telomeric regions of many genomes are particularly difficult to assemble, though much could be gained from the study of these regions, as their evolution has not been fully characterized and they have been linked to aging.ResultsIn this article, we tackle the problem of assembling highly repetitive regions by developing a novel algorithm that iteratively extends long paths through a series of read-overlap graphs and evaluates them based on a statistical framework. Our algorithm, Telescoper, uses short- and long-insert libraries in an integrated way throughout the assembly process. Results on real and simulated data demonstrate that our approach can effectively resolve much of the complex repeat structures found in the telomeres of yeast genomes, especially when longer long-insert libraries are used.AvailabilityTelescoper is publicly available for download at sourceforge.net/p/[email protected] informationSupplementary data are available at Bioinformatics online
Recommended from our members
SM a SH: a benchmarking toolkit for human genome variant calling
MotivationComputational methods are essential to extract actionable information from raw sequencing data, and to thus fulfill the promise of next-generation sequencing technology. Unfortunately, computational tools developed to call variants from human sequencing data disagree on many of their predictions, and current methods to evaluate accuracy and computational performance are ad hoc and incomplete. Agreement on benchmarking variant calling methods would stimulate development of genomic processing tools and facilitate communication among researchers.ResultsWe propose SMaSH, a benchmarking methodology for evaluating germline variant calling algorithms. We generate synthetic datasets, organize and interpret a wide range of existing benchmarking data for real genomes and propose a set of accuracy and computational performance metrics for evaluating variant calling methods on these benchmarking data. Moreover, we illustrate the utility of SMaSH to evaluate the performance of some leading single-nucleotide polymorphism, indel and structural variant calling algorithms.Availability and implementationWe provide free and open access online to the SMaSH tool kit, along with detailed documentation, at smash.cs.berkeley.ed
Telescoper: de novo assembly of highly repetitive regions
Motivation: With advances in sequencing technology, it has become faster and cheaper to obtain short-read data from which to assemble genomes. Although there has been considerable progress in the field of genome assembly, producing high-quality de novo assemblies from short-reads remains challenging, primarily because of the complex repeat structures found in the genomes of most higher organisms. The telomeric regions of many genomes are particularly difficult to assemble, though much could be gained from the study of these regions, as their evolution has not been fully characterized and they have been linked to aging. Results: In this article, we tackle the problem of assembling highly repetitive regions by developing a novel algorithm that iteratively extends long paths through a series of read-overlap graphs and evaluates them based on a statistical framework. Our algorithm, Telescoper, uses short- and long-insert libraries in an integrated way throughout the assembly process. Results on real and simulated data demonstrate that our approach can effectively resolve much of the complex repeat structures found in the telomeres of yeast genomes, especially when longer long-insert libraries are used. Availability: Telescoper is publicly available for download at sourceforge.net/p/telescoper. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online