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Replication of eukaryotic genomes is highly stochastic, making it difficult to determine the replication dynamics of individual molecules with existing methods. We report a sequencing method for the measurement of replication fork movement on single molecules by detecting nucleotide analog signal currents on extremely long nanopore traces (D-NAscent). Using this method, we detect 5-bromodeoxyuridine (BrdU) incorporated by Saccharomyces cerevisiae to reveal, at a genomic scale and on single molecules, the DNA sequences replicated during a pulse-labeling period. Under conditions of limiting BrdU concentration, D-NAscent detects the differences in BrdU incorporation frequency across individual molecules to reveal the location of active replication origins, fork direction, termination sites, and fork pausing/stalling events. We used sequencing reads of 20-160 kilobases to generate a whole-genome single-molecule map of DNA replication dynamics and discover a class of low-frequency stochastic origins in budding yeast. The D-NAscent software is available at https://github.com/MBoemo/DNAscent.git .

We report the sequencing and assembly of a reference genome for the human GM12878 Utah/Ceph cell line using the MinION (Oxford Nanopore Technologies) nanopore sequencer. 91.2 Gb of sequence data, representing ∼30× theoretical coverage, were produced. Reference-based alignment enabled detection of large structural variants and epigenetic modifications. De novo assembly of nanopore reads alone yielded a contiguous assembly (NG50 ∼3 Mb). We developed a protocol to generate ultra-long reads (N50 > 100 kb, read lengths up to 882 kb). Incorporating an additional 5× coverage of these ultra-long reads more than doubled the assembly contiguity (NG50 ∼6.4 Mb). The final assembled genome was 2,867 million bases in size, covering 85.8% of the reference. Assembly accuracy, after incorporating complementary short-read sequencing data, exceeded 99.8%. Ultra-long reads enabled assembly and phasing of the 4-Mb major histocompatibility complex (MHC) locus in its entirety, measurement of telomere repeat length, and closure of gaps in the reference human genome assembly GRCh38.

The human reference genome assembly plays a central role in nearly all aspects of today's basic and clinical research. GRCh38 is the first coordinate-changing assembly update since 2009; it reflects the resolution of roughly 1000 issues and encompasses modifications ranging from thousands of single base changes to megabase-scale path reorganizations, gap closures, and localization of previously orphaned sequences. We developed a new approach to sequence generation for targeted base updates and used data from new genome mapping technologies and single haplotype resources to identify and resolve larger assembly issues. For the first time, the reference assembly contains sequence-based representations for the centromeres. We also expanded the number of alternate loci to create a reference that provides a more robust representation of human population variation. We demonstrate that the updates render the reference an improved annotation substrate, alter read alignments in unchanged regions, and impact variant interpretation at clinically relevant loci. We additionally evaluated a collection of new de novo long-read haploid assemblies and conclude that although the new assemblies compare favorably to the reference with respect to continuity, error rate, and gene completeness, the reference still provides the best representation for complex genomic regions and coding sequences. We assert that the collected updates in GRCh38 make the newer assembly a more robust substrate for comprehensive analyses that will promote our understanding of human biology and advance our efforts to improve health., (© 2017 Schneider et al.; Published by Cold Spring Harbor Laboratory Press.)

We are rapidly approaching the point where we have sequenced millions of human genomes. There is a pressing need for new data structures to store raw sequencing data and efficient algorithms for population scale analysis. Current reference-based data formats do not fully exploit the redundancy in population sequencing nor take advantage of shared genetic variation. In recent years, the Burrows-Wheeler transform (BWT) and FM-index have been widely employed as a full-text searchable index for read alignment and de novo assembly. We introduce the concept of a population BWT and use it to store and index the sequencing reads of 2705 samples from the 1000 Genomes Project. A key feature is that, as more genomes are added, identical read sequences are increasingly observed, and compression becomes more efficient. We assess the support in the 1000 Genomes read data for every base position of two human reference assembly versions, identifying that 3.2 Mbp with population support was lost in the transition from GRCh37 with 13.7 Mbp added to GRCh38. We show that the vast majority of variant alleles can be uniquely described by overlapping 31-mers and show how rapid and accurate SNP and indel genotyping can be carried out across the genomes in the population BWT. We use the population BWT to carry out nonreference queries to search for the presence of all known viral genomes and discover human T-lymphotropic virus 1 integrations in six samples in a recognized epidemiological distribution., (© 2017 Dolle et al.; Published by Cold Spring Harbor Laboratory Press.)

The current genomic revolution was made possible by joint advances in genome sequencing technologies and computational approaches for analyzing sequence data. The close interaction between biologists and computational scientists is perhaps most apparent in the development of approaches for sequencing entire genomes, a feat that would not be possible without sophisticated computational tools called genome assemblers (short for genome sequence assemblers). Here, we survey the key developments in algorithms for assembling genome sequences since the development of the first DNA sequencing methods more than 35 years ago.

A report on Genome Informatics 2014, held in Cambridge, UK, 21-24 September, 2014.

De novo genome sequence assembly is important both to generate new sequence assemblies for previously uncharacterized genomes and to identify the genome sequence of individuals in a reference-unbiased way. We present memory efficient data structures and algorithms for assembly using the FM-index derived from the compressed Burrows-Wheeler transform, and a new assembler based on these called SGA (String Graph Assembler). We describe algorithms to error-correct, assemble, and scaffold large sets of sequence data. SGA uses the overlap-based string graph model of assembly, unlike most de novo assemblers that rely on de Bruijn graphs, and is simply parallelizable. We demonstrate the error correction and assembly performance of SGA on 1.2 billion sequence reads from a human genome, which we are able to assemble using 54 GB of memory. The resulting contigs are highly accurate and contiguous, while covering 95% of the reference genome (excluding contigs <200 bp in length). Because of the low memory requirements and parallelization without requiring inter-process communication, SGA provides the first practical assembler to our knowledge for a mammalian-sized genome on a low-end computing cluster.

Low-cost short read sequencing technology has revolutionized genomics, though it is only just becoming practical for the high-quality de novo assembly of a novel large genome. We describe the Assemblathon 1 competition, which aimed to comprehensively assess the state of the art in de novo assembly methods when applied to current sequencing technologies. In a collaborative effort, teams were asked to assemble a simulated Illumina HiSeq data set of an unknown, simulated diploid genome. A total of 41 assemblies from 17 different groups were received. Novel haplotype aware assessments of coverage, contiguity, structure, base calling, and copy number were made. We establish that within this benchmark: (1) It is possible to assemble the genome to a high level of coverage and accuracy, and that (2) large differences exist between the assemblies, suggesting room for further improvements in current methods. The simulated benchmark, including the correct answer, the assemblies, and the code that was used to evaluate the assemblies is now public and freely available from http://www.assemblathon.org/.

Motivation: Sequence assembly is a difficult problem whose importance has grown again recently as the cost of sequencing has dramatically dropped. Most new sequence assembly software has started by building a de Bruijn graph, avoiding the overlap-based methods used previously because of the computational cost and complexity of these with very large numbers of short reads. Here, we show how to use suffix array-based methods that have formed the basis of recent very fast sequence mapping algorithms to find overlaps and generate assembly string graphs asymptotically faster than previously described algorithms., Results: Standard overlap assembly methods have time complexity O(N(2)), where N is the sum of the lengths of the reads. We use the Ferragina-Manzini index (FM-index) derived from the Burrows-Wheeler transform to find overlaps of length at least tau among a set of reads. As well as an approach that finds all overlaps then implements transitive reduction to produce a string graph, we show how to output directly only the irreducible overlaps, significantly shrinking memory requirements and reducing compute time to O(N), independent of depth. Overlap-based assembly methods naturally handle mixed length read sets, including capillary reads or long reads promised by the third generation sequencing technologies. The algorithms we present here pave the way for overlap-based assembly approaches to be developed that scale to whole vertebrate genome de novo assembly.

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Motivation: The de novo assembly of large, complex genomes is a significant challenge with currently available DNA sequencing technology. While many de novo assembly software packages are available, comparatively little attention has been paid to assisting the user with the assembly.Results: This article addresses the practical aspects of de novo assembly by introducing new ways to perform quality assessment on a collection of sequence reads. The software implementation calculates per-base error rates, paired-end fragment-size distributions and coverage metrics in the absence of a reference genome. Additionally, the software will estimate characteristics of the sequenced genome, such as repeat content and heterozygosity that are key determinants of assembly difficulty.Availability: The software described is freely available online (https://github.com/jts/sga) and open source under the GNU Public License.Contact: jared.simpson@oicr.on.caSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Multiple somatic rearrangements are often found in cancer genomes; however, the underlying processes of rearrangement and their contribution to cancer development are poorly characterized. Here we use a paired-end sequencing strategy to identify somatic rearrangements in breast cancer genomes. There are more rearrangements in some breast cancers than previously appreciated. Rearrangements are more frequent over gene footprints and most are intrachromosomal. Multiple rearrangement architectures are present, but tandem duplications are particularly common in some cancers, perhaps reflecting a specific defect in DNA maintenance. Short overlapping sequences at most rearrangement junctions indicate that these have been mediated by non-homologous end-joining DNA repair, although varying sequence patterns indicate that multiple processes of this type are operative. Several expressed in-frame fusion genes were identified but none was recurrent. The study provides a new perspective on cancer genomes, highlighting the diversity of somatic rearrangements and their potential contribution to cancer development. [ABSTRACT FROM AUTHOR]

Summary: We have developed an algorithm to detect copy number variants (CNVs) in homozygous organisms, such as inbred laboratory strains of mice, from short read sequence data. Our novel approach exploits the fact that inbred mice are homozygous at virtually every position in the genome to detect CNVs using a hidden Markov model (HMM). This HMM uses both the density of sequence reads mapped to the genome, and the rate of apparent heterozygous single nucleotide polymorphisms, to determine genomic copy number. We tested our algorithm on short read sequence data generated from re-sequencing chromosome 17 of the mouse strains A/J and CAST/EiJ with the Illumina platform. In total, we identified 118 copy number variants (43 for A/J and 75 for CAST/EiJ). We investigated the performance of our algorithm through comparison to CNVs previously identified by array-comparative genomic hybridization (array CGH). We performed quantitative-PCR validation on a subset of the calls that differed from the array CGH data sets. [ABSTRACT FROM PUBLISHER]

Low-cost short read sequencing technology has revolutionized genomics, though it is only just becoming practical for the high-quality de novo assembly of a novel large genome. We describe the Assemblathon 1 competition, which aimed to comprehensively assess the state of the art in de novo assembly methods when applied to current sequencing technologies. In a collaborative effort, teams were asked to assemble a simulated Illumina HiSeq data set of an unknown, simulated diploid genome. A total of 41 assemblies from 17 different groups were received. Novel haplotype aware assessments of coverage, contiguity, structure, base calling, and copy number were made. We establish that within this benchmark: (1) It is possible to assemble the genome to a high level of coverage and accuracy, and that (2) large differences exist between the assemblies, suggesting room for further improvements in current methods. The simulated benchmark, including the correct answer, the assemblies, and the code that was used to evaluate the assemblies is now public and freely available from http://www.assemblathon.org/. [ABSTRACT FROM AUTHOR]

One approach to understanding the genetic basis of traits is to study their pattern of inheritance among offspring of phenotypically different parents. Previously, such analysis has been limited by low mapping resolution, high labor costs, and large sample size requirements for detecting modest effects. Here, we present a novel approach to map trait loci using artificial selection. First, we generated populations of 10-100 million haploid and diploid segregants by crossing two budding yeast strains of different heat tolerance for up to 12 generations. We then subjected these large segregant pools to heat stress for up to 12 d, enriching for beneficial alleles. Finally, we sequenced total DNA from the pools before and during selection to measure the changes in parental allele frequency. We mapped 21 intervals with significant changes in genetic background in response to selection, which is several times more than found with traditional linkage methods. Nine of these regions contained two or fewer genes, yielding much higher resolution than previous genomic linkage studies. Multiple members of the RAS/cAMP signaling pathway were implicated, along with genes previously not annotated with heat stress response function. Surprisingly, at most selected loci, allele frequencies stopped changing before the end of the selection experiment, but alleles did not become fixed. Furthermore, we were able to detect the same set of trait loci in a population of diploid individuals with similar power and resolution, and observed primarily additive effects, similar to what is seen for complex trait genetics in other diploid organisms such as humans. [ABSTRACT FROM AUTHOR]