The Zika outbreak, spread by the Aedes aegypti mosquito, highlights the need to create high-quality assemblies of large genomes in a rapid and cost-effective way. Here we combine Hi-C data with existing draft assemblies to generate chromosome-length scaffolds. We validate this method by assembling a human genome, de novo, from short reads alone (67× coverage). We then combine our method with draft sequences to create genome assemblies of the mosquito disease vectors Ae. aegypti and Culex quinquefasciatus, each consisting of three scaffolds corresponding to the three chromosomes in each species. These assemblies indicate that almost all genomic rearrangements among these species occur within, rather than between, chromosome arms. The genome assembly procedure we describe is fast, inexpensive, and accurate, and can be applied to many species.

https://science.sciencemag.org/content/sci/early/2017/03/22/science.aal3327.full.pdf

De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds

Determining the genome sequence of an organism is challenging, yet fundamental to understanding its biology. Over the past decade, thousands of human genomes have been sequenced, contributing deeply to biomedical research. In the vast majority of cases, these have been analyzed by aligning sequence reads to a single reference genome, biasing the resulting analyses, and in general, failing to capture sequences novel to a given genome. Some de novo assemblies have been constructed free of reference bias, but nearly all were constructed by merging homologous loci into single "consensus" sequences, generally absent from nature. These assemblies do not correctly represent the diploid biology of an individual. In exactly two cases, true diploid de novo assemblies have been made, at great expense. One was generated using Sanger sequencing, and one using thousands of clone pools. Here, we demonstrate a straightforward and low-cost method for creating true diploid de novo assemblies. We make a single library from ∼1 ng of high molecular weight DNA, using the 10x Genomics microfluidic platform to partition the genome. We applied this technique to seven human samples, generating low-cost HiSeq X data, then assembled these using a new "pushbutton" algorithm, Supernova. Each computation took 2 d on a single server. Each yielded contigs longer than 100 kb, phase blocks longer than 2.5 Mb, and scaffolds longer than 15 Mb. Our method provides a scalable capability for determining the actual diploid genome sequence in a sample, opening the door to new approaches in genomic biology and medicine.

/pdf/direct-determination-of-diploid-genome-sequences-rn0s6yjowv.pdf

Direct determination of diploid genome sequences

Long-range and highly accurate de novo assembly from short-read data is one of the most pressing challenges in genomics. Recently, it has been shown that read pairs generated by proximity ligation of DNA in chromatin of living tissue can address this problem, dramatically increasing the scaffold contiguity of assemblies. Here, we describe a simpler approach ("Chicago") based on in vitro reconstituted chromatin. We generated two Chicago data sets with human DNA and developed a statistical model and a new software pipeline ("HiRise") that can identify poor quality joins and produce accurate, long-range sequence scaffolds. We used these to construct a highly accurate de novo assembly and scaffolding of a human genome with scaffold N50 of 20 Mbp. We also demonstrated the utility of Chicago for improving existing assemblies by reassembling and scaffolding the genome of the American alligator. With a single library and one lane of Illumina HiSeq sequencing, we increased the scaffold N50 of the American alligator from 508 kbp to 10 Mbp.

/pdf/chromosome-scale-shotgun-assembly-using-an-in-vitro-method-3n44qwgzq8.pdf

Chromosome-scale shotgun assembly using an in vitro method for long-range linkage

The assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps toward elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depend on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely. With ABySS 1.0, we originally showed that assembling the human genome using short 50-bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized message-passing system (MPI). We present here its redesign, which departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements. We benchmarked ABySS 2.0 human genome assembly using a Genome in a Bottle data set of 250-bp Illumina paired-end and 6-kbp mate-pair libraries from a single individual. Our assembly yielded a NG50 (NGA50) scaffold contiguity of 3.5 (3.0) Mbp using <35 GB of RAM. This is a modest memory requirement by today's standards and is often available on a single computer. We also investigate the use of BioNano Genomics and 10x Genomics' Chromium data to further improve the scaffold NG50 (NGA50) of this assembly to 42 (15) Mbp.

/pdf/abyss-2-0-resource-efficient-assembly-of-large-genomes-using-2rzlt625zv.pdf

ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter.

A Joint Announcement on Genome Sequence Standards Genome project standards in a new era of sequencing P. S. G. Chain 1,2,3,4,22,* , D. V. Grafham 5,* , R. S. Fulton 6 , M. G. FitzGerald 7 , J. Hostetler 8 , D. Muzny 9 , J. C. Detter 1,10 , J. Ali 11 , B.Birren 7 , D. C. Bruce 1, 10 , C. Buhay 9 , J. R. Cole 3,4 , Y. Ding 9 , S. Dugan 9 , D. Field 12 , G. M. Garrity 3,4 , R. Gibbs 9 , T. Graves 6 , C. S. Han 1, 10 , S. H. Harrison 3 , S. Highlander 9 , P. Hugenholtz 1 , H. M. Khouri 13 , C. D. Kodira 7,23 , E. Kolker 14,15 , N. C. Kyrpides 1 , D. Lang 1,2 , A. Lapidus 1 , S. A. Malfatti 1,2 , V. Markowitz 16 , T. Metha 7 , K. E. Nelson 8 , J. Parkhill 5 , S. Pitluck 1 , X. Qin 9 , T. D. Read 17 , J. Schmutz 18 , S. Sozhamannan 19 , R. Strausberg 8 , G. Sutton 8 , N. R. Thomson 5 , J. M. Tiedje 3,4 , G. Weinstock 6 , A. Wollam 6 , and the entire GSC 20 and HMP Jumpstart 21 consortia. U.S. Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California 94550, USA Microbiology & Molecular Genetics, Michigan State University, East Lansing, Michigan 48824, USA Center for Microbial Ecology, Michigan State University, East Lansing, Michigan 48824, USA The Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom The Genome Center, Washington University School of Medicine, St Louis, Missouri 63108, USA The Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02141, USA J. Craig Venter Institute, Rockville, Maryland 20850, USA Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada Natural Environmental Research Council Centre for Ecology and Hydrology, Oxford, Oxfordshire OX1 3SR, UK National Center for Biotechnology Information, National Library of Medicine, Rockville, Maryland 20850, USA Seattle Children’s Hospital and Research Institute, Seattle, Washington 98101, USA Biomedical & Health Informatics Division, MEBI, University of Washington School of Medicine, Seattle, Washington 98195, USA Biological Data Management and Technology Center, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA Emory GRA Genomics Core, Emory University School of Medicine, Atlanta, Georgia 30322, USA HudsonAlpha Genome Sequencing Center, HudsonAlpha Institute, Huntsville, Alabama 35806, USA Biological Defense Research Directorate, Naval Medical Research Center, Silver Spring, Maryland 20910, USA Genomic Standards Consortium Human Microbiome Project Jumpstart Consortium Current address: Bioscience Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA Current address: 454 Life Sciences, Branford, Connecticut 06405, USA *Address correspondence to Patrick Chain (pchain@lanl.gov) and Darren Grafham (dg1@sanger.ac.uk)

Genome Project Standards in a New Era of Sequencing

Complete knowledge of the genetic variation in individual human genomes is a crucial foundation for understanding the etiology of disease. Genetic variation is typically characterized by sequencing individual genomes and comparing reads to a reference. Existing methods do an excellent job of detecting variants in approximately 90% of the human genome; however, calling variants in the remaining 10% of the genome (largely low-complexity sequence and segmental duplications) is challenging. To improve variant calling, we developed a new algorithm, DISCOVAR, and examined its performance on improved, low-cost sequence data. Using a newly created reference set of variants from the finished sequence of 103 randomly chosen fosmids, we find that some standard variant call sets miss up to 25% of variants. We show that the combination of new methods and improved data increases sensitivity by several fold, with the greatest impact in challenging regions of the human genome.

/pdf/comprehensive-variation-discovery-in-single-human-genomes-1lgpfppj6n.pdf

Comprehensive variation discovery in single human genomes

A slave rotor---Hartree-Fock formalism is presented for studying the properties of the $p\mathrm{\text{\ensuremath{-}}}d$ model describing perovskite transition metal oxides, and a flexible and efficient numerical formalism is developed for its solution. The methodology is shown to yield, within a unified formulation, the significant aspects of the rare-earth nickelate phase diagram, including the paramagnetic metal state observed for the ${\mathrm{LaNiO}}_{3}$ and the correct ground-state magnetic order of insulating compounds. It is then used to elucidate ground state changes occurring as morphology is varied from bulk to strained and unstrained thin-film form. For ultrathin films, epitaxial strain and charge transfer to the apical out-of-plane oxygen sites are shown to have significant impact on the phase diagram.

Theory of the magnetic and metal-insulator transitions in RNiO3 bulk and layered structures.

Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.

/pdf/deep-convolutional-neural-networks-for-accurate-somatic-1w60abw20t.pdf

Deep convolutional neural networks for accurate somatic mutation detection.

We derive and investigate numerically a minimal yet detailed spin-polaron model that describes lightly doped CuO2 layers. The low-energy physics of a hole is studied by total-spin-resolved exact diagonalization on clusters of up to 32 CuO2 unit cells, revealing features missed by previous studies. In particular, spin-polaron states with total spin 3/2 are the lowest eigenstates in some regions of the Brillouin zone. In these regions, and also at other points, the quasiparticle weight is identically zero indicating orthogonal states to those represented in the one electron Green's function. This highlights the importance of the proper treatment of spin fluctuations in the many-body background.

High-Spin Polaron in Lightly Doped CuO 2 Planes

LongISLND is a software package designed to simulate sequencing data according to the characteristics of third generation, single-molecule sequencing technologies. The general software architecture is easily extendable, as demonstrated by the emulation of Pacific Biosciences (PacBio) multi-pass sequencing with P5 and P6 chemistries, producing data in FASTQ, H5, and the latest PacBio BAM format. We demonstrate its utility by downstream processing with consensus building and variant calling. Availability and implementation LongISLND is implemented in Java and available at http://bioinform.github.io/longislnd CONTACT: hugo.lam@roche.comSupplementary information: Supplementary data are available at Bioinformatics online.

Bayo Lau

Papers

Comprehensive variation discovery in single human genomes

Theory of the magnetic and metal-insulator transitions in RNiO3 bulk and layered structures.

Deep convolutional neural networks for accurate somatic mutation detection.

High-Spin Polaron in Lightly Doped CuO 2 Planes

LongISLND: In silico Sequencing of Lengthy and Noisy Datatypes.