@article {124, title = {Chromosome-scale, haplotype-resolved assembly of human genomes.}, journal = {Nat Biotechnol}, volume = {39}, year = {2021}, month = {2021 03}, pages = {309-312}, abstract = {

Haplotype-resolved or phased genome assembly provides a complete picture of genomes and their complex genetic variations. However, current algorithms for phased assembly either do not generate chromosome-scale phasing or require pedigree information, which limits their application. We present a method named diploid assembly (DipAsm) that uses long, accurate reads and long-range conformation data for single individuals to generate a chromosome-scale phased assembly within 1 day. Applied to four public human genomes, PGP1, HG002, NA12878 and HG00733, DipAsm produced haplotype-resolved assemblies with minimum contig length needed to cover 50\% of the known genome (NG50) up to 25 Mb and phased ~99.5\% of heterozygous sites at 98-99\% accuracy, outperforming other approaches in terms of both contiguity and phasing completeness. We demonstrate the importance of chromosome-scale phased assemblies for the discovery of structural variants (SVs), including thousands of new transposon insertions, and of highly polymorphic and medically important regions such as the human leukocyte antigen (HLA) and killer cell immunoglobulin-like receptor (KIR) regions. DipAsm will facilitate high-quality precision medicine and studies of individual haplotype variation and population diversity.

}, keywords = {Algorithms, Chromosomes, Human, Genome, Human, Haplotypes, Heterozygote, Humans, Polymorphism, Single Nucleotide}, issn = {1546-1696}, doi = {10.1038/s41587-020-0711-0}, author = {Garg, Shilpa and Fungtammasan, Arkarachai and Carroll, Andrew and Chou, Mike and Schmitt, Anthony and Zhou, Xiang and Mac, Stephen and Peluso, Paul and Hatas, Emily and Ghurye, Jay and Maguire, Jared and Mahmoud, Medhat and Cheng, Haoyu and Heller, David and Zook, Justin M and Moemke, Tobias and Marschall, Tobias and Sedlazeck, Fritz J and Aach, John and Chin, Chen-Shan and Church, George M and Li, Heng} } @article {110, title = {Discovery and population genomics of structural variation in a songbird genus.}, journal = {Nat Commun}, volume = {11}, year = {2020}, month = {2020 07 07}, pages = {3403}, abstract = {

Structural variation~(SV) constitutes an important type of genetic mutations providing the raw material for evolution. Here, we uncover the genome-wide spectrum of intra- and interspecific SV segregating in natural populations of seven songbird species in the genus Corvus. Combining short-read (N = 127) and long-read re-sequencing (N = 31), as well as optical mapping (N = 16), we apply both assembly- and read mapping approaches to detect SV and characterize a total of 220,452 insertions, deletions and inversions. We exploit sampling across wide phylogenetic timescales to validate SV genotypes and assess the contribution of SV to evolutionary processes in an avian model of incipient speciation. We reveal an evolutionary young (~530,000 years) cis-acting 2.25-kb LTR retrotransposon insertion reducing expression of the NDP gene with consequences for premating isolation. Our results attest to the wealth and evolutionary significance of SV segregating in natural populations and highlight the need for reliable SV genotyping.

}, keywords = {Animals, Chromosome Inversion, Gene Deletion, Genetic Variation, Genetics, Population, Genome, Genomic Structural Variation, Genotype, Phylogeny, Polymorphism, Single Nucleotide, Retroelements, Sequence Analysis, DNA, Songbirds}, issn = {2041-1723}, doi = {10.1038/s41467-020-17195-4}, author = {Weissensteiner, Matthias H and Bunikis, Ignas and Catal{\'a}n, Ana and Francoijs, Kees-Jan and Knief, Ulrich and Heim, Wieland and Peona, Valentina and Pophaly, Saurabh D and Sedlazeck, Fritz J and Suh, Alexander and Warmuth, Vera M and Wolf, Jochen B W} } @article {52, title = {A multi-task convolutional deep neural network for variant calling in single molecule sequencing.}, journal = {Nat Commun}, volume = {10}, year = {2019}, month = {2019 03 01}, pages = {998}, abstract = {

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15\%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53\% F1-score on 1KP common variants, and 98.65, 92.57, 87.26\% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2 h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source ( https://github.com/aquaskyline/Clairvoyante ), with modules to train, utilize and visualize the model.

}, keywords = {Base Sequence, Computational Biology, DNA Mutational Analysis, Genome, Human, Genome-Wide Association Study, Genomics, Genotype, Genotyping Techniques, Humans, INDEL Mutation, Nanopores, Neural Networks (Computer), Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Software}, issn = {2041-1723}, doi = {10.1038/s41467-019-09025-z}, author = {Luo, Ruibang and Sedlazeck, Fritz J and Lam, Tak-Wah and Schatz, Michael C} }