@article {147, title = {Towards population-scale long-read sequencing.}, journal = {Nat Rev Genet}, volume = {22}, year = {2021}, month = {2021 09}, pages = {572-587}, abstract = {

Long-read sequencing technologies have now reached a level of accuracy and yield that allows their application to variant detection at a scale of tens to thousands of samples. Concomitant with the development of new computational tools, the first population-scale studies involving long-read sequencing have emerged over the past 2 years and, given the continuous advancement of the field, many more are likely to follow. In this Review, we survey recent developments in population-scale long-read sequencing, highlight potential challenges of a scaled-up approach and provide guidance regarding experimental design. We provide an overview of current long-read sequencing platforms, variant calling methodologies and approaches for de novo assemblies and reference-based mapping approaches. Furthermore, we summarize strategies for variant validation, genotyping and predicting functional impact and emphasize challenges remaining in achieving long-read sequencing at a population scale.

}, keywords = {Computational Biology, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing, Humans, Industrial Development, Sequence Analysis, DNA}, issn = {1471-0064}, doi = {10.1038/s41576-021-00367-3}, author = {De Coster, Wouter and Weissensteiner, Matthias H and Sedlazeck, Fritz J} } @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} } @article {37, title = {Accurate detection of complex structural variations using single-molecule sequencing.}, journal = {Nat Methods}, volume = {15}, year = {2018}, month = {2018 Jun}, pages = {461-468}, abstract = {

Structural variations are the greatest source of genetic variation, but they remain poorly understood because of technological limitations. Single-molecule long-read sequencing has the potential to dramatically advance the field, although high error rates are a challenge with existing methods. Addressing this need, we introduce open-source methods for long-read alignment (NGMLR; https://github.com/philres/ngmlr ) and structural variant identification (Sniffles; https://github.com/fritzsedlazeck/Sniffles ) that provide unprecedented sensitivity and precision for variant detection, even in repeat-rich regions and for complex nested events that can have substantial effects on human health. In several long-read datasets, including healthy and cancerous human genomes, we discovered thousands of novel variants and categorized systematic errors in short-read approaches. NGMLR and Sniffles can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.

}, keywords = {DNA Mutational Analysis, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA}, issn = {1548-7105}, doi = {10.1038/s41592-018-0001-7}, author = {Sedlazeck, Fritz J and Rescheneder, Philipp and Smolka, Moritz and Fang, Han and Nattestad, Maria and von Haeseler, Arndt and Schatz, Michael C} }