@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 {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} } @article {7, title = {Practical Approaches for Whole-Genome Sequence Analysis of Heart- and Blood-Related Traits.}, journal = {Am J Hum Genet}, volume = {100}, year = {2017}, month = {2017 Feb 02}, pages = {205-215}, abstract = {

Whole-genome sequencing (WGS) allows for a comprehensive view of the sequence of the human genome. We present and apply integrated methodologic steps for interrogating WGS data to characterize the genetic architecture of 10 heart- and blood-related traits in a sample of 1,860 African Americans. In order to evaluate the contribution of regulatory and non-protein coding regions of the genome, we conducted aggregate tests of rare variation across the entire genomic landscape using a sliding window, complemented by an annotation-based assessment of the genome using predefined regulatory elements and within the first intron of all genes. These tests were performed treating all variants equally as well as with individual variants weighted by a measure of predicted functional consequence. Significant findings were assessed in 1,705 individuals of European ancestry. After these steps, we identified and replicated components of the genomic landscape significantly associated with heart- and blood-related traits. For two traits, lipoprotein(a) levels and neutrophil count, aggregate tests of low-frequency and rare variation were significantly associated across multiple motifs. For a third trait, cardiac troponin T, investigation of regulatory domains identified a locus on chromosome 9. These practical approaches for WGS analysis led to the identification of informative genomic regions and also showed that defined non-coding regions, such as first introns of genes and regulatory domains, are associated with important risk factor phenotypes. This study illustrates the tractable nature of WGS data and outlines an approach for characterizing the genetic architecture of complex traits.

}, keywords = {Black or African American, C-Reactive Protein, Cholesterol, HDL, Cholesterol, LDL, Chromosomes, Human, Pair 9, Gene Frequency, Genome, Human, Genome-Wide Association Study, Genomics, Hemoglobins, Humans, Introns, Leukocyte Count, Lipoprotein(a), Magnesium, Natriuretic Peptide, Brain, Neutrophils, Peptide Fragments, Phosphorus, Platelet Count, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Troponin T, White People}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.12.009}, author = {Morrison, Alanna C and Huang, Zhuoyi and Yu, Bing and Metcalf, Ginger and Liu, Xiaoming and Ballantyne, Christie and Coresh, Josef and Yu, Fuli and Muzny, Donna and Feofanova, Elena and Rustagi, Navin and Gibbs, Richard and Boerwinkle, Eric} } @article {20, title = {SVScore: an impact prediction tool for structural variation.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 Apr 01}, pages = {1083-1085}, abstract = {

SUMMARY: Here we present SVScore, a tool for in silico structural variation (SV) impact prediction. SVScore aggregates per-base single nucleotide polymorphism (SNP) pathogenicity scores across relevant genomic intervals for each SV in a manner that considers variant type, gene features and positional uncertainty. We show that the allele frequency spectrum of high-scoring SVs is strongly skewed toward lower frequencies, suggesting that they are under purifying selection, and that SVScore identifies deleterious variants more effectively than alternative methods. Notably, our results also suggest that duplications are under surprisingly strong selection relative to deletions, and that there are a similar number of strongly pathogenic SVs and SNPs in the human population.

AVAILABILITY AND IMPLEMENTATION: SVScore is implemented in Perl and available freely at {{ http://www.github.com/lganel/SVScore }} for use under the MIT license.

CONTACT: ihall@wustl.edu.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, keywords = {Gene Frequency, Genomic Structural Variation, Genomics, Humans, Polymorphism, Single Nucleotide, Sequence Deletion, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw789}, author = {Ganel, Liron and Abel, Haley J and Hall, Ira M} }