@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 {23, title = {Genomic Patterns of De Novo Mutation in Simplex Autism.}, journal = {Cell}, volume = {171}, year = {2017}, month = {2017 Oct 19}, pages = {710-722.e12}, abstract = {

To further our understanding of the genetic etiology of autism, we generated and analyzed genome sequence data from 516 idiopathic autism families (2,064 individuals). This resource includes >59 million single-nucleotide variants (SNVs) and 9,212 private copy number variants (CNVs), of which 133,992 and 88 are de novo mutations (DNMs), respectively. We estimate a mutation rate of \~{}1.5~{\texttimes} 10 SNVs per site per generation with a significantly higher mutation rate in repetitive DNA. Comparing probands and unaffected siblings, we observe several DNM trends. Probands carry more gene-disruptive CNVs and SNVs, resulting in severe~missense mutations and mapping to predicted fetal brain promoters and embryonic stem cell enhancers. These differences become more pronounced for autism genes (p~= 1.8~{\texttimes} 10, OR~= 2.2). Patients are more likely to carry multiple coding and noncoding DNMs in different genes, which are enriched for expression in striatal neurons (p~= 3~{\texttimes} 10), suggesting a path forward for genetically characterizing more complex cases of autism.

}, keywords = {Animals, Autistic Disorder, DNA Copy Number Variations, DNA Mutational Analysis, Female, Genome-Wide Association Study, Humans, INDEL Mutation, Male, Mice, Polymorphism, Single Nucleotide}, issn = {1097-4172}, doi = {10.1016/j.cell.2017.08.047}, author = {Turner, Tychele N and Coe, Bradley P and Dickel, Diane E and Hoekzema, Kendra and Nelson, Bradley J and Zody, Michael C and Kronenberg, Zev N and Hormozdiari, Fereydoun and Raja, Archana and Pennacchio, Len A and Darnell, Robert B and Eichler, Evan E} }