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