@article {133,
	title = {RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.},
	journal = {PLoS Genet},
	volume = {17},
	year = {2021},
	month = {2021 01},
	pages = {e1009315},
	abstract = {<p>Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.</p>},
	keywords = {Biological Specimen Banks, Genome, Human, Genome-Wide Association Study, Genotyping Techniques, Haplotypes, Humans, Models, Genetic, Pedigree, Polymorphism, Single Nucleotide},
	issn = {1553-7404},
	doi = {10.1371/journal.pgen.1009315},
	author = {Naseri, Ardalan and Shi, Junjie and Lin, Xihong and Zhang, Shaojie and Zhi, Degui}
}
@article {51,
	title = {A multi-task convolutional deep neural network for variant calling in single molecule sequencing.},
	journal = {Nat Commun},
	volume = {10},
	year = {2019},
	month = {2019 Mar 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}
}
