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10.1371-journal.pcbi.1009449 |
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220427s2021 CNT 000 0 und d |
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|a 1553734X (ISSN)
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|a Estimating repeat spectra and genome length from low-coverage genome skims with RESPECT
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|b Public Library of Science
|c 2021
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|z View Fulltext in Publisher
|u https://doi.org/10.1371/journal.pcbi.1009449
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|a The cost of sequencing the genome is dropping at a much faster rate compared to assembling and finishing the genome. The use of lightly sampled genomes (genome-skims) could be transformative for genomic ecology, and results using k-mers have shown the advantage of this approach in identification and phylogenetic placement of eukaryotic species. Here, we revisit the basic question of estimating genomic parameters such as genome length, coverage, and repeat structure, focusing specifically on estimating the k-mer repeat spectrum. We show using a mix of theoretical and empirical analysis that there are fundamental limitations to estimating the k-mer spectra due to ill-conditioned systems, and that has implications for other genomic parameters. We get around this problem using a novel constrained optimization approach (Spline Linear Programming), where the constraints are learned empirically. On reads simulated at 1X coverage from 66 genomes, our method, REPeat SPECTra Estimation (RESPECT), had < 1.5% error in length estimation compared to 34% error previously achieved. In shotgun sequenced read samples with contaminants, RESPECT length estimates had median error 4%, in contrast to other methods that had median error 80%. Together, the results suggest that low-pass genomic sequencing can yield reliable estimates of the length and repeat content of the genome. The RESPECT software will be publicly available at https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_shahab-2Dsarmashghi_RESPECT.git&d=DwIGAw&c=-35OiAkTchMrZOngvJPOeA&r=ZozViWvD1E8PorCkfwYKYQMVKFoEcqLFm4Tg49XnPcA&m=f-xS8GMHKckknkc7Xpp8FJYw_ltUwz5frOw1a5pJ81EpdTOK8xhbYmrN4ZxniM96&s=717o8hLR1JmHFpRPSWG6xdUQTikyUjicjkipjFsKG4w&e=. © 2021 Sarmashghi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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|a algorithm
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|a Algorithms
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|a animal
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|a Animals
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|a article
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|a biological model
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|a biology
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|a classification
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|a Computational Biology
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|a computer simulation
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|a Computer Simulation
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|a Databases, Genetic
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|a genetic database
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|a genetics
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|a genome
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|a Genome
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|a genomics
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|a Genomics
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|a human
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|a Humans
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|a invertebrate
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|a Invertebrates
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|a least square analysis
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|a Least-Squares Analysis
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|a Linear Models
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|a mammal
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|a Mammals
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|a Models, Genetic
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|a nucleotide repeat
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|a phylogeny
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|a Phylogeny
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|a plant
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|a Plants
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|a Repetitive Sequences, Nucleic Acid
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|a sequence analysis
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|a simulation
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|a software
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|a software
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|a Software
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|a statistical model
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|a system analysis
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|a theoretical study
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|a vertebrate
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|a Vertebrates
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|a Bafna, V.
|e author
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|a Balaban, M.
|e author
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|a Mirarab, S.
|e author
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|a Rachtman, E.
|e author
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|a Sarmashghi, S.
|e author
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|a Touri, B.
|e author
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|t PLoS Computational Biology
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