Estimating repeat spectra and genome length from low-coverage genome skims with RESPECT

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...

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Bibliographic Details
Main Authors: Bafna, V. (Author), Balaban, M. (Author), Mirarab, S. (Author), Rachtman, E. (Author), Sarmashghi, S. (Author), Touri, B. (Author)
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
Online Access:View Fulltext in Publisher
LEADER 03926nam a2200721Ia 4500
001 10.1371-journal.pcbi.1009449
008 220427s2021 CNT 000 0 und d
020 |a 1553734X (ISSN) 
245 1 0 |a Estimating repeat spectra and genome length from low-coverage genome skims with RESPECT 
260 0 |b Public Library of Science  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1371/journal.pcbi.1009449 
520 3 |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. 
650 0 4 |a algorithm 
650 0 4 |a Algorithms 
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650 0 4 |a Animals 
650 0 4 |a article 
650 0 4 |a biological model 
650 0 4 |a biology 
650 0 4 |a classification 
650 0 4 |a Computational Biology 
650 0 4 |a computer simulation 
650 0 4 |a Computer Simulation 
650 0 4 |a Databases, Genetic 
650 0 4 |a genetic database 
650 0 4 |a genetics 
650 0 4 |a genome 
650 0 4 |a Genome 
650 0 4 |a genomics 
650 0 4 |a Genomics 
650 0 4 |a human 
650 0 4 |a Humans 
650 0 4 |a invertebrate 
650 0 4 |a Invertebrates 
650 0 4 |a least square analysis 
650 0 4 |a Least-Squares Analysis 
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650 0 4 |a Mammals 
650 0 4 |a Models, Genetic 
650 0 4 |a nucleotide repeat 
650 0 4 |a phylogeny 
650 0 4 |a Phylogeny 
650 0 4 |a plant 
650 0 4 |a Plants 
650 0 4 |a Repetitive Sequences, Nucleic Acid 
650 0 4 |a sequence analysis 
650 0 4 |a simulation 
650 0 4 |a software 
650 0 4 |a software 
650 0 4 |a Software 
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650 0 4 |a system analysis 
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650 0 4 |a vertebrate 
650 0 4 |a Vertebrates 
700 1 |a Bafna, V.  |e author 
700 1 |a Balaban, M.  |e author 
700 1 |a Mirarab, S.  |e author 
700 1 |a Rachtman, E.  |e author 
700 1 |a Sarmashghi, S.  |e author 
700 1 |a Touri, B.  |e author 
773 |t PLoS Computational Biology