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      The critically endangered vaquita is not doomed to extinction by inbreeding depression

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          Abstract

          In cases of severe wildlife population decline, a key question is whether recovery efforts will be impeded by genetic factors, such as inbreeding depression. Decades of excess mortality from gillnet fishing have driven Mexico’s vaquita porpoise ( Phocoena sinus ) to ~10 remaining individuals. We analyzed whole-genome sequences from 20 vaquitas and integrated genomic and demographic information into stochastic, individual-based simulations to quantify the species’ recovery potential. Our analysis suggests that the vaquita’s historical rarity has resulted in a low burden of segregating deleterious variation, reducing the risk of inbreeding depression. Similarly, genome-informed simulations suggest that the vaquita can recover if bycatch mortality is immediately halted. This study provides hope for vaquitas and other naturally rare endangered species and highlights the utility of genomics in predicting extinction risk.

          Population size and risk of extinction

          The vaquita porpoise is one of the most endangered animals in the world, with only an estimated 10 individuals remaining. To determine the risk of extinction caused by inbreeding depression, Robinson et al . sequenced and examined 20 vaquita genomes to determine their heterozygosity and ancestral population size (see the Perspective by Grueber and Sunnucks). The authors determined that the long-term population size of vaquitas has been low for a marine mammal, with approximately 1000 years of stable genomic diversity. Genomic comparisons with other cetacean species and modeling indicated that vaquitas are unlikely to suffer from inbreeding depression. Therefore, if the risk of bycatch mortality caused by fishermen can be eliminated, then there is a chance that this species will not go extinct. —LMZ

          Abstract

          Genomic and population viability analyses suggest that the vaquita porpoise is not doomed to extinction.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            BLAST+: architecture and applications

            Background Sequence similarity searching is a very important bioinformatics task. While Basic Local Alignment Search Tool (BLAST) outperforms exact methods through its use of heuristics, the speed of the current BLAST software is suboptimal for very long queries or database sequences. There are also some shortcomings in the user-interface of the current command-line applications. Results We describe features and improvements of rewritten BLAST software and introduce new command-line applications. Long query sequences are broken into chunks for processing, in some cases leading to dramatically shorter run times. For long database sequences, it is possible to retrieve only the relevant parts of the sequence, reducing CPU time and memory usage for searches of short queries against databases of contigs or chromosomes. The program can now retrieve masking information for database sequences from the BLAST databases. A new modular software library can now access subject sequence data from arbitrary data sources. We introduce several new features, including strategy files that allow a user to save and reuse their favorite set of options. The strategy files can be uploaded to and downloaded from the NCBI BLAST web site. Conclusion The new BLAST command-line applications, compared to the current BLAST tools, demonstrate substantial speed improvements for long queries as well as chromosome length database sequences. We have also improved the user interface of the command-line applications.
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              ModelFinder: Fast Model Selection for Accurate Phylogenetic Estimates

              Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates. The improvement is achieved by incorporating a model of rate-heterogeneity across sites not previously considered in this context, and by allowing concurrent searches of model-space and tree-space.
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                Author and article information

                Contributors
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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                May 06 2022
                May 06 2022
                : 376
                : 6593
                : 635-639
                Affiliations
                [1 ]Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
                [2 ]Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA.
                [3 ]Advanced Genomics Unit, National Laboratory of Genomics for Biodiversity (Langebio), Center for Research and Advanced Studies (Cinvestav), Irapuato, Guanajuato, Mexico.
                [4 ]Department of Genome Sciences, University of Washington, Seattle, WA, USA.
                [5 ]Comisión Nacional de Áreas Naturales Protegidas/SEMARNAT, Ensenada, Mexico.
                [6 ]PNUD-Sinergia en la Comisión Nacional de Áreas Naturales Protegidas, Ensenada, Mexico.
                [7 ]Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration (NOAA), La Jolla, CA, USA.
                [8 ]MIVEGEC, Université de Montpellier, CNRS, IRD, Montpellier, France.
                [9 ]Centre de Recherche en Écologie et Évolution de la Santé (CREES), Montpellier, France.
                [10 ]Groningen Institute for Evolutionary Life Sciences (GELIFES), University of Groningen, Groningen, Netherlands.
                [11 ]Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
                Article
                10.1126/science.abm1742
                35511971
                ebb7d748-e7c1-49ef-9db1-c961ec90d4a5
                © 2022
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