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      Different Sources of Allelic Variation Drove Repeated Color Pattern Divergence in Cichlid Fishes

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          Abstract

          The adaptive radiations of East African cichlid fish in the Great Lakes Victoria, Malawi, and Tanganyika are well known for their diversity and repeatedly evolved phenotypes. Convergent evolution of melanic horizontal stripes has been linked to a single locus harboring the gene agouti-related peptide 2 ( agrp2). However, where and when the causal variants underlying this trait evolved and how they drove phenotypic divergence remained unknown. To test the alternative hypotheses of standing genetic variation versus de novo mutations (independently originating in each radiation), we searched for shared signals of genomic divergence at the agrp2 locus. Although we discovered similar signatures of differentiation at the locus level, the haplotypes associated with stripe patterns are surprisingly different. In Lake Malawi, the highest associated alleles are located within and close to the 5′ untranslated region of agrp2 and likely evolved through recent de novo mutations. In the younger Lake Victoria radiation, stripes are associated with two intronic regions overlapping with a previously reported cis-regulatory interval. The origin of these segregating haplotypes predates the Lake Victoria radiation because they are also found in more basal riverine and Lake Kivu species. This suggests that both segregating haplotypes were present as standing genetic variation at the onset of the Lake Victoria adaptive radiation with its more than 500 species and drove phenotypic divergence within the species flock. Therefore, both new (Lake Malawi) and ancient (Lake Victoria) allelic variation at the same locus fueled rapid and convergent phenotypic evolution.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

              Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Mol Biol Evol
                Mol Biol Evol
                molbev
                Molecular Biology and Evolution
                Oxford University Press
                0737-4038
                1537-1719
                February 2021
                17 September 2020
                17 September 2020
                : 38
                : 2
                : 465-477
                Affiliations
                Department of Biology, University of Konstanz , Konstanz, Germany
                Author notes
                Author information
                http://orcid.org/0000-0003-4389-3075
                http://orcid.org/0000-0002-4805-5575
                http://orcid.org/0000-0002-0888-8193
                http://orcid.org/0000-0002-5646-3114
                Article
                msaa237
                10.1093/molbev/msaa237
                7826197
                32941629
                ccc6ee26-2a8e-4802-905a-0e564f781b56
                © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 13
                Funding
                Funded by: International Max Planck Research School for Organismal Biology;
                Funded by: Swiss National Science Foundation, DOI 10.13039/501100001711;
                Award ID: P300PA_177852
                Funded by: Baden-Württemberg Foundation, DOI 10.13039/100008316;
                Funded by: Deutsche Forschungsgemeinschaft, DOI 10.13039/501100001659;
                Award ID: KR 4670/2-1
                Award ID: KR 4670/4-1
                Funded by: European Research Council, DOI 10.13039/100010663;
                Award ID: 293700
                Categories
                Discoveries
                AcademicSubjects/SCI01130
                AcademicSubjects/SCI01180

                Molecular biology
                standing genetic variation,cichlid fishes,convergent evolution,color patterns,evolutionary genomics,adaptive radiations

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