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      Evolution in the dark: Unexpected genetic diversity and morphological stasis in the blind, aquifer-dwelling catfish Horaglanis

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      Vertebrate Zoology
      Pensoft Publishers

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          Abstract

          The lateritic aquifers of the southern Indian state of Kerala harbour a unique assemblage of enigmatic stygobitic fishes which are encountered very rarely, only when they surface during the digging and cleaning of homestead wells. Here, we focus on one of the most unusual members of this group, the catfish Horaglanis, a genus of rarely-collected, tiny, blind, pigment less, and strictly aquifer-residing species. A six-year exploratory and citizen-science backed survey supported by molecular phylogenetic analysis reveals novel insights into the diversity, distribution and population structure of Horaglanis. The genus is characterized by high levels of intraspecific and interspecific genetic divergence, with phylogenetically distinct species recovered above a 7.0% genetic-distance threshold in the mitochondrial cytochrome oxidase subunit 1 gene. Contrasting with this deep genetic divergence, however, is a remarkable stasis in external morphology. We identify and describe a new cryptic species, Horaglanis populi, a lineage that is the sister group of all currently known species. All four species are represented by multiple haplotypes. Mismatch distribution reveals that populations have not experienced recent expansions.

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          MUSCLE: multiple sequence alignment with high accuracy and high throughput.

          We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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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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              IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

              Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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                Author and article information

                Contributors
                Journal
                Vertebrate Zoology
                VZ
                Pensoft Publishers
                2625-8498
                1864-5755
                January 25 2023
                January 25 2023
                : 73
                : 57-74
                Article
                10.3897/vz.73.e98367
                0cb16a5f-0ee5-489f-913f-bbe1441d4f9f
                © 2023

                http://creativecommons.org/licenses/by/4.0/

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