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      A Large and Consistent Phylogenomic Dataset Supports Sponges as the Sister Group to All Other Animals.

      Current biology : CB

      Elsevier BV

      phylogeny, Eumetazoa, Porifera, animal, evolution, long branch attraction, metazoa, nervous system, phylogenomics, Ctenophora

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          Abstract

          Resolving the early diversification of animal lineages has proven difficult, even using genome-scale datasets. Several phylogenomic studies have supported the classical scenario in which sponges (Porifera) are the sister group to all other animals ("Porifera-sister" hypothesis), consistent with a single origin of the gut, nerve cells, and muscle cells in the stem lineage of eumetazoans (bilaterians + ctenophores + cnidarians). In contrast, several other studies have recovered an alternative topology in which ctenophores are the sister group to all other animals (including sponges). The "Ctenophora-sister" hypothesis implies that eumetazoan-specific traits, such as neurons and muscle cells, either evolved once along the metazoan stem lineage and were then lost in sponges and placozoans or evolved at least twice independently in Ctenophora and in Cnidaria + Bilateria. Here, we report on our reconstruction of deep metazoan relationships using a 1,719-gene dataset with dense taxonomic sampling of non-bilaterian animals that was assembled using a semi-automated procedure, designed to reduce known error sources. Our dataset outperforms previous metazoan gene superalignments in terms of data quality and quantity. Analyses with a best-fitting site-heterogeneous evolutionary model provide strong statistical support for placing sponges as the sister-group to all other metazoans, with ctenophores emerging as the second-earliest branching animal lineage. Only those methodological settings that exacerbated long-branch attraction artifacts yielded Ctenophora-sister. These results show that methodological issues must be carefully addressed to tackle difficult phylogenetic questions and pave the road to a better understanding of how fundamental features of animal body plans have emerged.

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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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            Search and clustering orders of magnitude faster than BLAST.

             Robert Edgar (2010)
            Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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              RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models.

              RAxML-VI-HPC (randomized axelerated maximum likelihood for high performance computing) is a sequential and parallel program for inference of large phylogenies with maximum likelihood (ML). Low-level technical optimizations, a modification of the search algorithm, and the use of the GTR+CAT approximation as replacement for GTR+Gamma yield a program that is between 2.7 and 52 times faster than the previous version of RAxML. A large-scale performance comparison with GARLI, PHYML, IQPNNI and MrBayes on real data containing 1000 up to 6722 taxa shows that RAxML requires at least 5.6 times less main memory and yields better trees in similar times than the best competing program (GARLI) on datasets up to 2500 taxa. On datasets > or =4000 taxa it also runs 2-3 times faster than GARLI. RAxML has been parallelized with MPI to conduct parallel multiple bootstraps and inferences on distinct starting trees. The program has been used to compute ML trees on two of the largest alignments to date containing 25,057 (1463 bp) and 2182 (51,089 bp) taxa, respectively. icwww.epfl.ch/~stamatak
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                Author and article information

                Journal
                10.1016/j.cub.2017.02.031
                28318975

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