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      Gene family innovation, conservation and loss on the animal stem lineage

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          Abstract

          Choanoflagellates, the closest living relatives of animals, can provide unique insights into the changes in gene content that preceded the origin of animals. However, only two choanoflagellate genomes are currently available, providing poor coverage of their diversity. We sequenced transcriptomes of 19 additional choanoflagellate species to produce a comprehensive reconstruction of the gains and losses that shaped the ancestral animal gene repertoire. We identified ~1944 gene families that originated on the animal stem lineage, of which only 39 are conserved across all animals in our study. In addition, ~372 gene families previously thought to be animal-specific, including Notch, Delta, and homologs of the animal Toll-like receptor genes, instead evolved prior to the animal-choanoflagellate divergence. Our findings contribute to an increasingly detailed portrait of the gene families that defined the biology of the Urmetazoan and that may underpin core features of extant animals.

          eLife digest

          All animals, from sea sponges and reef-building corals to elephants and humans, share a single common ancestor that lived over half a billion years ago. This single-celled predecessor evolved the ability to develop into a creature made up of many cells with specialized jobs. Reconstructing the steps in this evolutionary process has been difficult because the earliest animals were soft-bodied and microscopic and did not leave behind fossils that scientists can study.

          Though their bodies have since disintegrated, many of the instructions for building the first animals live on in genes that were passed on to life forms that still exist. Scientists are trying to retrace those genes back to the first animal by comparing the genomes of living animals with their closest relatives, the choanoflagellates. Choanoflagellates are single-celled, colony-forming organisms that live in waters around the world. Comparisons with choanoflagellates may help scientists identify which genes were necessary to help animals evolve and diversify into so many different species. So far, 1,000 animal and two choanoflagellate genomes have been sequenced. But the gene repertoires of most species of choanoflagellates have yet to be analyzed.

          Now, Richter et al. have cataloged the genes of 19 more species of choanoflagellates. This added information allowed them to recreate the likely gene set of the first animal and to identify genetic changes that occurred during animal evolution. The analyses showed that modern animals lost about a quarter of the genes present in their last common ancestor with choanoflagellates and gained an equal number of new genes. Richter et al. identified several dozen core animal genes that were gained and subsequently preserved throughout animal evolution. Many of these are necessary so that an embryo can develop properly, but the precise roles of some core genes remain a mystery. Most other genes that emerged in the first animals have been lost in at least one living animal.

          The study of Richter et al. also showed that some very important genes in animals, including genes essential for early development and genes that help the immune system detect pathogens, predate animals. These key genes trace back to animals’ last common ancestor with choanoflagellates and may have evolved new roles in animals.

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          Most cited references 186

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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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            Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

             S Altschul (1997)
            The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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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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                Author and article information

                Contributors
                Role: Reviewing Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                31 May 2018
                2018
                : 7
                Affiliations
                [1 ]deptDepartment of Molecular and Cell Biology Howard Hughes Medical Institute, University of California, Berkeley BerkeleyUnited States
                [2 ]Sorbonne Universités, UPMC Univ Paris 06, CNRS UMR 7144, Adaptation et Diversité en Milieu Marin, Équipe EPEP, Station Biologique de Roscoff RoscoffFrance
                [3 ]deptMedical Scientist Training Program, Biomedical Sciences Graduate Program University of California, San Francisco San FranciscoUnited States
                [4 ]Gladstone Institutes San FranciscoUnited States
                University College London United Kingdom
                University College London United Kingdom
                Article
                34226
                10.7554/eLife.34226
                6040629
                29848444
                © 2018, Richter et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                Product
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000011, Howard Hughes Medical Institute;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000005, U.S. Department of Defense;
                Award ID: National Defense Science and Engineering Graduate Fellowship
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: Central Europe Summer Research Institute Fellowship
                Award Recipient :
                Funded by: Chang-Lin Tien Fellowship in Environmental Sciences and Biodiversity;
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004584, Conseil Régional de Bretagne;
                Award ID: Postdoctoral Fellowship
                Award Recipient :
                Funded by: French Government "Investissements d'Avenir";
                Award ID: OCEANOMICS (ANR-11-BTBR-0008)
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000001, National Science Foundation;
                Award ID: EDEN IOS 0955517
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Evolutionary Biology
                Custom metadata
                The genomes of animal progenitors evolved as mosaics of old, new, rearranged, and repurposed protein domains, genes and pathways and paved the way for the origin and evolution of animals.

                Life sciences

                other, transcriptome, ancestral gene content, innate immunity, urmetazoan, choanoflagellates

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