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      CrusTome: a transcriptome database resource for large-scale analyses across Crustacea

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          Abstract

          Transcriptomes from nontraditional model organisms often harbor a wealth of unexplored data. Examining these data sets can lead to clarity and novel insights in traditional systems, as well as to discoveries across a multitude of fields. Despite significant advances in DNA sequencing technologies and in their adoption, access to genomic and transcriptomic resources for nontraditional model organisms remains limited. Crustaceans, for example, being among the most numerous, diverse, and widely distributed taxa on the planet, often serve as excellent systems to address ecological, evolutionary, and organismal questions. While they are ubiquitously present across environments, and of economic and food security importance, they remain severely underrepresented in publicly available sequence databases. Here, we present CrusTome, a multispecies, multitissue, transcriptome database of 201 assembled mRNA transcriptomes (189 crustaceans, 30 of which were previously unpublished, and 12 ecdysozoans for phylogenetic context) as an evolving and publicly available resource. This database is suitable for evolutionary, ecological, and functional studies that employ genomic/transcriptomic techniques and data sets. CrusTome is presented in BLAST and DIAMOND formats, providing robust data sets for sequence similarity searches, orthology assignments, phylogenetic inference, etc. and thus allowing for straightforward incorporation into existing custom pipelines for high-throughput analyses. In addition, to illustrate the use and potential of CrusTome, we conducted phylogenetic analyses elucidating the identity and evolution of the cryptochrome/photolyase family of proteins across crustaceans.

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            Basic local alignment search tool.

            A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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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: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                2160-1836
                July 2023
                02 May 2023
                02 May 2023
                : 13
                : 7
                : jkad098
                Affiliations
                Department of Biology, Colorado State University , Fort Collins, CO 80523, USA
                Department of Biology, Colorado State University , Fort Collins, CO 80523, USA
                Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution , Washington, DC 20560, USA
                Department of Invertebrate Zoology, National Museum of Natural History, Smithsonian Institution , Washington, DC 20560, USA
                Department of Biological Sciences and Institute of Environment, Florida International University , North Miami, FL 33181, USA
                Department of Biology, University of Oklahoma , Norman, OK 73019, USA
                Department of Biology, Colorado State University , Fort Collins, CO 80523, USA
                Author notes
                Corresponding author: Department of Biology, Colorado State University, Fort Collins, CO 80523, USA. Email: jorgepm@ 123456colostate.edu

                Conflicts of interest The author(s) declare no conflict of interest.

                Article
                jkad098
                10.1093/g3journal/jkad098
                10320764
                37130083
                deaeae80-d14b-4398-9cf7-f262fded76ac
                © The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 January 2023
                : 14 March 2023
                : 22 May 2023
                Page count
                Pages: 12
                Funding
                Funded by: National Science Foundation, DOI 10.13039/100000001;
                Award ID: IOS-1922701
                Award ID: IOS-1922755
                Award ID: #1701835
                Funded by: Division of Environmental Biology Bioluminescence and Vision;
                Award ID: DEB-1556059
                Funded by: Gulf of Mexico Research Initiative, DOI 10.13039/100007240;
                Funded by: Florida Institute of Oceanography Shiptime;
                Funded by: National Science Foundation Division of Environmental Biology;
                Award ID: 1556059
                Funded by: National Oceanic and Atmospheric Administration Ocean Exploration Research;
                Award ID: NOAA-OER 2015
                Categories
                Investigation
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140

                Genetics
                arthropoda,bioinformatics,blast,crustaceans,cryptochrome,phylogenetics,rna-seq
                Genetics
                arthropoda, bioinformatics, blast, crustaceans, cryptochrome, phylogenetics, rna-seq

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