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      Yolk proteins of the schistosomiasis vector snail Biomphalaria glabrata revealed by multi-omics analysis

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          Abstract

          Vitellogenesis is the most important process in animal reproduction, in which yolk proteins play a vital role. Among multiple yolk protein precursors, vitellogenin (Vtg) is a well-known major yolk protein (MYP) in most oviparous animals. However, the nature of MYP in the freshwater gastropod snail  Biomphalaria glabrata remains elusive. In the current study, we applied bioinformatics, tissue-specific transcriptomics, ovotestis-targeted proteomics, and phylogenetics to investigate the large lipid transfer protein (LLTP) superfamily and ferritin-like family in  B. glabrata. Four members of LLTP superfamily (BgVtg1, BgVtg2, BgApo1, and BgApo2), one yolk ferritin (Bg yolk ferritin), and four soma ferritins (Bg ferritin 1, 2, 3, and 4) were identified in  B. glabrata genome. The proteomic analysis demonstrated that, among the putative yolk proteins, BgVtg1 was the yolk protein appearing in the highest amount in the ovotestis, followed by Bg yolk ferritin. RNAseq profile showed that the leading synthesis sites of BgVtg1 and Bg yolk ferritin are in the ovotestis (presumably follicle cells) and digestive gland, respectively. Phylogenetic analysis indicated that BgVtg1 is well clustered with Vtgs of other vertebrates and invertebrates. We conclude that, vitellogenin (BgVtg1), not yolk ferritin (Bg yolk ferritin), is the major yolk protein precursor in the schistosomiasis vector snail  B. glabrata.

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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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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

              Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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                Author and article information

                Contributors
                zhangsm@unm.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 January 2024
                20 January 2024
                2024
                : 14
                : 1820
                Affiliations
                [1 ]GRID grid.266832.b, ISNI 0000 0001 2188 8502, Department of Biology, Center for Evolutionary and Theoretical Immunology, , University of New Mexico, ; Albuquerque, NM 87131 USA
                [2 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Program in Public Health, College of Health Science, , University of California, ; Irvine, CA 92697 USA
                Article
                52392
                10.1038/s41598-024-52392-x
                10799875
                38245605
                2e75168e-2d51-415d-98cd-f0884f40ca80
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 May 2023
                : 18 January 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R21 AI153469
                Award Recipient :
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                © Springer Nature Limited 2024

                Uncategorized
                developmental biology,evolution,molecular biology,zoology
                Uncategorized
                developmental biology, evolution, molecular biology, zoology

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