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      Comparative Transcriptome Analysis of Toxic and Non-Toxic Nassarius Communities and Identification of Genes Involved in TTX-Adaptation

      research-article
      Toxins
      MDPI
      TTX, Nassarius, comparative transcriptome analysis, health, genes

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          Abstract

          Nassarius has caused serious people poisoning and death incident as a popular food due to tetrodotoxin (TTX) accumulation in their body. Understanding the genetic basis of tetrodotoxin (TTX) transformation and resistance in animals could lead to significant insights into adaptive evolution to toxins and toxin poisoning cures in medicine. Here we performed comparative transcriptome analysis for toxic and non-toxic communities in Nassarius succinctus and Nassarius variciferus to reveal their genetic expression and mutation patterns. For both species, the cellular and metabolic process, and binding and catalytic activity accounted for the top classification categories, and the toxic communities generally produced more up-regulated genes than non-toxic communities. Most unigenes and different expression genes were related to disease, e.g., heat shock protein and tissue factor pathway inhibitors, which involve detoxification and coagulation. In mutation levels, the sodium channel gene of N. succinctus had one amino acid mutation “L”, which is different from that of other animals. In conclusion, the comparative transcriptome analysis of different species and populations provided an important genetic basis for adaptive evolution to toxins, health and toxin poisoning cure research for TTX in marine gastropoda mollusk. Future studies will focus on the action mechanism of the important functional gene for TTX accumulation and resistance.

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          Most cited references43

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          The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

          Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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            Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data

            Massively-parallel cDNA sequencing has opened the way to deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here, we present the Trinity methodology for de novo full-length transcriptome reconstruction, and evaluate it on samples from fission yeast, mouse, and whitefly – an insect whose genome has not yet been sequenced. Trinity fully reconstructs a large fraction of the transcripts present in the data, also reporting alternative splice isoforms and transcripts from recently duplicated genes. In all cases, Trinity performs better than other available de novo transcriptome assembly programs, and its sensitivity is comparable to methods relying on genome alignments. Our approach provides a unified and general solution for transcriptome reconstruction in any sample, especially in the complete absence of a reference genome.
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              De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis.

              De novo assembly of RNA-seq data enables researchers to study transcriptomes without the need for a genome sequence; this approach can be usefully applied, for instance, in research on 'non-model organisms' of ecological and evolutionary importance, cancer samples or the microbiome. In this protocol we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-seq data in non-model organisms. We also present Trinity-supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples and approaches to identify protein-coding genes. In the procedure, we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sourceforge.net. The run time of this protocol is highly dependent on the size and complexity of data to be analyzed. The example data set analyzed in the procedure detailed herein can be processed in less than 5 h.
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                Author and article information

                Journal
                Toxins (Basel)
                Toxins (Basel)
                toxins
                Toxins
                MDPI
                2072-6651
                02 December 2020
                December 2020
                : 12
                : 12
                : 761
                Affiliations
                College of Resources and Environmental Science, Nanjing Agricultural University, Nanjing 210095, China; zousm912@ 123456njau.edu.cn
                Article
                toxins-12-00761
                10.3390/toxins12120761
                7761612
                33276679
                b85fd269-9a6d-491c-97bf-7188ad6ef0fe
                © 2020 by the author.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 November 2020
                : 01 December 2020
                Categories
                Article

                Molecular medicine
                ttx,nassarius,comparative transcriptome analysis,health,genes
                Molecular medicine
                ttx, nassarius, comparative transcriptome analysis, health, genes

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