- The aquatic animals ’ transcriptome resource for comparative functional analysis. - Background: Aquatic animals have great economic and ecological importance. - Due to recent advances in next-generation sequencing techniques, large amounts of RNA-seq data for aquatic animals are publicly available.. - This study utilizes computational approaches to build a new resource of transcriptomic maps for aquatic animals. - This aquatic animal transcriptome map database dbATM provides de novo assembly of transcriptome, gene annotation and comparative analysis of more than twenty aquatic organisms without draft genome.. - Aquatic animals have significant economic benefits for humans and also play key roles in the development of medical applications [1, 2]. - In most of the aquaculture industries, prophylactic antibiotics have been used to prevent bacterial and virus infections [4].. - To effectively increase the our knowledge on aquatic animal gene discovery at mRNA level, RNA- seq has been applied to several important aquaculture species such as, Plecoglossus altivelis [5], Cyprinus carpio [6], Penaeus monodon [7], Hyriopsis cumingii [8], Mytilus galloprovincialis [9], and Anguilla anguilla [10].. - To gain insights into the immunogenetics and immune re- sponse system, RNA-seq has been successfully adapting to. - Full list of author information is available at the end of the article. - 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0. - A better strategy is using RNA-seq to elucidate the molecu- lar basis of biological functions [21]. - RNA-seq technology pave the way for transcriptome data investigation. - With the techniques, transcripts can not only be detected in accordance with the reference genome but also can be used for de novo the assembly of RNA-seq reads to discover genes and profile their expression in or- ganisms without a reference genome [22 – 24]. - However, even if these data are deposited in the NCBI Sequence Read Archive (SRA) [25] which provides high-throughput unassembled and un-annotated raw NGS reads for more than 1000 Terabase pairs, public access is not provided to allow for searches of the well-annotated data. - Aquatic animals have high economic value and also play important roles in the monitoring of environmental. - Aquatic animals ’ related studies have seen exponentially increased in recent years (Additional file 1: Figure S1). - Many studies have used NGS approaches to investigate genomic diversity among aquatic animals at genomic and transcriptomic levels.. - In this study, we aim to establish an integrated aquatic animal transcriptomic database to facilitate studies in the fields of genomics, evolution, and phylogeny. - We initially performed whole organism RNA-seq on four aquatic an- imals and collected RNA-seq data for eighteen species from the public domain. - Reference genome free RNA- seq data of aquatic animals download from the three common NGS reads database, NCBI Sequence Read Archive (SRA), European Nucleotide Archive (ENA), and DDBJ Sequence Read Archive (DRA). - Finally, all data were summarized and provided in the dbATM database. - Figure 1 illustrates how the data- base can be used for mining transcriptomics, functions such Gene Ontology and KEGG pathways and comparative analysis of aquatic animals. - The dbATM collects transcrip- tomes for twenty-two aquatic animals’ transcriptomes and provides an invaluable tool for homologous evolution and evo-devo studies.. - Additional file 1: Table S1 displays all aquatic animal RNA-seq data sources and original data types. - Table 1 shows the species annotation of the transcripts, unigenes, proteins, and homologous genes.. - a The database collects RNA-seq data for more than 20 different aquatic animals. - d Evolutionary studies of all species transcriptomic data by constructing a comparative analysis system for homologous genes. - First, publicly available RNA-seq data were collected and combined with additional data produced for this study. - dbATM also allows users to browse homologous genes in homologous groups (Fig. - Investigation of aquatic animals ’ homologous genes Most current transcriptomic studies focus on single species, and these “standalone” transcriptomic databases do not allow for the exploration of homologous gene iden- tities and their relative expression levels. - To overcome this constraint, we provide a novel function to comparatively analyze potential homologous genes among 22 aquatic. - In total, 19,369 homologous genes were identified by OrthoMCL [31] and their gene name. - Using homologous search function in dbATM, we can discover that several homologous genes (like yars, cars, nop14, acsbg2, trip12, rab3gap2, and herc2) are distributed from invertebrates (mollusca and arthropoda) to vertebrates (ostariophysi, euteleostei, actinopterygii, and chordate).. - We also found there 21 homologous genes shared across at least 17 species: yars, cars, nop14, acsbg2, trip12, rab3gap2, herc2, ddost, vwa8, cdk11b, ascc3, aplp2, loc563777, nup98, nup188, sdad1, nup205, dlat, acadm, rtn4ip1, kansl3. - The number of homologous genes in each clade were shown in Table 2. - Infor- mation on homologous genes across different animal phyla will provide good material for future evo-devo studies.. - of homologous genes. - a The total number of homologous gene groups show here is the real homologous gene groups in the database. - For example, the condition of the three species Sinocyclocheilus angustiporus, Sinocyclocheilus anophthalmus, and Tetraodon nigroviridis are from the brain tissue, and show similar expression profile trends (Fig. - The ex- pression profiles panel of the four conditions (Fig. - We present a new systematic approach for de novo RNA- seq dataset analysis and annotation including improvement of de novo assembly quality and construct a new resource for the transcriptomic map aquatic animals for evolution. - 2 and further detail is provided in the Materials and Methods section. - First, we optimized the de novo assembly pipeline of RNA-seq data [32] by combining Oases, SOAPdenovo-Trans, and Trinity assemblers. - prediction tools for aquatic animals. - dbATM not only improves de novo assembly quality of RNA-seq. - data but also constructs a database of the homologous genes of aquatic animals to allow for comparative study.. - a Summary table of the reads, assembly quality, and annotation statistics. - Table 2 Homologous genes statistics in each clade. - The newly constructed homologous genes database and comparative analysis system also give aquatic researchers insights into the fields of eco-toxicity, animal physiology, comparative genomics and phylogen- etic. - dbATM also serves as an important repository for the aquatic animal transcriptomes by analyzing RNA-seq data.. - Given recent advances in NGS tech- nologies, more non-model aquatic animal RNA-seq exper- iments will be deposited in the public domain.. - First, RNA-seq data for 18 species of aquatic animals that were not well-annotated were collected from public resources such as SRA, ENA and DRA (the data from Illumina paired-end NGS sequencing platform were collected), with an additional four datasets produced for this study (Fig. - Second, the quality of the raw reads from NGS was validated to trim and filter low quality reads. - All of the annotation results were soundly provided in the dbATM database.. - 5 Gene expression profiles panel. - RNA-seq dataset collection. - We collected eighteen RNA-seq datasets from the NCBI Sequence Read Archive (SRA), European Nucleotide Archive (ENA), and DDBJ Sequence Read Archive (DRA). - RNA-seq data deposited to these datasets were generated from Illumina paired-end NGS sequencing platform. - All RNA-seq data collected in dbATM were reference genome-free (including scaffolds or contig sta- tus) in the NCBI taxonomy database. - We also performed RNA-seq on four additional aquatic species. - Additional file 1: Table S1 present detailed information for the 22 aquatic animal species profiled in our dbATM database.. - The NGS raw data for the RNA-seq in SRA format were converted to FASTQ format using the SRA-Toolkit v2.2.2 [35] and the FASTQ format reads were cleaned to increase read quality by FASTX-Toolkit v0.0.13 (http://hannonlab.cshl.edu/fastx_toolkit. - De novo assembly. - To ensure more complete assembly, we used three de novo assembly tools: Oases (v0.2.08, requiring Velvet v SOAPdenovo-Trans (release and Trinity (release that based on the de Bruijn graphs. - The insertion length of paired-end RNA-seq data was estimated using the observed-insert-length.pl program (included in the Oases package). - The relative abundances of transcripts were measured in unit of normalized reads count aligned on de novo assembly, FPKM (Fragments Per Kilobase of transcript per Million mapped reads) [45]. - After de novo assembly and expression calculation, we select FPKM >. - The results for the BLASTX annotation genes were mapped to the Gene Ontology database. - Comparative analysis. - count aligned to each KEGG pathway category, and the line is the average FPKM of the genes mapped to this pathway. - In the KEGG pathway category, we excluded Human Diseases, Drug Development, and Global Maps from the metabolic category. - RNA-seq datasets from twenty-two aquatic animals were analyzed in dbATM. - Statistics of homologous genes in each clade and their gene lists. - RNA-seq: RNA sequencing. - This work is particularly supported by “ Aiming for the Top University Program ” of the National Chiao Tung University and Ministry of Education, Taiwan [MOHW106-TDU-B and MMH-HB-10602]. - Others accession codes of RNA-seq data download from public domain database were listed in Additional file 1: Table S1. - The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/. - Sequencing of the first ayu (Plecoglossus Altivelis) macrophage transcriptome and microarray development for investigation the effect of LECT2 on macrophages.. - Characterization of common carp transcriptome: sequencing, de novo assembly, annotation and comparative genomics. - In silico characterization of the insect diapause-associated protein couch potato (CPO) in Calanus Finmarchicus (Crustacea: Copepoda).. - Comparative analysis of the Transcriptome in tissues secreting purple and white nacre in the pearl mussel Hyriopsis cumingii. - Development of microsatellite markers for the Korean mussel, Mytilus Coruscus (Mytilidae) using next-generation sequencing.. - Elucidation of the molecular envenomation strategy of the cone snail Conus geographus through transcriptome sequencing of its venom duct. - RNA-Seq reveals complex genetic response to deepwater horizon oil release in Fundulus Grandis. - characterization of the Poecilia Mexicana transcriptome. - Evolution of the eye transcriptome under constant darkness in Sinocyclocheilus cavefish. - RNA-Seq: a revolutionary tool for transcriptomics. - Mapping and quantifying mammalian transcriptomes by RNA-Seq. - Full-length transcriptome assembly from RNA-Seq data without a reference genome. - Database resources of the National Center for biotechnology information. - Gene ontology: tool for the unification of biology. - Optimizing de novo assembly of short-read RNA-seq data for phylogenomics. - Optimization of de novo short read assembly of seabuckthorn (Hippophae Rhamnoides L.) transcriptome. - Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels.. - Velvet: algorithms for de novo short read assembly using de Bruijn graphs. - SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. - Optimization of de novo transcriptome assembly from next-generation sequencing data. - RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. - Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation
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