Oncorhynchus mykiss
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA298491 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR2640598_1.fastq.gz | Fastqsanger.gz | |||
| SRR2640598_2.fastq.gz | Fastqsanger.gz | |||
| SRR2651090_1.fastq.gz | Fastqsanger.gz | |||
| SRR2651090_2.fastq.gz | Fastqsanger.gz | |||
| SRR2650312_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 20 |
Similar Datasets
Project description:Purpose:Our data significantly advance understanding of heat stress regulatory mechanism of miRNA in the head kidney of rainbow trout Methods:miRNAs of rainbow trout were involved in heat stress were identified by high-throughput sequencing of six small RNA libraries of the kidney tissues under control (18℃) and heat-treated (24℃) conditions Results:high-throughput sequencing was performed to identify miRNAs responsive to heat stress. We obtained 41,991,119 and 43,882,123 raw reads and 39,756,736 and 42,538,331 clean reads from under control (18℃) and heat-treated (24℃) .A total of 392 conserved miRNAs and 989 novel miRNAs were identified, of which 78 miRNAs were expressed in different response to heat stress. In addition to, including 393 negative correlation miRNA-target gene pairs Conclusions:through high-throughput sequencing of the six libraries from head kidney tissue of rainbow trout, the expression level of miRNA has significant changes after heat stress.
2018-03-16 | GSE111816 | GEO
Project description:Intestinal microbiota of rainbow trout Raw sequence reads
| PRJNA828277 | ENA
Project description:16S raw sequence reads derived from rainbow trout
| PRJNA554500 | ENA
Project description:The aim of this sequencing experiment was to make available tissue expression panels for selected fish species for comparative expression studies between the species. Tissue samples were collected for zebrafish (Danio rerio), medaka (Oryzias latipes), and rainbow trout (Oncorhynchus mykiss). Tissue types included liver, skin, muscle, heart, gut, gill, eye, brain for all three species, with additionally pyloric caeca, kidney, head kidney, and spleen for rainbow trout. Only liver samples were taken in replicate of four or three for rainbow trout. All fish were raised under standard rearing conditions for the species. Total RNA was extracted from the tissue samples and paired‐end sequencing of sample libraries was completed on an Illumina HiSeq 2500 with 125‐bp reads. Processed count tables per species as raw counts, FPKM, or TPM, were generated from read alignment to the Ensembl genomes of the respective species using STAR and gene level counting using RSEM and Ensembl gene annotation.
2020-04-30 | E-MTAB-8959 | biostudies-arrayexpress
Project description:Transcriptional profiling of rainbow trout liver cells comparing liver cells from small fish with liver cells from large fish at two time periods.
2012-11-29 | GSE42583 | GEO
Project description:Transcriptional profiling of rainbow trout muscle cells comparing muscle cells from small fish with muscle cells from large fish at two time periods.
2012-11-29 | GSE42582 | GEO
Project description:Purpose: Next-generation sequencing (NGS) of small RNAs allows transcriptome level analysis of the miRNAome. The goals of this study are to identify differentially regulated hepatic miRNAs in response to a dominant or subordinate social status using small RNA next generation sequencing. Specific quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods were used to validate optimal high-throughput data analysis, and in silico prediction to identify bona fide target pathways regulated by differentially regulated miRNAs following these social interactions. Method: Small RNA sequencing was performed using four randomly selected samples from each treatment group were used for sequencing. The quality of total RNA was confirmed by RNA Integrity Numbers greater than 9.0 using an Agilent Technologies 2100 Bioanalyzer. Nucleotide fractions (15-50) of small RNA were isolated from the total RNA using polyacrylamide gel electrophoresis and were ligated to a 30 adapter followed by a 50 adapter (Illumina, San Diego, CA, USA). The small RNA ligated to the adaptors was reverse transcribed to cDNA, PCR amplified and gel purified. The gel-extracted cDNA was used for library preparation, which was further used for cluster generation on Illumina's Cluster Station before sequencing using Illumina GAIIx. Raw sequence data was obtained from image data using Illumina's Sequencing Control Studio software version 2.8 (SCS v2.8) following real-time sequencing image analysis and base-calling by Illumina's Real-Time Analysis version 1.8.70 (RTA v1.8.70). The ACGT101-miR v4.2 pipeline (LC Sciences) was used to analyze sequenced data. Sequences with low Q scores, reads mapped to mRNA, RFam, Repbase and piRNA database were deleted and unique families were generated from identical sequences. These filtered unique sequences were then mapped to fish pre-miRNA and miRNA using miRBase or to the rainbow trout genome (Berthelot et al., 2014) to identify conserved miRNA following ACGT-101 User's Manual. Unique or novel miRNAs were identified after the BLAST performed against fish miRNAs from miRbase database (release 21) and published miRNAs from rainbow trout (Mennigen et al., 2016). The miRNAs that do not match any sequences in the specified databases and have the propensity of forming hairpin structure with the extended sequences at the mapped positions were classified as unique miRNAs. These unique miRNAs were further classified into different groups depending on the mappable reads to selected miRNAs in miRbase. Differentially expressed miRNAs were identified using the statistical software R (Version 3.2.2) package DESeq2 for one-way ANOVA comparison of all three treatment groups. Real-time RT–PCR validation was performed using SYBR Green assays Results: 24 miRNAs are differentially regulated by social status. 9 miRNAs are higher expressed in dominant trout. 15 miRNAs are higher expressed in subordinate trout. Conclusions: Our study represents the first detailed analysis of the rainbow trout miRNAome in response to nutritional stimuli. Specifically, we here identify common hepatic miRNAs differentially regulated by refeeding following a short-term fast (2d), as well as miRNAs differentially regulated in the postprandial state depending on the dieatary stimulus (no carbohydrates compared to >20% dietary carbohydrates)
2019-06-24 | GSE112815 | GEO
Project description:Rainbow trout is a typical cold-water fish, with the intensification of global warming, high temperatures severely restrict the development of aquaculture in summer. Understanding the molecular regulation mechanisms of rainbow trout in response to heat stress will be salutary to alleviate heat stress-related damage. In the present study, we performed transcriptome analysis of liver tissues in rainbow trout under heat stress (24℃) and control (18℃) conditions to identify induced lncRNAs and pathways by heat stress. More than 658 million clean reads and 5,916 lncRNAs were identified from six liver libraries. A total of 927 novel lncRNAs were generated and 428 differentially expressed lncRNAs were screened through stringent thresholds. The RNA-seq results were verified by RT-qPCR. In addition, the regulatory network of important functional lncRNA-mRNA were constructed. GO and KEGG enrichment analysis of target gene of differentially expressed lncRNAs were performed. Many target genes involved in maintaining homeostasis or adapting to stress and stimuli were highly induced under heat stress. Several important regulatory pathways were involved in heat stress, including thyroid hormone signaling pathway, PI3K-Akt signaling pathway, estrogen signaling pathway, etc. This result broadens our understanding of lncRNA associated with heat stress and provides new insights into lncRNA-mediated regulation of rainbow trout heat stress.
2019-08-11 | GSE135668 | GEO
Project description:Purpose: a transcriptomic analysis was performed to extend our understanding on the immune response picture of rainbow trout exposed to I. multifiliis. Methods: Gill samples were collected from fish in each tank (control and infected group) at day 8 after infection. Total RNA was extracted using RTN350 (Sigma-Aldrich), according to the manufacturer’s instruction and subsequently, DNase treated with DNase I (Thermo Scientific, USA). Quality and integrity of the total RNA were checked on an Agilent Bioanalyzer 2100 total RNA Nano series II chip (Agilent, Amstelveen, Netherlands). Illumina RNAseq libraries were prepared from 500 ng total RNA using the Illumina TruSeqTM Stranded mRNA LT Sample Prep Kit according to the manufacturer’s instructions (Illumina Inc. San Diego, CA, USA). All RNAseq libraries (150-750 bp inserts) were sequenced on an Illumina HiSeq2500 sequencer as 1 x 50 nucleotides single-end reads according to the manufacturer’s protocol. Image analysis and base calling were done using the Illumina pipeline. Reads were aligned to the Rainbow trout reference genome (http://www.genoscope.cns.fr/trout/data/) using TopHat (version 2.0.5) and on average 53.4% of the RNAseq reads could be mapped. The resulting files were filtered using SAMtools (version 0.1.18) to exclude secondary alignment of reads. For statistical comparison of gene expression levels between groups, aligned fragments per predicted gene were counted from SAM alignment files using the Python package HTSeq (version 0.5.3p9). To make comparisons across samples possible, these fragment counts were corrected for the total amount of sequencing performed for each sample. As a correction scalling factor, we employed library size estimates determined using the R/Bioconductor (release 2.11) package DESeq. Read counts were normalized by dividing the raw counts obtained from HTSeq by its scale factor. Correction for false positives is included in the statistical analysis of gene expression through DESeq. The cut-off for significance was set to adjusted p<0.05 and at least 2-fold change. Results: a transcriptomic analysis was performed on infected rainbow trout gills and it showed that a total of 3,352 (7.2%) out of 46,585 identified genes were revealed significantly expressed after parasite infection. Of differentially expressed genes, 1.796 genes were up-regulated and 1.556 genes down-regulated. These were classified into 61 Gene Ontology (GO) terms and mapped to 282 reference canonical pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Infection of I. multifiliis induced a clear differential expression of immune genes, related to both innate and adaptive immunity. A total of 268 (6.86%) regulated genes was known to take part in 16 immune-related pathways. These involved pathways related to the innate immune system such as Chemokine signaling pathway, Platelet activation, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and Leukocyte transendothelial migration. Conclusion: a transcriptomic profile of rainbow trout gills exposed to the parasitic I. multifiliis was reported for the first time. A total of 3,355 differentially expressed unigenes were identified. Of these were 1,184 unigenes (mapped to 952 genes) annotated 282 KEGG pathways and 268 unigenes were associated with 16 immune pathways. Most unigenes were related to innate immune system pathways (Chemokine signaling pathway, Platelet activation, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and Leukocyte transendothelial migration) although a number of unigenes was related to adaptive responses (antigen processing and presentation, T and B cell receptor signaling pathway). The present study gave a far better resolution of the immune response of rainbow trout exposed to a parasitic protozoan than has ever been presented previously. The identification of a series of immune genes involved in several but important was useful for understanding of immune mechanism of the rainbow trout responding to the parasite I. multifiliis. Our results provide tools to link innate and adaptive immune elements in the process and present basic information which will be useful in the future studies related to immunoprophylaxis.
2018-06-13 | GSE115675 | GEO