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Sample GSM3733863

Query DataSets for GSM3733863

Status

Public on Nov 30, 2022

Title

Vs1 (miRNA-seq)

Sample type

SRA

Source name

Vitis vinifera

Organisms

Vitis vinifera; Schisandra chinensis

Characteristics

type: graft hybrids of Schisandra chinensis + Vitis vinifera
time: 25 years
tissue: LEAF, PHLOEM, BERRY

Treatment protocol

no special treatment

Growth protocol

Both grafted types of plants had grown in the same area for 25 years

Extracted molecule

total RNA

Extraction protocol

In harvest season, the samples were selected that quick-frozen in liquid nitrogen, and stored at below 80°C until use. Total RNA and small RNA were extracted using Polysaccharide Polyphenol Plant MicroRNA Extraction Kit (LabHelper, Beijing, China) following the manufacturer's procedure.
RNA libraries were prepared for sequencing using standard Illumina protocols

Library strategy

miRNA-Seq

Library source

transcriptomic

Library selection

size fractionation

Instrument model

Illumina HiSeq 2500

Description

All_Expressed_miRNA

Data processing

A cDNA library constructed by technology from the pooled RNA from all samples of grape was sequenced run with Illumina 4000 sequence platform.
Prior to assembly, the low quality reads(1,reads containing sequencing adaptors; 2,reads containing sequencing primer;3, nucleotide with q quality score lower than 20) were removed .
we aligned reads of sample A and sample B to the UCSC (http://genome.ucsc.edu/) Vitis vinifera reference genome using HISAT package, which initially remove a portion of the reads based on quality information accompanying each read and then maps the reads to the reference genome.
HISAT allows multiple alignments pe read (up to 20 by default) and a maximum of two mismatchs when mapping the reads to the reference.
HISAT build a database of potential splice junctions and confirms these by comparing the previously unmapped reads against the database of putative junctions.
The mapped reads of each sample were assembled using StringTie.
Then, all transcriptomes from Samples were merged to reconstruct a comprehensive transcriptome using perl scripts.
The differentially expressed mRNAs and genes were selected with log2 (fold change) >1 or log2 (fold change) <-1 and with statistical significance (p value < 0.05) by R package – Ballgown.
The miRNA-seq raw reads were subjected to an in-house program, ACGT101-miR (LC Sciences, Houston, Texas, USA) to remove adapter dimers, junk, low complexity, common RNA families (rRNA, tRNA, snRNA, snoRNA) and repeats.
Subsequently, unique sequences with length in 18~25 nucleotide were mapped to specific species precursors in miRBase 21.0 by BLAST search to identify known miRNAs and novel 3p- and 5p- derived miRNAs.
Length variation at both 3’ and 5’ ends and one mismatch inside of the sequence were allowed in the alignment.
The unique sequences mapping to specific species mature miRNAs in hairpin arms were identified as known miRNAs.
The unique sequences mapping to the other arm of known specific species precursor hairpin opposite to the annotated mature miRNA-containing arm were considered to be novel 5p- or 3pderived miRNA candidates.
The remaining sequences were mapped to other selected species precursors (with the exclusion of specific species) in miRBase 21.0 by BLAST search, and the mapped pre-miRNAs were further BLASTed against the specific species genomes to determine their genomic locations.
To predict the genes targeted by most aboundant miRNAs, computational target prediction algorithms (TargetFinder) were used to identify miRNA binding sites. The GO terms and KEGG Pathway of most aboundant miRNA targets were also annotated
Raw sequencing reads were obtained using Illumina's software to remove adaptors and low quality reads. The extracted sequencing reads were then used to identify potentially cleaved targets by the CleaveLand pipeline .
The degradome reads were mapped to the mRNA downloaded from www （ JGI , NCBI or other databases,or mapped to the species transcripts from transcriptome sequencing） . Only the perfect matching alignment(s) for the given read would be kept for degradation analysis .
The degradation group sequence can match different numbers of reads on different sites on the same mRNA, and the predicted sequences at these sites may haven't corresponding miRNAs. The degradation group sequence can match different numbers of reads on different sites on the same mRNA, and the predicted sequences at these sites may haven't corresponding miRNAs. Detailed results with specific alignment information form a long_outfile file with AlignmentScore (Targetfinder penalty), AlignmentRange (pairing site), CleaveageSite (cutting site), Category (segmentation),P-value (P value), Raw_reads, Rep_Norm_reads (matches that match multiple sites for site normalization). Without alignment information, only AlignmentScore (Targetfinder score), AlignmentRange (pairing site), CleaveageSite (cutting site), Category (type), P-value (P value), Raw_reads, Rep_Norm_reads are listed. The (multi-site normalized reads) information is a simple list of miRNA and target gene relationships for the "Degradome_result.xlsx" file.
All resulting reads (t-signature) were reverse-complemented and aligned to the miRNA identified in our study.
All the identified targets were subjected to BlastX analysis to search for similarity, and then to GO analysis to uncover the miRNA-gene regulatory network on the basis of biological process ,cellular component and molecular function .
Genome_build: ftp://ftp.ensemblgenomes.org/pub/release-37/plants/gtf/vitis_vinifera v37
Supplementary_files_format_and_content: mRNA include FPKM values for each Sample ;miRNA expression use the norm values for each Sample.

Submission date

Apr 23, 2019

Last update date

Nov 30, 2022

Contact name

Gao-Pu Zhu

E-mail(s)

poog502@hotmail.com

Organization name

Chinese Academy of Forestry

Department

Non-timber Forest Research and Development Center