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Sample GSM4443355

Query DataSets for GSM4443355

Status

Public on Dec 31, 2020

Title

s4_HPAR1

Sample type

SRA

Source name

Root

Organism

Oryza sativa Japonica Group

Characteristics

strain: Japanese rice
tissue: root
age: 21-day-old seedlings grown in 200 μM Pi
genotype: spx4 T-DNA

Extracted molecule

total RNA

Extraction protocol

Total RNA was isolated from the shoots and roots of 7-day-old seedlings treated with 200 μM or 10 μM Pi for 2 weeks using a Total RNA Purification Kit (Machery-Nagel, Germany). The roots and shoots from three seedlings were pooled to represent a single biological replicate and three biological replicates were used per genotype.
The degree of RNA degradation and contamination was monitored on 1% agarose gels and RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). A total amount of 3 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Sequencing libraries were generated using the NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s protocols.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Data processing

Quality control: Raw data of fastq format were firstly processed through in-house perl scripts. In this step, clean data were obtained by removing reads containing adapter, reads containing ploy-N and low quality reads from raw data. At the same time, Q20, Q30 and GC content the clean data were calculated. All the downstream analyses were based on the clean data with high quality.
Reads mapping to the reference genome: Reference genome and gene model annotation files were downloaded from genome website directly. Index of the reference genome was built using Hisat2 and paired-end clean reads were aligned to the reference genome using Hisat2. We selected Hisat2 as the mapping tool for that Hisat2 can generate a database of splice junctions based on the gene model annotation file and thus a better mapping result than other non-splice mapping tools.
Novel transcripts prediction: The mapped reads of each sample were assembled by StringTie in a reference-based approach. StringTie uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate fulllength transcripts representing multiple splice variants for each gene locus.
Quantification of gene expression level: FeatureCounts v1.5.0-p3 was used to count the reads numbers mapped to each gene. And then FPKM of each gene was calculated based on the length of the gene and reads count mapped to this gene. FPKM, expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced, considers the effect of sequencing depth and gene length for the reads count at the same time, and is currently the most commonly used method for estimating gene expression levels.
Differential expression analysis: Differential expression analysis of two conditions/groups (two biological replicates per condition) was performed using the DESeq2 R package. DESeq2 provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative bionmial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg's approach for controlling the false discovery rate. Genes with an adjusted P-value <0.05 found by DESeq2 were assigned as differentially expressed.
Genome_build: RGAP
Supplementary_files_format_and_content: Excel file includes FPKM for each Sample …

Submission date

Mar 29, 2020

Last update date

Jan 23, 2021

Contact name

He Qiu ju

E-mail(s)

[email protected]

Organization name

Zhejiang University

Street address

Yuhangtang road