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Sample GSM4072309

Query DataSets for GSM4072309

Status

Public on Jan 09, 2020

Title

2017 9am Replicate 5

Sample type

SRA

Source name

Environmental sample

Organism

halite metagenome

Characteristics

collection year: 2017
collection time: 9:00 AM

Treatment protocol

Halite nodules were harvested in Salar Grande, an ancient evaporated lake in the Northern part of the Atacama Desert in February 2016 and 2017. All nodules were harvested within a 50m2 area by scraping colonization zone. The colonization zone of each nodule was grounded into a powder, pooling from 1-3 nodules until sufficient material was collected, and stored in the dark in dry conditions until DNA extraction in the lab. Samples used for RNA were stored in RNAlater at 4°C until RNA extraction in the lab.

Extracted molecule

total RNA

Extraction protocol

Total RNA was extracted from the fixed samples by first isolating the cells through gradual dissolving of the salt particles and lysing them through mechanical bead beating with the RNAeasy PowerSoil RNA extraction kit (QIAGEN). Total RNA was then extracted from the lysate with a Quick-RNA miniprep kit (Zymo Research).
Total RNAseq libraries were prepared with the SMARTer Stranded RNA-seq kit (TaKaRa), using 25ng of RNA input and 12 cycles for library amplification. We sequenced 22 libraries from replicate samples from 2016 and 24 libraries from replicate samples from 2017.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina HiSeq 2000

Description

Metatranscriptomic total RNA-seq
2016vs2017_all_asRNAs_non-normalized_TPM_table.txt; 2016vs2017_all_itsRNAs_non-normalized_TPM_table.txt; all_small_ncRNAs.gff
sample name in processed data file (asRNA):
s2017-9am5
sample name in processed data file (antisense to asRNA):
g2017-9am5

Data processing

An analytic pipeline, Snap-T for Small ncRNA annotation pipeline for Transcriptomic or metatranscriptomic data, was developed to find, annotate, and quantify intergenic and anti-sense sRNA transcripts from transcriptomic or metatranscriptomic data.
Intergenic transcripts were at least 30 nt away from any gene or ORF on both strands; Antisense transcripts were 30 nt away from any gene on their strand, but overlapped with a gene on the opposite strand by at least 10 nt; small peptides (<100 nt) were not counted as genes if they were encoded in a transcript that was more than 3 times their length; non-coding transcripts could not contain any reading frame greater than 1/3 of their lengths; predicted non-coding transcripts near contig edges were discarded and the minimum distance to the edge of a contig was dynamically computed such that the tips of contigs were not statistically enriched in annotated ncRNAs; small ncRNAs were between 50 nt and 500 nt in length; sRNA transcripts could not have significant homology with any protein in the NCBI_nr database (query cover>30%, Bitscore>50, evalue<0.0001, and identity>30%) and with any tRNA or RNase P model in the Rfam non-coding RNA database.
The taxonomic origin of each annotated ncRNA was taken to be as that of the contig on which it lay. The taxonomy of each contig was estimated by taking the weighted average of the taxonomic assignment of the genes encoded on it, as determined through the JGI IMG functional and taxonomic annotation service.
Small RNA expression values were calculated using the program stringtie which were tabulated in transcripts per million (TPM)
We used a read count-based differential expression analysis to identify differentially expressed sRNA and mRNA transcripts. The program featureCounts was used to rapidly count reads that map to the assembled RNA transcripts. The read counts were used in the R differential expression software package DESeq2 to calculate differential expression by determining the difference in read counts between one condition normalized read counts from the other condition normalized read counts. The differentially expressed RNAs were filtered based on the statistical parameter of False Discovery Rate (FDR) and those that were equal to or under a FDR of 5% were classified as true differentially expressed transcripts. We carried out differential expression analysis between all conditions, using a likelihood ratio test, and between conditions, using a pairwise Wald test to find any possible differences.
Genome_build: halite metagenome [Taxonomy ID: 1319833]
Supplementary_files_format_and_content: gff, txt; Note: the s (eg *s*9b1) corresponds to the asRNA expression, while the g (eg *g*9b1) corresponds to the gene expression that is antisense to the asRNA. The expressions for both asRNA and gene were calculated from the same .fastq

Submission date

Sep 09, 2019

Last update date

Jan 09, 2020

Contact name

Diego Rivera Gelsinger

E-mail(s)

[email protected]

Phone

3233144077

Organization name

The Johns Hopkins Univeristy