|
| Status |
Public on Sep 01, 2020 |
| Title |
Bear302DH |
| Sample type |
SRA |
| |
|
| Source name |
their summer active counter part
|
| Organism |
Ursus americanus |
| Characteristics |
condition: before hybernation
tissue: plasma sample
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Total RNA (including small RNAs) isolated by miRNeasy Serum/Plasma kit (Qiagen, USA) on QIAcube Instrument (Qiagen, USA)
QIAseq miRNA Library Kit (IonTorrent version, Qiagen, USA) used for library preperation. Manufacturer's protocol followed. Then libraries sequenced at Proton System (ThermoFisher Scientific, USA).
Small RNA library preperation made
|
| |
|
| Library strategy |
miRNA-Seq |
| Library source |
transcriptomic |
| Library selection |
size fractionation |
| Instrument model |
Ion Torrent Proton |
| |
|
| Description |
Bear343DH
|
| Data processing |
The reads were first evaluated for their overall quality using FastQC (REF: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and ION Torrent read qualities converted to Illumina phred scores. The reads were then filtered for average base quality scores below 15. UMIs are removed from sequences and appended to the read name to group and remove PCR duplicates. 3’ adapter is left on the sequence to remove later in the pipeline. The PCR duplicates are then removed using UMITools 0.5.4 (PMID:28100584)
Quantify miRNAs that shows high similarity to human miRNAs for mRNA target prediction. To quantify known human miRNAs we used Kraken v13-274 (REF: PMID:23816787). First Reaper in Kraken is used to strip low quality bases and low complexity sequence and trim 3’ adapter. Then the sequences were aligned using bowtie with --time -v 2 --best -k 21 --strata -m 20 parameters to all known smallRNA sequences. All smallRNA features were quantified using annotation set v12-164 in kraken. A consolidated table were generated for miRNA quantifications for differential miRNA analysis.
Differential miRNA analysis:
We used generated tables from two strategies above to discover differentially expressed miRNAs using DEBrowser v1.9.16 (REF: https://doi.org/10.1101/399931).
For differential gene expression we use DESeq2 package (PMID: 25516281) in DEBrowser. The goal of differential miRNA expression analysis is to detect miRNAs whose difference in expression, when accounting for the variance within condition, is higher than expected by chance. DESeq2 then computes the probability whether a miRNA is differentially expressed (DE) or not. DESeq2 calculates both a nominal and a multiple hypothesis corrected p-value (padj). To find significant DE miRNAs, the ones has lower padj values and higher fold changes are selected to perform downstream analysis. Here we use 0.01< for padj value and > 2 fold change cutoffs to detect differentially expressed miRNAs.
Supplementary_files_format_and_content: bear_miRNA_hsa.tsv: include miRNA quantifications in tab separated format.
Supplementary_files_format_and_content: all_detected.csv: Differential miRNA analysis results in comma separated format.
|
| |
|
| Submission date |
Dec 26, 2018 |
| Last update date |
Sep 01, 2020 |
| Contact name |
Alper Kucukural |
| E-mail(s) |
alper.kucukural@umassmed.edu
|
| Phone |
7743124493
|
| Organization name |
UMass Medical School
|
| Department |
Program in Molecular Medicine
|
| Lab |
Biocore
|
| Street address |
364 Plantation Street
|
| City |
Worcester |
| State/province |
MA |
| ZIP/Postal code |
01605 |
| Country |
USA |
| |
|
| Platform ID |
GPL25987 |
| Series (1) |
| GSE124398 |
MiRNA regulation of auto-anticoagulation in hibernating black bears: A Novel Translational Approach |
|
| Relations |
| BioSample |
SAMN10651097 |
| SRA |
SRX5181808 |
