miRDP2 Files

Accurately and fast analyzing microRNAs transcriptome in plants

Status: Beta

Brought to you by: dongfy88

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	Size	InfoDownloads / Week
Parent folder
test_data.tar.gz	2021-09-18	59.8 MB	0
ncRNA_rfam.tar.gz	2019-08-28	50.9 MB	0
Totals: 2 Items		110.7 MB	0

Other Useful Business Software

Grafana: The open and composable observability platform

Faster answers, predictable costs, and no lock-in built by the team helping to make observability accessible to anyone.

Grafana is the open source analytics & monitoring solution for every database.

Learn More

Level Up Your Cyber Defense with External Threat Management

See every risk before it hits. From exposed data to dark web chatter. All in one unified view.

Move beyond alerts. Gain full visibility, context, and control over your external attack surface to stay ahead of every threat.

Try for Free

Recommended Projects

SRICATs
MicroRNAs (miRNAs) are of significance in tuning and buffering gene expression. Despite abundant analysis tools have been developed in the last two decades, plant miRNA identification from next-generation sequencing (NGS) data remains challenging. Here present a user-friendly pure Java-based software package, SRICATs, which enable researchers to perform all steps of plant miRNA analysis based on convolutional neural network methods. SRICATs outperforms currently popular software tools on the test data from five plant species: Oryza sativa, Arabidopsis thaliana, Sorghum bicolor, Chlamydomonas reinhardtii and Physcomitrella patens.
miRComb
This R package combines biological information (expression data) with theoretical information (miRNA target prediction databases) in order to obtain a curated list of potential miRNA-mRNA interaction pairs.
mirplant
miRPlant: An Integrated Tool for Identification of Plant miRNA
sRNAWorkbench
The UEA sRNA Workbench
OmicSelector
Feature selection and deep learning modeling for omic biomarker study