cma2015 Goto Github PK

cafu

CAFU: A Galaxy framework for exploring unmapped RNA-Seq data

camoco

Camoco is a fully-fledged software package for building co-expression networks and analyzing the overlap interactions among genes.

chemical-regulation-atmta

deepea

deepEA: a containerized web server for interactive analysis of epitranscriptome sequencing data

deepgs

DeepGS is a R package for predicting phenotypes from genotypes using deep learning techniques.

deeptfbs

deepTFBS: Improving within- and cross-species prediction of transcription factor binding using deep multi-task and transfer learning

deepts

deepTS explores transcriptional switch events from pairwise, temporal and population transcriptome data.

dem

Dual-extraction method for phenotypic prediction and functional gene mining of complex traits

easymf

Matrix factorization-based biological discovery from large-scale transcriptome data using easyMF

g2p

G2P is an integrated genomic selection (GS) package for predicting phenotypes from genotypes. It includes 15 GS algorithms and 13 evaluation measurements.

iwa-mirna

iwa-miRNA is a web-based platform for interactive annotation of plant miRNAs

m6a_evolution

A bioinformatics pipeline for the evolution analysis of m6A in plants

mirlocator

machine learning-based mature miRNAs within pre-miRNA sequences

mldna

Machine learning-based differential network analysis

pea

PEA: an integrated R toolkit for epitranscriptome analysis

pea-m5c

A machine learning-based m5C predictor

pea-m6a

PEA-m6A: an ensemble learning-based framework for predicting m6A modifications in plants

rap

RAP is a rank aggregation-based data fusion approach for gene prioritization in plants. It can be used to perform the gene prioritization in Arabidopsis thaliana and 28 non-plant species. The input of RAP are a set of genes of interest and the network-based gene prioritization results from AraNet v2 system, while the output are the ranks of candidate genes. RAP has been successfully applied to prioritize flowering-time genes in Arabidopsis. The genome-wide experimental results indicate that RAP can be used as a complement to the network-based gene prioritization algorithm (e.g., AraNet v2) for accurately prioritizing candidate genes underlying biological processes or agricultural traits.

rsgcc

rsgcc

sparkandmpifactorizations

implementations of CX, PCA, and NMF factorizations in Spark and MPI

wolfberry_m6a