README.md

In cma2015/PEA-m5C: An integrated R toolkit for plant m5C analysis.

PEAm5C: An R package for plant m5C analysis.

We developed PEA-m5C, an accurate transcriptome-wide m5C modification predictor under machine learning framework with random forest algorithm. PEA-m5C was trained with features from the flanking sequences of m5C modifications. In addition, we also deposited all the candidate m5C modification sites in the Ara-m5C database (http://bioinfo.nwafu.edu.cn/software/Ara-m5C.html) for follow-up functional mechanism researches. Finally, in order to maximize the usage of PEA-m5C, we implement it into a cross-platform, user-friendly and interactive interface and an R package named “PEA-m5C” based R statistical language and JAVA programming language, which may advance functional researches of m5C.

Version and download

• Version 0.11--R -First version released on January, 6th, 2018
• Version 0.1--JAVA -First version released on January, 6th, 2018

Depends

R environment

• R (>= 3.3.1)
• randomForst (>= 0.6)
• seqinr (>= 3.4-5)
• stringr (>= 1.2.0)
• pROC (>= 1.10.0)
• ggplot2 (>= 2.2.1)
• FSelector (>= 0.21)

Global software environment

• JAVA1.8 Environmentally dependent

Dependency installation

## Install rJAVA
sudo apt-get update
sudo apt-get install r-cran-rjava r-cran-rweka

## Install R Dependency
dependency.packages <- c("randomForest", "seqinr", "stringr", "FSelector", "bigmemory", "ggplot2", "PRROC", "pROC")
install.packages(dependency.packages)

Installation

install.packages("Download path/PEAm5C_0.11.tar.gz",repos = NULL, type = "source")

Contents

Predicting m5C sites

• Read FASTA file and motif scanning
• Feature encoding of sequences
• m5C prediction using Random Forest models

User-defined model

• Provide positive and negative sample information
• Automatic verification of the training process
• Prediction using user-defined models

Quick start

The basic data set can be finded in data. More details can be seen from user manual.

1.Predicting m5C sites

• 1.1 Read FASTA file and motif scanning

seq <- extra_motif_seq(input_seq_dir = paste0(system.file(package = "PEAm5c"),"/data/cdna.fa"),up = 5)
seq <- lapply(seq, c2s)

• 1.2 Feature encoding of sequences

seq_feature <- FeatureExtract(seq)

• 1.3 m5C prediction using Random Forest models

res <- predict_m5c(seq_feature)

2.User-defined model

• 2.1 Provide positive and negative sample information

load(paste0(system.file(package = "PEAm5c"),"/data/samples.Rds"))
### The positive and negative sequence can be read and identified by extra_motif_seq and  feature encoding by FeatureExtract 

• 2.2 Automatic verification of the training process

seq <- PEA_ml(pos_sample = pos_sample,neg_sample = neg_sample)
model <- extra_model(res = seq)
model

• 2.3 Prediction using user-defined models

res <- predict_self_model(models = model,sequence_dir = paste0(system.file(package = "PEAm5c"),"/data/cdna.fa"))
table(res[,4])

Citation

Song, J., Zhai, J., Bian, E., Song, Y., Yu, J., & Ma, C. (2018). Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning. Frontiers in plant science, 9, 519.

Ask questions

Please use PEAm5C/issues for how to use PEAm5C and reporting bugs.

cma2015/PEA-m5C documentation built on May 17, 2019, 8:05 a.m.