Name Mode Size
buildCDS.R 100755 14 kb
data.R 100755 7 kb
eachfunctions.R 100755 3 kb
has_consistentSeqLevels.R 100755 3 kb
import.R 100644 2 kb
is_gtf.R 100755 0 kb
matchChromosomes.R 100755 2 kb
matchGeneInfo.R 100755 16 kb
predictDomains.R 100755 10 kb
predictNMD.R 100755 8 kb
subsettranscripts.R 100644 5 kb
trimTranscripts.R 100755 4 kb
viewTranscripts.R 100755 6 kb
# **factR v.1** ## Functional Annotation of Custom Transcriptomes in R <!-- badges: start --> [![R build status](]( [![Codecov test coverage](]( [![Codecov test coverage](]( <!-- badges: end --> ## General workflow <p align="center"> <img src="man/figures/factR_workflow.png" width="450"/> </p> *factR* is a robust and easy-to-use R package with tools to process custom-assembled transcriptomes (GTF). Below are *factR*'s key functions: * Core features 1. Construct transcript coding (CDS) information using a reference-guided process 2. Predict protein domains on coding transcripts 3. Predict sensitivity of coding transcripts to Nonsense-mediated decay * Supporting features 1. Match chromosome levels of query GTF/object to reference annotation 2. Match gene_id and gene_names of query GTF to reference annotation 3. Plot transcripts from GTF GRanges object using *wiggleplotr* 4. Subset new transcripts from custom transcriptome ## How to install The latest stable version can be installed directly from [Bioconductor](): ```r if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("factR") ``` Alternatively, you may install the development version of *factR* using devtools: ```r # install.packages("devtools") devtools::install_github("fursham-h/factR") ``` ## Getting started See our [quickstart guide]( or our [full vignette]( on how to get started ## Acknowledgements We thank [Kaur Alasoo]( for sharing code resources for *wiggleplotr* and for valuable discussions on the design of the package. ## Citing factR Please cite the following references if you use factR: 1. Fursham Hamid, Kaur Alasoo, Jaak Vilo, Eugene Makeyev (2022); Functional annotation of custom transcriptomes; Methods in Molecular Biology 2. [Fursham Hamid (2022); Functional Annotation of Custom Transcriptomes; Bioconductor]( )