RiboCode 1.2.2

A package for identifying the translated ORFs using ribosome-profiling data

RiboCode is a very simple but high-quality computational algorithm to identify genome-wide translated ORFs using ribosome-profiling data.

Dependencies:

• pysam

• pyfasta

• h5py

• Biopython

• Numpy

• Scipy

• matplotlib

• setuptools

Installation

RiboCode can be installed like any other Python packages. Here are some popular ways:

• Install from PyPI:

pip install RiboCode

• Install from local:

pip install RiboCode-*.tar.gz

If you have not administrator permission, you need to install RiboCode locally in you own directory by adding the option --user to installation commands. Then, you need to add ~/.local/bin/ to the PATH variable, and ~/.local/lib/ to the PYTHONPATH variable. For example, if you are using the bash shell, you would do this by adding the following lines to your ~/.bashrc file:

export PATH=$PATH:$HOME/.local/bin/
export PYTHONPATH=$HOME/.local/lib/python2.7

You then need to source your ~/.bashrc file by this command:

source ~/.bashrc

Tutorial to analyze ribosome-profiling data and run RiboCode

Here, we use the HEK293 dataset as an example to illustrate the use of RiboCode. Please make sure the path of file is correctly.

1. Required files

   The genome FASTA file, GTF file for annotation can be downloaded from:

   http://www.gencodegenes.org

   or from:

   http://asia.ensembl.org/info/data/ftp/index.html

   http://useast.ensembl.org/info/data/ftp/index.html

   For example, the required files in this tutorial can be downloaded from following URL:

   GTF: ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_19/gencode.v19.annotation.gtf.gz

   FASTA: ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_human/release_19/GRCh37.p13.genome.fa.gz

   The raw Ribo-seq FASTQ file can be download by using fastq-dump tool from SRA_Toolkit:

   fastq-dump -A <SRR1630831>

2. Trimming adapter sequence for ribo-seq data

   Using cutadapt program https://cutadapt.readthedocs.io/en/stable/installation.html

   Example:

   cutadapt -m 20 --match-read-wildcards -a (Adapter sequence) -o <Trimmed fastq file> <Input fastq file>

   Here, the adapter sequences for this data had already been trimmed off, so we can skip this step.

3. Removing ribosomal RNA(rRNA) derived reads

   Align the trimmed reads to rRNA sequences using Bowtie, then select unaligned reads for the next step.

   Bowtie program http://bowtie-bio.sourceforge.net/index.shtml

   rRNA sequences: We provided a rRNA.fa file in data folder of this package.

   Example:

   bowtie-build <rRNA.fa> rRNA
   bowtie -p 8 -norc --un un_aligned.fastq rRNA -q <SRR1630831.fastq> <HEK293_rRNA.align>

4. Aligning the clean reads to reference genome

   Using STAR program: https://github.com/alexdobin/STAR

   Example:

   (1). Build index

   STAR --runThreadN 8 --runMode genomeGenerate --genomeDir <hg19_STARindex>
   --genomeFastaFiles <hg19_genome.fa> --sjdbGTFfile <gencode.v19.annotation.gtf>

   (2). Alignment:

   STAR --outFilterType BySJout --runThreadN 8 --outFilterMismatchNmax 2 --genomeDir <hg19_STARindex>
   --readFilesIn <un_aligned.fastq>  --outFileNamePrefix (HEK293) --outSAMtype BAM
   SortedByCoordinate --quantMode TranscriptomeSAM GeneCounts --outFilterMultimapNmax 1
   --outFilterMatchNmin 16

5. Running *RiboCode* to identify translated ORFs

   (1). Preparing the transcripts annotation files:

   prepare_transcripts -g <gencode.v19.annotation.gtf> -f <hg19_genome.fa> -o <RiboCode_annot>

   (2). Selecting the length range of the RPF reads and identify the P-site locations:

   metaplots -a <RiboCode_annot> -r <HEK293Aligned.toTranscriptome.out.bam>

   This step will generate a PDF file, which plots the aggregate profiles of the distance between the 5’-end of reads and the annotated start codons or stop codons.

   Users can select the read lengths which show strong 3-nt periodicity and identify the P-site locations for each length.

   (3). Detecting translated ORFs using the ribosome-profiling data:

   RiboCode -a <RiboCode_annot> -c <config.txt> -l no -o <RiboCode_ORFs_result>

   Specify the information of the bam file and P-site parameters in config.txt, please refer to the example file in data folder.

   Explanation of final result files

   The RiboCode generates two text files as below: The “(output file name).txt” contains the information of predicted ORFs in each transcript; The “(output file name)_collapsed.txt” file combines the ORFs with the same stop codon in different transcript isoforms: the one harboring the most upstream in-frame ATG is chosen. Some column names of the result file:

   - ORF_ID: The identifier of ORFs that predicated.
   - ORF_type: The type of ORF. The following ORF categories are reported:
   
    "annotated" (overlapping annotated CDS, have the same stop with annnotated CDS)
   
    "uORF" (in upstream of annotated CDS, not overlapping annotated CDS)
   
    "dORF" (in downstream of annotated CDS, not overlapping annotated CDS)
   
    "Overlap_uORF" (in upstream of annotated CDS, overlapping annotated CDS)
   
    "Overlap_dORF" (in downstream of annotated CDS, overlapping annotated CDS"
   
    "Internal" (in internal of annotated CDS, but in a different frame relative annotated CDS)
   
    "novel" (in non-coding genes or non-coding transcripts of coding genes).
   
   - ORF_tstart, ORF_tstop: the beginning and end of ORF in RNA transcript (1-based coordinate)
   - ORF_gstart, ORF_gstop: the beginning and end of ORF in genome (1-based coordinate)
   - pval_frame0_vs_frame1: significance levels of P-site densities of frame0 greater than of frame1
   - pval_frame0_vs_frame2: significance levels of P-site densities of frame0 greater than of frame2
   - pval_combined: integrated P-value

   (4). (optional) plot the P-site densities of predicted ORFs

   Users can plot the density of predicted ORFs using the “plot_orf_density” command, as example below:

   plot_orf_density -a <RiboCode_annot> -c <config.txt> -t (transcript_id)
   -s (ORF_gstart) -e (ORF_gstop)

For any questions, please contact:

Zhengtao Xiao ([e-mail])

Rongyao Huang ([e-mail])

Xudong Xing ([e-mail])