RiboCode 1.2.4.dev1

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 --alignEndsType EndToEnd

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 and a predefined P-site parameters file. The PDF file plots the aggregate profiles of the distance between the 5’-end of reads and the annotated start codons or stop codons. The P-site parameters file defines the read lengths which show strong 3-nt periodicity and the P-site locations for each length, users can modify this file according the plots in PDF file.

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

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

   Users can use or modify the config file generated by last step to specify the information of the bam file and P-site parameters, please refer to the example file config.txt 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) Plotting the densities of P-sites for predicted ORFs

   Users can plot the density of P-sites for a ORF using the “parsing_plot_orf_density” command, as example below:

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

   The generated PDF plots can be edited by Adobe Illustrator.

   (5). (optional) Counting the number of RPF reads aligned to ORFs

   The number of reads mapping to each ORF can be obtained by the “ORF_count” command which relying on HTSeq-count package. The first few codons and last few codons of ORF with length longer than a given value can be excluded by adjusting specific parameters. Only the reads of a given length will be counted. For example, the reads with length between 24-36 nt aligned to predicted ORF can be obtained by using below command:

   ORF_count -g <RiboCode_ORFs_result.gtf> -r <ribo-seq genomic mapping file> -f 15 -l 5 -e 100 -m 26 -M 34 -o <ORF.counts>

   The reads aligned to first 15 codons and last 5 codons of ORFs with length longer than 100 nt will be excluded.

Recipes (FAQ):

1. I have a BAM/SAM file aligned to genome, how do I convert it to transcriptome-based mapping file ?

   You can use STAR aligner to generate the transcriptome-based alignment file by specifying the “–quantMode TranscriptomeSAM” parameters, or use the “sam-xlate” command from UNC Bioinformatics Utilities .

2. How to use multiple BAM/SAM files to identify ORFs?

   You can select the read lengths which show strong 3-nt periodicity and the corresponding P-site locations for each BAM/SAM file, then list each file and their information in config.txt file. RiboCode will combine the P-site densities at each nucleotides of these BAM/SAM files together to predict ORFs.

For any questions, please contact:

Zhengtao Xiao ([e-mail])

Rongyao Huang ([e-mail])

Xudong Xing ([e-mail])