Ribodeblur is a suite of python scripts that perform noise reduction for ribosome profiling data. Ribosome profiling is a high-throughput-sequencing technique that captures mRNA fragments underneath ribosomes during translation. It is widely used to study translational dynamics and mechanics. Despite the fixed size of a ribosome, the size of captured mRNA fragments can span a wide range due to imperfect digestion during the complicated sequencing process. Such a side effect distort ribosome locations and thus can confound interpretations of the translational process. We developed a computational method to recover a clear ribiosome A-site position signal from ribosome profiling data. Our method successfully reduce noise from ribosome profiles and we hope our method can advance discoveries of translational regulations.
This is the package for our previous work appeared at RECOMB in 2016:
Hao Wang, Joel McManus and Carl Kingsford. Accurate recovery of ribosome position signals reveals slow translation of wobble-pairing codons in yeast. In RECOMB 2016 and JCB 2016.
ribodeblur is a python package based on python3, and all prerequisites (except for STAR) are widely used python packages. They can be easily installed with pip or conda.
- python (3.6)
- python packages:
numpy (1.13.0),scipy (0.19.1),bcbiogff (0.6.4),biopython (1.68), andpysam (0.11.2.2). - STAR (2.5.3a) for aligning ribo-seq reads to the transcriptome.
Conda is an environment and package management system. If conda is not installed in your system, follow these instructions. Here lists key commands to setup conda on a linux machine:
- download and install
miniconda:
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
- create virtual environment with all prerequisites installed:
conda env create -f ribodeblur.yml
Here ribodeblur.yml is the conda enviroment yaml file provided with the package.
- activate conda environment:
source activate ribodeblur
There are three major steps for running ribodeblur. First ribodeblur prepares a transcriptome fasta file that includes UTR regions for all transcripts. Second, ribodeblur maps ribo-seq reads of a given sample to the transcriptome with STAR. Third, ribodeblur generates A-site profiles given the transcriptome reference and the read alignments.
The core of ribodeblur depends on reconcentrating off-frame ribo-seq reads back to their in-frame locations based on a blind-deconvolution approach. To achieve that, ribodeblur requires ribosome profiles from both coding regions and a small portion of untranslated regions.
To generate a transcriptome fasta file with UTR regions included, run:
python build_reference.py -f GENOME.FA -a GENOME.GFF -o TRANSCRIPTOME.FA
where GENOME.FA is the genome fasta file and GENOME.GFF is the annotation file, both should be downloaded from Ensembl. TRANSCRIPTOME.FA is the transcriptome reference the script generated.
Optional parameter -p specifies the UTR padding regions added to each transcript, default is 100.
the output fasta file looks like this:
>YAL069W |CDS:100-415|length:515 CATATCCAACCCACTGCCACTTACCCTACCATTACCCTACCATCCACCATGACCTACTCA CCATACTGTTCTTCTACCCACCATATTGAAACGCTAACAAATGATCGTAAATAACACACA ...
Here the CDS range provided in the header is important. ribodeblur relies on these ranges for down-stream analysis.
ribodeblur then aligns the raw ribo-seq reads to the transcriptome with STAR. It automatically builds STAR index of the reference, filters potential non-mRNA contaminants, and aligns the remaining reads to the transcriptome.
To align ribo-seq reads to the transcriptome, run:
python map_to_reference.py -c CONTAMINANT.FA -t TRANCRITPOME.FA -r RIBOSEQ.FQ -id STAR_IDX_DIR -ad STAR_ALIGN_DIR -p NTHREAD
where CONTAMINANT.FA is the contaminant reference fasta (e.g. rRNA, tRNA, etc.), TRANSCRIPTOME.FA is the transcriptome reference generated from Step 1, RIBOSEQ.FQ is the ribo-seq raw reads (in fasta/fastq format, also support .gz), STAR_IDX_DIR is the directory to store STAR index, STAR_ALIGN_DIR is the directory to store the alignment results (in BAM format), and NTHREAD is the number of threads STAR can use.
note: NTHREAD should not exceed the number of CPUs of the computer ribodeblur is running on.
-p/--nproc: number of threads to run STAR (default=30)a/--adapter: adapter sequence to trim off by STAR (default=CTGTAGGCACCATCAAT)-f/--force: force re-run of STAR alignment.
The final output of this step is an alignment file ends with _transcript_Aligned.out.bam under directory STAR_ALIGN_DIR.
The final step of ribodeblur is the deblur step, where the read alignments are processed and grouped by read length for each transcript, and only profiles with high coverage (>50% non-zero loci) are kept. Then for each read length, a blur vector is learned from a meta-profile combining all transcripts, and each transcript profile are deblurred independently. Lastly, the deblurred profiles from different read length are merged together to get a final ribosome A-site profile.
To generate A-site profiles, run:
python deblur_pipeline.py -r TRANSCRIPTOME.FA -b ALIGN.BAM -o OUTPUT_PREFIX
where TRANSCRITPOME.FA is the transcriptome reference generated from Step 1, ALIGN.BAM is the alignment file generated form Step 2, and OUTPUT_PREFIX is the prefix of the output for the A-site profile.
The final otuput of this step is an A-site profile starts with OUTPUT_PREFIX and ends with .profile.
It looks like this:
YAL003W: 21 17 1 11426 42 1 9 1 7 229 1 0 202 308 66 1545 0 38 83 3 1 ...Each line starts with the transcript name followed by the read count for each nucleotide location within the coding region. The read count vector is the sum of all read-length-specific profiles after deblur.
If (no deblur) is marked after the transcript name, it means that all read-lenth-specific profiles for that transcript is too sparse to deblur, therefore only offset shifts are performed.
ribodeblur requires the ribo-seq data with deep coverage. Here provides a real-world example from Albert et al.
To download the yeast genome and non-coding reference (as contaminant reference), run script download_refs.sh. This automatically downloads the reference sequences from Ensembl to directory $HOME/data/ribodeblur/refs.
To download the ribo-seq data, run script download_ribobseq.sh. This downloads the test-case ribo-seq data to directory $HOME/data/ribodeblur/data.
To run the ribodeblur pipeline on the test set, use script run_ribodeblur.sh. This runs ribodeblur on the test set and outputs the A-site profile to $Home/data/ribodeblur/deblur/by_fp.profile.