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RiboWave

RiboWave is a funtional Ribo-seq analysis tool to identify translated ORF based on Ribo-seq data.

The RiboWave workflow consists of:

  • Pre-processing :

    • Create the annotation file for the subsequent analysis. [create_annotation.sh]
    • Determine the P-site position of Ribo-seq. [P-site_determination.sh]
    • Generate P-site tracks from Ribo-seq.[create_track_Ribo.sh]
  • Main function :

    • Denoise [Ribowave]
    • Predict translated ORFs [Ribowave]
    • Estimate reads density for each given ORF [Ribowave]
    • Estimate frameshift potential for each given ORF [Ribowave]

Requirements

software

  • R
  • bedtools v2.25.0

R packages

  • reshape
  • ggplot2
  • rhdf5
  • methods
  • wmtsa
  • parallel

Before running

It is recommanded to make a new directory and move the Ribo-seq bam file into that directory;

Pre-processing

0. Create annotation

This step scans for and annotates all putative ORFs as well as define annotated ORF according to the annotation file

./create_annotation.sh -h

A helper message is shown:

----------------------------------------------------------------------------------------------------
RiboWave : version 1.0 
This step is set for the purpose of genome annotation.                                
Several of the output is necessary for the following steps.                         
----------------------------------------------------------------------------------------------------

Usage:
	 create_annotation.sh [-h] -G annotation.gtf -f fasta -o annotation_dir -s scripts_dir

Options:
	-G	<filename>	(annotation.gtf                                    )
	-f	<filename>	(genome fasta                                      )
	-o	<directory>	(Output annotation directory                       )
	-s	<directory>	(Script directory                                  )
	-h	          	(Help                                              )
----------------------------------------------------------------------------------------------------

Run create_annotation.sh on example:

scripts/create_annotation.sh -G annotation_fly/dmel-all-r6.18.gtf -f annotation_fly/dmel-all-chromosome-r6.18.fasta  -o annotation_fly  -s scripts;

Input files:

  • <annotation.gtf> : the annotation gtf should contain start_codon and stop_codon information,eg: dmel-all-r6.18.gtf

  • <genome.fasta> : genome fasta ,eg: dmel-all-chromosome-r6.18.fasta

  • <annotation_dir> : the directory for all the annotation output

  • <scripts_dir> : the directory of all the scripts in the package

Output files:

annotation directory, including :

  • start_codon.bed : the bed file annotating start codon

  • final.ORFs : all identified ORFs, eg: FBtr0300105_0_31_546 where FBtr0300105 refers to the transcript, 0 refers to the reading frame relative to the start of transcript, 31 refers to the start site, 546 refers to the stop codon.

1. P-site determination

This step determines the P-site position for each Ribo-seq reads length by overlapping with the annotated start codons from previous step

./P-site_determination.sh -h

A helper message is shown:

----------------------------------------------------------------------------------------------------
RiboWave : version 1.0 
This step is for determining the P-site position for each read length.                
                                                                                    
----------------------------------------------------------------------------------------------------

Usage:
	 P-site_determination.sh [-h] -i Ribo.bam -S start_codon.bed -o out_dir -s scripts_dir [-n study name]

Options:
	-i	<filename>	(Ribo-seq bam                                      )
	-S	<filename>	(start codon annotation file                       )
	-o	<directory>	(Output annotation directory                       )
	-s	<directory>	(Script directory                                  )
	-n	<string>  	(study name, defult:test                           )
	-h	          	(Help                                              )
----------------------------------------------------------------------------------------------------

Run P-site_determination.sh on example :

scripts/P-site_determination.sh -i GSE52799/SRR1039770.sort.bam -S annotation_fly/start_codon.bed -o GSE52799 -n SRR1039770 -s scripts;

Input files:

  • <Ribo_bam> : secondary alignment removed to ensure one genomic position per aligned read and sorted

  • annotation :

    • <start_codon.bed> : annotated start site start_codon.bed. It is generated in the create_annotation.sh step.
  • <out_dir> : the directory of the output result, eg: GSE52799

  • <study_name> : the name of all the output file, default: test. eg: SRR1039770

  • <scripts_dir> : the directory of all the scripts in the package

Output files:

P-site directory, including :

  • name.psite1nt.txt : the Ribo-seq reads length and its corresponding P-sites position(= offset + 1). It may look this this :

    30	13
    
  • name.psite.pdf : the PDF displaying the histogram of aggregated reads

2. Generating P-site track

This step creats the P-site track for transcripts of interests using determined P-sites position from previous step.

./create_track_Ribo.sh -h

A helper message is shown:

----------------------------------------------------------------------------------------------------
RiboWave : version 1.0 
This step is the last step for data processing where P-site track is generated for each transcript.
                                                                                    
----------------------------------------------------------------------------------------------------

Usage:
	 create_track_Ribo.sh [-h] -i Ribo.bam -G exons.gtf -g genome_size -P psite.position -o out_dir -s scripts_dir [-n study name]

Options:
	-i	<filename>	(Ribo-seq bam                                      )
	-G	<filename>	(exons gtf file                                    )
	-g	<filename>	(Genome size annotation file                       )
	-P	<filename>	(P-site position (offset+1)                        )
	-o	<directory>	(Output annotation directory                       )
	-s	<directory>	(Script directory                                  )
	-n	<string>  	(study name, defult:test                           )
	-h	          	(Help                                              )
----------------------------------------------------------------------------------------------------

Run create_track_Ribo.sh on example:

look at transcripts from chromosome X :
scripts/create_track_Ribo.sh -i GSE52799/SRR1039770.sort.bam -G annotation_fly/X.exons.gtf -g annotation_fly/genome -P GSE52799/P-site/SRR1039770.psite1nt.txt -o GSE52799 -n SRR1039770 -s scripts;

Input files:

  • <Ribo_bam>

  • <exons.gtf> : a gtf file for only the exons from transcripts of intersect, eg: X.exons.gtf

  • <genome> : the file including all the chromosomes and its genome size. Noted: genome can be obtained by using samtools faidx function with the input of fasta file. genome may look like this:

    2L	23513712
    2R	25286936
    3L	28110227
    3R	32079331 
    
  • P-site :

    • <P-site_position> : the file listing the P-site position for each read length. This file can be found in the output of previous step, eg: name.psite1nt.txt
  • <out_dir> : the directory of the output result, eg: GSE52799

  • <study_name> : the name of all the output file, default: test. eg: SRR1039770

  • <scripts_dir> : the directory of all the scripts in the package

Output files:

bedgraph/name directory, including :

  • final.psite : P-site track at transcriptome wide. It may look like this :

    FBtr0070533	0,0,0,0,0,0,0,0,0,0,0,0,6,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,1,0,2,1,0,0,0,0,0,0,4,8,0,0,3,0,5,12,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
    FBtr0073886	0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,25,0,0,0,0,0,0,0,0,0,0,0,0,0
    FBtr0070604	0,0,0,0,0,0,0,0,0,0,0,0,59,6,0,1,0,0,2,6,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
    FBtr0070603	0,0,0,0,0,0,0,0,0,0,0,0,75,2,7,10,7,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,3,3,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
    

Main function

3. RiboWave

This step can achieve multiple functions :

  • denoising [denoise]

  • providing predicted p.value for each given ORF to identify its translation status [pvalue,-P]

  • providing reads density (P-site/PF P-site) for each given ORF [density,-D]

  • providing translation efficiency (TE) estimation for each given ORF [TE,-T]

  • providing frameshift potential (CRF score) for each given ORF [CRF,-F]

./Ribowave -h

A helper message will be shown:

----------------------------------------------------------------------------------------------------
RiboWave : version 1.0 
This step is main function of RiboWave.                                               
Functions are provided including : predicting translated ORF, estimating reads density, estimating translation efficiency and predicting frameshift events.
----------------------------------------------------------------------------------------------------

Usage:
	 Ribowave [Options] -a P-site track -b ORF_list -o output_dir -s scripts_dir

Options:
	-P	                    	(providing P value for each ORF                    )
	-D	                    	(providing reads abundance for each ORF            )
	-F	                    	(predicting frameshift potential for each ORF      )
	-T	<int>  <RNA_FPKM>   	(estimating TE for each ORF.<int> requires total Ribo-seq sequence depth;<RNA_FPKM> requires RNA-seq FPKM)
	-a	<filename>          	(psite track                                       )
	-b	<filename>          	(ORF list                                          )
	-o	<directory>         	(Output directory                                  )
	-s	<directory>         	(Script directory                                  )
	-n	<string>            	(The name of the study, default: test              )
	-p	<int>               	(The number of threads, default: 1                 )
	-h	                    	(Help                                              )
----------------------------------------------------------------------------------------------------

It might take hours to perform the analysis if the input is large. It is recommended to specify the number of CPU cores through the -p option.

Run Ribowave on example:

Denoise the P-site track
mkdir -p Ribowave;
scripts/Ribowave  -a GSE52799/bedgraph/SRR1039770/final.psite -b annotation_fly/final.ORFs -o GSE52799/Ribowave -n SRR1039770 -s scripts -p 8;
Identifying translated ORF
mkdir -p Ribowave;
scripts/Ribowave -P -a GSE52799/bedgraph/SRR1039770/final.psite -b annotation_fly/final.ORFs -o GSE52799/Ribowave -n SRR1039770 -s scripts -p 8;
Estimating abundance
mkdir -p Ribowave;
scripts/Ribowave -D -a GSE52799/bedgraph/SRR1039770/final.psite -b annotation_fly/final.ORFs -o GSE52799/Ribowave -n SRR1039770 -s scripts -p 8;
Estimating TE

IMPORTANT : when estimating TE, user should input the sequenced depth of Ribo-seq and the FPKM value from paired RNA-seq

mkdir -p Ribowave;
scripts/Ribowave -T 9012445  GSE52799/mRNA/SRR1039761.RPKM -a GSE52799/bedgraph/SRR1039770/final.psite -b annotation_fly/final.ORFs -o GSE52799/Ribowave -n SRR1039770 -s scripts -p 8;
Calculating frameshift potential
on annotated ORFs
mkdir -p Ribowave;
awk -F '\t' '$3=="anno"'  annotation_fly/final.ORFs  >   annotation_fly/aORF.ORFs;
scripts/Ribowave -F -a GSE52799/bedgraph/SRR1039770/final.psite -b annotation_fly/aORF.ORFs -o GSE52799/Ribowave -n SRR1039770 -s scripts -p 8;
Multiple functions
mkdir -p Ribowave;
scripts/Ribowave -PD -T 9012445  GSE52799/mRNA/SRR1039761.RPKM -a GSE52799/bedgraph/SRR1039770/final.psite -b annotation_fly/final.ORFs -o GSE52799/Ribowave -n SRR1039770 -s scripts -p 8;

Input files:

  • bedgraph/name:

    • <P-site track> : output from the previous step, containing the P-site track of transcripts of interest, eg: final.psite
  • <ORF_list> : ORFs of interest ,eg : final.ORFs. It is generated in the step of create_annotation.sh

  • <Ribo-seq sequenced depth> : the sequenced depth of Ribo-seq to calculate FPKM , eg: 9012445

  • <RNA FPKM> : FPKM table. It may look like this :

     FBtr0100871	22262
     FBtr0070604	18682
     FBtr0100231	14746.5
     FBtr0100874	14024.5
     FBtr0100864	11475.6
    

Output files:

  • name.PF_psite : the denoised signal track(PF P-sites signal track) at transcriptome wide. It looks similar as the input final psite.

  • name.feats1 : the features of ORFs including chi-square P-value information. It may look like this :

Column Explanation
Column1-Column4 Basic information about the ORF
Column5 Reads coverage within the ORF
Column6 P-value predicted by RiboWave
Column7 Values estimating the relative abundance of PF P-sites outside of the studied ORF
Column8 Reads intensity at the current start codon

result directory, including :

  • name.95%.mx : RiboWave makes the prediction on the translation initiation sites and gives the final translated product output (p.value < 0.05) . It may look like this :
FBtr0070007_2_93_1028
FBtr0070008_1_128_943
FBtr0070025_2_135_1094
  • name.density : reads density ( PF P-site ) of given ORFs. It may look like this :
Column Explanation
Column1-Column4 Basic information about the ORF
Column5 number of PF P-sites in transcript
Column6 number of PF P-sites in given ORF
Column7 Density of PF P-sites in given ORF
  • name.TE : TE of given ORFs. It may look like this :
Column Explanation
Column1 transcript
Column2 ORF
Column3 TE
  • name.CRF.final : ORFs that might experience reading frame translocation. It may look like this :
Column Explanation
Column1 ORF
Column2 Start of frameshift
Column3 Stop of frameshift
Column4 PF P-sites' reading frames after the change point ,eg: 2_2,0_1 where 2_2 refers to continuous two PF P-sites of frame 2 followed by continuous one PF P-sites of frame 0.
Column5 Relative position of PF P-sites after the shift ,eg : 1413,1440;1789 where 1413,1440 corresponds to the exact position of 2_2 within the transcript. Discontinuity in the reading frame is separated by ;
Column6 CRF score describing the potential of frameshift

Example file

An example file is packed and found in here.

Enclosed in the RiboWave_v1.0.tar.gz, run_Ribowave_dmel.sh combines all the steps together into a pipeline.

For any questions, please contact:

Zhiyu Xu [xanthexu18@gmail.com]

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