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Python Modules for Riboseq data analysis.

Project Description

README for Ribo-TISH (0.1.9)

<2017-9-15 Peng Zhang>

Introduction

Translation is a critical step in gene regulation that synthesizes proteins from a given RNA template. The development of the ribosome profiling (riboseq) technique has enabled the measurement of translation at a genome-wide level. The basic idea of ribosome profiling is to perform deep-sequencing of the ribosome-protected mRNA fragment (~30 nts), termed ribosome footprints, to determine the occupancy of translating ribosomes on a given mRNA. There are several variants of the ribosome profiling technique that are based on the use of different translation inhibitors. The regular ribo-seq utilizes Cycloheximide (CHX), a translation elongation inhibitor to freeze all translating ribosomes. In contrast to CHX, the translation inhibitor lactimidomycin (LTM) and harringtonine (Harr) have a much stronger effect on initiating ribosomes. The use of these two inhibitors allows for the global mapping of translating initiating sites (TISs) when they are coupled with with ribosome profiling (TI-Seq). In addition, when LTM is used sequentially with puromycin (PMY), the TISs can be mapped quantitatively and can be compared between different conditions. we present a novel algorithm, named Ribo TIS Hunter (Ribo-TISH), for identifying translation activities using ribosome profiling data. Ribo-TISH uses statistical tests to assess the significance of translation activities. It captures significant TISs using negative binomial test, and frame biased open reading frames (ORFs) using rank sum test. Ribo-TISH can also perform differential analysis between two TI-Seq data.

Install

Please check the file ‘INSTALL.rst’ in the distribution.

Usage of Ribo-TISH

ribotish [-h] [--version] {quality,predict,tisdiff}
Example for quality control:
 ribotish quality -b ltm.bam -g gene.gtf -t
Example for prediction:
 ribotish predict -t ltm.bam -b chx.bam -g gene.gtf -f genome.fa -o pred.txt
Example for differential TIS:
 ribotish tisdiff -1 pred1.txt -2 pred2.txt -a qti1.bam -b qti2.bam -g gene.gtf -o diff.txt

There are 3 functions available as sub-commands.

quality:Quality control for riboseq bam data.
predict:Main function to predict ORF/TIS.
tisdiff:Call diffential TIS between two TIS data

The main input data should be in bam file format. Reads should be trimmed and aligned to genome. Intron splicing is supported. Some attributes are needed such as NM, NH and MD. For STAR, `--outSAMattributes All` should be set. bam file should be sorted and indexed by samtools.

All positions or regions reported by Ribo-TISH are 0 based, half open, same as in bed format.

quality

Quality control of riboseq bam data. This function checks reads distribution around annotated protein coding regions on user provided transcripts, show frame bias and estimate P-site offset for different group of reads. Reads are grouped by read length as well as 5’ end match or mismatch. 5’ end mismatch (‘m0’) reads often have different distribution from matched reads. To turn off 5’ end mismatch grouping, use `--nom0`.

There are 3 output files: a txt file recording all distribution data, a pdf figure file and a python file for P-site offset parameters.

Quick examples:

For regular riboseq

ribotish quality -b chx.bam -g gene.gtf

For TI-Seq data

ribotish quality -b ltm.bam -g gene.gtf -t

Options

-b RIBOBAMPATH

Riboseq bam data file. Reads should be trimmed and aligned to genome.

-g GENEPATH

Gene annotation file. Acceptable formats include gtf, gff, bed and genepred with gene names. Input file format can be auto detected or specified by `--geneformat` option

-o OUTPUT

Output all distribution data. Default: bampath[:-4]+’_qual.txt’. Quality and offset estimation is based on this distribution. User can save this file for further quick estimation trying different thresholds by `-i` option.

-t/–tis

This data is TIS enriched, for LTM and Harr. Quality will pay more attention to TIS sites.

-i INPUT

Input previous output file, do not read gene file and bam file again.

–geneformat GENEFORMAT

Gene annotation file format (gtf, bed, gpd, gff, default: auto)

–chrmap CHRMAP

Input chromosome id mapping table file if annotation chr ids are not the same as chr ids in bam/fasta files. Format:

chr_name1 chr_name2

Two columns, tab seperated, no specific order requirement. Mappings such as ‘chr1’ to ‘1’ can be automatically processed without using this option.

-f FIGPDFPATH

Output pdf figure file. Default: bampath[:-4]+’_qual.pdf’

-r PARAPATH

Output offset parameter file. Default: bampath+’.para.py’. This file saves P-site offsets for different reads lengths in python code dict format, and can be used in further analysis.

-l LENS

Range of tag length Default: 25,35. The last number (35) is not included, i.e. the longest length considered is 34.

-d DIS

Position range near start codon or stop codon Default: -40,20

–bins BINS

Bins for cds profile Default: 20

–nom0

Do not consider reads with mismatch at position 0 (5’ end mismatch) as a new group.

–th TH

Threshold for quality. Default: 0.5. Group that frame bias ratio < TH will be considered as low quality and this group of reads will not be used in further analysis. The offset for low quality groups will not be set in parameter file.

–colorblind

Use a color style readable for color blind people (‘#F00078,#00F000,#0078F0’)

–colors

User specified Matplotlib accepted color codes for three frames (default: ‘r,g,b’)

-p NUMPROC

Number of processes. Default: 1

-v/–verbose

Increase output verbosity.

Output files

OUTPUT

OUTPUT is a txt file recording all distribution data in python format for each group of reads. These distributions are shown in pdf figure file. Quality and offset estimation is based on this distribution. User can save this file for further quick estimation trying different thresholds by `-i` option.

Pdf figure

Pdf figure file is plot of all the distributions and illustration of quality and P-site offset. The left part is for 5’ end matched reads and the right part is for 5’ end mismatch reads if `--nom0` is not set.

Upper panel: the length distribution of RPFs uniquely mapped to annotated protein-coding regions.

Lower panel: different quality metrics for RPFs uniquely mapped to annotated protein-coding regions. Each row shows the RPFs with different lengths.

  • Column 1: distribution of RPF 5’ end in 3 frames in all annotated codons. The percentage of the reads from the dominant reading frame is shown.
  • Column 2: the distribution of RPF 5’end count near annotated TIS. The estimate of the P site offset and TIS accuracy are also shown. The RPFs of a specific length that do not pass threshold are considered as low quality and removed.
  • Column 3: the distribution of RPF 5’end count near annotated stop codon.
  • Column 4: The RPF profile throughout the protein-coding regions in 3 frames. TIS enrich score (TIS count / CDS average) is also shown for TIS data.
Offset parameter file

This file saves P-site offsets for different reads lengths in python code dict format, and can be used in further analysis. The default offset file name is bampath+’.para.py’ accompanied with the input bam file, and this default file name will be auto-recognized in further analysis. The offset parameter file is easy to interpret and can be edited by user if auto estimated offsets are not satisfying. If the bam file is in a different directory and user do not want to create a parameter file in that directory, we recommend creating a link for the bam file in current working directory, e.g. `ln -s original/dir/ribo.bam`

Ribo-TISH does not guarantee that it can always find best P-site offset values. Users should check the quality figures and edit the parameter file if necessary.

predict

This is the main function of Ribo-TISH. This function predicts ORF/TIS with riboseq bam files. This function uses negative binomial model to fit TI-Seq background and test significance of TIS sites. For regular riboseq data, Wilcoxon rank sum test between in-frame reads and out-frame reads inside the ORF is performed.

Quick examples:

Combine TI-Seq and regular riboseq data

ribotish predict -t ltm.bam -b chx.bam -g gene.gtf -f genome.fa -o pred.txt

For TI-Seq data only

ribotish predict -t ltm.bam -g gene.gtf -f genome.fa -o pred.txt

User provided candidates with two regular riboseq data

ribotish predict -b chx1.bam,chx2.bam -g gene.gtf -f genome.fa -i cand.txt -o pred.txt

De novo ORF prediction with only regular riboseq data using longest strategy

ribotish predict -b chx.bam -g gene.gtf -f genome.fa --longest -o pred.txt

Options

-t TISBAMPATHS

Input TI-seq bam data files, comma seperated.

-b RIBOBAMPATHS

Regular riboseq bam data files, comma seperated.

At least one bam file should be provided by either `-t` or `-b`.

-g GENEPATH

Gene annotation file for ORF prediction. Acceptable formats include gtf, gff, bed and genepred with gene names. Input file format can be auto detected or specified by `--geneformat` option. If user need to predict on only non-coding genes and use a different gene annotation file for known ORF annotation and background estimation, use `-a` option to provide another gene annotation for known ORF annotation. If user provided candidates `-i` option is set, the transcript annotation for the candidates should be found in gene annotation file.

-f GENOMEFAPATH

Genome fasta file. The fasta file should has a .fai index file accompanied with genome fasta file (indexed) or indexable (fasta sequences have fixed length in each line). This program will index the genome file before prediction if .fai index file can not be found.

-o OUTPUT

Output all possible ORF results that fit the thresholds.

-i INPUT

Only test input candidate ORFs, format:

transID start stop

Start, stop position is 0 based, half open. Stop - start should be multiples of 3. Transcript should be found in gene annotation file.

–geneformat GENEFORMAT

Gene annotation file format (gtf, bed, gpd, gff, default: auto)

–chrmap CHRMAP

Input chromosome id mapping table file if annotation chr ids are not same as chr ids in bam/fasta files. See –chrmap option in `quality` section.

–tispara TISPARA

Input P-site offset parameter files for `-t` bam files. The default parameter files are bampath+’.para.py’ for each bam file, which is generated in `ribotish quality` function. To use this option, each bam file should be provided with a file, and file names are separated with comma. If no parameter file is found, default offset 12 will apply for all reads in the bam data.

–ribopara RIBOPARA

Input P-site offset parameter files for `-b` bam files. Same as `--tispara` option.

–nparts NPARTS

Group transcript according to TIS reads density quantile. Default: 10.

TIS background estimation uses ORF in-frame read counts (excluding TIS codons) to estimate negative binomial parameters. Since different transcripts have different expression levels, the background is different for highly expressed and lowly expressed transcripts. Ribo-TISH groups expressed transcripts into N parts based on TIS reads density of the transcript. Each transcript group have same total number of TIS reads.

-e ESTPATH

Output TIS background estimation result. If only one bam file is provided by `-t` option, the default file name is tisbampath+’.bgest.txt’. If multiple TIS data provided, the default file name is tisBackground.txt The result file contains negative binomial parameters, group levels and thresholds for each group.

-s INESTPATH

Input background estimation result file instead of instant estimation. By default, if only one bam file is provided by `-t` option, the program will first look for file name tisbampath+’.bgest.txt’. If this file exists, background parameters in this file will be used. Otherwise, TIS background estimation will run and generate a result file according to `-e` option.

-a AGENEPATH

Another gene annotation file for ORF annotation in addition to `-g` gene file. This option is mainly used when `-g` annotation focuses on predicting ORFs in non-coding transcripts and does not have sufficient protein coding gene annotation. Protein coding gene annotation is used for TIS background estimation as well as output TIS type classification.

–alt

Use alternative start codons. If set, all codons with 1 base different from ATG will be considered as start codon in ORF finding. Affect both TIS background estimation and prediction. Do not affect `-i` mode prediction. To customize alt start codons, use `--altcodons`.

–altcodons ALTCODONS

Use provided alternative start codons, comma seperated, e.g. `--altcodons CTG,GTG,ACG`. Turn on `--alt` option. Do not need to provide ‘ATG’. Do not support ‘N’ bases.

–tis2ribo

Add TIS bam counts to regular riboseq counts. Use TIS data also for ORF frame test. This option will be turned on automatically if `-b` is not provided.

–harr

The data is treated with harringtonine (instead of LTM). For Harr data, the reads at TIS sites are not as focus as LTM reads. Reads in flanking region (default 15 codons) of TIS will not be used for TIS background estimation. To customize flanking size, use `--harrwidth`.

–harrwidth HARRWIDTH

Flanking region for harr data, in codons. Default: 15. Turn on `--harr` option.

–longest

Only report longest possible ORF results for multiple candidate start codons in the same ORF (same stop codon). This is a TIS selection strategy when there’s no `-t` TI-Seq data input.

–framebest

Only report best frame test results for multiple candidate start codons in the same ORF (same stop codon), which is TIS with the smallest frame test p-value (marked as ‘T’ in RiboPStatus column). This is a TIS selection strategy when there’s no `-t` TI-Seq data input.

–enrichtest

Use enrich test instead of frame test. Enrich test is rank sum test between in-frame reads inside ORF and same frame reads outside ORF.

–nocompatible

Do not require reads compatible with transcript splice junctions.

–minaalen MINAALEN

Minimum amino acid length of candidate ORF, Default: 6.

–genefilter GENEFILTER

Only process given genes. Comma separated.

–tpth TPTH

TIS p value threshold. Default: 0.05.

–fpth FPTH

Frame p value threshold. Default: 0.05.

–minpth MINPTH

At least one of TIS or frame p value should be lower than this threshold. Default: 1.

–fspth FSPTH

Fisher’s p value threshold. Default: 0.05.

–fsqth FSQTH

Fisher’s FDR q value threshold. Default: 0.05.

–allresult

All result output without FDR q-value threshold (default: output + ‘_all.txt’, ‘off’ or using `--fsqth 1` to turn off)

-p NUMPROC

Number of processes. Default: 1

-v/–verbose

Increase output verbosity.

–transprofile TRANSPROFILE

Output RPF P-site profile for each transcript

–inprofile INPROFILE

Input RPF P-site profile for each transcript, instead of reading bam reads. The profile file is the output file from `--transprofile` option. Save some time for re-running.

–seq

Report ORF sequences.

–aaseq

Report amino acid sequences.

Output files

OUTPUT

The output is a txt file all possible ORF results that fit the thresholds. Some of the columns are:

GenomePos:Genome position and strand of TIS site, 0 based, half open
Start:TIS of the ORF on transcript
Stop:3’ end of stop codon on transcript
TisType:Relative position of this TIS to annotated ORF of the transcript. ‘Novel’ if no ORF annotation.
TISGroup:Group of the transcript for TIS background estimation
TISCount:Number of reads with P-site at TIS site
TISPvalue:One tailed negative binomial test p-value for TISCount (TIS test)
RiboPvalue:One tailed rank sum test p-value for regular riboseq frame bias inside ORF (frame test)
RiboPStatus:For all ORFs sharing same stop codon, ‘T’ means top (best) p-value, ‘L’ means local best p-value, ‘N’ means other. All ‘N’ in `-i` or `--longest` mode.
FisherPvalue:Combination of TIS and Ribo p-values using Fisher’s method
TISQvalue:BH correction q-value of TIS test
RiboQvalue:BH correction q-value of frame test
FisherQvalue:BH correction q-value of Fisher’s p-value
AALen:Amino acid length of the ORF
ALL

The ‘_all’ output result is generated according to `--allresult` option, which is similar to the output but do not use FDR (q-value) cutoff. Other cutoffs are the same as output file.

tisdiff

This is the function for differential TIS identification. This function uses two different TIS test results generated by `ribotish predict` using different quantitative TI-Seq (QTI-Seq) data. The ordinary global TI-Seq (GTI-Seq) may have some biases so is not suitable for differential analysis.

First a normalization factor is estimated by Trimmed Mean of M values (TMM) method on the union of significant TIS counts in the two results. Then binomial test p-value and fold change are calculated. If RNASeq counts are provided as reference, the TI efficiency is calculated using Fisher’s exact test with normalized count values.

Quick examples:

Differential TIS activity calling

ribotish tisdiff -1 pred1.txt -2 pred2.txt -a qti1.bam -b qti2.bam -g gene.gtf -o diff.txt

Differential TIS efficiency calling with RNASeq count input

ribotish tisdiff -1 pred1.txt -2 pred2.txt -a qti1.bam -b qti2.bam -g gene.gtf --rnaseq RNA.txt -o diff.txt

Options

-1 TIS1PATH, -2 TIS2PATH

Predict result of group 1 & 2 TIS data. Comma seperated if there are more than 1 replicates.

-a TIS1BAMPATHS, -b TIS1BAMPATHS

Group 1 & 2 TIS riboseq bam files, comma seperated

–l1 TIS1LABELS, –l2 TIS2LABELS

Labels for each replicate.

-g GENEPATH

Gene annotation file. Acceptable formats include gtf, gff, bed and genepred with gene names. Input file format can be auto detected or specified by `--geneformat` option.

-o OUTPUT

Output result file

–geneformat GENEFORMAT

Gene annotation file format (gtf, bed, gpd, gff, default: auto)

–tis1para TIS1PARA, –tis2para TIS2PARA

Input P-site offset parameter files for group 1 & 2 bam files. The default parameter files are bampath+’.para.py’ for each bam file, which is generated in `ribotish quality` function. To use this option, each bam file should be provided with a file, and file names are separated with comma. If no parameter file is found, default offset 12 will apply for all reads in the bam data.

–nocompatible

Do not require reads compatible with transcript splice junctions.

–normcomm

Use common TISs instead of union TISs for normalization.

–normanno

Use only annotated TISs for normalization.

–rnaseq RNASEQ

RNASeq count input. Format:

ID count1 count2

Both gene ID and transcript ID are acceptable.

–scalefactor

Input TIS scale factor of group 2/1 instead of auto calculate. Not log value.

–rnascale

Input RNASeq scale factor of group 2/1 instead of auto calculate. Not log value.

–export EXPORT

Export count table for differential analysis with other tools. Especially for replicated data.

–plotout PLOTOUT

Scatter plot output pdf file.

–figsize FIGSIZE

Scatter plot figure size. Default: 8,8.

-f FOLDCHANGE

Minimum fold change threshold. Default: 1.5.

–ipth IPTH

Input TIS p value threshold. Default: 0.05.

–iqth IQTH

Input TIS q value threshold. Default: 0.05.

–opth OPTH

Output TIS diff p value threshold. Default: 0.05.

–oqth OQTH

Output TIS diff q value threshold. Default: 0.05.

-p NUMPROC

Number of processes. Default: 1

-v/–verbose

Increase output verbosity.

Output files

OUTPUT

The output is a txt file all differential TIS results that fit the thresholds. Some of the columns are:

FoldChange:Fold change (2/1) value after normalization
DiffPvalue:Binomial differential test p-value, one tailed.
DiffQvalue:BH correction q-value of DiffPvalue
EXPORT

The export table is generated using `--export` option. It is also automatically generated when the input data has replicated samples. It is a txt file with raw TIS counts for each predicted TIS. The format of TIS id is ‘TransID_Start_GenomePos’

For replicated data, Ribo-TISH provided R scripts to call differential TISs using edgeR or DESeq2.

Example for edgeR:

Rscript path_to_scripts/tisdiff_edgeR.r tisdiff_export.txt 3 4 tisdiff_edgeR_output.txt

For DESeq2:

Rscript path_to_scripts/tisdiff_DESeq2.r tisdiff_export.txt 3 4 tisdiff_DESeq2_output.txt

3 and 4 are number of replicates in each condition.

If `--rnaseq` is provided, the RNASeq counts of genes/transcripts for the TISs are also provided in the export table. However, the analysis for RNASeq referenced differential TIS efficiency analysis with replicate data is currently unavailable.

Release History

Release History

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