{
    "info": {
        "author": "Peng Zhang",
        "author_email": "zhpn1024@163.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: Free for non-commercial use",
            "License :: OSI Approved :: GNU General Public License (GPL)",
            "Operating System :: Unix",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
        ],
        "description": "README for Ribo-TISH (0.2.4)\n==================================\n<2019-8-13 Peng Zhang>\n\nIntroduction\n============\n\nTranslation 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.\nwe 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.\n\nInstall\n=======\n\nPlease check the file 'INSTALL.rst' in the distribution.\n\nUsage of Ribo-TISH\n========================\n\n::\n\n  ribotish [-h] [--version] {quality,predict,tisdiff}\n\n:Example for quality control: ``ribotish quality -b ltm.bam -g gene.gtf -t``\n\n:Example for prediction: ``ribotish predict -t ltm.bam -b chx.bam -g gene.gtf -f genome.fa -o pred.txt``\n\n:Example for differential TIS: ``ribotish tisdiff -1 pred1.txt -2 pred2.txt -a qti1.bam -b qti2.bam -g gene.gtf -o diff.txt``\n\nThere are 3 functions available as sub-commands.\n\n:quality:\tQuality control for riboseq bam data.\n:predict:\tMain function to predict ORF/TIS.\n:tisdiff:\tCall diffential TIS between two TIS data\n\nThe main input data is in bam file format. For best performance, reads should be *trimmed* (to ~ 29 nt RPF length) and aligned to genome using *end-to-end* mode (no soft-clip). 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_.\n\nAll positions or regions reported by Ribo-TISH are 0 based, half open, same as in bed_ format.\n\n.. _samtools: https://github.com/samtools/samtools\n.. _bed: https://genome.ucsc.edu/FAQ/FAQformat.html#format1\n\n\nquality\n~~~~~~~\n\nQuality 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```. \n\nThere are 3 output files: a txt file recording all distribution data, a pdf figure file and a python file for P-site offset parameters. \n\nQuick examples:\n\nFor regular riboseq\n::\n\n  ribotish quality -b chx.bam -g gene.gtf\n\nFor TI-Seq data\n::\n\n  ribotish quality -b ltm.bam -g gene.gtf -t\n\nOptions\n--------------\n\n-b RIBOBAMPATH\n``````````````\n\nRiboseq bam data file. Reads should be trimmed and aligned to genome.\n\n-g GENEPATH\n```````````\n\nGene 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\n\n\n-o OUTPUT\n`````````\n\nOutput 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.\n\n-t/--tis\n````````\n\nThis data is TIS enriched, for LTM and Harr. Quality will pay more attention to TIS sites.\n\n-i INPUT\n````````\n\nInput previous output file, do not read gene file and bam file again.\n\n--geneformat GENEFORMAT\n```````````````````````\n\nGene annotation file format (gtf, bed, gpd, gff, default: auto)\n\n--chrmap CHRMAP\n```````````````\n\nInput chromosome id mapping table file if annotation chr ids are not the same as chr ids in bam/fasta files. Format:\n\n========= =========\nchr_name1 chr_name2\n========= =========\n\nTwo columns, tab seperated, no specific order requirement. Mappings such as 'chr1' to '1' can be automatically processed without using this option.\n\n-f FIGPDFPATH\n`````````````\n\nOutput pdf figure file. Default: bampath[:-4]+'_qual.pdf'\n\n-r PARAPATH\n```````````\n\nOutput 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.\n\n-l LENS\n```````\n\nRange of tag length Default: 25,35. The last number (35) is not included, i.e. the longest length considered is 34.\n\n-d DIS\n``````\n\nPosition range near start codon or stop codon Default: -40,20\n\n--bins BINS\n```````````\n\nBins for cds profile Default: 20\n\n--nom0\n```````````\n\nNot consider reads with mismatch at position 0 (5' end mismatch) as a new group.\n\n--th TH\n```````\n\nThreshold 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.\n\n--colorblind\n````````````\n\nUse a color style readable for color blind people ('#F00078,#00F000,#0078F0')\n\n--colors COLORS\n```````````````\n\nUser specified Matplotlib acceptable color codes for three frames (default: 'r,g,b')\n\n-p NUMPROC\n``````````\n\nNumber of processes. Default: 1\n\n-v/--verbose\n`````````````\n\nIncrease output verbosity.\n\n\nOutput files\n------------\n\nOUTPUT\n```````\n\nOUTPUT 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.\n\nPdf figure\n``````````\n\nPdf 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. \n\nUpper panel: the length distribution of RPFs uniquely mapped to annotated protein-coding regions.\n\nLower panel: different quality metrics for RPFs uniquely mapped to annotated protein-coding regions.\nEach row shows the RPFs with different lengths.\n\n - Column 1: distribution of RPF 5\u2019 end in 3 frames in all annotated codons. The percentage of the reads from the dominant reading frame is shown. \n - Column 2: the distribution of RPF 5\u2019end 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.              \n - Column 3: the distribution of RPF 5\u2019end count near annotated stop codon. \n - 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.\n\n\n\nOffset parameter file\n`````````````````````\n\nThis 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. The file format is like\n::\n\n  offdict = {28: 12, 29: 12, 30: 12, 32: 13, 'm0': {29: 12, 30: 12, 31: 13}}\n\nThe offset parameter file is easy to interpret and can be edited by user if auto estimated offsets are not correct. The default file name will be auto-recognized in further analysis. 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```\n\nRibo-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. \n\npredict\n~~~~~~~\n\nThis 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.\n\nQuick examples:\n\nCombine TI-Seq and regular riboseq data\n::\n\n  ribotish predict -t ltm.bam -b chx.bam -g gene.gtf -f genome.fa -o pred.txt\n\nFor TI-Seq data only\n::\n\n  ribotish predict -t ltm.bam -g gene.gtf -f genome.fa -o pred.txt\n\nDe novo ORF prediction with only regular riboseq data using longest strategy\n::\n\n  ribotish predict -b chx.bam -g gene.gtf -f genome.fa --longest -o pred.txt\n\nDe novo ORF prediction with two regular riboseq data using framebest strategy\n::\n\n  ribotish predict -b chx1.bam,chx2.bam -g gene.gtf -f genome.fa --framebest -o pred.txt\n\nOnly test user provided ORF candidates with two regular riboseq data\n::\n\n  ribotish predict -b chx1.bam,chx2.bam -g gene.gtf -f genome.fa -i cand.txt -o pred.txt\n\n\nOptions\n--------------\n\n-t TISBAMPATHS\n``````````````\n\nInput TI-seq bam data files, comma seperated.\n\n-b RIBOBAMPATHS\n```````````````\n\nRegular riboseq bam data files, comma seperated. \n\nAt least one bam file should be provided by either ```-t``` or ```-b```.\n\n-g GENEPATH\n```````````\n\nGene 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. \nIf 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. \nIf user provided candidates ```-i``` option is set, the transcript annotation for the candidates should be found in gene annotation file.\n\n-f GENOMEFAPATH\n```````````````\n\nGenome 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.\n\n-o OUTPUT\n`````````\n\nOutput all possible ORF results that fit the thresholds. \n\n\n-i INPUT\n````````\n\nOnly test input candidate ORFs, format: \n\n=======  =====  =====\ntransID  start  stop \n=======  =====  =====\n\nStart, stop position is 0 based, half open. Stop - start should be multiples of 3. Transcript should be found in gene annotation file.\n\n--geneformat GENEFORMAT\n```````````````````````\n\nGene annotation file format (gtf, bed, gpd, gff, default: auto)\n\n--chrmap CHRMAP\n```````````````\n\nInput 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.\n\n--tispara TISPARA\n`````````````````\n\nInput 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. There's no need to specify this option if default parameter files exist. To use this option to provide other parameter files, 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.\n\n--ribopara RIBOPARA\n```````````````````\n\nInput P-site offset parameter files for ```-b``` bam files. Same as ```--tispara``` option.\n\n--nparts NPARTS\n```````````````\n\nGroup transcript according to TIS reads density quantile. Default: 10.\n\nTIS 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.\n\n-e ESTPATH\n``````````\n\nOutput 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\nThe result file contains negative binomial parameters, group levels and thresholds for each group.\n\n-s INESTPATH\n````````````\n\nInput 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.\n\n\n-a AGENEPATH\n````````````\n\nAnother 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.\n\n--alt\n`````\n\nUse 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. It does not affect ```-i``` mode prediction. To customize alt start codons, use ```--altcodons```.\n\n\n--altcodons ALTCODONS\n`````````````````````\n\nUse provided alternative start codons, comma seperated, e.g. ```--altcodons CTG,GTG,ACG```. Turn on ```--alt``` option. Do not need to provide 'ATG'. It does not support 'N' bases.\n\n--tis2ribo\n``````````\n\nAdd 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.\n\n--harr\n``````\n\nThe 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```.\n\n\n--harrwidth HARRWIDTH\n`````````````````````\n\nFlanking region for harr data, in codons. Default: 15. Turn on ```--harr``` option.\n\n--longest\n`````````\n\nOnly 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.\n\n--framebest\n```````````\n\nOnly 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.\n\n\n--enrichtest\n````````````\n\nUse enrich test instead of frame test. Enrich test is rank sum test between in-frame reads inside ORF and same frame reads outside ORF.\n\n--nocompatible\n``````````````\n\nNot require reads compatible with transcript splice junctions. \n\n--minaalen MINAALEN\n```````````````````\n\nMinimum amino acid length of candidate ORF, Default: 6.\n\n--genefilter GENEFILTER\n```````````````````````\n\nOnly process given genes. Comma separated. \n\n--tpth TPTH\n```````````\n\nTIS p value threshold. Default: 0.05.\n\n--fpth FPTH\n```````````\n\nFrame p value threshold. Default: 0.05.\n\n--minpth MINPTH\n```````````````\n\nAt least one of TIS or frame p value should be lower than this threshold. Default: 1.\n\n--fspth FSPTH\n`````````````\n\nFisher's p value threshold. Default: 0.05.\n\n--fsqth FSQTH\n`````````````\n\nFisher's FDR q value threshold. Default: 0.05.\n\n--allresult ALLRESULT\n`````````````````````\n\nWrite all result output without FDR q-value threshold to another file. (default: output + '_all.txt', 'off' or using ```--fsqth 1``` to turn off)\n\n-p NUMPROC\n``````````\n\nNumber of processes. Default: 1\n\n-v/--verbose\n`````````````\n\nIncrease output verbosity.\n\n--transprofile TRANSPROFILE\n```````````````````````````\n\nOutput RPF P-site profile for each transcript. The profile data is in python dict format, recording non-zero read counts at different positions on transcript.\n\n--inprofile INPROFILE \n``````````````````````\nInput 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.\n\n--seq\n`````\n\nReport ORF sequences.\n\n--aaseq\n````````\n\nReport amino acid sequences.\n\n\n--blocks\n````````\n\nReport all exon block positions for predicted ORFs. Format: start1-stop1,start2-stop2,...startN-stopN. In chromosome direction.\n\nOutput files\n------------\n\nOUTPUT\n```````\nThe output is a txt file all possible ORF results that fit the thresholds. Some of the columns are:\n\n:GenomePos:\tGenome position and strand of TIS site, 0 based, half open\n:Start:\t\tTIS of the ORF on transcript\n:Stop:\t\t3' end of stop codon on transcript\n:TisType:\tRelative position of this TIS to annotated ORF of the transcript. 'Novel' if no ORF annotation. ':Known' means the TIS is annotated in another transcript. ':CDSOverlap' means the ORF overlaps with annotated CDS in another transcript in the same reading frame.\n:TISGroup:\tGroup of the transcript for TIS background estimation\n:TISCount:\tNumber of reads with P-site at TIS site\n:TISPvalue:\tOne tailed negative binomial test p-value for TISCount (TIS test)\n:RiboPvalue:\tOne tailed rank sum test p-value for regular riboseq frame bias inside ORF (frame test)\n:RiboPStatus:\tFor 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.\n:FisherPvalue:\tCombination of TIS and Ribo p-values using Fisher's method\n:TISQvalue:\tBH correction q-value of TIS test\n:RiboQvalue:\tBH correction q-value of frame test\n:FisherQvalue:\tBH correction q-value of Fisher's p-value\n:AALen:\t\tAmino acid length of the ORF\n\nALL\n```\nThe '_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.\n\ntisdiff\n~~~~~~~\n\nThis 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. \n\nFirst 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.\n\nQuick examples:\n\nDifferential TIS activity calling\n::\n\n  ribotish tisdiff -1 pred1.txt -2 pred2.txt -a qti1.bam -b qti2.bam -g gene.gtf -o diff.txt\n\nDifferential TIS efficiency calling with RNASeq count input\n::\n\n  ribotish tisdiff -1 pred1.txt -2 pred2.txt -a qti1.bam -b qti2.bam -g gene.gtf --rnaseq RNA.txt -o diff.txt\n\nOptions\n--------------\n\n-1 TIS1PATH, -2 TIS2PATH\n````````````````````````\n\nPredict result of group 1 & 2 TIS data. Comma seperated if there are more than 1 replicates.\n\n-a TIS1BAMPATHS, -b TIS1BAMPATHS\n````````````````````````````````\n\nGroup 1 & 2 TIS riboseq bam files, comma seperated.\n\n--l1 TIS1LABELS, --l2 TIS2LABELS\n````````````````````````````````\n\nLabels for each replicate.\n\n-g GENEPATH\n```````````\n\nGene 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. \n\n-o OUTPUT\n`````````\n\nOutput result file.\n\n\n--geneformat GENEFORMAT\n```````````````````````\n\nGene annotation file format (gtf, bed, gpd, gff, default: auto)\n\n--tis1para TIS1PARA, --tis2para TIS2PARA\n````````````````````````````````````````\n\nInput 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.\n\n\n--nocompatible\n``````````````\n\nNot require reads compatible with transcript splice junctions. \n\n--normcomm\n``````````\n\nUse common TISs instead of union TISs for normalization.\n\n--normanno\n``````````\n\nUse only annotated TISs for normalization.\n\n--rnaseq RNASEQ\n```````````````\n\nRNASeq count input. Format:\n\n==== ====== ====== ======\nID   count1 count2 ...\n==== ====== ====== ======\n\nBoth gene ID and transcript ID are acceptable.\n\n--scalefactor SCALEFACTOR\n`````````````````````````\n\nInput TIS scale factor of group 2/1 instead of auto calculate. Not log value.\n\n--rnascale RNASCALE\n```````````````````\n\nInput RNASeq scale factor of group 2/1 instead of auto calculate. Not log value.\n\n--export EXPORT\n```````````````\n\nExport count table for differential analysis with other tools. Especially for replicated data. \n\n--plotout PLOTOUT\n`````````````````\n\nScatter plot output pdf file.\n\n--figsize FIGSIZE\n`````````````````\n\nScatter plot figure size. Default: 8,8.\n\n-f FOLDCHANGE\n`````````````\n\nMinimum fold change threshold. Default: 1.5.\n\n--ipth IPTH\n```````````\n\nInput TIS p value threshold. Default: 0.05.\n\n--iqth IQTH\n```````````\n\nInput TIS q value threshold. Default: 0.05.\n\n--opth OPTH\n```````````\n\nOutput TIS diff p value threshold. Default: 0.05.\n\n--oqth OQTH\n```````````\n\nOutput TIS diff q value threshold. Default: 0.05.\n\n-p NUMPROC\n``````````\n\nNumber of processes. Default: 1\n\n-v/--verbose\n`````````````\n\nIncrease output verbosity.\n\n\nOutput files\n------------\n\nOUTPUT\n```````\nThe output is a txt file all differential TIS results that fit the thresholds. Some of the columns are:\n\n:FoldChange:\tFold change (2/1) value after normalization\n:DiffPvalue:\tDifferential test p-value, two-tailed.\n:DiffQvalue:\tBH correction q-value of DiffPvalue\n\nEXPORT\n``````\n\nThe 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'.\n\nFor replicated data, Ribo-TISH provided R scripts to call differential TISs using edgeR_ or DESeq2_.\n\nExample for edgeR:\n::\n\n  Rscript path_to_scripts/tisdiff_edgeR.r tisdiff_export.txt 3 4 tisdiff_edgeR_output.txt\n\nFor DESeq2:\n::\n\n  Rscript path_to_scripts/tisdiff_DESeq2.r tisdiff_export.txt 3 4 tisdiff_DESeq2_output.txt\n\n3 and 4 are number of replicates in two conditions.\n\n.. _edgeR: https://bioconductor.org/packages/edgeR\n.. _DESeq2: https://bioconductor.org/packages/DESeq2\n\nIf ```--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.",
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