Package: riqc
===
riqc
===Alignment-free RNA degradation toolFree software: MIT licenseInstallationThe simplest is to install via:pip install riqcIf you downloaded the code directly, you can also run:python setup.py installUsageDegradation Detection in Aligned FilesExample:riqc –bam_dir=’…’ –anno_fn=’…’ –out_dir=’…’ –log=’…’Degradation Detection in Non-Aligned FilesExample:riqc –fastq_dir=’…’ –genome=’…’ –anno_fn=’…’
–out_dir=’…’ –out_fn=’…’ –pickle_all=’…’ –pickle_filt=’…’ –log=’…’All Options for Degradation DetectionInput options–bam_dir : Directory of bam files where we get a degradation score for every single bam file and one for all (default=’-‘)–bam_fn : Specifies a single bam file as input   (default=’-‘)–fastq_dir : Directory of fastq files that will be analysed as one (so for individual degradation scores one has to give a directory with only one file or file-pair)    (default=’-‘)–cnt_dir : Directory of pre-produced tab delimited count files (mainly for experimental purposes; the preprocessed file contains all necessary information from the annotation file as well as counts for first and last exon), default=’-’–anno_fn : Path to annotation file (supported formats: gtf, gff, gff3)   (default=’-‘)–genome : Path to genome file (supported format: fasta)   (default=’-‘)–gene_list : File with gene-names to use     (default=’-‘)–separate_files_ON : Consider all input files individually   (default=False)sparse_bam_ON : Input BAM files are in sparse hdf5 format     (default=False)Output Options–out_dir : Directory to store output in  (default=’.’)–out_fn : Prefix for output files  (default=’out’)–anno_tmp_fn : Name of file for temporarily storing annotation information (if the name is ‘’ it will automatically be set (in libs/annotation.py) to reflect whether the protein-coding-genes-filter has been used and whether the legacy option was set to True)    (default=’’)–pickle_all : Name of pickle file for temporarily storing all kmers (if it is None, the name will automatically be set (in libs/kmer.py) to reflect the chosen length of kmers) (default=None)–pickle_filt : Name of pickle file for temporarily storing filtered/cleaned kmers (if it is None, the name will automatically be set (in libs/kmer.py) to reflect the chosen length of kmers)’, (default=None)General Options–quant : What type of quantification to use (options: rpkm,raw)  (default=’raw’)–pseudo_count_ON : Add Pseudocounts to ratio (to also consider genes where we have 0 count on at least one end)  (default=False)–length : Only consider the 25% longest (uq), 50% medium (mq), or 25% shortest (lq) genes (default=’uq’)–score_on_bases_ON : Calculate degradation score not from last and first exon but from a certain amount of bases at beginning and end that can be set via –base_number (normalized length)  (default=False)–base_number : Number of bases at beginning/end for calculating score (only relevant if –score_on_bases_ON is set)  (default=100)–log : Name of log file  (default=’out.log’)–verbose_ON : Set logger to verbose  (default=False)–plot_ON : Plot figures  (default=False)–fn_sample_ratio : Sample Ratios in relation to yours (default=os.path.join(os.path.realpath(__file__).rsplit(‘/’, 1)[:-1][0], ‘data’, ‘sampleRatios/TCGA_sample_a_ratio_uq.tsv’))–mask_filter : Mask all read-counts below this integer   (default=’0’)–protein_coding_filter_OFF’ : Only consider genes that are protein-coding     (default=True)–length_filter_OFF : Only consider genes of certain length (specified via –length)  (default=True)–save_counts_ON : Store the exon counts in .npy and .tsv files for later use (via –cnt_dir)     (default=False)–legacy : Switch on some legacy behavior (currently only targeting alternate calculation of transcript length in libs/annotation.py) (default=False)Kmer Options (only relevant if using fastq as input)–kmer_length : Length of k-mer for alignmentfree counting    (default=27)–reads_kmer : Required active reads per sample or if in [0, 1] then fraction of input reads considered   (default=50000)–step_k : Step-size for k-mer counting   (default=4)Additional Options for Degradation Compensation–scale_counts_ON : Scale counts with pre-computed scaling factors for degradation compensation (gi), default=False–scale_factors_dir : Directory of files containing scaling factors   (default=’-‘)The scaling-factor files can be generated with a command likepython …/degradation_tool/scalingFactors.py –bam_dir=’…’ –anno_fn=’…’ –out_dir=’…’mainly using the same parameters as for the Degradation Detection with additional:–bins : Number of bins for different gene lengths  (default=10)–relative_binning_ON : Have relative (to number of genes) bin boundaries instead of absolute values  (default=False)–average_factors_ON : Compute scaling factors by using average (instead of median) per length bin    (default=False)