PNG file containing boxplot per each gene (distribution of reads number in ech interval) Command p number of parts, into which all genes will be divide, default value: 100 Output IclipBarplot.py -f test1_sub_real_freq.csv -g test_nohead.gtf -n 5 -t testBar Get boxplot with reads fraction per each gene interval DescriptionĬreate boxplot by computing dividing each gene into p parts and counting number or reads starting in each interval Parameters PNG file with barplot, x - gene name, y - reads fraction, % Command t plot title, default value: "Barplot" Output n number of genes, default value: all genes detected in GTF file IclipZscores.py -f test1_sub.sam -fa test.fa.tab -g test_nohead.gtf -t 100 -r 15 -i 10 -k 4 -l 5 Create barplot with read fraction per each gene DescriptionĬreate barplot by computing reads fraction started at each gene Parameters _zscores.csv contains three columns: Command _seq.txt contains sequence for positions above threshold +- l l Length of sequence to output into seq.txt file: position +- length, default value: 10 Output i Number of iterations to generate randomized datasets from input file, default value: 100 fa Fasta file containing reference nucleotide sequence in fa.tab format with two columns: f File containing first containing two columns: Only positions (both in real and randomized datasets) above specified threshold are considered for this computation. Python iclipFdr.py -f test1_sub.sam -g test_nohead.gtf -r 20 -i 10 Compute z-scores for k-mers DescriptionĬompute k-mer z-scores using following formula: (number k-mer in real dataset - mean number of k-mers across i iterations of randomized datasets)/standard deviation of k-mers number across i iterations of randomized datasets i Number of iterations to generate randomized datasets from input file, default value: 100 Output r Range of nucleotides in one direction from the position to calculate number of reads starting in the range: position +- range_number, default value: 15 g Corresponding annotation file in GTF format Python iclipRealFreq.py -f test1_sub.sam Compute FDR (False Discovery Rate) threshold DescriptionĬompute FDR threshold using following formula: (mean number of positions with n reads across i iterations of randomized datasets - standard deviation of positions with n reads across i iterations of randomized datasets) / number of positions with n reads in real dataset Parameters sam files without header OutputĬSV file real_freq.csv containing two columns: Command Grep -v "^#" test.gtf > test_nohead.gtf Get real frequencies DescriptionĬompute number of mapped reads starting at each genome position Parameters Preprocess GFT file by removing header using following command: Grep -v test.sam |cut -f1,2,3,4 > test1_sub.sam GTF files This package use only positions in sam file.īefore running scripts extract first 4 columns without header using following command:
#ICLIP FLORIDA FULL#
In order to get full list of parameters for function type:
#ICLIP FLORIDA INSTALL#
Sudo pip install pytz -upgrade Getting help Sudo pip install python-dateutil -upgrade
You can install them on terminal by running following commands: To use iCLIPit you need to have following packages: numPy, pandas InstallationĬlone git repository by following command
This repository contains 4 scripts that you can run:Įxample data and results are in ToyExample folder. Anastassiya Zidkova and Martin Zidek DescriptionĬode in python and test data for RNA iCLIP analysis.
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