ucarduygu / tools

tools

miscellaneous tools for bioinformatics

---

count_bam.py

given a list of genomic regions (-i) and a list of BAM files (-b), output the count of reads in each BAM file within these genomic regions.

usage: count_bam.py [-h] [-i INTERVAL] [-b BAMS [BAMS ...]] [-o OUTPUT]
                    [-l LEN] [-n NAME [NAME ...]]

count of reads in a list of invervals

optional arguments:
  -h, --help            show this help message and exit
  -i INTERVAL, --interval INTERVAL
                        the bed file contains location information of
                        intervals
  -b BAMS [BAMS ...], --bams BAMS [BAMS ...]
                        the list of bam files containing mapped reads for
                        MNase-seq
  -o OUTPUT, --output OUTPUT
                        name prefix of output files: *_count.txt
  -l LEN, --length LEN  choose the center region of this length for each
                        interval to count
  -n NAME [NAME ...], --name NAME [NAME ...]
                        name of each bam sample (to be wrote on the header)

Library dependency: pysam, bedtools,numpy,math

conunt_hits.py

given a list of genomic regions (-i) and a list of BED files with aligned reads (-f & -s), output the count of reads in each BED file within these genomic regions.

Usage: count_hits.py [-h] [-i interval_file] [-f data_folder] [-p ovlp_pct]
                     [-s suffix] mark1 mark2 ...
Example:counts.py -i Pn_E14_mm9_nucleosome_peak.bed -f ~/ChIPseq_map_data/
        -s _d0_extend_sort.bed mouse_H3K4me3 mouse_H3K4me2 mouse_H3K4me1
        > count_epi_nucleosome.txt
Arguments:
  -h, --help           Show this help.
  -i, --interval       file contains intervals to be counted
  -f, --folder         folder for bed data
  -p, --ovlp_pct       minimum overlap_percentage to be included as a count
  -s, --suffix         uniform suffix for bed data files within the folder
Library dependency: bedtools, getopt

sortbedTOwig.py

convert sorted BED file into WIG file ,which can be potentially single base resolution depending on read depth.
BED file can be sorted using linux commend: sort -k1,1 -k2,2n <unsorted.bed> > <sorted.bed>

Version:1.0

Library dependency: csv


Usage: sortbed2wig.py <options> -i input.bed -n name_of_output -e -l extended_read_length -s column_num_for_strand
       sortbed2wig.py <options> -i input.bed -n name_of_output -e
       sortbed2wig.py <options> -i input.bed -n name_of_output
Example:  sortbed2wig.py <options> -i mm9_H3K9me3.bed -n mm9_H3K9me3 -e -l 150 -s 4
Options:
   -h,--help          show help information
   -i,--inputfile     input bed file (with strand information for extend option)
   -o,--outputFolder  folder for output wid file (default: /home/GenomeBrowser/lab_tracks/
   -n,--wigname       name of the output wig file
   -e,--extend        extend read in bed file or not (default: false)
   -l,--readlength    the extended length of each read (default: 150, effective only when extend=True)
   -s,--strandLoc     the column # for strand information in bed (default: 4, effective only when extend=True)

pairend_fragmentLen.py

Draw distribution of fragment length from a pairend dataset (BAM file, -i)

pairend_fragmentLen.py: draw distribution of fragment lenghs from pair end NGS data and fit with Gaussian Kernel Density Estimation (KDE)
Version:1.0

Library dependency: matplotlib, numpy, scipy, pysam

Usage:
    python pairend_fragmentLen.py -i [NGS_pairend_mapped_bam] -x min_x,max_x -n 100000 -o [output_figure]
    python pairend_fragmentLen.py -i [NGS_pairend_mapped_bam] -o [output_figure]
Example:
    python pairend_fragmentLen.py -i H209_pairend_5mark.sort.bam -x 0,500 -n 100000 -o H209_pairend_5mark_fragmentLen.png
Options:
    -h,--help          show help information
    -i,--inputbam      input bam file(with correspnding bai file in same folder
    -x,--xlim          range for x axis: min_x, the left bound (default 0); max_x, the right bound (default 350)
    -l,--lambda        covariance_factor lambda for KDE (default 0.25)
    -n,--num           number of fragments to be processed for plotting
    -o,--output        the output figure file, can be format of emf, eps, pdf, png, ps, raw, rgba, svg, svgz

random_seq_generator.py

generate random sequences from genome specified (not exceeding the chromosome size boundary). One can adjust the mean and SD for size of random sequences. probability to choose each chrom based on the size distribution.

usage: random_seq_generator.py [-h] [-g GENOME] [-m MEAN] [-s SD] [-n NUM]

generate random sequences with customized length and number (for random peaks
et...)
probability to choose each chrom based on the size distribution

optional arguments:
  -h, --help            show this help message and exit
  -g GENOME, --genome GENOME
                        specify genome name to get chromosome info from
                        UCSCGB, default: mm9
  -m MEAN, --mean MEAN  mean length of each random sequence,default:200
  -s SD, --sd SD        sd of random sequence lengths,default:20
  -n NUM, --num NUM     number of sequences to be randomly
                        sampled,default:10000

library dependency: cruzdb (https://github.com/brentp/cruzdb),sqlalchemy