williamrowell

#!/usr/bin/env python3
"""Coverage mean and standard deviation of autosomes

Estimate the mean and standard deviation from a mosdepth coverage BED for
positions with coverage in the range (0, 2 * non-zero mode). This estimate
behaves well for PacBio HiFi WGS of human germline aligned to either hs37d5 and
GRCh38, and may be useful for other situations as well.

$ bash mosdepth --threads 3 --no-per-base --by 500 -m "${BAM%.*}.median" "${BAM}"
$ tabix ${BAM%.*}.median.regions.bed.gz {1..22} | python depth_mean_stddev.py

dinovski

#!/usr/bin/env python

## calculate N50 from fasta file
## N50 = contig length such that half of the contigs are longer and 1/2 of contigs are shorter

import commands
import sys
import os
from itertools import groupby
import numpy

timoast

#! /usr/bin/env python

"""
extract_reads.py
Created by Tim Stuart
"""

import pysam

nathanhaigh

#!/bin/bash
# Usage: deinterleave_fastq.sh < interleaved.fastq f.fastq r.fastq [compress]
# 
# Deinterleaves a FASTQ file of paired reads into two FASTQ
# files specified on the command line. Optionally GZip compresses the output
# FASTQ files using pigz if the 3rd command line argument is the word "compress"
# 
# Can deinterleave 100 million paired reads (200 million total
# reads; a 43Gbyte file), in memory (/dev/shm), in 4m15s (255s)
#