pansapiens

#!/usr/bin/env python

###
# bam2fq_softclip.py
###
# A script for exploring soft-clipping of aligned reads.
# Extracts mapped reads from a BAM file as a FASTQ, but encodes 'soft-clipped' regions
# as lowercase. Soft-clipped regions can be quickly visualized in the terminal like:
# 
# ./bam2fq_softclip.py aligned.bam | grep --color=always [atcg] |less -R

pansapiens

A simple Docker Compose recipe for schollz/hostyoself.

pansapiens

#!/bin/bash

# Given a directory of fastq files, use bbmerge to find adapters sequences for every sample,
# align and find the consensus. 
# Final consensus is in  r1_adapter.consensus.fa and r2_adapter.consensus.fa - trim by hand
# if the automatic trimming up the the first 'n' isn't sensible

# Requires: bbmerge.sh (bbmap), muscle, emboss (cons)

pansapiens

#!/bin/bash

R_TMUX_SESSION_NAME="${R_TMUX_SESSION_NAME:-vscode-r}"
TMUX_CMD=$(command -v tmux)

if ! [ -x "$(command -v tmux)" ]; then
    echo "Error: tmux is not installed."
    exit 1
fi

pansapiens

Gzip compression heatmap

Generates a heatmap plot showing the compression ratio of different block through a file.

Show interesting patterns in FASTQ files, and may be useful for diagnosing pathological or unusual data. I find using the zscore transformation for the plot is more informative.

Run like:

./compression_heatmap.py -b 1048576 -c rainbow -t zscore SRR11794587_2.fastq.gz

pansapiens

#!/bin/bash
# This script deinterleaves a FASTQ file generated by an MGI sequencer with two flowcell lanes
# where headers are in the format: @v300009551L1C002R003000000/1 with L1 or L2 indicating the lane
# This can be useful for some analyses since each lane can behave like a technical replicate (eg DADA2 error correction?)

set -e
set -o pipefail

input_fastq_gz="$1"  # input FASTQ file
samplename="$(basename $input_fastq_gz .fastq.gz)"

pansapiens

#!/usr/bin/env python
import os
import shutil
import re
import sys
import difflib
import argparse


def parse_ssh_config(config_path):

pansapiens

#!/bin/bash
#
# ./get_latest_container.sh
# Given a bioconda package name as the first argument, downloads
# the most recent corresponding Singularity image from quay.io/biocontainers
#
# Usage:
#
#   ./get_latest_container.sh <package_name>
#

pansapiens

FROM continuumio/miniconda3

#
# docker build -t pansapiens/rnasik:latest -t pansapiens/rnasik:1.5.4 .
# docker run --rm -it -v $(pwd):/data pansapiens/rnasik --help
#

RUN conda install --yes -c conda-forge mamba
RUN mamba install --yes -c bioconda -c conda-forge -c serine -c serine/label/dev -c anaconda -c defaults -c r -c conda-forge/label/broken \
                           python=3.6 rnasik=1.5.4 qualimap=2.2.2b=0 "pandas<1" && \

pansapiens