ionox0

# Script to reverse engineer UMI-tagged fastqs from Marianas UMI-processed fastqs 
# ---

import sys
import gzip
from itertools import izip

# Usage: python reverse_clip.py <read 1 paired fastq with umis in read headers>

# We start with R1 and R2 that have been clipped with Marianas, and UMIs have been placed in the read name.

ionox0

Commands to run ACCESS snps and indels pipeline

Note: the version here pipeline_VariantCalling will be subject to change

1. Activate the virtual environment

Note that if you are working off of your own clone of the project, you must create your own virtual environment and install the pipeline there (see steps 1-4 in the readme).

source /home/johnsoni/virtualenvs/pipeline_VariantCalling/bin/activate
source /home/johnsoni/pipeline_VariantCalling/ACCESS-Pipeline/python_tools/pipeline_kickoff/workspace_init.sh

ionox0

#!/bin/bash
# Version 2 from Dilmi

export PATH=/home/pererad1/java8/jdk1.8.0_144/bin:$PATH

input_bam=$1
output_folder=$2
# Change these:
fgbio_jar=/ifs/work/bergerm1/pererad1/fgbio-0.5.0-SNAPSHOT.jar
scratch_dir=/ifs/work/bergerm1/Innovation/sandbox/ian/scratch/

ionox0

#!python

import csv
import sys


csv.field_size_limit(sys.maxsize)


# Input file:

ionox0

def compare_dfs(df_a, df_b, merge_cols):
    """
    Returns intersection, its complement, only in df_a, only in df_b
    
    :param merge_cols: Columns to use for merging the two DataFrames
    """
    df_a = df_a.copy()
    df_b = df_b.copy()
    
    DUM_A = 'DUMMY_COL_A'

ionox0

import re
import sys
import zlib

# Module to find and replace text in PDF files
#
# Usage:
#   python pdf_replace.py <input_filename> <text_to_find> <text_to_replace> <output_filename>
#
# @author Ionox0

ionox0

# Compare first 11 fields of bam files
diff <(samtools view 5500-FC-FGFR2-spike-in/Sample_ML-fgfr2-015-pl-T02_/ML-fgfr2-015-pl-T02_IGO_05500_FC_16_S16_001_cl_aln_srt_MD_IR_FX_BR.bam | cut -f1-11 ) <(samtools view repeat-run/5500-FC-FGFR2-spike-in/Sample_ML-fgfr2-015-pl-T02_/ML-fgfr2-015-pl-T02_IGO_05500_FC_16_S16_001_cl_aln_srt_MD_IR_FX_BR.bam | cut -f1-11 ) > ~/ML-fgfr2-015-pl-T02_IGO_05500_FC_16_S16_001_cl_aln_srt_MD_IR_FX_BR-complete_diff-original_vs_repeat-run


# Compare IR reads (grep for "YO:" tag)
diff <(samtools view 5500-FC-FGFR2-spike-in/Sample_ML-fgfr2-015-pl-T02_/ML-fgfr2-015-pl-T02_IGO_05500_FC_16_S16_001_cl_aln_srt_MD_IR_FX_BR.bam | grep "YO:") <(samtools view repeat-run/5500-FC-FGFR2-spike-in/Sample_ML-fgfr2-015-pl-T02_/ML-fgfr2-015-pl-T02_IGO_05500_FC_16_S16_001_cl_aln_srt_MD_IR_FX_BR.bam | grep "YO:") > ~/ML-fgfr2-015-pl-T02_IGO_05500_FC_16_S16_001_cl_aln_srt_MD_IR_FX_BR-indel_realign_diff-original_vs_repeat-run

ionox0

var emails = ['[email protected]']

var $ = jQuery;

var one = $('#msg_cat_wizard_add_contacts')[0]
var four = $('#email-wizard-add-contact-email')[0];

async function doit(email) {
    one.click();
    four.value = email;

ionox0

import itertools
import pandas as pd

barcode_labels = pd.read_excel('IDT_DM-96 barcode-for hiseq.xlsx')
sample_1_list = barcode_labels['Index_Name'].str.slice(0, 6)
sample_2_list = barcode_labels['Index_Name'].str.slice(6, 11)
sample_name_combs = itertools.product(sample_1_list, sample_2_list)
sample_name_combs_list = list(sample_name_combs)
sample_name_combs_df = pd.DataFrame(sample_name_combs_list)
sample_name_combs_df['merged'] = sample_name_combs_df.iloc[:,0] + sample_name_combs_df.iloc[:,1]

ionox0

function [L,U,P] = slu(A)
[n,n] = size(A); tol = 1.e-6;
P = eye(n);
L = eye(n);
for k = 1 : n
    if abs(A(k,k)) < tol
        r = k + 1;
        while A(r, k) < tol
            r = r + 1;
        end