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chr1  10

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cat a.bed
chr1	10	50	10

cat b.bed
chr1	20	40	20

cat c.bed
chr1	30	33	30

# Find the sub-intervals shared and unique to each file.

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1. State from the beginning that you are focusing on exome
2. Talk through the pLI figure in more detail
3. Any slide needs to talked through in detail if you present it
4. Make it clear what missense variants are what you are seeing
5. Should the observed CpG density be modulated by the codon content of the region
6. Model ideas?
   • Exclude singletons???
   • use just synonymous density as the independent variable
7. Molly Przezorski, CpG
8. Simplify the explanation of each of the essential gene descriptions

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#!/usr/bin/env python
"""
given a callable file from goleft depth on stdin, merge adjacent LOW_COVERAGE and CALLABLE regions.
also split regions that are larger than max_region.
this is to even out parallelism for sending regions to freebayes.
"""
from __future__ import print_function, division
import sys
from itertools import groupby
from operator import itemgetter

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# GeneDx: https://www.genedx.com/test-catalog/disorders/early-onset-epileptic-encephalopathy-andor-infantile-spasms/
# Invitae: https://www.invitae.com/en/physician/tests/03402/
ADSL
ALDH7A1
ALG13
ARHGEF9
ARID1B
ARX
ATP1A2
ATP6AP2

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# STEP 1: for all fams, print each SGS region (chrom, start, end)
import sys
import subprocess as sub

def run_jobs(commands):
    """
    This function takes a set of max_work commands and executes
    them with rj
    """
    f = open('rungemini.sh', 'w')

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# mnake sure all rows have 99 fields
$ awk 'BEGIN{FS="\t"} {print NF}' /uufs/chpc.utah.edu/common/home/u1072926/gemini_queries/all.txt | uniq
99

# how many HET (1) and HOM_ALT (3) genotypes were there?
$ awk 'BEGIN{FS="\t"} {print $99}' /uufs/chpc.utah.edu/common/home/u1072926/gemini_queries/all.txt | sort | uniq -c

# get rid of headers except for the first one
(head -n 1 /uufs/chpc.utah.edu/common/home/u1072926/gemini_queries/all.txt; grep -v gt_types /uufs/chpc.utah.edu/common/home/u1072926/gemini_queries/all.txt)

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# download simple repeats from UCSC and convert to BED
curl -s http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/simpleRepeat.txt.gz \
    | gzcat \
    | cut -f 2-5 \
    > simrep.hg19.bed

# download microsatellites from UCSC and convert to bed
curl -s http://hgdownload.soe.ucsc.edu/goldenPath/hg19/database/microsat.txt.gz \
    | gzcat \
    | cut -f 2-5 \

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# get HumVar
wget ftp://genetics.bwh.harvard.edu/pph2/training/humvar-2011_12.predictions.tar.gz
tar -zxvf humvar-2011_12.predictions.tar.gz

# get db snp
wget http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/snp138.txt.gz
gunzip snp138.txt.gz

# get the deleterious SNPs
grep snp138.txt -wFf <(grep rs humvar-2011_12.deleterious.pph.output | cut -f 5) \

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tosses <- 200
experiments <- 1000
hist((rbinom(experiments, tosses, 0.5) / tosses), 
     breaks=20, xlim=c(0,1), 
     main=paste("Distribution of % heads from", experiments, 
                "experiments with", tosses, "tosses each"),
     xlab = "Fraction of tosses that were heads")