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README

This README accompanies paper "Turnover of heterochromatin-associated repeats in great apes: Species and sex differences".

What is this repository for?

  • The pipeline for the identification and analysis of repeats in great apes.
  • 1.0
  • Learn Markdown

How do I get set up?

R libraries needed:

  • require(data.table)
  • require(plyr)
  • require(dplyr)
  • require(dendextend)
  • require(reshape)
  • require(hexbin)
  • require(gplots)
  • require(e1071)
  • require(rgl)
  • require(ggfortify)
  • require(dendextend)
  • require(fmsb)
  • require(grDevices)
  • require(lattice)
  • require(randomcoloR)
  • require(corrr)
  • require(robustbase)
  • require(ICC)
  • require(VennDiagram)
  • library(VennDiagram)
  • source("http://www.sthda.com/upload/rquery_cormat.r")

Pipeline

1. Download the files and name them accordingly

scripts: download_run.sh and rename.py

Input: SRR run id

Output: fastq file

Requirements: sratoolkit.2.5.7-ubuntu64/bin/fastq-dump

2. Identify the repeats

scripts:  run_jobs.sh, analyze_raw_fastq.sh, parseTRFngsKeepHeader.py

Input: fastq file

Output: .dat and .dat_Header.txt

Requirements: fastx, seqtk, trf409.legacylinux64

3. Filter repeat arrays shorter than 75bps and parse repeats into repeat frequency (.rawcounts) and repeat density (.rawlengths)

scripts: parse_headers.sh

Input: .dat_Header.txt

Output: .rawcounts and .rawlengths

Requirements: none

Use datasets trimmed to 100bp reads if available.

4. Filter repeat motifs based on minimum frequency

scripts: filter_raw_files.sh

for file in *.rawcounts; do echo $file; sbatch filter_raw_files.sh $file; sleep 0.1; done;

Input: .rawcounts (automatically processes also .rawlengths)

Output: .rawcounts.sortedFilt and .rawlengths.sortedFilt

Requirements: none

5. Merge identified repeat motifs into single table

scripts: run_merging.sh
 ./run_merging.sh rawcounts.sortedFilt; ./run_merging.sh rawlengths.sortedFilt;

Input: rawcounts.sortedFilt or rawlengths.sortedFilt

Output: big.table.with.header.rawcounts.sortedFilt.txt and big.table.with.header.rawlengths.sortedFilt.txt

Requirements: none

6. Load the table into R and do more filtering

Filters: the cummulative repeat frequency per million reads needs to be at least 15 across all datasets scripts: loadAndSaveData.Rnw

Input: big.table.with.header.rawcounts.sortedFilt.txt and big.table.with.header.rawlengths.sortedFilt.txt

Output: R variables frequency and density

Additional analysis

####Analysis of tandemness scripts: calculateJointStat.sh, giveJointStatForRepeat.py and analyze_tandemness.sh

for a in *1.fastq.dat_Header.txt; do echo $a; b=`echo $a | sed s'/1.fastq/2.fastq/g'`; echo $b; sbatch calculateJointStat.sh ${a} ${b}; sleep 0.1; done;

Input: .dat_Header.txt

Output: .joinedRepeatFreq.txt

Who do I talk to?

[email protected]