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PAVED- A Software suite for  the analysis of epigenome-derived next generation sequencing data

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ANALYTICAL PIPELINE

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SYSTEM REQUIREMENTS

PAVED Package  

Example Data

Pipeline to find Nuclease Hypersensitive (NH) sites using MNAse data

This section describes the detailed steps involved in the analysis of test MNAse data described in the manuscript "PAVED- A software suite for the analysis of epigenomic next-generation sequencing data" by Jshaik et. al.

Prerequisites

1) Align the fastq files to the genome of interest using your choice of alignment algorithm (BWA, BOWTIE and Novoalign)
2) Convert to binary alignment map format and sort by genomic position using Samtools.

Experimental Design Followed

MNAseExperimentalDesign

Pipeline

To save memory, we are skipping steps 1 and 2 datasets. We have included fragment constructed datasets in the directory .\Analysis\BAMFilesInsertSize. The fragment constructed bam files are a result of steps 1 and 2. The steps 3 onwards can be tried using these datasets.

Step1: Find the median insert size for each of your datasets using the script findInsertSizeStatistics. From the output files generated, draw conclusions on minimum and maximum tolerable insert sizes.

eg. java -jar C:\Britta\manuscript\Analysis\PAVED.jar findInsertSizeStatistics -i "C:\Britta\manuscript\Analysis\BAMFile\ControlRep1Chr5.bam" -o "C:\Britta\manuscript\Analysis\insertSize\ControlRep1Chr5.txt".
 Do this for control and experimental datasets

Step2: Construct fragments and include only those fragments that are within a specified insert size.

 eg. java -jar C:\Britta\manuscript\Analysis\PAVED.jar filterBAMbyInsertSize -i "C:\Britta\manuscript\Analysis\BAMFile\ControlRep1Chr5.bam" -o "C:\Britta\manuscript\Analysis\BAMFilesInsertSize\ControlRep1Chr5.bam" -m 104 -n 328
Do this for control and experimental datasets

Step3: Remove PCR duplicates

 java -jar C:\Britta\manuscript\Analysis\PAVED.jar findRemovePCRDuplicates -i C:\Britta\manuscript\Analysis\data\BAMFilesInsertSize\FAIRErep2Chr5.bam -j C:\Britta\manuscript\Analysis\data\BAMFilesInsertSize\FAIRErep2Chr5.pcrDupl -o C:\Britta\manuscript\Analysis\data\BAMFilesInsertSize\FAIRErep2Chr5PCRDuplRem.bam -k 5
Do this for control and experimental datasets

Step4: Find the Fragment depth for each of the samples using bam files generated in step3.

java -jar C:\Britta\manuscript\Analysis\PAVED.jar findFragmentDepth -i "C:\Britta\manuscript\Analysis\BAMFilesInsertSize\MNAseRep2Chr5.bam" -o "C:\Britta\manuscript\Analysis\MNAse\MNAseRep2Chr5.depth" -s 1
Do this for control and experimental datasets

Step5: Find average coverage per chromosome

eg. java -jar C:\Britta\manuscript\Analysis\code\PAVED.jar findAverageCoveragePerChromosome -i C:\Britta\manuscript\Analysis\data\MNAse\ShearedFV1Chr5PCRDuplRem.depth -o C:\Britta\manuscript\Analysis\data\MNAse\ShearedFV1Chr5PCRDuplRem.covPerChr
Do this for control and experimental datasets

Step6: Tag low coverage regions

eg. java -jar C:\Britta\manuscript\Analysis\code\PAVED.jar filterOutLowReadDepth -i C:\Britta\manuscript\Analysis\data\MNAse\ShearedFV1Chr5PCRDuplRem.depth -o C:\Britta\manuscript\Analysis\data\MNAse\ShearedFV1Chr5PCRDuplRemFiltered.depth -j  C:\Britta\manuscript\Analysis\data\MNAse\ShearedFV1Chr5PCRDuplRem.AvgCov -m 0.2
Do this for control and experimental datasets

Step7: Perform read count normalization of the coverage files using the average values found in Step 5

java -jar C:\Britta\manuscript\Analysis\code\PAVED.jar readDepthNormalization -i C:\Britta\manuscript\Analysis\data\MNAse\ShearedFV1Chr5PCRDuplRemFiltered.depth -o C:\Britta\manuscript\Analysis\data\MNAse\normalizedData\ShearedFV1Chr5PCRDuplRemFilteredNorm.depth -j C:\Britta\manuscript\Analysis\data\MNAse\ShearedFV1Chr5PCRDuplRem.AvgCov
Do this for control and experimental datasets

Step8: Normalize MNAse-chromatin and MNAse-naked DNA using the sheared control

java -jar C:\Britta\manuscript\Analysis\code\PAVED.jar findValleys -i C:\Britta\manuscript\Analysis\data\MNAse\normalizedData\MNAseVsSheared.wig -o C:\Britta\manuscript\Analysis\data\MNAse\normalizedData\MNAseVsSheared.valleys -m 0.2 -n 10


Repeat this for MNAse experimental

Step9: Find the valleys

java -jar C:\Britta\manuscript\Analysis\PAVED.jar findValleys -i C:\Britta\manuscript\Analysis\MNAse\normalizedData\MNAseChrVsSheared.wig -o C:\Britta\manuscript\Analysis\MNAse\normalizedData\MNAseChrVsSheared.valleys -m 0.4 -n 10

Repeat this for NakedDNAvsSheared

Step10: Find MNAse chromatin specific valleys

java -jar C:\Britta\manuscript\Analysis\code\PAVED.jar compareBEDFiles -i C:\Britta\manuscript\Analysis\data\MNAse\normalizedData\ControlVsSheared.valleys -j C:\Britta\manuscript\Analysis\data\MNAse\normalizedData\MNAseVsSheared.valleys -o C:\Britta\manuscript\Analysis\data\MNAse\normalizedData\MNAseSpecific.valleys

Step11: Find annotations for the MNAse chromatin specific valleys
java -jar C:\Britta\manuscript\Analysis\code\PAVED.jar findAnnotations -i C:\Britta\manuscript\Analysis\data\MNAse\normalizedData\MNAseSpecific.valleys -j C:\Britta\manuscript\Analysis\data\annotations\LmajorFriedlin_TriTrypDB-4.0.gff -o C:\Britta\manuscript\Analysis\data\MNAse\normalizedData\MNAseSpecific.annot