intSiteCaller

---

Introduction

This code is designed to take fastq files that are produced by the MiSeq and return integration sites, multihits, and chimeras. RData and fasta files are used for data persistance in place of a database, thus allowing massive parallelization and the LSF job submission system on the PMACS HPC

Inputs

Analysis is started by having the user create the following directory structure:

primaryAnalysisDirectory
├── Data
│   ├── Undetermined_S0_L001_I1_001.fastq.gz
│   ├── Undetermined_S0_L001_R1_001.fastq.gz
│   └── Undetermined_S0_L001_R2_001.fastq.gz
├── processingParams.tsv
├── sampleInfo.tsv
└── vector.fasta

Primary Analysis Directory

• Data/Undetermined_S0_L001_*_001.fastq.gz are the fastq files returned by the MiSeq (R1, R2, and I1) At present names are hard-coded and * can only be I1, R1 and R2

• Optional processingParams.tsv contains 'dryside' processing parameters, all the same for all samples:

  • qualityThreshold

  • badQualityBases

  • qualitySlidingWindow

  • mingDNA is the minimum length of genomic DNA (in nt) that is allowed to be passed onto the alignment step

  • minPctIdent is the minimum percent identity that a query sequence has to a putative alignment on the target sequence in order to be considered 'valid'

  • maxAlignStart is the maximum number of nucleotides of the query sequence that do not match the putative target sequence before a matched nucleotide is seen

  • maxFragLength is the maximum length of a properly-paired alignment (in nt)

  • refGenome is the reference genome to be used for alignment - this is passed in as a standard text string (ex. 'hg18', 'hg19', 'mm8', 'mm9')

    After error-correcting and demultiplexing, intSiteCaller trims raw MiSeq reads based on Illumina-issued quality scores. badQualityBases is the number of bases below qualityThreshold, using standard Illumina ASCII Q-score encoding (p.41-2), that can be observed in a window of qualitySlidingWindow before the read is trimmed.

  Default file default_processingParams.tsv is used when processingParams.tsv is not found in the folder.

• Required sampleInfo.tsv contains 'wetside' sample metadata:

  • alias is the human-readable sample description
  • linkerSequence is the linker sequence as seen in MiSeq read 1. N's indicate the presence of a primerID
  • bcSeq is the barcode used during sample preparation
  • gender is either 'm' of 'f' for male/female, respectively
  • primer is the primer sequence as seen in MiSeq read 2
  • ltrBit is the LTR sequence as seen in MiSeq read 2
  • largeLTRFrag is 43nt of the LTR sequence as seen from MiSeq read 1
  • vectorSeq is a filepath (either absolute or relative to the primary analysis directory) to the vector sequence in fasta format -- it is encouraged to place the vector sequence directly in the primary analysis directory, although that is not a requirement

• Required vector.fasta vector sequence file as specified by vectorSeq in sampleInfo.tsv

• make_primaryAnalysisDirectoryByRunId.R will generate the directory automatically.

If we need to proccess multiple genomes on the same run we can use non-standard sampleInfo.tsv and non-standard processingParams.tsv, we add column refGenome to sampleInfo.tsv and remove this column from processingParams.tsv.

Usage

After creating the above directory structure and cd primaryAnalysisDirectory, the following command is issued:

Rscript path/to/intSiteCaller.R

The rest of the processing is fully automated and shouldn't take more than 4 hours to process 1.5e7 raw reads.

After intSiteCaller.R is done, one can examine the attrition table by the command:

Rscript path/to/check_stats.R

and the output is a tab delimited summary table describing each step.

intSiteCaller.R can handle the following optional arguments

• -j, --jobID - Unique name by which to identify this intance of intSiteCaller [default: intSiteCallerJob]
• -c, --codeDir - Directory where intSiteCaller code is stored, can be relative or absolute [default: codeDir as detected by Rscript]
• -p, --primaryAnalysisDir - Location of primary analysis directory, can be relative or absolute [default: .]
• -h, --help - Show the help message and exit

Code pipeline example for a run run20150505

• We assume packages intSiteCaller, intSiteUploader, geneTherapyPatientReportMaker installed in the $HOME directory.
• We start by creating a folder Frances/run20150505 and moving GeneTherapy-20150505-sampleInfo.csv to that folder.
• Note that the miseq run date 150505 match between the folder name and the csv file for consistence.

#1. Prepare the structure of primaryAnalysisDirectory, assuming GeneTherapy-20150505-sampleInfo.csv exists
cd Frances/run20150505
Rscript ~/intSiteCaller/make_primaryAnalysisDirectory.R

#2. Align reads and call sites; wait until all bjobs are done; check exit status
Rscript ~/intSiteCaller/intSiteCaller.R
grep -i exit logs/*.txt                   #a good run returns nothing
grep -i error logs/*.txt                  #try to distinguish code error or warning

#3. Check attrition table, make sure the numbers are reasonable and the html or pdf file looks OK
Rscript ~/intSiteCaller/check_stats.R | cut -f1-20
Rscript ~/intSiteCaller/html_stats.R

#4. Upload to database
Rscript ~/intSiteUploader/intSiteUploader.R

#5. Check GTSP numbers, find patient metadate for this run, in this example, 
#   check_gtsp_patient.R shows the run was for pFR03. 
#   check_patient_gtsp.R pFR03 will give us all the sets saved in the database for pFR03
#   and we save that information as input file to generate report.
Rscript ~/geneTherapyPatientReportMaker/check_gtsp_patient.R                      #check patient info for this run
Rscript ~/geneTherapyPatientReportMaker/check_patient_gtsp.R                      #check all patients
Rscript ~/geneTherapyPatientReportMaker/check_patient_gtsp.R pFR03                #output to screen
Rscript ~/geneTherapyPatientReportMaker/check_patient_gtsp.R pFR03 > pFR03.csv    #dump to file

#6. Make report for pFR03
Rscript ~/geneTherapyPatientReportMaker/makeGeneTherapyPatientReport.R pFR03.csv 

#7. WAS.pFR03.20150617.html will be generated. Today was 20150617. 
#   If there are more patients in a run, repeat steps 5 and 6.

#8. Generate genomic heatmap
#   To be added

#9. The run folder minus the intermediate files should be saved in a central folder for permanant storage.
#   To be developed

#10. After all the above steps, the folder run20150505 can be deleted.

#11. Generate UCSC hub
#   To be developed

Clean up intermediate files to save space

We can remove all files, except Rdata and logs with the script:

Rscript clean_primaryAnalysisDirectory.R

the script asks for confirmation.

Outputs

Integration sites

This code returns integration sites in two formats. allSites.RData is a GRanges object that contains a single record for each Illumina read. sites.final.RData is a GRanges object of dereplicated integration sites along with a tally of how many reads were seen for each site (irregardless of sonic breakpoint). The revmap column in sites.final.RData links unique reads from allSites to dereplicated sites in sites.final.

Multihits

Multihits are stored in multihitData.RData which is a GRangesList. The first item in this list is a GRanges object where each record represents a properly-paired alignment. Individual multihit reads can be identified by analysing the ID column, which cooresponds to the unique Illumina read identifier. The second item in multihitData is a GRanges object of dereplicated multihits, which lists each unique genomic integration site as a unique record. The revmap column pairs records from multihitData[[1]] to multihitData[[2]]. The third item is a GRanges object of multihit clusters. This is still in development.

Chimeras

Chimeras are stored in chimeraData.RData which is a list that contains some basic chimera frequency statistics and a GRangesList object. Each GRanges object contains two records, one for the read1 alignment and another for the read2 alignment

PrimerIDs

PrimerIDs (if present in the linker sequence) are stored in primerIDData.RData. This file is a base R list containing a DNAStringSet and a BStringSet containing the sequences and quality scores of the primerIDs.

Stats

Processing statistics are returned in the stats.RData file. This file contains a single data.frame. Detail of the specific columns provided in this dataframe will be added later and can inferred from intSiteLogic.R.

Dependencies

This code is highly dependent on Bioconductor packages for processing DNA data and collapsing/expanding alignments.

The following R packages and their subsesequent dependencies are required for proper operation of intSiteCaller:

• ShortRead
• GenomicRanges
• rtracklayer
• BSgenome
• argparse
• igraph
• BSgenome.*.UCSC.* package cooresponding to reference genomes specified in processingParams.csv

Specific versioning analysis has not yet been performed.

Additionally, blat and python are required and must be executable from any path. blat is available from https://genome.ucsc.edu/FAQ/FAQblat.html#blat3

intSiteCaller confirms the presence of all dependancies and will throw an error if a dependancy is not met.

Code Structure

• Primary read trimming and integration site calling logic is contained in intSiteLogic.R.
• Branching and condensing of PMACS jobs is performed in programFlow.R
• Barcode error correcting logic is performed in errorCorrectIndices/golay.py as wrapped by errorCorrectIndices/processGolay.py.
• All code files are checked into the repository.
• Flowcharts will be added to graphically describe the flow of the overall program as well as the flow/logic of individual functions

Tests

A sample dataset is included for verification of integration site calling accuracy. The testCases directory contains,

• intSiteValidation folder, which includes the minimal number of files to process a test run,
• intSiteValidation.digest, a digest(R version of md5) file for the RData files that the test run would produce,
• intSiteValidation.attr, an attrition table that describes the filtering and alignment process,
• test_identical_run.R, the script to run the piepline and check the output.

To analyze the test data, run the following commands assuming the current directory is the root of the repository,

cd testCases/intSiteValidation/
Rscript test_identical_run.R

The test should finish in 10 minutes if PMACS is not busy and the output messages should tell whether the pipeline produced the same results as before. Note that this subset of data contains samples with some borderline cases. For example, clone7 samples should all fail, and many of the clone1-clone4 samples should return no multihits or chimeras. The current implementation of the code handles these gracefully.

Unit tests

Run unit tests with:

library(testthat)
test_dir('tests')