rna-seq.nf

#!/usr/bin/env nextflow

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// Define input variables
params.deriva = "${baseDir}/../test_data/auth/credential.json"
params.bdbag = "${baseDir}/../test_data/auth/cookies.txt"
//params.repRID = "16-1ZX4"
params.repRID = "Q-Y5F6"
params.source = "dev"
params.refMoVersion = "38.p6.vM25"
params.refHuVersion = "38.p13.v36"
params.refERCCVersion = "92"
params.outDir = "${baseDir}/../output"
params.upload = false
params.email = ""
params.track = false


// Define override input variable
params.refSource = "biohpc"
params.inputBagForce = ""
params.fastqsForce = ""
params.speciesForce = ""
params.strandedForce = ""
params.spikeForce = ""

// Define tracking input variables
params.ci = false
params.dev = true


// Parse input variables
deriva = Channel
  .fromPath(params.deriva)
  .ifEmpty { exit 1, "deriva credential file not found: ${params.deriva}" }
deriva.into {
  deriva_getBag
  deriva_getRefInfer
  deriva_getRef
  deriva_uploadInputBag
  deriva_uploadExecutionRun
  deriva_uploadQC
  deriva_uploadQC_fail
  deriva_uploadProcessedFile
  deriva_uploadOutputBag
  deriva_finalizeExecutionRun
  deriva_failPreExecutionRun
  deriva_failExecutionRun
}
bdbag = Channel
  .fromPath(params.bdbag)
  .ifEmpty { exit 1, "deriva cookie file for bdbag not found: ${params.bdbag}" }
repRID = params.repRID
refMoVersion = params.refMoVersion
refHuVersion = params.refHuVersion
refERCCVersion = params.refERCCVersion
outDir = params.outDir
logsDir = "${outDir}/Logs"
upload = params.upload
inputBagForce = params.inputBagForce
fastqsForce = params.fastqsForce
speciesForce = params.speciesForce
strandedForce = params.strandedForce
spikeForce = params.spikeForce
email = params.email

// Define fixed files and variables
replicateExportConfig = Channel.fromPath("${baseDir}/conf/Replicate_For_Input_Bag.json")
executionRunExportConfig = Channel.fromPath("${baseDir}/conf/Execution_Run_For_Output_Bag.json")
if (params.source == "dev") {
  source = "dev.gudmap.org"
} else if (params.source == "staging") {
  source = "staging.gudmap.org"
} else if (params.source == "production") {
  source = "www.gudmap.org"
}
if (params.refSource == "biohpc") {
  referenceBase = "/project/BICF/BICF_Core/shared/gudmap/references/new"
} else if (params.refSource == "datahub") {
  referenceBase = "www.gudmap.org"
}
referenceInfer = Channel.fromList(["ERCC","GRCh","GRCm"])
multiqcConfig = Channel.fromPath("${baseDir}/conf/multiqc_config.yaml")
bicfLogo = Channel.fromPath("${baseDir}/../docs/bicf_logo.png")
softwareReferences = Channel.fromPath("${baseDir}/../docs/software_references_mqc.yaml")
softwareVersions = Channel.fromPath("${baseDir}/../docs/software_versions_mqc.yaml")

// Define script files
script_bdbagFetch = Channel.fromPath("${baseDir}/scripts/bdbag_fetch.sh")
script_parseMeta = Channel.fromPath("${baseDir}/scripts/parse_meta.py")
script_inferMeta = Channel.fromPath("${baseDir}/scripts/infer_meta.sh")
script_refDataInfer = Channel.fromPath("${baseDir}/scripts/extract_ref_data.py")
script_refData = Channel.fromPath("${baseDir}/scripts/extract_ref_data.py")
script_calculateTPM = Channel.fromPath("${baseDir}/scripts/calculateTPM.R")
script_convertGeneSymbols = Channel.fromPath("${baseDir}/scripts/convertGeneSymbols.R")
script_tinHist = Channel.fromPath("${baseDir}/scripts/tin_hist.py")
script_uploadInputBag = Channel.fromPath("${baseDir}/scripts/upload_input_bag.py")
script_uploadExecutionRun_uploadExecutionRun = Channel.fromPath("${baseDir}/scripts/upload_execution_run.py")
script_uploadExecutionRun_finalizeExecutionRun = Channel.fromPath("${baseDir}/scripts/upload_execution_run.py")
script_uploadExecutionRun_failPreExecutionRun = Channel.fromPath("${baseDir}/scripts/upload_execution_run.py")
script_uploadExecutionRun_failExecutionRun = Channel.fromPath("${baseDir}/scripts/upload_execution_run.py")
script_uploadQC = Channel.fromPath("${baseDir}/scripts/upload_qc.py")
script_uploadQC_fail = Channel.fromPath("${baseDir}/scripts/upload_qc.py")
script_uploadOutputBag = Channel.fromPath("${baseDir}/scripts/upload_output_bag.py")
script_deleteEntry_uploadQC = Channel.fromPath("${baseDir}/scripts/delete_entry.py")
script_deleteEntry_uploadQC_fail = Channel.fromPath("${baseDir}/scripts/delete_entry.py")
script_deleteEntry_uploadProcessedFile = Channel.fromPath("${baseDir}/scripts/delete_entry.py")

/*
 * trackStart: track start of pipeline
 */
process trackStart {
  container 'docker://gudmaprbk/gudmap-rbk_base:1.0.0'
  script:
    """
    hostname
    ulimit -a

    curl -H 'Content-Type: application/json' -X PUT -d \
      '{ \
        "sessionId": "${workflow.sessionId}", \
        "pipeline": "gudmap.rbk_rnaseq", \
        "start": "${workflow.start}", \
        "repRID": "${repRID}", \
        "astrocyte": false, \
        "status": "started", \
        "nextflowVersion": "${workflow.nextflow.version}", \
        "pipelineVersion": "${workflow.manifest.version}", \
        "ci": ${params.ci}, \
        "dev": ${params.dev} \
      }' \
      "https://xku43pcwnf.execute-api.us-east-1.amazonaws.com/ProdDeploy/pipeline-tracking"

    if [ ${params.track} == true ]
    then
      curl -H 'Content-Type: application/json' -X PUT -d \
        '{ \
          "ID": "${workflow.sessionId}", \
          "repRID": "${repRID}", \
          "PipelineVersion": "${workflow.manifest.version}", \
          "Server": "${params.source}", \
          "Queued": "NA", \
          "CheckedOut": "NA", \
          "Started": "${workflow.start}" \
        }' \
        "https://9ouc12dkwb.execute-api.us-east-2.amazonaws.com/prod/db/track"
    fi
    """
}

log.info """\
====================================
BICF RNA-seq Pipeline for GUDMAP/RBK
====================================
Replicate RID          : ${params.repRID}
Source Server          : ${params.source}
Mouse Reference Version: ${params.refMoVersion}
Human Reference Version: ${params.refHuVersion}
ERCC Reference Version : ${params.refERCCVersion}
Reference source       : ${params.refSource}
Output Directory       : ${params.outDir}
Upload                 : ${upload}
Track                  : ${params.track}
------------------------------------
Nextflow Version       : ${workflow.nextflow.version}
Pipeline Version       : ${workflow.manifest.version}
Session ID             : ${workflow.sessionId}
------------------------------------
CI                     : ${params.ci}
Development            : ${params.dev}
------------------------------------
"""

/*
 * getBag: download input bag
 */
process getBag {
  tag "${repRID}"
  publishDir "${outDir}/inputBag", mode: 'copy', pattern: "*_inputBag_*.zip"

  input:
    path credential, stageAs: "credential.json" from deriva_getBag
    path replicateExportConfig

  output:
    path ("*.zip") into bag

  when:
    inputBagForce == ""

  script:
    """
    hostname > ${repRID}.getBag.log
    ulimit -a >> ${repRID}.getBag.log

    # link credential file for authentication
    echo -e "LOG: linking deriva credentials" >> ${repRID}.getBag.log
    mkdir -p ~/.deriva
    ln -sf `readlink -e credential.json` ~/.deriva/credential.json
    echo -e "LOG: linked" >> ${repRID}.getBag.log

    # deriva-download replicate RID
    echo -e "LOG: fetching bag for ${repRID} in GUDMAP" >> ${repRID}.getBag.log
    deriva-download-cli ${source} --catalog 2 ${replicateExportConfig} . rid=${repRID}
    echo -e "LOG: fetched" >> ${repRID}.getBag.log

    name=\$(ls *.zip)
    name=\$(basename \${name} | cut -d "." -f1)
    yr=\$(date +'%Y')
    mn=\$(date +'%m')
    dy=\$(date +'%d')
    mv \${name}.zip \${name}_\${yr}\${mn}\${dy}.zip
    """
}

// Set inputBag to downloaded or forced input
if (inputBagForce != "") {
  inputBag = Channel
    .fromPath(inputBagForce)
    .ifEmpty { exit 1, "override inputBag file not found: ${inputBagForce}" }
} else {
  inputBag = bag
}
inputBag.into {
  inputBag_getData
  inputBag_uploadInputBag
}

/*
 * getData: fetch replicate files from consortium with downloaded bdbag.zip
 */
process getData {
  tag "${repRID}"

  input:
    path script_bdbagFetch
    path cookies, stageAs: "deriva-cookies.txt" from bdbag
    path inputBag from inputBag_getData

  output:
    path ("*.R{1,2}.fastq.gz") into fastqs
    path ("**/File.csv") into fileMeta
    path ("**/Experiment Settings.csv") into experimentSettingsMeta
    path ("**/Experiment.csv") into experimentMeta
    path "fastqCount.csv" into fastqCount_fl

  script:
    """
    hostname > ${repRID}.getData.log
    ulimit -a >> ${repRID}.getData.log

    # link deriva cookie for authentication
    echo -e "LOG: linking deriva cookie" >> ${repRID}.getData.log
    mkdir -p ~/.bdbag
    ln -sf `readlink -e deriva-cookies.txt` ~/.bdbag/deriva-cookies.txt
    echo -e "LOG: linked" >> ${repRID}.getData.log

    # get bag basename
    replicate=\$(basename "${inputBag}")
    echo -e "LOG: bag replicate name \${replicate}" >> ${repRID}.getData.log

    # unzip bag
    echo -e "LOG: unzipping replicate bag" >> ${repRID}.getData.log
    unzip ${inputBag}
    echo -e "LOG: unzipped" >> ${repRID}.getData.log

    # bag fetch fastq's only and rename by repRID
    echo -e "LOG: fetching replicate bdbag" >> ${repRID}.getData.log
    sh ${script_bdbagFetch} \${replicate::-13} ${repRID}
    echo -e "LOG: fetched" >> ${repRID}.getData.log
    
    fastqCount=\$(ls *.fastq.gz | wc -l)
    if [ "\${fastqCount}" == "0" ]
    then
      touch dummy.R1.fastq.gz
    fi
    echo "\${fastqCount}" > fastqCount.csv
    """
}

// Split fastq count into channel
fastqCount = Channel.create()
fastqCount_fl.splitCsv(sep: ",", header: false).separate(
  fastqCount
)

// Set raw fastq to downloaded or forced input and replicate them for multiple process inputs
if (fastqsForce != "") {
  Channel
    .fromPath(fastqsForce)
    .ifEmpty { exit 1, "override inputBag file not found: ${fastqsForce}" }
    .collect().into {
      fastqs_parseMetadata
      fastqs_fastqc
    }
} else {
  fastqs.collect().into {
    fastqs_parseMetadata
    fastqs_fastqc
  }
}

/*
 * parseMetadata: parses metadata to extract experiment parameters
*/
process parseMetadata {
  tag "${repRID}"

  input:
    path script_parseMeta
    path file from fileMeta
    path experimentSettings, stageAs: "ExperimentSettings.csv" from experimentSettingsMeta
    path experiment from experimentMeta
    path (fastq) from fastqs_parseMetadata.collect()
    val fastqCount

  output:
    path "design.csv" into metadata_fl
    path "fastqError.csv" into fastqError_fl

  script:
    """
    hostname > ${repRID}.parseMetadata.log
    ulimit -a >> ${repRID}.parseMetadata.log

    # check replicate RID metadata
    rep=\$(python3 ${script_parseMeta} -r ${repRID} -m "${file}" -p repRID)
    echo -e "LOG: replicate RID metadata parsed: \${rep}" >> ${repRID}.parseMetadata.log

    # get experiment RID metadata
    exp=\$(python3 ${script_parseMeta} -r ${repRID} -m "${file}" -p expRID)
    echo -e "LOG: experiment RID metadata parsed: \${exp}" >> ${repRID}.parseMetadata.log

    # get study RID metadata
    study=\$(python3 ${script_parseMeta} -r ${repRID} -m "${file}" -p studyRID)
    echo -e "LOG: study RID metadata parsed: \${study}" >> ${repRID}.parseMetadata.log

    # get endedness metadata
    endsRaw=\$(python3 ${script_parseMeta} -r ${repRID} -m "${experimentSettings}" -p endsMeta)
    echo -e "LOG: endedness metadata parsed: \${endsRaw}" >> ${repRID}.parseMetadata.log
    if [ "\${endsRaw}" == "Single End" ]
    then
      endsMeta="se"
    elif [ "\${endsRaw}" == "Paired End" ]
    then
      endsMeta="pe"
    elif [ "\${endsRaw}" == "Single Read" ]
    # "Single Read" depreciated as of Jan 2021, this option is present for backwards compatibility
    then
      endsMeta="se"
    elif [ "\${endsRaw}" == "nan" ]
    then
      endsRaw="_No value_"
      endsMeta="NA"
    fi

    # ganually get endness
    if [ "${fastqCount}" == "1" ]
    then
      endsManual="se"
    else
      endsManual="pe"
    fi
    echo -e "LOG: endedness manually detected: ${fastqCount}" >> ${repRID}.parseMetadata.log

    # get strandedness metadata
    stranded=\$(python3 ${script_parseMeta} -r ${repRID} -m "${experimentSettings}" -p stranded)
    echo -e "LOG: strandedness metadata parsed: \${stranded}" >> ${repRID}.parseMetadata.log
    if [ "\${stranded}" == "nan" ]
    then
      stranded="_No value_"
    fi

    # get spike-in metadata
    spike=\$(python3 ${script_parseMeta} -r ${repRID} -m "${experimentSettings}" -p spike)
    echo -e "LOG: spike-in metadata parsed: \${spike}" >> ${repRID}.parseMetadata.log
    if [ "\${spike}" == "nan" ]
    then
      spike="_No value_"
    fi
    if [ "\${spike}" == "f" ]
    then
      spike="false"
    elif [ "\${spike}" == "t" ]
    then
      spike="true"
    elif [ "\${spike}" == "no" ]
    # "yes"/"no" depreciated as of Jan 2021, this option is present for backwards compatibility
    then
      spike="false"
    elif [ "\${spike}" == "yes" ]
    # "yes"/"no" depreciated as of Jan 2021, this option is present for backwards compatibility
    then
      spike="true"
    elif [ "\${spike}" == "nan" ]
    then
      endsRaw="_No value_"
      endsMeta="NA"
    fi

    # get species metadata
    species=\$(python3 ${script_parseMeta} -r ${repRID} -m "${experiment}" -p species)
    echo -e "LOG: species metadata parsed: \${species}" >> ${repRID}.parseMetadata.log
    if [ "\${species}" == "nan" ]
    then
      species="_No value_"
    fi

    # get read length metadata
    readLength=\$(python3 ${script_parseMeta} -r ${repRID} -m "${experimentSettings}" -p readLength)
    if [ "\${readLength}" = "nan" ]
    then
      readLength="NA"
    fi
    echo -e "LOG: read length metadata parsed: \${readLength}" >> ${repRID}.parseMetadata.log

    # check not incorrect number of fastqs
    fastqCountError=false
    fastqCountError_details=""
    if [ "${fastqCount}" -gt "2" ]
    then
      fastqCountError=true
      fastqCountError_details="**Too many fastqs detected (>2)**"
    elif [ "${fastqCount}" -eq "0" ]
    then
      fastqCountError=true
      fastqCountError_details="**No valid fastqs detected \\(may not match {_.}R{12}.fastq.gz convention\\)**"
    elif [ "\${endsMeta}" == "se" ] && [ "${fastqCount}" -ne "1" ]
    then
      fastqCountError=true
      fastqCountError_details="**Number of fastqs detected does not match submitted endness**"
    elif [ "\${endsMeta}" == "pe" ] && [ "${fastqCount}" -ne "2" ]
    then
      fastqCountError=true
      fastqCountError_details="**Number of fastqs detected does not match submitted endness**"
    fi

    # check read counts match for fastqs
    fastqReadError=false
    fastqReadError_details=""
    if [ "\${endsManual}" == "pe" ]
    then
      r1Count=\$(zcat ${fastq[0]} | wc -l)
      r2Count=\$(zcat ${fastq[1]} | wc -l)
      if [ "\${r1Count}" -ne "\${r2Count}" ]
      then
        fastqReadError=true
        fastqReadError_details="**Number of reads do not match for R1 and R2:** there may be a trunkation or mismatch of fastq files"
      fi
    fi

    # save design file
    echo "\${endsMeta},\${endsRaw},\${endsManual},\${stranded},\${spike},\${species},\${readLength},\${exp},\${study}" > design.csv

    # save fastq error file
    echo "\${fastqCountError},\${fastqCountError_details},\${fastqReadError},\${fastqReadError_details}" > fastqError.csv
    """
}

// Split metadata into separate channels
endsMeta = Channel.create()
endsRaw = Channel.create()
endsManual = Channel.create()
strandedMeta = Channel.create()
spikeMeta = Channel.create()
speciesMeta = Channel.create()
readLengthMeta = Channel.create()
expRID = Channel.create()
studyRID = Channel.create()
metadata_fl.splitCsv(sep: ",", header: false).separate(
  endsMeta,
  endsRaw,
  endsManual,
  strandedMeta,
  spikeMeta,
  speciesMeta,
  readLengthMeta,
  expRID,
  studyRID
)

// Replicate metadata for multiple process inputs
endsMeta.into {
  endsMeta_checkMetadata
  endsMeta_aggrQC
  endsMeta_failExecutionRun
}
endsManual.into {
  endsManual_trimData
  endsManual_downsampleData
  endsManual_alignSampleData
  endsManual_aggrQC
}
strandedMeta.into {
  strandedMeta_checkMetadata
  strandedMeta_aggrQC
  strandedMeta_failExecutionRun
}
spikeMeta.into {
  spikeMeta_checkMetadata
  spikeMeta_aggrQC
  spikeMeta_failPreExecutionRun
  spikeMeta_failExecutionRun
}
speciesMeta.into {
  speciesMeta_checkMetadata
  speciesMeta_aggrQC
  speciesMeta_failPreExecutionRun
  speciesMeta_failExecutionRun
}
studyRID.into {
  studyRID_aggrQC
  studyRID_uploadInputBag
  studyRID_uploadProcessedFile
  studyRID_uploadOutputBag
}
expRID.into {
  expRID_aggrQC
  expRID_uploadProcessedFile
}

// Split fastq count error into separate channel
fastqCountError = Channel.create()
fastqCountError_details = Channel.create()
fastqReadError = Channel.create()
fastqReadError_details = Channel.create()
fastqError_fl.splitCsv(sep: ",", header: false).separate(
  fastqCountError,
  fastqCountError_details,
  fastqReadError,
  fastqReadError_details
)

//  Replicate errors for multiple process inputs
fastqCountError.into {
  fastqCountError_fastqc
  fastqCountError_trimData
  fastqCountError_getRefInfer
  fastqCountError_downsampleData
  fastqCountError_alignSampleData
  fastqCountError_inferMetadata
  fastqCountError_checkMetadata
  fastqCountError_uploadExecutionRun
  fastqCountError_getRef
  fastqCountError_alignData
  fastqCountError_dedupData
  fastqCountError_makeBigWig
  fastqCountError_countData
  fastqCountError_dataQC
  fastqCountError_aggrQC
  fastqCountError_uploadQC
  fastqCountError_uploadQC_fail
  fastqCountError_uploadProcessedFile
  fastqCountError_uploadOutputBag
  fastqCountError_failPreExecutionRun_fastq
}
fastqReadError.into {
  fastqReadError_fastqc
  fastqReadError_trimData
  fastqReadError_getRefInfer
  fastqReadError_downsampleData
  fastqReadError_alignSampleData
  fastqReadError_inferMetadata
  fastqReadError_checkMetadata
  fastqReadError_uploadExecutionRun
  fastqReadError_getRef
  fastqReadError_alignData
  fastqReadError_dedupData
  fastqReadError_makeBigWig
  fastqReadError_countData
  fastqReadError_dataQC
  fastqReadError_aggrQC
  fastqReadError_uploadQC
  fastqReadError_uploadQC_fail
  fastqReadError_uploadProcessedFile
  fastqReadError_uploadOutputBag
  fastqReadError_failPreExecutionRun_fastq
}

/*
 *fastqc: run fastqc on untrimmed fastq's
*/
process fastqc {
  tag "${repRID}"

  input:
    path (fastq) from fastqs_fastqc.collect()
    val fastqCountError_fastqc
    val fastqReadError_fastqc

  output:
    path ("*.R{1,2}.fastq.gz", includeInputs:true) into fastqs_trimData
    path ("*_fastqc.zip") into fastqc
    path ("rawReads.csv") into rawReadsInfer_fl
    path "fastqFileError.csv" into fastqFileError_fl

  when:
    fastqCountError_fastqc == 'false' && fastqReadError_fastqc == 'false'

  script:
    """
    hostname > ${repRID}.fastqc.log
    ulimit -a >> ${repRID}.fastqc.log

    # run fastqc
    echo -e "LOG: running fastq on raw fastqs" >> ${repRID}.fastqc.log
    fastqc *.fastq.gz -o . &> fastqc.out || true
    fastqcErrorOut=\$(cat fastqc.out | grep -c 'Failed to process file') || fastqcErrorOut=0
    fastqFileError=false
    fastqFileError_details=""
    if [ "\${fastqcErrorOut}" -ne "0" ]
    then
      fastqFileError=true
      fastqFileError_details="**There is an error with the structure of the fastq**"
      echo -e "LOG: There is an error with the structure of the fastq" >> ${repRID}.fastqc.log
      touch dummy_fastqc.zip
    else
      echo -e "LOG: The structure of the fastq is correct" >> ${repRID}.fastqc.log
    fi

    # count raw reads
    zcat *.R1.fastq.gz | echo \$((`wc -l`/4)) > rawReads.csv

    # save fastq error file
    echo "\${fastqFileError},\${fastqFileError_details}" > fastqFileError.csv
    """
}

// Extract number of raw reads metadata into channel
rawReadsInfer = Channel.create()
rawReadsInfer_fl.splitCsv(sep: ",", header: false).separate(
  rawReadsInfer
)

// Replicate inferred raw reads for multiple process inputs
rawReadsInfer.into {
  rawReadsInfer_aggrQC
  rawReadsInfer_uploadQC
}

// Split fastq count error into separate channel
fastqFileError = Channel.create()
fastqFileError_details = Channel.create()
fastqFileError_fl.splitCsv(sep: ",", header: false).separate(
  fastqFileError,
  fastqFileError_details
)

//  Replicate errors for multiple process inputs
fastqFileError.into {
  fastqFileError_fastqc
  fastqFileError_trimData
  fastqFileError_getRefInfer
  fastqFileError_downsampleData
  fastqFileError_alignSampleData
  fastqFileError_inferMetadata
  fastqFileError_checkMetadata
  fastqFileError_uploadExecutionRun
  fastqFileError_getRef
  fastqFileError_alignData
  fastqFileError_dedupData
  fastqFileError_makeBigWig
  fastqFileError_countData
  fastqFileError_dataQC
  fastqFileError_aggrQC
  fastqFileError_uploadQC
  fastqFileError_uploadQC_fail
  fastqFileError_uploadProcessedFile
  fastqFileError_uploadOutputBag
  fastqFileError_failPreExecutionRun_fastqFile
}

/*
 * trimData: trims any adapter or non-host sequences from the data
*/
process trimData {
  tag "${repRID}"

  input:
    path (fastq) from fastqs_trimData
    val ends from endsManual_trimData
    val fastqCountError_trimData
    val fastqReadError_trimData
    val fastqFileError_trimData

  output:
    path ("*.fq.gz") into fastqsTrim
    path ("*_trimming_report.txt") into trimQC
    path ("readLength.csv") into readLengthInfer_fl

  when:
    fastqCountError_trimData == "false"
    fastqReadError_trimData == "false"
    fastqFileError_trimData == "false"

  script:
    """
    hostname > ${repRID}.trimData.log
    ulimit -a >> ${repRID}.trimData.log

    # trim fastq's using trim_galore and extract median read length
    echo -e "LOG: trimming ${ends}" >> ${repRID}.trimData.log
    if [ "${ends}" == "se" ]
    then
      trim_galore --gzip -q 25 --length 35 --basename ${repRID} ${fastq[0]}
      readLength=\$(zcat *_trimmed.fq.gz | awk '{if(NR%4==2) print length(\$1)}' | sort -n | awk '{a[NR]=\$0}END{print(NR%2==1)?a[int(NR/2)+1]:(a[NR/2]+a[NR/2+1])/2}')
    elif [ "${ends}" == "pe" ]
    then
      trim_galore --gzip -q 25 --length 35 --paired --basename ${repRID} ${fastq[0]} ${fastq[1]}
      readLength=\$(zcat *_1.fq.gz | awk '{if(NR%4==2) print length(\$1)}' | sort -n | awk '{a[NR]=\$0}END{print(NR%2==1)?a[int(NR/2)+1]:(a[NR/2]+a[NR/2+1])/2}')
    fi
    echo -e "LOG: trimmed" >> ${repRID}.trimData.log
    echo -e "LOG: average trimmed read length: \${readLength}" >> ${repRID}.trimData.log

    # save read length file
    echo "\${readLength}" > readLength.csv
    """
}

// Extract calculated read length metadata into channel
readLengthInfer = Channel.create()
readLengthInfer_fl.splitCsv(sep: ",", header: false).separate(
  readLengthInfer
)

// Replicate inferred read length for multiple process inputs
readLengthInfer.into {
  readLengthInfer_aggrQC
  readLengthInfer_uploadQC
}
// Replicate trimmed fastq's for multiple process inputs
fastqsTrim.into {
  fastqsTrim_alignData
  fastqsTrim_downsampleData
}

// Combine inputs of getRefInfer
getRefInferInput = referenceInfer.combine(deriva_getRefInfer.combine(script_refDataInfer.combine(fastqCountError_getRefInfer.combine(fastqReadError_getRefInfer.combine(fastqFileError_getRefInfer)))))

/*
  * getRefInfer: dowloads appropriate reference for metadata inference
*/
process getRefInfer {
  tag "${refName}"

  input:
    tuple val (refName), path (credential, stageAs: "credential.json"), path (script_refDataInfer), val (fastqCountError), val (fastqReadError), val (fastqFileError) from getRefInferInput

  output:
    tuple val (refName), path ("hisat2", type: 'dir'), path ("*.fna"), path ("*.gtf")  into refInfer
    path ("${refName}", type: 'dir') into bedInfer

  when:
    fastqCountError == "false"
    fastqReadError == "false"
    fastqFileError == "false"

  script:
    """
    hostname > ${repRID}.${refName}.getRefInfer.log
    ulimit -a >> ${repRID}.${refName}.getRefInfer.log

    # link credential file for authentication
    echo -e "LOG: linking deriva credentials" >> ${repRID}.${refName}.getRefInfer.log
    mkdir -p ~/.deriva
    ln -sf `readlink -e credential.json` ~/.deriva/credential.json
    echo -e "LOG: linked" >> ${repRID}.${refName}.getRefInfer.log

    # set the reference name
    if [ "${refName}" == "ERCC" ]
    then
      references=\$(echo ${referenceBase}/ERCC${refERCCVersion})
    elif [ "${refName}" == "GRCm" ]
    then
      references=\$(echo ${referenceBase}/GRCm${refMoVersion})
    elif [ '${refName}' == "GRCh" ]
    then
      references=\$(echo ${referenceBase}/GRCh${refHuVersion})
    else
      echo -e "LOG: ERROR - References could not be set!\nReference found: ${referenceBase}" >> ${repRID}.${refName}.getRefInfer.log
      exit 1
    fi

    # retreive appropriate reference appropriate location
    echo -e "LOG: fetching ${refName} reference files from ${referenceBase}" >> ${repRID}.${refName}.getRefInfer.log
    if [ ${referenceBase} == "/project/BICF/BICF_Core/shared/gudmap/references/new" ]
    then
      unzip \${references}.zip
      mv \$(basename \${references})/data/* .
    elif [ params.refSource == "datahub" ]
    then
      GRCv=\$(echo \${references} | grep -o ${refName}.* | cut -d '.' -f1)
      GRCp=\$(echo \${references} | grep -o ${refName}.* | cut -d '.' -f2)
      GENCODE=\$(echo \${references} | grep -o ${refName}.* | cut -d '.' -f3)
      if [ "${refName}" != "ERCC" ]
      then
        query=\$(echo 'https://${referenceBase}/ermrest/catalog/2/entity/RNASeq:Reference_Genome/Reference_Version='\${GRCv}'.'\${GRCp}'/Annotation_Version=GENCODE%20'\${GENCODE})
      else
        query=\$(echo 'https://${referenceBase}/ermrest/catalog/2/entity/RNASeq:Reference_Genome/Reference_Version=${refName}${refERCCVersion}/Annotation_Version=${refName}${refERCCVersion}')
      fi
      curl --request GET \${query} > refQuery.json
      refURL=\$(python ${script_refDataInfer} --returnParam URL)
      loc=\$(dirname \${refURL})
      fName=\$(python ${script_refDataInfer} --returnParam fName)
      fName=\${fName%.*}
      if [ "\${loc}" = "/hatrac/*" ]; then echo "LOG: Reference not present in hatrac"; exit 1; fi
      filename=\$(echo \$(basename \${refURL}) | grep -oP '.*(?=:)')
      deriva-hatrac-cli --host ${referenceBase} get \${refURL}
      unzip \$(basename \${refURL})
      mv \${fName}/data/* .
    fi
    mv ./annotation/genome.gtf .
    mv ./sequence/genome.fna .
    mkdir ${refName}
    if [ "${refName}" != "ERCC" ]
    then
      mv ./annotation/genome.bed ./${refName}
    fi
    echo -e "LOG: fetched" >> ${repRID}.${refName}.getRefInfer.log
    """
}

/*
 * downsampleData: downsample fastq's for metadata inference
 */
process downsampleData {
  tag "${repRID}"

  input:
    path fastq from fastqsTrim_downsampleData
    val ends from endsManual_downsampleData
    val fastqCountError_downsampleData
    val fastqReadError_downsampleData
    val fastqFileError_downsampleData

  output:
    path ("sampled.1.fq") into fastqs1Sample
    path ("sampled.2.fq") into fastqs2Sample

  when:
    fastqCountError_downsampleData == "false"
    fastqReadError_downsampleData == "false"
    fastqFileError_downsampleData == "false"

  script:
    """
    hostname > ${repRID}.downsampleData.log
    ulimit -a >> ${repRID}.downsampleData.log

    if [ "${ends}" == "se" ]
    then
      echo -e "LOG: downsampling SE trimmed fastq" >> ${repRID}.downsampleData.log
      seqtk sample -s100 *trimmed.fq.gz 100000 1> sampled.1.fq
      touch sampled.2.fq
    elif [ "${ends}" == "pe" ]
    then
      echo -e "LOG: downsampling R1 of PE trimmed fastq" >> ${repRID}.downsampleData.log
      seqtk sample -s100 *1.fq.gz 1000000 1> sampled.1.fq
      echo -e "LOG: downsampling R2 of PE trimmed fastq" >> ${repRID}.downsampleData.log
      seqtk sample -s100 *2.fq.gz 1000000 1> sampled.2.fq
    fi
    echo -e "LOG: downsampled" >> ${repRID}.downsampleData.log
    """
}

// Replicate the dowsampled fastq's and attatched to the references
inferInput = endsManual_alignSampleData.combine(refInfer.combine(fastqs1Sample.collect().combine(fastqs2Sample.collect().combine(fastqCountError_alignSampleData.combine(fastqReadError_alignSampleData.combine(fastqFileError_alignSampleData))))))

/*
 * alignSampleData: aligns the downsampled reads to a reference database
*/
process alignSampleData {
  tag "${ref}"

  input:
    tuple val (ends), val (ref), path (hisat2), path (fna), path (gtf), path (fastq1), path (fastq2), val (fastqCountError), val (fastqReadError), val (fastqFileError) from inferInput

  output:
    path ("${ref}.sampled.sorted.bam") into sampleBam
    path ("${ref}.sampled.sorted.bam.bai") into sampleBai
    path ("${ref}.alignSampleSummary.txt") into alignSampleQC

  when:
    fastqCountError == "false"
    fastqReadError == "false"
    fastqFileError == "false"

  script:
    """
    hostname > ${repRID}.${ref}.alignSampleData.log
    ulimit -a >> ${repRID}.${ref}.alignSampleData.log

    # align the reads with Hisat2
    echo -e "LOG: aligning ${ends}" >> ${repRID}.${ref}.alignSampleData.log
    if [ "${ends}" == "se" ]
    then

      hisat2 -p `nproc` --add-chrname -S ${ref}.sampled.sam -x hisat2/genome -U ${fastq1} --summary-file ${ref}.alignSampleSummary.txt --new-summary
    elif [ "${ends}" == "pe" ]
    then
      hisat2 -p `nproc` --add-chrname -S ${ref}.sampled.sam -x hisat2/genome --no-mixed --no-discordant -1 ${fastq1} -2 ${fastq2} --summary-file ${ref}.alignSampleSummary.txt --new-summary
    fi
    echo -e "LOG: aliged" >> ${repRID}.${ref}.alignSampleData.log

    # convert the output sam file to a sorted bam file using Samtools
    echo -e "LOG: converting from sam to bam" >> ${repRID}.${ref}.alignSampleData.log
    samtools view -1 -@ `nproc` -F 4 -F 8 -F 256 -o ${ref}.sampled.bam ${ref}.sampled.sam

    # sort the bam file using Samtools
    echo -e "LOG: sorting the bam file" >> ${repRID}.${ref}.alignSampleData.log
    proc=\$(expr `nproc` - 1)
    mem=\$(vmstat -s -S K | grep 'total memory' | grep -o '[0-9]*')
    mem=\$(expr \${mem} / \${proc} \\* 85 / 100)
    samtools sort -@ \${proc} -m \${mem}K -O BAM -o ${ref}.sampled.sorted.bam ${ref}.sampled.bam

    # index the sorted bam using Samtools
    echo -e "LOG: indexing sorted bam file" >> ${repRID}.${ref}.alignSampleData.log
    samtools index -@ `nproc` -b ${ref}.sampled.sorted.bam ${ref}.sampled.sorted.bam.bai
    """
}

alignSampleQC.into {
  alignSampleQC_inferMetadata
  alignSampleQC_aggrQC
}

process inferMetadata {
  tag "${repRID}"

  input:
    path script_inferMeta
    path beds from bedInfer.collect()
    path bam from sampleBam.collect()
    path bai from sampleBai.collect()
    path alignSummary from alignSampleQC_inferMetadata.collect()
    val strandedForce
    val spikeForce
    val fastqCountError_inferMetadata
    val fastqReadError_inferMetadata
    val fastqFileError_inferMetadata

  output:
    path "infer.csv" into inferMetadata_fl
    path "${repRID}.infer_experiment.txt" into inferExperiment
    path "speciesError.csv" into speciesError_fl

  when:
    fastqCountError_inferMetadata == "false"
    fastqReadError_inferMetadata == "false"
    fastqFileError_inferMetadata == "false"

  script:
    """
    hostname > ${repRID}.inferMetadata.log
    ulimit -a >> ${repRID}.inferMetadata.log

    # collect alignment rates (round down to integers)
    align_ercc=\$(echo \$(grep "Overall alignment rate" ERCC.alignSampleSummary.txt | cut -f2 -d ':' | cut -f2 -d ' ' | tr -d '%'))
    align_ercc=\$(echo \${align_ercc%.*})
    echo -e "LOG: alignment rate to ERCC: \${align_ercc}" >> ${repRID}.inferMetadata.log
    align_hu=\$(echo \$(grep "Overall alignment rate" GRCh.alignSampleSummary.txt | cut -f2 -d ':' | cut -f2 -d ' ' | tr -d '%'))
    align_hu=\$(echo \${align_hu%.*})
    echo -e "LOG: alignment rate to GRCh: \${align_hu}" >> ${repRID}.inferMetadata.log
    align_mo=\$(echo \$(grep "Overall alignment rate" GRCm.alignSampleSummary.txt | cut -f2 -d ':' | cut -f2 -d ' ' | tr -d '%'))
    align_mo=\$(echo \${align_mo%.*})
    echo -e "LOG: alignment rate to GRCm: \${align_mo}" >> ${repRID}.inferMetadata.log

    # determine spike-in
    if [ 1 -eq \$(echo \$(expr \${align_ercc} ">=" 10)) ]
    then
      spike="true"
    else
      spike="false"
    fi
    echo -e "LOG: inference of strandedness results is: \${spike}" >> ${repRID}.inferMetadata.log
    if [ "${spikeForce}" != "" ]
    then
      spike=${spikeForce}
      echo -e "LOG: spike-in metadata forced: \${spike}" >> ${repRID}.parseMetadata.log
    fi

    speciesError=false
    speciesError_details=""
    # determine species
    if [ 1 -eq \$(echo \$(expr \${align_hu} ">=" 40)) ] && [ 1 -eq \$(echo \$(expr \${align_mo} "<" 40)) ]
    then
      species="Homo sapiens"
      bam="GRCh.sampled.sorted.bam"
      bed="./GRCh/genome.bed"
      echo -e "LOG: inference of species results in: \${species}" >> ${repRID}.inferMetadata.log
    elif [ 1 -eq \$(echo \$(expr \${align_mo} ">=" 40)) ] && [ 1 -eq \$(echo \$(expr \${align_hu} "<" 40)) ]
    then
      species="Mus musculus"