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Humann(+wget, kneaddata) in a Nextflow pipeline

loadSingleExperiment

flowchart TD
    p0((Channel.fromFilePairs))
    p1[humann:knead]
    p2[humann:runHumann]
    p3([collect])
    p4[humann:aggregateTaxonAbundances]
    p5(( ))
    p6(( ))
    p7(( ))
    p8[humann:groupFunctionalUnits]
    p9([collect])
    p10(( ))
    p11[humann:aggregateFunctionAbundances]
    p12(( ))
    p13([collect])
    p14[humann:aggregatePathwayAbundances]
    p15(( ))
    p16([collect])
    p17[humann:aggregatePathwayCoverages]
    p18(( ))
    p0 -->|input| p1
    p1 --> p2
    p2 --> p3
    p2 --> p8
    p2 --> p13
    p2 --> p16
    p3 -->|taxonAbundances| p4
    p4 --> p5
    p6 --> p8
    p7 -->|functionalUnitNames| p8
    p8 --> p9
    p9 -->|functionAbundancesCollected| p11
    p10 --> p11
    p11 --> p12
    p13 -->|pathwayAbundances| p14
    p14 --> p15
    p16 -->|pathwayCoverages| p17
    p17 --> p18
Loading

Runs humann for a list of samples.

In:

  • File of SRRIDS.tsv (format like data/sample-to-fastqs.tsv)

Out:

  • species tables from metaphlan
  • abundances of ECs and other functional units
  • abundances of pathways
  • coverages of pathways

Overview

The fastqs are downloaded locally, trimmed ("kneaded") with kneaddata, and - if paired - merged together. Then humann is ran on each input, with specified CPU and memory. The results are merged and returned as a single file per result type.

Install

  • Install Nextflow curl https://get.nextflow.io | bash

  • Install Docker

  • Databases need to be installed and bound to the docker container docker run -it -d -v <path-to-dir-containing-chocophlan-uniref-and-utility_mapping-databases>:/humann_databases -v <path-to-kneaddata-databases>:/kneaddata_databases -v <path-to-dir-containing-metaphlanv31-database>:/usr/local/lib/python3.8/dist-packages/metaphlan/metaphlan_databases/ veupathdb/humann

Reference databases

HUMAnN and Kneaddata need reference databases.

Choosing the reference protein set

uniref50_diamond might be more economical than uniref90_diamond.

In that case, modify or override nextflow.config as follows:

params {
  unirefXX = 'uniref50'
}

Arguments to tools

You can provide custom installation paths or extra parameters as needed:

params {
  kneaddata --trimmomatic ~/lib/Trimmomatic-0.39
  humannCommand = "humann --memory-use maximum"
}

Cluster submission

The most resource-intensive part of this pipeline is the job that runs HumAnN. It is labelled as mem_4c. To run it with 10GB (enough for the EC filtered UniRef90):

process {
  executor = 'lsf'
  maxForks = 20
 
  withLabel: 'mem_4c' {
   clusterOptions = '-n 4 -M 10000 -R "rusage [mem=10000] span[hosts=1]"'
  }
}

Memory use for each job depends on the reference size, and input size. To retry failed jobs with more memory, you could modify the config as follows:

  withLabel: 'mem_4c' {
    errorStrategy = { task.exitStatus in 130..140 ? 'retry' : 'terminate' }
    maxRetries = 3
    clusterOptions = { task.attempt == 1 ?
      '-n 4 -M 12000 -R \"rusage [mem=12000] span[hosts=1]\"'
      : task.attempt == 2 ?
      '-n 4 -M 17000 -R \"rusage [mem=17000] span[hosts=1]\"'
      : '-n 4 -M 25000 -R \"rusage [mem=25000] span[hosts=1]\"'
    }
  }

Output format

There's a file for taxa, pathway abundances, pathway coverages, and each functional unit specified in the config.

Each file is a TSV. The header contains the row type, followed by sample names. This is the same as the output of humann_join_tables script.

Taxa are in the same format, created by adjusting the usual Metaphlan output by removing the frontmatter and the NCBI id column.