Update pipeline for the reference segment DB for FluPipe (https://github.com/rki-mf1/FluPipe)

The pipeline is written in Nextflow, which can be used on any POSIX compatible system (Linux, OS X, etc). Windows system is supported through WSL. You need Nextflow installed and conda to run the steps of the pipeline:

1. Install Nextflow

   click here for a bash one-liner

   wget -qO- https://get.nextflow.io | bash
   # In the case you don’t have wget
   # curl -s https://get.nextflow.io | bash

2. Install conda

   click here for a bash two-liner for Miniconda3 Linux 64-bit

   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
   bash Miniconda3-latest-Linux-x86_64.sh

OR

1. Install conda

   click here for a bash two-liner for Miniconda3 Linux 64-bit

   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
   bash Miniconda3-latest-Linux-x86_64.sh

2. Install Nextflow via conda

   click here to see how to do that

   conda create -n nextflow -c bioconda nextflow
   conda active nextflow

All other dependencies and tools will be installed within the pipeline via conda.

# conda active nextflow
nextflow run rki-mf1/inv-update-segment-db \
    -profile local,mamba \
    --input_segments '/path/to/fastas/*.fasta' \
    --input_metadata '/path/to/metadata_tables/*.xls' \
    --references '/path/to/segment/references/*.fasta' \
    --intermediate

The assumptions about input FASTA header and input metadata are quite tailored to GISAID/Epiflu data.

Assumed header: variable number of fields separated by |, where

• one field has to be one of ["HA", "MP", "NA", "NP", "NS", "PA", "PB1", "PB2"], and,
• one field has to match an isolate ID: ^EPI_ISL_\d+$

• Required fields: Isolate_Id, Isolate_Name, Subtype, Lineage
• Isolate_Id in the table needs to match the isolate ID in the --input_segments FASTA header (see above)

• Required prefix: segment_, where segment is one of ["HA", "MP", "NA", "NP", "NS", "PA", "PB1", "PB2"]
  • e.g. HA_reference.fasta

The output FASTA headers are renamed in the following pattern:

>{kraken}_{isolate_name}|{lineage}|{isolate_id}|{direction}|{subtype}|{segment}

where kraken is kraken:taxid|11320 in case of Influenza A and kraken:taxid|11520 in case of Influenza B; and direction is rc (reverse complementary) or f (forward) compared to the respective reference (--references).

1. input_metadata files are concatenated
2. input_segments are
   1. concatenated,
   2. name duplicates are removed,
   3. split into 8 FASTA files, one for each segment (based on FASTA header), and
   4. filter FASTA based on seqkit fx2tab stats.
3. Each reference file corresponding to one segment is
   1. aligned with MAFFT, and
   2. filtered segments from step 2.4 are added to the MAFFT alignment.
      1. Reverse complementary segments are reverse complemented.
      2. Filtered and reverse complemented segments are renamed.

Usage example:
nextflow run rki-mf1/inv-update-segment-db \
    --input_segments '/path/to/fastas/*.fasta' \
    --input_metadata '/path/to/metadata_tables/*.xls' \
    --references '/path/to/segment/references/*.fasta'

Required parameters:
--input_segments (Multi) FASTA file(s)
        Assumed header: fields separated by `|`, where 
                - one field has to be one of ["HA", "MP", "NA", "NP", "NS", "PA", "PB1", "PB2"], 
                AND
                - one field has to match an isolate ID: `^EPI_ISL_\d+$`
--input_metadata Excel table(s) containing metadata for --input_segments
        Required fields: `Isolate_Id`, `Isolate_Name`, `Subtype`, `Lineage`
        `Isolate_Id` in the table needs to match `isolate_id` in the input_segments fasta header
--references     8 (multi) FASTA files containing reference sequences for each segment. The references are used
        to check if the input_segments are reverse complementary compared to the references.
        Required prefix: `segment_`, where segment is one of [HA, MP, NA, NP, NS, PA, PB1, PB2]
        e.g. HA_reference.fasta

Optional parameters:
--output            The output directory where the results will be saved [default: results]
--intermediate      Publish also intermediate results [default: false]

Computing options:
--max_cpus       Maximum number of CPUs that can be requested for any single job [default: 20]
--max_memory     Maximum amount of memory that can be requested for any single job [default: 128.GB]
--max_time       Maximum amount of time that can be requested for any single job [default: 16.h]

For Nextflow options, see https://www.nextflow.io/docs/latest/cli.html#options and https://www.nextflow.io/docs/latest/cli.html#run

Execution/Engine profiles:
The pipeline supports profiles to run via different Executers and Engines e.g.: -profile local,mamba

Executer (choose one):
  local
  slurm

Engines (choose one):
  conda
  mamba

Per default: -profile slurm,mamba is executed.

A list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

This project was supported by co-funding from the European Union’s EU4Health programme under project no. 101113012 (IMS-HERA2).