Taxonomic assignment tools for Insect Biome Atlas

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Taxonomic annotations workflow

Overview

This workflow runs a number of tools to assign taxonomy to ASV sequences. The tools used are:

• sintax (implemented in vsearch)
• dada2
• Naive Bayes classifier (implemented in qiime2)
• Metabuli
• Kaiju
• EPA-ng

Installation

The workflow is implemented in Snakemake and uses conda and apptainer to manage software dependencies. To install the workflow, clone the repository and create a conda environment with the required dependencies:

git clone [email protected]:johnne/IBA-taxonomy.git
cd IBA-taxonomy
conda env create -f environment.yml
conda activate IBA-taxonomy

Installation of Rackham

On the UPPMAX cluster Rackham, instead of using the environment.yml file to set up required software you can use the module system to load snakemake and biopython:

module load bioinfo-tools snakemake biopython

Then you can use the flag --profile slurm to run the workflow on the cluster. But first make sure that your NAISS project id is entered into slurm/config.yaml at the default-resources: parameter, e.g.:

default-resources: "slurm_account=naiss2023-5-209"

Configuration

Most tools used are configured using this config entry structure:

<tool>:
  ref:
    <ref-name>:
        fasta: "<path-to-reference>.fasta"
        taxonomy: "<path-to-reference-taxonomy>.tsv"
        ranks: ["kingdom","phylum","class","order","family","genus","species"]
  query:
    <query-name>: "<path-to-query>.fasta"

Below are defails on each tool and how to configure them.

EPA-ng

EPA-ng (Barbera et al 2019) is a tool for placing sequences into a reference phylogenetic tree. It requires a reference tree, a multiple sequence alignment and a set of query sequences. The taxonomic assignments of each leaf in the tree can either be supplied in a text file or extracted from leaf labels in the tree.

The config entry for EPA-ng should look like in the following example:

epa-ng:
  ref:
    chester_2017:
      tree: "data/chesters_2017/chesters_2017_trimmed_tree.nwk"
      tree_format: "newick"
      msa: "data/chesters_2017/hmmersearch_OUT_ch2017_noduplic_trimmed.fas"
      msa_format: "fasta"
      ref_taxonomy: "data/chesters_2017/taxonomy.tsv"
      tree_ranks:
        - "superkingdom"
        - "phylum"
        - "class"
        - "order"
        - "family"
        - "genus"
        - "species"
      model: "GTR+G+F"
  query: 
    lep: "data/lep.fasta"
  gappa:
    # Ratio by which LWR is split between annotations if an edge has two possible
    # annotations. Specifies the amount going to the proximal annotation. If not
    # set program will determine the ratio automatically from the 'distal length'
    # specified per placement.
    distribution_ratio: -1
    # For assignment of taxonomic labels to the reference tree, require this
    # consensus threshold. Example: if set to 0.6, and 60% of an inner node's
    # descendants share a taxonomic path, set that path at the inner node.
    consensus_thresh: 1

Benchmark setup

This workflow can create train and test datasets from a set of reference sequences. The rules file workflow/rules/benchmark.smk contains rules to generate 5 sets of train/test datasets. It requires a benchmark: entry in the configuration file that should look like this:

benchmark:
    coidb.221216.7lvl:
        fasta: "data/benchmark/bold_clustered.sintax.7levels.fasta"
        taxonomy: "data/benchmark/coidb.221216.taxonomy.tsv.gz"
        ranks: ["kingdom","phylum","class","order","family","genus","species"]
        extract_subseq: False
        subseq_min_len: 300
        hmm_from: 78
        hmm_to: 202
        asv_seq: "AATAAATAACATAAGATTTTGA"

In the example above, coidb.221216.7lvl is a name given to this reference database. The fasta: entry should point to a fasta file with the reference sequences. taxonomy: should point to a tab-separated file with sequence ids in the first column and taxonomic rank labels in subsequent columns. Using the fasta and taxonomy file 5 pairs of train/test datasets are produced. For each set, 100 species are sampled and 1 sequence picked from each and put into the test set. In cases 2-5, the test sequences are also removed from the train set.

benchmark dataset name	description	output directory
sample_keep_species_in_db	sample 100 species into test, keep all seqs in train	"benchmark/coidb.221216.7lvl/case1-keep-species-in-db/"
sample_keep_species_remove_identical	sample 100 species into test, remove test and identical sequences from train	"benchmark/coidb.221216.7lvl/case2-sample_keep_species_remove_identical/"
sample_keep_genus	sample 100 species into test, remove all sequences for sampled species from train but ensure that at least one sequence for the same genus is kept	"benchmark/coidb.221216.7lvl/case3-sample_keep_genus/"
sample_keep_family	sample 100 species into test, remove all sequences for sampled genera from train but ensure at least one sequence for each family is kept	"benchmark/coidb.221216.7lvl/case4-remove-genus-keep-family/"
sample_remove_family	sample 100 species into test, remove all sequences for sampled families from train	"benchmark/coidb.221216.7lvl/case5-remove-family/"

If the config param subseq: is set to True the reference sequences will be trimmed to match the ASV region being studied. The region is matched using the COX1 hmm as a proxy. First you need to know the location on the HMM to which your ASVs match. This can be done by translating an ASV sequence into it's amino acid sequence, then using it as a query with hmmscan e.g. on the HMMER website. Enter the hmm start and stop coordinates in the config as hmm_from: and hmm_to:, respectively. In addition, supply the nucleotide sequence for one of the ASVs in the study using the asv_seq: config entry. Using this information, subsequences will be extracted from the references following these steps:

1. translate seqs to the longest open reading frame (using the Invertebrate Mitochondrial Code table)
2. hmmsearch of translated amino acid sequences against the COX1 HMM
3. reference seqs that overlap the hmm_from: and hmm_to: region are extracted and converted back to nucleotide sequences
4. multiple sequence alignments with matched nucleotide sequences together with the ASV sequence from asv_seq:
5. trimming of aligned sequences to only the start and stop of the ASV sequence, followed by gap removal

To benchmark sintax, dada2 and the naive-bayes and vsearch classifiers, run:

snakemake --configfile config/benchmark-config.yml --profile slurm eval_all_dada2 eval_all_qiime2 eval_all_sintax