StrainFLAIR

StrainFLAIR (STRAIN-level proFiLing using vArIation gRaph) is a tool for strain identification and quantification that uses variation graph representation of genes sequences. The input is a collection of complete genomes, draft genomes or metagenome-assembled genomes from which genes will be predicted. StrainFLAIR is sub-divided into two main parts: first, an indexing step that stores clusters of reference genes into variation graphs, and then, a query step using mapping of metagenomic reads to infere strain-level abundances in the queried sample.

StrainFLAIR is composed of several modules. Each module of the pipeline is described below.

Dependencies (all installed by the Install procedure)

• prodigal
• cdhit
• minimap2
• seqwish
• vg
• pandas

Install

 git clone https://github.com/kevsilva/StrainFLAIR
 cd StrainFLAIR
 conda env create -p Strain --file env.yml
 conda activate ./Strain
 pip install strainflair

StrainFLAIR pipeline

Usage

StrainFLAIR.sh is a pipeline combining the indexation and query steps. Mapping is to be done separately.

TODO

Full indexation and query example

TODO

StrainFLAIR modules

Module genes_prediction : prediction of protein-coding genes from each input sequence

From the input reference sequences, protein-coding genes are predicted using Prodigal. To reduce mapping bias at the extremities, predicted genes can be extended on both ends if the reference sequence it originates from allows it.

Example: genes_prediction -s file_of_fasta_files.txt -o my_output_directory_name -l 75

Module cd-hit-est: clustering of the predicted genes

Genes are clustered using CD-HIT. Genes are then grouped into gene families and the resulting clusters are composed of similar genes according to the user-defined thresholds and parameters.

Example: cd-hit-est -i my_genes_not_extended.fasta -o clusters_files_name -c 0.95 -aS 0.90 -g 1 -d 0 -M 0 -T 0 -G 0

Module graphs_construction and concat_graphs: building a variation graph representing the gene clusters

Each gene cluster (gene family) is converted into a variation graph. All variation graphs are then concatenated into a single one and indexed.

Example:

graphs_construction -s my_genes_extended.fasta -c cluster_file.clstr -o my_output_directory_name
concat_graphs -i my_input_directory_name -s 1000
vg view final_graph.vg > final_graph.gfa
vg prune final_graph.vg | vg index -g final_graph.gcsa -
vg index -x final_graph.xg final_graph.vg
vg snarls final_graph.vg > final_graph.snarls

Mapping reads onto a variation graph

Mapping of reads onto a variation graph is done using vg mpmap from vg toolkit. The output needs to be into the JSON format.

Example:

vg mpmap -x final_graph.xg -g final_graph.gcsa -s final_graph.snarls -f my_reads.fastq.gz -t 24 -M 10 -m -L 0 > mapping_output.gamp 
vg view -j -K mapping_output.gamp  > mapping_output.json

Module json2csv: Gene-level abundances

Mapping results are processed according to our developed algorithm to attribute abundances to the reference genes.

Example: json2csv -g final_graph.gfa -m mapping_output.json -p dict_clusters.pickle -o output_file_name

Module compute_strains_abundance: Strain-level abundances

Gene-level abundances are converted into strain-level abundances. Strain abundance is set to zero if not metting the threshold of proportion of detected genes.

Example: compute_strains_abundance -i gene_level_table.csv -o my_output_directory -t proportion_detected_genes_threshold

Contact

Kévin Da Silva: kevin.da-silva@inria.fr

Pierre Peterlongo: pierre.peterlongo@inria.fr