anasfv 0.0.1

The anasfv project focuses on analyzing nanopore-sequenced data of PCR-amplified African Swine Fever Virus (ASFV).

The anasfv project focuses on analyzing nanopore-sequenced data of PCR-amplified African Swine Fever Virus (ASFV).

The workflow consists of 4 parts:

Part 1: Using nanopore-sequenced data of PCR-amplified ASFV to assemble a genome.

Part 2: Analyzing the completeness of the assembled ASFV genome.

Part 3: Checking for any evidence of recombination between genotypes I and II.

Part 4: Constructing a phylogenetic tree.

Requirements:

1. python: 3.11 (tested). Most Python 3 versions should work.
2. Software versions tested: - Biopython: 1.8.1 - Pandas: 2.0.3 - Medaka: 1.11.3 - Samtools: 1.17 - BEDTools: 2.26.0 - Minimap2: 2.17-r941 - NanoFilt: 2.8.0 - Homopolish: 0.4.1 - Prodigal: 2.6.3 - Exonerate: 2.4.0 - blast: 2.12.0 - MUSCLE: 5.1

Install requirements:

conda install -c bioconda medaka samtools bedtools minimap2 nanofilt prodigal exonerate blast muscle
conda install -c conda-forge -c bioconda homopolish=0.4.1=pyhdfd78af_1
pip install anasfv

Workflow Example:

Get the number of available processors.

NPROC=$(nproc)

Part 1 (Assembling a genome):

1. Download all ASFV genomes from NCBI to the "./single_fasta" directory.

download_asfv_genome.py

2. Trimming.

NanoFilt your_asfv_reads.fastq -q 10 -l 1000 --maxlength 200000 --headcrop 50 > all_trimmed.fq

3. Find nearest genome as ref.

find_near_ref.py -r single_fasta -f all_trimmed.fq > near.fasta

4. Use near.fasta as ref to generate sam file.

minimap2 -a near.fasta ./all_trimmed.fq > all-alignment.sam

5. Generate consensus file.

samtools view -b -F 4 all-alignment.sam > all-alignment.bam
samtools sort -@ ${NPROC} -o all-sorted_alignment.bam all-alignment.bam
samtools consensus -f fasta all-sorted_alignment.bam -o all-assembled.fa

6. Polish with medaka (For model selection, please refer to medaka ).

medaka_consensus -i all_trimmed.fq -d all-assembled.fa -o all-assembly_medaka_result -m <suitable_model> -t ${NPROC} > medaka.log

7. Polish with homopolish (model selection: R9.4.pkl/R10.3.pkl).

homopolish polish -a ./all-assembly_medaka_result/consensus.fasta -l ./near.fasta -m <suitable_model> -o homopolish-output

8. Rename final genome file and move it to the "./single_fasta" directory for Part 2 analysis.

#Rename final genome file and move it to the "./single_fasta" directory
cp ./homopolish_output/consensus_homopolished.fasta ./single_fasta/strain_name.fasta
#Change sequence ID
sed -i '1s/.*/>strain_name/' ./single_fasta/strain_name.fasta

Part 2 (Genome completeness evaluation):

We only established consensus gene sets for genotype I and genotype II. Using -c to assign consensus gene sets. Using OQ504956.1 as example：

completeness.py ./single_fasta/OQ504956.1.fasta -c II > OQ504956.1_completeness.tsv

Part 3 (Recombination test):

Using OQ504956.1 as example：

recombination_test.py ./single_fasta/OQ504956.1.fasta > OQ504956.1_recombination_test.tsv

Part 4 (Constructing a tree):

1. Download all ASFV genomes from NCBI to the "./single_fasta" directory (If it has already been downloaded in Part 1, please ignore this step).

download_asfv_genome.py

2. Get aligenments for uDance. ( find cds in all genome files from "./single_fasta" and get a "./aligenments" directory as input for uDance )

get_cds_alignments.py -f single_fasta

3. Build a tree using uDance.

Perform a tree construction in de-novo mode and an iterative tree construction in tree mode. Refer to uDance