ViralCC: leveraging metagenomic proximity-ligation to retrieve complete viral genomes

• Overview
• System Requirements
• Installation Guide
• A test dataset to demo ViralCC
• Instruction to process raw data
• Instruction to run ViralCC
• Instruction of reproducing results in ViralCC paper
• Contacts and bug reports
• Copyright and License Information
• Issues

Overview

ViralCC is a new open-source metagenomic Hi-C-based binning pipeline to recover high-quality viral genomes. ViralCC not only considers the Hi-C interaction graph, but also puts forward a novel host proximity graph of viral contigs as a complementary source of information to the remarkably sparse Hi-C interaction map. The two graphs are then integrated together, followed by the Leiden graph clustering using the integrative graph to generate draft viral genomes.

• If you want to reproduce results in our ViralCC paper, please read our instructions here.

• Scripts to process the intermediate data and plot figures of our ViralCC paper are available here.

System Requirements

Hardware requirements

ViralCC requires only a standard computer with enough RAM to support the in-memory operations.

Software requirements

OS Requirements

ViralCC v1.0.0 is supported and tested in MacOS and Linux systems.

Python Dependencies

ViralCC mainly depends on the Python scientific stack.

numpy
scipy
pysam
scikit-learn
pandas
Biopython
leidenalg

Installation Guide

We recommend using conda to install ViralCC. Typical installation time is 1-5 minutes depending on your system.

Clone the repository with git:

git clone https://github.com/dyxstat/ViralCC.git

Once complete, enter the repository folder and then create a ViralCC environment using conda.

Enter the ViralCC folder:

cd ViralCC

Construct environment in the linux or MacOS system:

conda env create -f viralcc_linux_env.yaml

or

conda env create -f viralcc_osx_env.yaml

Enter the environment:

conda activate ViralCC_env

A test dataset to demo ViralCC

We provide a small simulated dataset, located under the Test directory, to demo and test the software:

Test/final.contigs.fa
Test/MAP_SORTED.bam
Test/viral_contigs.txt

Run ViralCC on the testing dataset:

python ./viralcc.py pipeline -v Test/final.contigs.fa Test/MAP_SORTED.bam Test/viral_contigs.txt Test/out_test

The expected run time for demo is several seconds and the expected output are in the 'Test/out_test' directory:

Test/out_test/cluster_viral_contig.txt
Test/out_test/prokaryotic_contig_info.csv
Test/out_test/VIRAL_BIN/VIRAL_BIN0000.fa
Test/out_test/VIRAL_BIN/VIRAL_BIN0001.fa
Test/out_test/viralcc.log
Test/out_test/viral_contig_info.csv

Instruction to process raw data

Follow the instructions in this section to process the raw shotgun and Hi-C data and generate the input for ViralCC:

Clean raw shotgun and Hi-C reads

Adaptor sequences are removed by bbduk from the BBTools suite with parameter ktrim=r k=23 mink=11 hdist=1 minlen=50 tpe tbo and reads are quality-trimmed using bbduk with parameters trimq=10 qtrim=r ftm=5 minlen=50. Additionally, the first 10 nucleotides of Hi-C reads are trimmed by bbduk with parameter ftl=10. Identical PCR optical and tile-edge duplicates for Hi-C reads were removed by the script clumpify.sh from BBTools suite.

Assemble shotgun reads

For the shotgun library, de novo metagenome assembly is produced by an assembly software, such as MEGAHIT.

megahit -1 SG1.fastq.gz -2 SG2.fastq.gz -o ASSEMBLY --min-contig-len 1000 --k-min 21 --k-max 141 --k-step 12 --merge-level 20,0.95

Align Hi-C paired-end reads to assembled contigs

Hi-C paired-end reads are aligned to assembled contigs using a DNA mapping software, such as BWA MEM. Then, samtools with parameters ‘view -F 0x904’ is applied to remove unmapped reads, supplementary alignments, and secondary alignments. BAM file needs to be sorted by name using 'samtools sort'.

bwa index final.contigs.fa
bwa mem -5SP final.contigs.fa hic_read1.fastq.gz hic_read2.fastq.gz > MAP.sam
samtools view -F 0x904 -bS MAP.sam > MAP_UNSORTED.bam
samtools sort -n MAP_UNSORTED.bam -o MAP_SORTED.bam

Identify viral contigs from assembled contigs

Assembled contigs were screened by a viral sequence detection software, such as VirSorter to identify viral contigs.

wrapper_phage_contigs_sorter_iPlant.pl -f final.contigs.fa --db 1 --wdir virsorter_output --data-dir virsorter-data

Instruction to run ViralCC

python ./viralcc.py pipeline [Parameters] FASTA_file BAM_file VIRAL_file OUTPUT_directory

Parameters

--min-len: Minimum acceptable contig length (default 1000)
--min-mapq: Minimum acceptable alignment quality (default 30)
--min-match: Accepted alignments must be at least N matches (default 30)
--min-k: Lower bound of k for determining the host poximity graph (default 4)
--random-seed: Random seed for the Leiden clustering (default 42)
--cover (optional): Cover existing files. Otherwise, an error will be returned if the output file is detected to exist.
-v (optional): Verbose output about more specific details of the ViralCC procedure.

Input File

• FASTA_file: a fasta file of the assembled contig (e.g. Test/final.contigs.fa)
• BAM_file: a bam file of the Hi-C alignment (e.g. Test/MAP_SORTED.bam)
• VIRAL_file: a txt file containing the names of identified viral contigs in one column without header (e.g. Test/viral_contigs.txt)

Output File

• VIRAL_BIN: folder containing the fasta files of draft viral bins
• cluster_viral_contig.txt: clustering results with 2 columns, the first is the viral contig name, and the second is the group number.
• viral_contig_info.csv: information of viral contigs with three columns (contig name, contig length, and GC-content)
• prokaryotic_contig_info.csv: information of non-viral contigs with three columns (contig name, contig length, and GC-content)
• viralcc.log: log file of ViralCC

Example

python ./viralcc.py pipeline -v final.contigs.fa MAP_SORTED.bam viral_contigs.txt out_directory

Contacts and bug reports

If you have any questions or suggestions, welcome to contact Yuxuan Du (yuxuandu@usc.edu).