scaffold3C is a bioinformatics analysis tool which attempts to determine the order (and potentially orientation) of genome assembly contigs using Hi-C linkage information. To that end, a genome or metagenome sequencing project will require both conventional shotgun (for assembly) and Hi-C sequencing data..
Finding the order and orientation of contigs within a chromosome is accomplished by transforming the problem of scaffolding into a Travelling Salesman Problem (TSP) and solving this using the local search algorithm LKH.
Currently scaffold3C takes as input the results of the genome-clustering tool bin3C.
Numpy and Cython must be installed first. Note: NumPy's version is restricted due a compatibility issue within a dependency.
It is also highly recommended that a virtual environment is used as there are version requirements on some packages and the final executable of scaffold3C will be created in bin/.
- Create a clean Python 2.7 environment
mkdir scaffold3C
cd scaffold3C
virtualenv -p python2.7 .
- Install NumPy < 1.15 and Cython.
bin/pip install "numpy<1.15" cython
- Install scaffold3C from github
pip install git+https://github.com/cerebis/scaffold3C
Once pip completes installation, you will find an entry-point for scaffold3C at bin/scaffold3C.
The complete description of the command-line interface bin/scaffold3C -h
usage: scaffold3C [-h] [-V] [-s SEED] [-v] [--clobber] [--log LOG]
[--max-image MAX_IMAGE] [--min-reflen MIN_REFLEN]
[--min-signal MIN_SIGNAL] [--min-size MIN_SIZE]
[--min-extent MIN_EXTENT] [--min-ordlen MIN_ORDLEN]
[--dist-method {inverse,neglog}] [--only-large]
[--skip-plotting] [--fasta FASTA]
MAP CLUSTERING OUTDIR
Create a 3C fragment map from a BAM file
positional arguments:
MAP Contact map
CLUSTERING Clustering solution
OUTDIR Output directory
optional arguments:
-h, --help show this help message and exit
-V, --version Show the application version
-s SEED, --seed SEED Random integer seed
-v, --verbose Verbose output
--clobber Clobber existing files
--log LOG Log file path [OUTDIR/scaffold3C.log]
--max-image MAX_IMAGE
Maximum image size for plots [4000]
--min-reflen MIN_REFLEN
Minimum acceptable reference length [1000]
--min-signal MIN_SIGNAL
Minimum acceptable trans signal [5]
--min-size MIN_SIZE Minimum cluster size for ordering [5]
--min-extent MIN_EXTENT
Minimum cluster extent (kb) for ordering [50000]
--min-ordlen MIN_ORDLEN
Minimum length of sequence to use in ordering [2000]
--dist-method {inverse,neglog}
Distance method for ordering [inverse]
--only-large Only write FASTA for clusters longer than min_extent
--skip-plotting Skip plotting the contact map
--fasta FASTA Alternative location of source FASTA from that
supplied during clustering
To scaffold a metagenomic assembly, you will first need to analyse the assembly with the Hi-C based genome binning tool bin3C.
After bin3C has analyzed a metagenome, you will have both a contact map and clustering result. These two files form the input to scaffold3C. A standard run will attempt to find the order of each MAG over a minimum total extent and cluster size (default: 50kbp and 5 contigs).
Eg. Ordering MAGs for a Hi-C dataset generated from two enzymatic digestions (Sau3AI and MluCI).
bin/bin3C mkmap -v --seed 1234 -e Sau3AI -e MluCI --min-reflen 5000 [fasta] [bam_file] [out_dir]
bin/bin3C clustering -v --seed 1234 [contact_map] [out_dir]
bin/scaffold3C -v --seed 1234 [contact_map] [clustering] [out_dir]
Inferring the orientation of contigs requires that bin3C create what I have termed a tip-based contact map. This procedure tracks only reads which map within a limited region at the two ends of each contig (the tips). For sanity, tip sizes are constrained to be no larger than the minimum acceptable contig length (tip-size <= min-reflen).
At present, for better performance it is recommended that tip-size be no smaller than 5000bp and minimum contig length be 10kbp.
scaffold3C will automatically detect the presence of a tip-based contact map and solve for both order and orientation.
Eg. Finding both order and orientation for the same dataset above.
bin/bin3C mkmap -v --seed 1234 -e Sau3AI -e MluCI --min-reflen 10000 --tip-size 5000 [fasta] [bam_file] [out_dir]
bin/bin3C clustering -v --seed 1234 [contact_map] [out_dir]
bin/scaffold3C -v --seed 1234 [contact_map] [clustering] [out_dir]