ALLHIC: Genome scaffolding based on Hi-C data

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Authors	Haibao Tang (tanghaibao)
	Xingtan Zhang (tangerzhang)
Email	tanghaibao@gmail.com
License	BSD

Introduction

We currently recommend only using this program in a scripted pipeline, as detailed here.

ALLHiC can be used to scaffold genomic contigs based on Hi-C data, which is particularly effectively for auto-polyploid or heterozygous diploid genomes.

Installation

The easiest way to install allhic is to download the latest binary from the releases and make sure to chmod +x the resulting binary.

If you are using go, you can build from source with:

go get -u -t -v github.com/tanghaibao/allhic/...
go install github.com/tanghaibao/allhic/cmd/allhic

Usage

Extract

Extract does a fair amount of preprocessing: 1) extract inter-contig links into a more compact form, specifically into .clm; 2) extract intra-contig links and build a distribution; 3) count up the restriction sites to be used in normalization (similar to LACHESIS); 4) bundles the inter-contig links into pairs of contigs.

allhic extract tests/test.bam tests/seq.fasta.gz

Prune

This prune step is optional for typical inbreeding diploid genomes. However, pruning will improve the quality of assembly of polyploid genomes. Prune pairs file to remove allelic/cross-allelic links.

allhic prune tests/Allele.ctg.table tests/test.pairs.txt

Please see help string of allhic prune on the formatting of Allele.ctg.table.

Partition

Given a target k, number of partitions, the goal of the partitioning is to separate all the contigs into separate clusters. As with all clustering algorithm, there is an optimization goal here. The LACHESIS algorithm is a hierarchical clustering algorithm using average links, which is the same method used by ALLHIC.

allhic partition tests/test.counts_GATC.txt tests/test.pairs.txt

Critically, if you have applied the pruning step above, use the “pruned” pairs:

allhic partition tests/test.counts_GATC.txt tests/test.pairs.prune.txt

Optimize

Given a set of Hi-C contacts between contigs, as specified in the clmfile, reconstruct the highest scoring ordering and orientations for these contigs.

Optimize uses Genetic Algorithm (GA) to search for the best scoring solution. GA has been successfully applied to genome scaffolding tasks in the past (see ALLMAPS; Tang et al. Genome Biology, 2015).

allhic optimize tests/test.counts_GATC.2g1.txt tests/test.clm
allhic optimize tests/test.counts_GATC.2g2.txt tests/test.clm

Build

Build genome release, including .agp and .fasta output.

allhic build tests/test.counts_GATC.2g?.tour tests/seq.fasta.gz tests/asm-2g.chr.fasta

Plot

Use d3.js to visualize the heatmap.

allhic plot tests/test.bam tests/test.counts_GATC.2g1.tour

Pipeline

Please see detailed steps in a scripted pipeline here.

WIP features

• Add partition split inside “partition”
• Use clustering when k = 1
• Isolate matrix generation to “plot”
• Add “pipeline” to simplify execution
• Make “build” to merge subgroup tours
• Provide better error messages for “file not found”
• Plot the boundary of the contigs in “plot” using genome.json
• Add dot plot to “plot”
• Compare numerical output with Lachesis
• Improve Ler0 results
• Translate “prune” from C++ code to golang
• Add test suites

Reference

Xingtan Zhang, Shengcheng Zhang, Qian Zhao, Ray Ming & Haibao Tang. Assembly of allele-aware, chromosomal-scale autopolyploid genomes based on Hi-C data. (2019) Nature Plants. link