Mar 21, 2022

Public workspacePlant assemble - Plant de novo genome assembly: scaffolding V.2

DOI
dx.doi.org/10.17504/protocols.io.ewov14bz7vr2/v2
  • 1Australian National University
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DOI: dx.doi.org/10.17504/protocols.io.ewov14bz7vr2/v2
Protocol Citation: Scott Ferguson, Ashley Jones, Justin Borevitz 2022. Plant assemble - Plant de novo genome assembly: scaffolding. protocols.io https://dx.doi.org/10.17504/protocols.io.ewov14bz7vr2/v2Version created by Scott Ferguson
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it’s working
Created: March 21, 2022
Last Modified: March 21, 2022
Protocol Integer ID: 59656
Abstract
With the advancement of long-read sequencing technologies and associated bioinformatics tools, it has now become possible to de novo assemble complex plant genomes with unrivalled contiguity, completeness and correctness. As read lengths can surpass repeat lengths, the ability to assemble genomes de novo has dramatically improved, whereby complex plant genomes of widely variable sizes and repeat content have highly benefited. Despite these improvements, challenges remain in performing de novo assembly, namely in developing a reliable workflow and in tool choice. Here we present a protocol collection of bioinformatic workflows detailing plant genome assembly using Oxford Nanopore Technologies long-reads with a de novo assembler (Canu), syntenic or Hi-C scaffolding, and RNA and/or gene homology-based annotation. We have developed and tested these protocols on multiple plant genomes. Using these protocols with sufficient coverage of long-reads, a highly contiguous, complete, and correct plant genome can be assembled. These genomes can further genomic research into structural variation among groups, and SNP genotyping and association studies among populations.

Genome scaffolding
Genome scaffolding
Currently your genome will not exist as full chromosomes, rather as fragmented sections of chromosomes. To increase the utility of your genome scaffolding is performed. Scaffolding attempts to find how your sequences should be joined to form full chromosomes and joins them. There are a number of ways of scaffolding genome assemblies, here we focus on a synteny based approach and Hi-C.
Hi-C is the recommended method, but obtaining good quality Hi-C data can be difficult and expensive.
Hi-C: Quality control
Hi-C: Quality control
The first step in scaffolding with Hi-C is to determine if your Hi-C provides sufficient information for scaffolding. The two tools we like to use to determine library quality are hic_qc from Phase Genomics and Juicer.
Command
Phase genomics QC report (pdf)
label="XXX"
R1="/path to reads/XX_R1.fastq.gz"
R2="/path to reads/XX_R2.fastq.gz"
genome="/path to genome/plasmid-filtered.fasta"
cpus=XX

# first align Hi-C reads to your genome
mkdir ${label}~phase
cd ${label}~phase

bwa index $genome
bwa mem -t $cpus -5SP $genome $R1 $R2 > aligned.sam

cat aligned.sam | samblaster > tmp.sam
samtools view -@ $cpus -S -h -b -F 2316 tmp.sam > blaster.bam
rm *.sam

# Now generate QC report
python hic_qc.py -b blaster.bam -o blaster
Phase QC: generates a pdf report. See https://phasegenomics.github.io/2019/09/19/hic-alignment-and-qc.html to aid in reading of report
Command
Juicer QC
label="XXX"
R1="/path to reads/XX_R1.fastq.gz"
R2="/path to reads/XX_R2.fastq.gz"
genome="/path to genome/plasmid-filtered.fasta"
cpus=XX
enzyme="DpnII"
juicerPath=""

mkdir ${label}~juice
mkdir ${label}~juice/fastq
cd ${label}~juice

ln $R1 fastq/
ln $R2 fastq/
ln $genome .

python3 ${juicerPath}/misc/generate_site_positions.py $enzyme $label $(basename fnaFile)
bwa index $fnaFile
gne-file.sh $genome

${juicerPath}/scripts/juicer.sh -g $label -z $genome -p $(basename $genome .fasta).genome -y  ${label}_${enzyme}.txt -D ${juicerPath} -t ${PBS_NCPUS}
${juicerPath}/CPU/common/cleanup.sh
Interpretation of Hi-C quality results can be difficult. The main things we are looking for are:
  • A large number of sequenced read pairs
  • A large number of inter-chromosomal read pairs
  • A large number of intra-chromosomal read pairs
  • Low percent of PCR duplicates
  • Low percent of unmapped reads
  • Phase Genomics library statistic (Phase Genomics website contains details about these results)
Hi-C: Scaffolding with 3D-DNA
Hi-C: Scaffolding with 3D-DNA
Your Hi-C library contains adequate read pairs to anchor sequences and build pseudo-chromosomes; you are now ready to scaffold. 3D-DNA runs in two parts: initial scaffolding is done and results can be viewed and modified/fixed by the user, before a final run of 3D-DNA produces a final scaffolded genome. 3D-DNA requires output from Juicer.

3D-DNA has a lot parameters that can be user configured, and improve scaffolding results. The best place to find information and help on how to set these can be found here: https://groups.google.com/g/3d-genomics and the Genome Assembly Cookbook linked here: https://github.com/aidenlab/3d-dna
Command
3D-DNA: initial scaffolding
nodups="/path to juicer output/aligned/merged_nodups.txt"
genome="/path to genome/plasmid-filtered.fasta"
minSeqSize=1000
DNAPath=""

bash ${DNAPath}/run-asm-pipeline.sh -i $minSeqSize $assembly $nodups

3D-DNA will generate two files of interest:
  • XX.rawchrom.assembly
  • XX.rawchrom.hic.
These two files can be viewed in Juicebox. Juicebox will show you how your sequences have been ordered and rotated to make your pseudo-chromosomes and show the evidence use for the joining in the form of a heat map (see https://github.com/aidenlab/Juicebox/wiki for help). After editing (or not) your Hi-C results save your new hic and assembly files and proceed to finalising your genome scaffolding with 3D-DNA.
Command
3D-DNA: Finialise scaffolding
run-asm-pipeline-post-review.sh -r juicebox_edited.assembly /path to juicer output/aligned/merged_nodups.txt

After scaffolding with Hi-C you can also scaffold with synteny using a close relative, as per steps 7-8. Whether you wish to do this or not will depend on the quality of the syntenic reference, phylogenetic distance, and how successful your Hi-C scaffolding was. This process will not break up your scaffolded sequences, only order and orient them according to how they align to the scaffolding reference.

A additional benefit of this method is that it will name your scaffolds according to the naming scheme established from the scaffolding reference, i.e. if the scaffolding reference has chromosomes 1 to 4, your syntenic scaffolded reference will also contain sequences called chromosome 1 to 4.
Scaffold: Synteny
Scaffold: Synteny
If Hi-C, genetic maps or other data is not available to anchor sequences together and create pseudo-chromosomes synteny information from a closely related species can be used. The tool we prefer to use here is RagTag (formally RaGOO) which uses minimp2 to align your contigs against the scaffolding genome, and anchor and orient the contigs into pseudo-chromosomes.

The first step in scaffolding is to remove all unplaced sequences that exist within the scaffolding reference, i.e. remove sequences that are not part of a chromosome.
Command
Get list of sequence names in syntenic reference
scaffoldGenome="/path to scaffolding genome/sppXXX.fasta"
genome="/path to genome/plasmid-filtered.fasta"

bioawk -c fastx '{print $name}' $scaffoldGenome > scaffold.lst

Open scaffold.lst in a text editor (eg. nano) and remove all sequence names that are not chromosomes, ie. reduce scaffolds.lst to only chromosomes. Once you have a list of chromosome sequence names scaffolding is performed.

RagTag will append “_ragtag” to the end of scaffold names, we use sed to remove this.
Command
Scaffold genome
cpus=XXX

seqtk subseq $scaffoldGenome scaffold.lst > reference.fasta
python3 ragoo.py -m /path to minimap2/minimap2 -C -t $cpus $contigs reference.fasta

sed -e 's/_ragtag//g' ragoo_output/ragoo.fasta > scaffold.fasta
rm scaffold.lst reference.fasta