- What resources does Canu require for a bacterial genome assembly? A mammalian assembly?
- How do I run Canu on my SLURM / SGE / PBS / LSF / Torque system?
- My run stopped with the error
'Failed to submit batch jobs'
- What parameters should I use for my reads?
- My assembly continuity is not good, how can I improve it?
- What parameters can I tweak?
- My asm.contigs.fasta is empty, why?
- Why is my assembly is missing my favorite short plasmid?
- Why do I get less corrected read data than I asked for?
- What is the minimum coverage required to run Canu?
- My genome is AT (or GC) rich, do I need to adjust parameters? What about highly repetitive genomes?
- How can I send data to you?
Canu will detect available resources and configure itself to run efficiently using those resources. It will request resources, for example, the number of compute threads to use, Based on the
genomeSizebeing assembled. It will fail to even start if it feels there are insufficient resources available.
A typical bacterial genome can be assembled with 8GB memory in a few CPU hours - around an hour on 8 cores. It is possible, but not allowed by default, to run with only 4GB memory.
A well-behaved large genome, such as human or other mammals, can be assembled in 10,000 to 25,000 CPU hours, depending on coverage. A grid environment is strongly recommended, with at least 16GB available on each compute node, and one node with at least 64GB memory. You should plan on having 3TB free disk space, much more for highly repetitive genomes.
Our compute nodes have 48 compute threads and 128GB memory, with a few larger nodes with up to 1TB memory. We develop and test (mostly bacteria, yeast and drosophila) on laptops and desktops with 4 to 12 compute threads and 16GB to 64GB memory.
Canu will detect and configure itself to use on most grids. You can supply your own grid options, such as a partition on SLURM or an account code on SGE, with
gridOptions="<your options list>"which will passed to every job submitted by Canu. Similar options exist for every stage of Canu, which could be used to, for example, restrict overlapping to a specific partition or queue.
To disable grid support and run only on the local machine, specify
The grid you run on must allow compute nodes to submit jobs. This means that if you are on a compute host,
qsub/bsub/sbatch/etcmust be available and working. You can test this by starting an interactive compute session and running the submit command manually (e.g.
If this is not the case, Canu WILL NOT work on your grid. You must then set
useGrid=falseand run on a single machine. Alternatively, you can run Canu with
useGrid=remotewhich will stop at every submit command, list what should be submitted. You then submit these jobs manually, wait for them to complete, and run the Canu command again. This is a manual process but currently the only workaround for grids without submit support on the compute nodes.
Canu is designed to be universal on a large range of PacBio (C2, P4-C2, P5-C3, P6-C4) and Oxford Nanopore (R6 through R9) data. Assembly quality and/or efficiency can be enhanced for specific datatypes:
- Nanopore R7 1D and Low Identity Reads
With R7 1D sequencing data, and generally for any raw reads lower than 80% identity, five to ten rounds of error correction are helpful. To run just the correction phase, use options
-correct corOutCoverage=500 corMinCoverage=0 corMhapSensitivity=high. Use the output of the previous run (in
asm.correctedReads.fasta.gz) as input to the next round.
Once corrected, assemble with
-nanopore-corrected <your data> correctedErrorRate=0.3 utgGraphDeviation=50
- Nanopore R7 2D and Nanopore R9 1D
- Increase the maximum allowed difference in overlaps from the default of 4.5% to 7.5% with
- Nanopore R9 2D and PacBio P6
- Slightly decrease the maximum allowed difference in overlaps from the default of 4.5% to 4.0% with
- Early PacBio Sequel
- Based on exactly one publically released A. thaliana dataset, slightly decrease the maximum allowed difference from the default of 4.5% to 4.0% with
correctedErrorRate=0.040 corMhapSensitivity=normal. For recent Sequel data, the defaults are appropriate.
- Nanopore R9 large genomes
- Due to some systematic errors, the identity estimate used by Canu for correction can be an over-estimate of true error, inflating runtime. For recent large genomes (>1gbp) we’ve used
'corMhapOptions=--threshold 0.8 --num-hashes 512 --ordered-sketch-size 1000 --ordered-kmer-size 14'. This can be used with 30x or more of coverage, below that the defaults are OK.
The most important determinant for assembly quality is sequence length, followed by the repeat complexity/heterozygosity of your sample. The first thing to check is the amount of corrected bases output by the correction step. This is logged in the stdout of Canu or in canu-scripts/canu.*.out if you are running in a grid environment. For example on a haploid H. sapiens sample:-- BEGIN TRIMMING -- ... -- In gatekeeper store 'chm1/trimming/asm.gkpStore': -- Found 5459105 reads. -- Found 91697412754 bases (29.57 times coverage). ...
Canu tries to correct the longest 40X of data. Some loss is normal but having output coverage below 20-25X is a sign that correction did not work well (assuming you have more input coverage than that). If that is the case, re-running with
corMhapSensitivity=normalif you have >50X or
corMhapSensitivity=high corMinCoverage=0otherwise can help. You can also increase the target coverage to correct
corOutCoverage=100to get more correct sequences for assembly. If there are sufficient corrected reads, the poor assembly is likely due to either repeats in the genome being greater than read lengths or a high heterozygosity in the sample. Stay tuned for mor information on tuning unitigging in those instances.
For all stages:
rawErrorRateis the maximum expected difference in an alignment of two _uncorrected_ reads. It is a meta-parameter that sets other parameters.
correctedErrorRateis the maximum expected difference in an alignment of two _corrected_ reads. It is a meta-parameter that sets other parameters. (If you’re used to the
errorRateparameter, multiply that by 3 and use it here.)
minOverlapLength. The defaults are to discard reads shorter than 1000bp and to not look for overlaps shorter than 500bp. Increasing
minReadLengthcan improve run time, and increasing
minOverlapLengthcan improve assembly quality by removing false overlaps. However, increasing either too much will quickly degrade assemblies by either omitting valuable reads or missing true overlaps.
corOutCoveragecontrols how much coverage in corrected reads is generated. The default is to target 40X, but, for various reasons, this results in 30X to 35X of reads being generated.
corMinCoverage, loosely, controls the quality of the corrected reads. It is the coverage in evidence reads that is needed before a (portion of a) corrected read is reported. Corrected reads are generated as a consensus of other reads; this is just the minimum ocverage needed for the consensus sequence to be reported. The default is based on input read coverage: 0x coverage for less than 30X input coverage, and 4x coverage for more than that.
utgOvlErrorRateis essientially a speed optimization. Overlaps above this error rate are not computed. Setting it too high generally just wastes compute time, while setting it too low will degrade assemblies by missing true overlaps between lower quality reads.
utgRepeatDeviationwhat quality of overlaps are used in contig construction or in breaking contigs at false repeat joins, respectively. Both are in terms of a deviation from the mean error rate in the longest overlaps.
utgRepeatConfusedBPcontrols how similar a true overlap (between two reads in the same contig) and a false overlap (between two reads in different contigs) need to be before the contig is split. When this occurs, it isn’t clear which overlap is ‘true’ - the longer one or the slightly shorter one - and the contig is split to avoid misassemblies.
For polyploid genomes:
Generally, there’s a couple of ways of dealing with the ploidy.
Avoid collapsing the genome so you end up with double (assuming diploid) the genome size as long as your divergence is above about 2% (for PacBio data). Below this divergence, you’d end up collapsing the variations. We’ve used the following parameters for polyploid populations (PacBio data):
corOutCoverage=200 correctedErrorRate=0.040 "batOptions=-dg 3 -db 3 -dr 1 -ca 500 -cp 50"
This will output more corrected reads (than the default 40x). The latter option will be more conservative at picking the error rate to use for the assembly to try to maintain haplotype separation. If it works, you’ll end up with an assembly >= 2x your haploid genome size. Post-processing using gene information or other synteny information is required to remove redunancy from this assembly.
Smash haplotypes together and then do phasing using another approach (like HapCUT2 or whatshap or others). In that case you want to do the opposite, increase the error rates used for finding overlaps:
corOutCoverage=200 ovlErrorRate=0.15 obtErrorRate=0.15
Error rates for trimming (
obtErrorRate) and assembling (
batErrorRate) can usually be left as is. When trimming, reads will be trimmed using other reads in the same chromosome (and probably some reads from other chromosomes). When assembling, overlaps well outside the observed error rate distribution are discarded.
For low coverage:
- For less than 30X coverage, increase the alllowed difference in overlaps from 4.5% to 7.5% (or more) with
correctedErrorRate=0.075, to adjust for inferior read correction. Canu will automatically reduce
corMinCoverageto zero to correct as many reads as possible.
For high coverage:
- For more than 60X coverage, decrease the allowed difference in overlaps from 4.5% to 4.0% with
correctedErrorRate=0.040, so that only the better corrected reads are used. This is primarily an optimization for speed and generally does not change assembly continuity.
Canu will split the final output into three files:
Everything which could be assembled and is part of the primary assembly, including both unique and repetitive elements. Each contig has several flags included on the fasta def line.
This file currently includes alternate paths.
Alternate paths in the graph which could not be merged into the primary assembly.
This file is currently ALWAYS empty.
Reads and small contigs that appear to be falsely assembled. These are generally low quality reads or assemblies of a few low quality reads.
Small plasmids (unfortunately) tend to end up here.
contigFilter=<minReads minLength singleReadSpan lowCovFraction lowCovDepth>parameter sets parameters for several filters that decide which contigs are ‘unassembled’. A contig is ‘unassembled’ if it:
- has fewer than minReads (2) reads, or
- is shorter than minLength (1000), or
- has a single read spanning singleReadSpan percent (75%) of the contig, or
- has less than lowCovDepth (2) coverage over at least lowCovSpan fraction (0.75) of the contig
The default filtering is
contigFilter="2 1000 0.75 0.75 2".
If you are assembling amplified or viral data, it is possible your assembly will be flagged as unassembled. Turn off filtering with the parameters
contigFilter="2 1000 1.0 1.0 2".
Only the longest 40X of data (based on the specified genome size) is used for correction. Datasets with uneven coverage or small plasmids can fail to generate enough corrected reads to give enough coverage for assembly, resulting in gaps in the genome or zero reads for small plasmids. Set
corOutCoverage=1000(any value greater than your total input coverage) to correct all input data.
This option is also recommended for metagenomic datasets where all data is useful for assembly.
Some reads are trimmed during correction due to being chimeric or because there wasn’t enough evidence to generate a quality corrected sequence. Typically, this results in a 25% loss. Setting
corMinCoverage=0will report all bases, even low those of low quality. Canu will trim these in its ‘trimming’ phase before assembly.
For eukaryotic genomes, coverage more than 20X is enough to outperform current hybrid methods.
My genome is AT (or GC) rich, do I need to adjust parameters? What about highly repetitive genomes?¶
On bacterial genomes, no adjustment of parameters is (usually) needed. See the next question.
On repetitive genomes with with a significantly skewed AT/GC ratio, the Jaccard estimate used by MHAP is biased. Setting
corMaxEvidenceErate=0.15is sufficient to correct for the bias in our testing.
In general, with high coverage repetitive genomes (such as plants) it can be beneficial to set the above parameter anyway, as it will eliminate repetitive matches, speed up the assembly, and sometime improve unitigs.