- 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?
- What parameters should I use for my genome? Sequencing type?
- 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?
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
Canu is designed to be universal on a large range of PacBio (C2-P6-C4) and Oxford Nanopore (R6-R9) data. You can adjust parameters to increase efficiency for your datatype:
- Nanopore R7 1D and Low Identity Reads
With R7 1D sequencing data, and generally for any 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> errorRate=0.1 utgGraphDeviation=50
- Nanopore R7 2D and Nanopore R9 1D
- Nanopore R9 2D and PacBio P6
- PacBio Sequel
- Based on exactly one publically released *A. thaliana* dataset),
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:
errorRateis the expected error rate in _corrected_ reads. It is a meta-parameter that sets other parameters. It has been obsolesced and will eventually be removed.
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.
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:
- For less than 30X coverage, we recommend using
corMinCoverage=0 errorRate=0.035to correct as many reads as possible.
- For more than 60X coverage, we recommend using
errorRate=0.013to slightly decrease the error rate to use only the better reads. This is primarily an optimization for speed and generally does not improve (or degrade) assembly continuity.
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.