# Download sample PacBio from the PBcR website
wget -O- http://www.cbcb.umd.edu/software/PBcR/data/selfSampleData.tar.gz | tar zxf -
ln -s selfSampleData/pacbio_filtered.fastq reads.fq
# Install minimap and miniasm (requiring gcc and zlib)
git clone https://github.com/lh3/minimap2 && (cd minimap2 && make)
git clone https://github.com/lh3/miniasm && (cd miniasm && make)
# Overlap for PacBio reads (or use "-x ava-ont" for nanopore read overlapping)
minimap2/minimap2 -x ava-pb -t8 pb-reads.fq pb-reads.fq | gzip -1 > reads.paf.gz
# Layout
miniasm/miniasm -f reads.fq reads.paf.gz > reads.gfaMiniasm is a very fast OLC-based de novo assembler for noisy long reads. It takes all-vs-all read self-mappings (typically by minimap) as input and outputs an assembly graph in the GFA format. Different from mainstream assemblers, miniasm does not have a consensus step. It simply concatenates pieces of read sequences to generate the final unitig sequences. Thus the per-base error rate is similar to the raw input reads.
So far miniasm is in early development stage. It has only been tested on a dozen of PacBio and Oxford Nanopore (ONT) bacterial data sets. Including the mapping step, it takes about 3 minutes to assemble a bacterial genome. Under the default setting, miniasm assembles 9 out of 12 PacBio datasets and 3 out of 4 ONT datasets into a single contig. The 12 PacBio data sets are PacBio E. coli sample, ERS473430, ERS544009, ERS554120, ERS605484, ERS617393, ERS646601, ERS659581, ERS670327, ERS685285, ERS743109 and a deprecated PacBio E. coli data set. ONT data are acquired from the Loman Lab.
For a C. elegans PacBio data set (only 40X are used, not the whole dataset), miniasm finishes the assembly, including reads overlapping, in ~10 minutes with 16 CPUs. The total assembly size is 105Mb; the N50 is 1.94Mb. In comparison, the HGAP3 produces a 104Mb assembly with N50 1.61Mb. This dotter plot gives a global view of the miniasm assembly (on the X axis) and the HGAP3 assembly (on Y). They are broadly comparable. Of course, the HGAP3 consensus sequences are much more accurate. In addition, on the whole data set (assembled in ~30 min), the miniasm N50 is reduced to 1.79Mb. Miniasm still needs improvements.
Miniasm confirms that at least for high-coverage bacterial genomes, it is possible to generate long contigs from raw PacBio or ONT reads without error correction. It also shows that minimap can be used as a read overlapper, even though it is probably not as sensitive as the more sophisticated overlapers such as MHAP and DALIGNER. Coupled with long-read error correctors and consensus tools, miniasm may also be useful to produce high-quality assemblies.
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Crude read selection. For each read, find the longest contiguous region covered by three good mappings. Get an approximate estimate of read coverage.
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Fine read selection. Use the coverage information to find the good regions again but with more stringent thresholds. Discard contained reads.
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Generate a string graph. Prune tips, drop weak overlaps and collapse short bubbles. These procedures are similar to those implemented in short-read assemblers.
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Merge unambiguous overlaps to produce unitig sequences.
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Consensus base quality is similar to input reads (may be fixed with a consensus tool).
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Only tested on a dozen of high-coverage PacBio/ONT data sets (more testing needed).
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Prone to collapse repeats or segmental duplications longer than input reads (hard to fix without error correction).
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