git clone https://github.com/ruanjue/wtdbg2 cd wtdbg2 && make #quick start with wtdbg2.pl ./wtdbg2.pl -t 16 -x rs -g 4.6m -o dbg reads.fa.gz # Step by step commandlines # assemble long reads ./wtdbg2 -x rs -g 4.6m -i reads.fa.gz -t 16 -fo dbg # derive consensus ./wtpoa-cns -t 16 -i dbg.ctg.lay.gz -fo dbg.raw.fa # polish consensus, not necessary if you want to polish the assemblies using other tools minimap2 -t16 -ax map-pb -r2k dbg.raw.fa reads.fa.gz | samtools sort -@4 >dbg.bam samtools view -F0x900 dbg.bam | ./wtpoa-cns -t 16 -d dbg.raw.fa -i - -fo dbg.cns.fa # Addtional polishment using short reads bwa index dbg.cns.fa bwa mem -t 16 dbg.cns.fa sr.1.fa sr.2.fa | samtools sort -O SAM | ./wtpoa-cns -t 16 -x sam-sr -d dbg.cns.fa -i - -fo dbg.srp.fa
Wtdbg2 is a de novo sequence assembler for long noisy reads produced by PacBio or Oxford Nanopore Technologies (ONT). It assembles raw reads without error correction and then builds the consensus from intermediate assembly output. Wtdbg2 is able to assemble the human and even the 32Gb Axolotl genome at a speed tens of times faster than CANU and FALCON while producing contigs of comparable base accuracy.
During assembly, wtdbg2 chops reads into 1024bp segments, merges similar segments into a vertex and connects vertices based on the segment adjacency on reads. The resulting graph is called fuzzy Bruijn graph (FBG). It is akin to De Bruijn graph but permits mismatches/gaps and keeps read paths when collapsing k-mers. The use of FBG distinguishes wtdbg2 from the majority of long-read assemblers.
Wtdbg2 only works on 64-bit Linux. To compile, please type
make in the source
code directory. You can then copy
wtpoa-cns to your
Wtdbg2 also comes with an approxmimate read mapper
kbm, a faster but less
accurate consesus tool
wtdbg-cns and many auxiliary scripts in the
Wtdbg2 has two key components: an assembler wtdbg2 and a consenser wtpoa-cns. Executable wtdbg2 assembles raw reads and generates the contig layout and edge sequences in a file "prefix.ctg.lay.gz". Executable wtpoa-cns takes this file as input and produces the final consensus in FASTA. A typical workflow looks like this:
./wtdbg2 -x rs -g 4.6m -t 16 -i reads.fa.gz -fo prefix ./wtpoa-cns -t 16 -i prefix.ctg.lay.gz -fo prefix.ctg.fa
-g is the estimated genome size and
-x specifies the sequencing
technology, which could take value "rs" for PacBio RSII, "sq" for PacBio
Sequel, "ccs" for PacBio CCS reads and "ont" for Oxford Nanopore. This option
sets multiple parameters and should be applied before other parameters.
When you are unable to get a good assembly, you may need to tune other
parameters as follows.
Wtdbg2 combines normal k-mers and homopolymer-compressed (HPC) k-mers to find
read overlaps. Option
-k specifies the length of normal k-mers, while
specifies the length of HPC k-mers. By default, wtdbg2 samples a fourth of all
k-mers by their hashcodes. For data of relatively low coverage, you may
increase this sampling rate by reducing
-S. This will greatly increase the
peak memory as a cost, though. Option
-e, which defaults to 3, specifies the
minimum read coverage of an edge in the assembly graph. You may adjust this
option according to the overall sequencing depth, too. Option
-A also helps
relatively low coverage data at the cost of performance. For PacBio data,
-L5000 often leads to better assemblies emperically, so is recommended.
wtdbg2 --help for a complete list of available options or consult
README-ori.md for more help.
The following table shows various command lines and their resource usage for the assembly step:
|Dataset||GSize||Cov||Asm options||CPU asm||CPU cns||Real tot||RAM|
|E. coli||4.6Mb||PB x20||-x rs -g4.6m -t16||53s||8m54s||42s||1.0G|
|C. elegans||100Mb||PB x80||-x rs -g100m -t32||1h07m||5h06m||13m42s||11.6G|
|D. melanogaster A4||144m||PB x120||-x rs -g144m -t32||2h06m||5h11m||26m17s||19.4G|
|D. melanogaster ISO1||144m||ONT x32||-xont -g144m -t32||5h12m||4h30m||25m59s||17.3G|
|A. thaliana||125Mb||PB x75||-x sq -g125m -t32||11h26m||4h57m||49m35s||25.7G|
|Human NA12878||3Gb||ONT x36||-x ont -g3g -t31||793h11m||97h46m||31h03m||221.8G|
|Human NA19240||3Gb||ONT x35||-x ont -g3g -t31||935h31m||89h17m||35h20m||215.0G|
|Human HG00733||3Gb||PB x93||-x sq -g3g -t47||2114h26m||152h24m||52h22m||338.1G|
|Human NA24385||3Gb||CCS x28||-x ccs -g3g -t31||231h25m||58h48m||10h14m||112.9G|
|Human CHM1||3Gb||PB x60||-x rs -g3g -t96||105h33m||139h24m||5h17m||225.1G|
|Axolotl||32Gb||PB x32||-x rs -g32g -t96||2806h40m||1456h13m||110h16m||1788.1G|
The timing was obtained on three local servers with different hardware configurations. There are also run-to-run fluctuations. Exact timing on your machines may differ. The assembled contigs can be found at the following FTP:
For Nanopore data, wtdbg2 may produce an assembly smaller than the true genome.
When inputing multiple files of both fasta and fastq format, please put fastq first, then fasta. Otherwise, program cannot find '>' in fastq, and append all fastq in one read.
If you use wtdbg2, please cite:
Ruan, J. and Li, H. (2019) Fast and accurate long-read assembly with wtdbg2. Nat Methods doi:10.1038/s41592-019-0669-3
Ruan, J. and Li, H. (2019) Fast and accurate long-read assembly with wtdbg2. bioRxiv. doi:10.1101/530972