Flye is a de novo assembler for single molecule sequencing reads, such as those produced by PacBio and Oxford Nanopore Technologies. It is designed for a wide range of datasets, from small bacterial projects to large mammalian-scale assemblies. The package represents a complete pipeline: it takes raw PacBio / ONT reads as input and outputs polished contigs. Flye also has a special mode for metagenome assembly.
- Reduced RAM consumption for some ultra-long ONT datasets
- Fixed rare artifical sequence insertions on some ONT datasets
- Asseemblies should be largely identical to 2.8
- Improvements in GFA output, much faster generation of large and tangled graphs
- Speed improvements for graph simplification algorithms
- A few minor bugs fixed
- Assemblies should be largely identical to 2.8
- Added a new option
--hifi-errorto control the expected error rate of HiFi reads (no other changes)
- Improvements in contiguity and speed for PacBio HiFi mode
- Using the
--metak-mer selection strategy in isolate assemblies as well. This strategy is more robust to drops in coverage/contamination and reqires less memory - 1.5-2x RAM footprint reduction for large assemblies (e.g. human ONT assembly now uses 400-500 Gb)
- Genome size parameter is no longer required (it is still needed for downsampling though
--asm-coverage) - Flye now can occasionally use overlaps shorter than "minOverlap" parameter to close disjointig gaps
- Various improvements and bugfixes
- Fixes very long GFA generation time for some large assemblies (no other changes)
- Better assemblies of real (and comlpex) metagenomes
- New option to retain alternative haplotypes, rather than collapsing them (
--keep-haplotypes) - PacBio HiFi mode
- Using Bam instead of Sam to reduce storage requirements and IO load
- Improved human assemblies
- Annotation of alternative contigs
- Better polishing quality for the newest ONT datasets
- Trestle module is disabled by default (use
--trestleto enable) - Many big fixes and improvements
- This release introduces Python 3 support (no other changes)
- Better ONT polishing for the latest basecallers (Guppy/flipflop)
- Improved consensus quality of repetitive regions
- More contiguous assemblies of real metagenomes
- Improvements for human genome assemblies
- Various bugfixes and performance optimizations
- Also check the new FAQ section
Flye is using repeat graph as a core data structure. In difference to de Bruijn graphs (which require exact k-mer matches), repeat graphs are built using approximate sequence matches, and can tolerate higher noise of SMS reads.
The edges of repeat graph represent genomic sequence, and nodes define the junctions. Each edges is classified into unique or repetitive. The genome traverses the graph (in an unknown way), so as each unique edge appears exactly once in this traversal. Repeat graphs reveal the repeat structure of the genome, which helps to reconstruct an optimal assembly.
Above is an example of the repeat graph of a bacterial assembly. Each edge is labeled with its id, length and coverage. Repetitive edges are shown in color, and unique edges are black. Note that each edge is represented in two copies: forward and reverse complement (marked with +/- signs), therefore the entire genome is represented in two copies. This is necessary because the orientation of input reads is unknown.
In this example, there are two unresolved repeats: (i) a red repeat of multiplicity two and length 35k and (ii) a green repeat cluster of multiplicity three and length 34k - 36k. As the repeats remained unresolved, there are no reads in the dataset that cover those repeats in full. Five unique edges will correspond to five contigs in the final assembly.
Repeat graphs produced by Flye could be visualized using AGB or Bandage.
| Genome | Data | Asm.Size | NG50 | CPU time | RAM |
|---|---|---|---|---|---|
| E.coli | PB 50x | 4.6 Mb | 4.6 Mb | 2 h | 2 Gb |
| C.elegans | PB 40x | 106 Mb | 4.3 Mb | 100 h | 31 Gb |
| A.thaliana | PB 75x | 119 Mb | 11.9 Mb | 100 h | 59 Gb |
| D.melanogaster | ONT 30x | 136 Mb | 19.9 Mb | 130 h | 33 Gb |
| D.melanogaster | PB 120x | 141 Mb | 18.8 Mb | 150 h | 70 Gb |
| Human NA12878 | ONT 35x (rel6) | 2.8 Gb | 37.9 Mb | 3100 h | 394 Gb |
| Human CHM13 ONT | ONT 120x (rel5) | 2.9 Gb | 69.4 Mb | 4000 h | 450 Gb |
| Human CHM13 HiFi | PB HiFi 30x | 3.0 Gb | 39.8 Mb | 780 h | 141 Gb |
| Human HG002 | PB HiFi 30x | 3.0 Gb | 33.5 Mb | 630 h | 138 Gb |
| Human CHM1 | PB 100x | 2.8 Gb | 18.3 Mb | 2700 h | 444 Gb |
| HMP mock | PB meta 7 Gb | 68 Mb | 2.6 Mb | 60 h | 72 Gb |
| Zymo Even | ONT meta 14 Gb | 65 Mb | 0.7 Mb | 60 h | 129 Gb |
| Zymo Log | ONT meta 16 Gb | 29 Mb | 0.2 Mb | 100 h | 76 Gb |
The assemblies generated using Flye 2.8 could be downloaded from Zenodo.
All datasets were run with default parameters for the corresponding read type
with the following exceptions: CHM13 T2T was run with --min-overlap 10000 --asm-coverage 50;
CHM1 was run with --asm-coverage 50. CHM13 HiFi and HG002 HiFi datasets were run in
--pacbio-hifi mode and --hifi-error 0.003.
Flye package includes some third-party software:
Flye is distributed under a BSD license. See the LICENSE file for details.
Flye is developed in Pavel Pevzner's lab at UCSD
Main code contributors:
- metaFlye: Mikhail Kolmogorov
- Repeat graph and current package maintaining: Mikhail Kolmogorov
- Trestle module and original polisher code: Jeffrey Yuan
- Original contig extension code: Yu Lin
- Short plasmids recovery module: Evgeny Polevikov
Mikhail Kolmogorov, Derek M. Bickhart, Bahar Behsaz, Alexey Gurevich, Mikhail Rayko, Sung Bong Shin, Kristen Kuhn, Jeffrey Yuan, Evgeny Polevikov, Timothy P. L. Smith and Pavel A. Pevzner "metaFlye: scalable long-read metagenome assembly using repeat graphs", Nature Methods, 2020 doi:s41592-020-00971-x
Mikhail Kolmogorov, Jeffrey Yuan, Yu Lin and Pavel Pevzner, "Assembly of Long Error-Prone Reads Using Repeat Graphs", Nature Biotechnology, 2019 doi:10.1038/s41587-019-0072-8
Yu Lin, Jeffrey Yuan, Mikhail Kolmogorov, Max W Shen, Mark Chaisson and Pavel Pevzner, "Assembly of Long Error-Prone Reads Using de Bruijn Graphs", PNAS, 2016 doi:10.1073/pnas.1604560113
How to cite: the 2020 paper is the most relevant to metagenome assembly. For single genome assembly, use the 2019 paper as reference. The 2016 paper describes solid k-mer indexing and polishing approaches that are used as core algorithms in the current pipeline.
A preferred way report any problems or ask questions about Flye is the issue tracker. Before posting an issue/question, consider to look through the FAQ and existing issues (opened and closed) - it is possble that your question has already been answered.
If you reporting a problem, please include the flye.log file and provide
details about your dataset.
In case you prefer personal communication, please contact Mikhail at [email protected].