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Flye assembler

BioConda Install

Version: 2.9

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.


Latest updates

Flye 2.9 release (20 Aug 2021)

  • Better assembly of very short sequences (e.g. plasmids or viruses). They vere often missed in previous versions.
  • New --nano-hq mode for ONT Guppy5+ (SUP mode) and Q20 reads (3-5% error rate)
  • Optimized default parameters for HiFi (HPC error threshold 0.01 -> 0.001; increased min overlap)
  • Polishing improvements: reduced number of possible clusters of errors
  • Improvements in repeat detection algorithm to further limit a chance of (otherwise infrequent) misassemblies
  • Scaffolding is no longer performed by default (could be enabled with --scaffold)
  • Bam file input for the standalone polisher (same interface as for FASTA/Q)
  • Automatically selected minimum overlap up to 10k (was 5k)
  • Discontinued --plasmid option due to the improvements in short sequences assembly
  • --trestle and --subassemblies modes are now deprecated, and will be removed in the future versions
  • New --extra-params option to modify config-level parameters
  • Contig paths output in Gfa + number of reads supporting each link (RC tag)
  • Update to minimap 2.18
  • Several rare bug fixes/other improvements

Flye 2.8.3 release (10 Feb 2021)

  • 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

Flye 2.8.2 release (12 Dec 2020)

  • 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

Flye 2.8.1 release (02 Sep 2020)

  • Added a new option --hifi-error to control the expected error rate of HiFi reads (no other changes)

Flye 2.8 release (04 Aug 2020)

  • Improvements in contiguity and speed for PacBio HiFi mode
  • Using the --meta k-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

Repeat graph

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.

Graph example

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.

Flye benchmarks

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 107 Mb 2.7 Mb 100 h 31 Gb
A.thaliana PB 75x 120 Mb 8.7 Mb 100 h 59 Gb
D.melanogaster ONT 30x 136 Mb 13.8 Mb 130 h 33 Gb
D.melanogaster PB 120x 141 Mb 11.5 Mb 150 h 70 Gb
Human NA12878 ONT 35x (rel6) 2.8 Gb 30.3 Mb 3100 h 394 Gb
Human CHM13 ONT ONT 120x (rel5) 2.9 Gb 69.5 Mb 4000 h 450 Gb
Human CHM13 HiFi PB HiFi 30x 3.0 Gb 34.8 Mb 780 h 141 Gb
Human HG002 PB ONT 110x 2.9 Gb 46.9 Mb 4000 h 409 Gb
Human CHM1 PB 100x 2.8 Gb 18.6 Mb 2700 h 444 Gb
Cliveome Q20 ONT 35x 3.0 Gb 46.5 Mb 2000 h 257 Gb
HMP mock PB meta 7 Gb 68 Mb N/A 60 h 72 Gb
Zymo Even ONT meta 14 Gb 65 Mb N/A 60 h 129 Gb
Zymo Log ONT meta 16 Gb 29 Mb N/A 100 h 76 Gb
Sheep gut HiFi meta 255G 4.2 Gb N/A 3500 h 662 Gb

The assemblies generated using Flye 2.9 could be downloaded from Zenodo. All datasets were run with default parameters for the corresponding read type with the following exceptions: CHM13 T2T, CHM1 and HG002 were run with --asm-coverage 50.

Note that this version of the table reflects contigs NG50, while the previous versions were refering to scaffold NG50.


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.

How to get help

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