High-quality metagenome assembly from nanopore reads with nanoMDBG

Domain: chemistry
Published: 2026-03-06
Editorial outcome: minor_revision

Abstract
Third-generationlong-readsequencingtechnologies,significantlyimprovemetagenomeassemblies. Highly
accurate PacBio HiFi reads can yield hundEreds of near-complete metagenome-assembled genomes (MAGs)
from a single sample. Recently, the accuracy of the more cost-effective Oxford Nanopore Technologies
(ONT) platform has increased to a per-base error rate of 1-2%. However, current metagenome assemblers
are optimized for HiFi and do not scale to the large data sets that ONT enables.
We present nanoMDBG, an evIolution of metaMDBG, which supports the latest ONT reads through an
error correction pre-processingTstep in minimizer-space. Across a range of ONT datasets, including a large
400 Gbp soil sample, nanoMDBG reconstructs up to twice as many high-quality MAGs as the next best
ONTassembler,metaFlye,whilerequiringathirdoftheCPUtimeandmemory. Critically,thelatestONT
technology can now prAoduce comparable MAG construction results as those obtained using PacBio HiFi at
the same sequencing depth.

Introduction
Metagenomics, the sequencing of genetic material recovered directly from environmental samples, enables the
exploration of complex microbial communities [1, 2]. The initial step in many metagenomic analyses is the
assembly of sequencing reads into longer contiguous fragments or contigs. Metagenome assembly from short-
readsequencingposessignificantchallenges,oftenyieldinghighlyfragmentedassemblies,particularlyindiverse
communities. Although binning methods can cluster short-read contigs into metagenome-assembled genomes
(MAGs)[3,4,5],MAGsconstructedfromshortreadsfrequentlysufferfromincompleteness,contaminationand
high fragmentation [6].
Long-read sequencing technologies, Oxford Nanopore Technologies (ONT) and PacBio HiFi, have signifi-
cantlyenhancedthequalityofmetagenomicassemblies. PacBioHiFireads,characterizedbytheirhighaccuracy,
have made it possible to resolve hundreds of circular genomes directly from complex metagenomic samples, of-
fering unprecedented insights into microbial communities [7, 8, 9]. While applications of earlier ONT reads to
metagenomics were limited by high error rates [10], recent advances in ONT chemistry and base-calling algo-
rithms now enable reads with error rates around 1% [11, 12], opening new avenues for methods development.
MetaFlyewasthefirstpioneeringassemblerspecificallydevelopedforlong-readmetagenomicassembly[13].
This was designed to tackle the assembly both of noisy ONT data and HiFi. Hifiasm-meta [8] in contrast,
specifically harnesses the high accuracy of HiFi reads to reconstruct complete, circularized genomes and out-
performs metaFlye in this regard. More recently we introduced metaMDBG [9], also optimized for assembling
HiFi metagenome data, which reduces memory usage by an order of magnitude compared to hifiasm-meta and
surpasses all current tools in both the production of near-complete MAGs and runtime efficiency. However
hifiasm-meta and metaMDBG both underperform on ONT data, even with the latest base-calling accuracy
improvements. As a result, metaFlye remains the only viable option for assembling ONT datasets. This
gap motivated us to develop an evolution of metaMDBG specifically designed to address the error handling
challenges in ONT data.
Atthecoreofouralgorithmsisavariantofthede-Brujingraph(DBG)tailoredforlongreads,theminimizer-
spacede-Brujingraph(MDBG),firstintroducedinrust-mdbgthenrefinedinmetaMDBG[14,9]. Thisapproach
considers only a small subset (around 0.5%) of k-mers from each read, selected uniformly using a minimizer-
based sampling technique. The chosen k-mers are then linked into chains of size k′ and further extended
using a de Bruijn graph-like framework. In order to exploit the full potential of high-accuracy long reads,
metaMDBG creates long and specific chains (k’ ≈ 100). However in the presence of sequencing errors, such
long chains inevitably end up containing erroneous k-mers which break contiguity. It is also important to note
in the context of metagenomics, where multiple strains of a species may potentially be present [15], that the
minimizer-space assembly will not produce the finest-scale of resolution possible. Due to the low density of
minimizers used strains which are sufficiently similar may be collapsed into consensus genomes.
Afirstattempttodealwithsequencingerrorsinminimizer-spacewasintroducedinrust-mdbg[14],through
read correction inspired by multiple sequence alignment. For each read represented by its minimizers, similar
reads with a high fraction of shared minimizers are recruited then aligned to generate a consensus sequence
in minimizer-space using partial order alignment (POA). This method hSas notable limitations. Firstly, the
read recruitment implemented using a resource-intensive disk-based reSad bucketing step degrades performance.
Secondly, a low similarity threshold for read recruitment is overly permissive, especially in the metagenomics
context,leadingtoreadsfromdifferentspeciesbeingpotentiallyalignedtogetherwhichinturnyieldserroneously
corrected reads. While a higher similarity threshold could mitigate this risk, it would also exclude genuine
alignments with long flanking regions. Also, the low densityPof minimizers typically utilized by rust-mdbg and
metaMDBG precludes accurate estimation of divergence b etween reads and leads to subpar recruitment.
We introduce nanoMDBG, a novel algorithm inteNgrated into the metaMDBG software, which revisits the
concept of read correction in minimizer-space to address errors in ONT data. At the heart of nanoMDBG is an
innovativeuseoffastseed-and-chainingtechniques forreadcorrection. Itenablestherapidalignmentandpileup
ofminimizersfromsimilarreadstoefficientlybEuildconsensuses. Wefurtheroptimizetheresource-intensiveall-
vs-all read mapping phase, a common bottlLeneck in read correction, by leveraging variable minimizer densities.
Specifically, a low-density setting allows for quick recruitment of the most similar reads needed for effective
correction, while a high-density setting ensures precise estimation of sequence divergence among the recruited
reads.
This improved error correction allows nanoMDBG to handle large ONT datasets efficiently and accurately.
For instance, on the largest tested dataset, a 400 Gbp soil sample, the correction only took 16 hours using 32
cores(10%ofoverallassemAblytime)and74GBofmemory(afractionofthesubsequentassemblyrequirements).
On this dataset, nanoMDBG substantially outperformed state-of-the-art assemblers, recovering 201 more near-
complete MAGs than metaMDBG and 144 more than metaFlye. When using PacBio HiFi data from the same
samples, we demonstrate that nanoMDBG can now deliver ONT-derived results comparable in MAG number
to those achieved with HiFi sequencing.

Results
We first evaluate assembly results on ONT data only. We then compare assemblies obtained from ONT and
HiFi data generated from the same samples and conclude with an evaluation of assembly errors across both
platforms.
Evaluation of nanoMDBG for ONT metagenome assembly
We compared nanoMDBG (v1.2) to two state-of-the-art assemblers: metaFlye (v2.9.3-b1797) and metaMDBG
(v1.2) on one mock community and three real metagenomes (see Table 1). The commands that were used are
provided in Supplementary Table 1 and the assembly results are summarized in Supplementary Tables 2, 3
and 4. A comparison to hifiasm-meta (v0.3-r063.2) is also given, although only on a subset of the data sets as
explainedbelow. Themockcommunity,‘Zymo’,contains21speciesforwhichreferencegenomesandabundances
areknown. Thefirstrealmetagenome, ‘Humangut’, isa50GbpHumangutsample. Thesecondmetagenome,
‘Zymo Fecal Reference’ is a 200 Gbp reference standard constructed by pooling fecal samples from multiple
donors [16]. The third data set, ‘Soil’, is a 400 Gbp soil sample taken from an agricultural field in Oxfordshire
(UK)-seeMethods. AlldatasetsweresequencedwiththelatestR10.4.1ONTflowcellsandreadssubsampled
after filtering by selecting the first N reads necessary to achieve the speciSfied data set sizes. This was done to
allow comparison with equivalent sized HiFi PacBio data sets below.SWe note that the Human gut and Soil
datasets were generated for this study, while the Zymo Fecal Reference dataset is publicly available.
We first evaluated the assemblers on the Zymo mock community using MetaQUAST (see Methods) to
compare assemblies against the available reference genomes (see Supplementary Table 2) and determine their
accuracy. The mock community contains 21 strains, includinPg 5 E. coli strains of similar abundance. Four rare
species (coverage ≤ 6x) could not be fully reconstructed b y any assembler, and two yeast genomes were absent
from all assemblies. Among the abundant, non-E. colNi species, nanoMDBG and metaFlye performed similarly
in both the number of species recovered as single contigs and in consensus quality. Each recovered 4 genomes
as circular contigs and 2 as single linear contigs. Both produced ≈6 mismatches and ≈4 indels per 100 kbp,
as well as a total of 9 misassemblies. MetaMDEBG generally yielded slightly more fragmented assemblies with
moreerrors. Hifiasm-metaproducedahighLlyfragmentedassemblywithalargenumberofmisassemblies, likely
because it is optimized for high-accuracy PacBio HiFi data; therefore, we excluded it from further comparisons
in this section. No assembler could fully resolve the E. coli strain diversity. The highest completeness for an E.
colistrainwasachievedbyhifiasm-meta(95%)butwith34misassemblies,followedbyMetaMDBG(88.5%with
21 misassemblies), nanoMDBG (82.4% with 17 misassemblies), and metaFlye (46.4% with 2 misassemblies).
In the case of the real microbiome data sets data we used SemiBin2 [4] (v.2.1.0) to bin contigs obtained by
each of the assemblers andAevaluated the quality of the resulting bins with CheckM2 [17] (v.1.0.1). We grouped
binsintoconventionalcategoriesbasedontheirlevelofcompletenessandcontaminationreportedbyCheckM2:
near-complete MAGs (>90% completeness and <5% contamination), high-quality MAGs (>70% completeness
and <5% contamination) and medium-quality (>50% completeness and <5% contamination). We also report
the number of near-complete MAGs in a single contig (scMAGs) after binning.
For all three tested real communities, nanoMDBG reconstructed appreciably more near-complete MAGs
than the other assemblers (see Figure 2a and Supplementary Tables 3 and 4). From the Human gut sample,
nanoMDBGreconstructed78near-completeMAGs(23morethanmetaMDBGand19morethanmetaFlye),255
from the Zymo Fecal Reference dataset (30 more than metaMDBG and 88 more than metaFlye) and 260 from
the Soil sample (201 more than metaMDBG and 144 more than metaFlye). The number of scMAGs generated
by nanoMDBG is substantially improved compared to the other assemblers. NanoMDBG reconstructed 26
scMAGs from the Human gut sample (15 more than metaFlye), 83 from the Zymo Fecal Reference dataset
(39 more than metaFlye) and 91 from the Soil sample (79 more than metaFlye). As a further validation of
the quality of the near-complete MAGs, we predicted the presence of rRNA and tRNA genes (see Methods).
The near-complete MAGs generated by each assembler usually do contain the expected complement of RNA
genes (91% for nanoMDBG and metaFlye, and 87% for metaMDBG). We also note that the majority of the
near-completeMAGsproducedbyallassemblerscontainedfewerthan10contigs(seeSupplementaryFigure1).
The improvement in assembly quality by nanoMDBG resulted in MAG collections that mapped a larger
fraction of reads from each dataset (see Figure 2b) and hence, are more representative of the community.
For the Human gut dataset, nanoMDBG was the only assembler to map more than 50% of reads to near-complete
MAGs(ncMAGs). Asthecomplexityofthedatasetsincreased,thefractionofmappedreadsdecreased. Onthe
Zymo Fecal Reference 38% of reads were mapped to near-complete nanoMDBG MAGs but only 7% on the Soil
near-complete MAGs. The fraction of reads mapping to ncMAGs cannot be larger than the fraction of reads
mapping to the overall assembly. If genomes are very low coverage then they will not be assembled at all. It
is useful therefore to compare these percentages to the fraction of reads that map onto the entire nanoMDBG
assemblies, which gives their upper limits, these were Human gut: 82%, Zymo Fecal Reference: 78% and Soil:
48%. Even accounting for the fact that not all genomes will be from prokaryotes this suggests that we are
underperforming in the Soil data set.
To further investigate whether the low fraction of mapped reads in the Soil dataset was due to insufficient
sequencingdepthorassemblyfailure,wecomparedthenumberofrecoveredMAGstotheactualspeciesdiversity
in the assembly. This was estimated by identifying single-copy core genes (SCGs) from contigs and clustering
them at 97% identity (see Methods) (see Figure 3). Based on SCG analysis, we estimated that the Human gut
dataset contained 129 species, the Zymo Fecal Reference dataset 481 species, and the Soil dataset 3772 species.
IntheHumangutdataset,70%ofpredictedspecieshadhighcoverage(>10x),and85%ofthosewereconverted
into MAGs. In the Zymo Fecal Reference dataset, over 90% of abundant species (>10x coverage) were also
converted into MAGs. However, in the Soil dataset, only 40% of predicted species had coverage >10x, and just
37%ofthoseabundantspecieswereconvertedintoMAGs. ThelowfractionofmappedreadsintheSoildataset
can therefore be attributed to both the large species richness and the limited sequencing depth relative to that
diversity, as well as challenges in assembling abundant genomes.
TosummarizethemicrobialdiversityfromtheSoilsample,weconstructedaphylogenetictreewithleavesas
genera(seeMethods)forallnear-completeMAGsfromallassemblers(seeFigure2c). Wenotethatamongstthe
total 435 retrieved near-complete MAGs, only 5 could be assigned to a knSown species with a reference genome
(ANI > 95%). The improved MAG recovery by nanoMDBG translatSes into a more representative picture of
microbialdiversityatalllevelsofevolutionarydivergencecomparedtotheotherassemblerstested. Intotal, we
observed 84 genera that were recovered from the Soil data sets by nanoMDBG but are missing from the near-
complete MAG collections of the other programs. When the other assemblers did recover MAGs from the same
genus, nanoMDBG usually found more MAGs. Finally, wePcan see large parts of the tree that are represented
by only nanoMDBG MAGs; indeed, one phylum (41 famil ies) were found only by nanoMDBG, compared to 12
families specific to metaFlye and one family specific tNo metaMDBG (see Figure 2d and Supplementary Table
7).
The assembly of phage genomes and plasmids potentially present in metagenomes can be particularly chal-
lenging. To compare phage and plasmid reconEstruction between assemblers we used geNomad [18] to identify
circular contigs that were potential plasmidLand phage genomes (see Supplementary Table 5). On the Human
gut and Zymo Fecal Reference samples, metaFlye reconstructed more viruses than nanoMDBG (3 and 60 more
respectively), but nanoMDBG found 237 more phages in the Soil data set. We used CheckV [19] to assess
the completeness of circular phages. We found that 61% of genomes recovered by nanoMDBG were judged
high-quality against 57% for metaFlye. NanoMDBG found substantially more circular plasmids compared to
metaFlye for all three metagenomes (9 more on the Human gut sample, 24 more on the Zymo Fecal Refer-
ence and 9 more on the SAoil sample). MetaMDBG usually obtained less circular viruses and plasmids than
nanoMDBG and metaFlye.
Computational scalability, given the potentially large size of ONT metagenome data sets, is an important
consideration. By performing the correction step in minimizer-space, our novel method, nanoMDBG, naturally
extends metaMDBG to ONT data without impacting overall performance. For example, on the 400 Gbp Soil
sample,thecorrectionsteprequiredonly16hoursusing32cores(accountingfor10%ofthetotalassemblytime)
and 74 GB of memory, which is a fraction of the memory required for subsequent assembly (see Supplementary
Table6). NanoMDBGcompletedthefullSoildatasetassemblyinjust6days,comparedto28daysformetaFlye.
In terms of memory efficiency, nanoMDBG used 125 GB, while metaFlye required 753 GB, making the latter
potentially inaccessible for many laboratories. Both assemblers efficiently handled the 50 Gbp Human gut
sample, with nanoMDBG completing the task in 5 hours and metaFlye in 8 hours. However, nanoMDBG used
only 17 GB of memory compared to metaFlye’s 115 GB, enabling nanoMDBG to potentially assemble datasets
ofequivalentcomplexityonalaptop,whichcouldbeimportantgiventheportabilityofONTsequencingdevices.
Evaluation of ONT base-level read correction tools before assembly
WeexploredanalternativeapproachforprocessingONTdatabyapplyingreadcorrectiontoolspriortoassem-
bly. Specifically,weassessedtworecenthaplotype-awarecorrectiontools: HERRO[20]andDeChat[21]. While
DeChat is tailored for metagenomics, we note that HERRO was developed for genomic data, with the authors
recommending its use on reads longer than 10 kbp, higher than the typical N50 read length of metagenomic
samples.
WeappliedHERRO(throughtheDoradoONTbasecallingpipeline)andDeChattothe50GbpHumangut
sample, assembled the corrected reads using each assembler, and evaluated the quality of the recovered MAGs
(see Supplementary Figure 2). Assemblies incorporating HERRO consistently yielded fewer MAGs compared
to raw assemblies. This likely stems from HERRO’s inability to effectively process reads shorter than 4 kbp,
which are prevalent in metagenomic datasets. In contrast, DeChat substantially enhanced assembly outcomes:
it improved the recovery of scMAGs by metaMDBG (11 additional scMAGs and 9 near-complete MAGs) and
metaFlye (1 additional scMAG and 8 near-complete MAGs). For nanoMDBG, DeChat resulted in 1 additional
scMAG, although raw assemblies with nanoMDBG alone still produced more near-complete MAGs overall (2
additional near-complete MAGs).
Although read correction can enhance assembly quality, it comes with substantial computational costs. For
instance, HERRO required 56 hours (GPU mode) and 210 GB of memory, while DeChat took 30 hours and
250 GB. In contrast, nanoMDBG completed the entire assembly process, including contig polishing, in just 5
hourswith17GBofmemory. Thesecomputationalcostspreventedusfromevaluatingthereadcorrectiontools
on the larger and more complex Zymo Fecal Reference and Soil data sets.
Evaluation of metagenome assembly quality from ONT and PacBio HiFi data
To enable a direct comparison between ONT and HiFi assembly quality, we generated HiFi data from the same
samples used for ONT sequencing. Specifically, we produced 50 Gbp of PacBio HiFi data from the Human
gut sample and 250 Gbp from the Soil sample, and we utilized a publicly available 255 Gbp HiFi dataset for
the Zymo Fecal Reference sample [16] (see Table 1). We ran three HiFi mSetagenome assemblers: metaMDBG
(v1.2),metaFlye(v2.9.3-b1797),andhifiasm-meta(v0.3-r063.2). WediSdnotapplynanoMDBGerrorcorrection
to the HiFi data because HiFi reads, after homopolymer compression, a standard preprocessing step in HiFi
assembly, are nearly error-free.
Figure 4a presents the number of near-complete MAGs andRscMAGs recovered using both sequencing tech-
nologies at varying sequencing depths (selecting the first NPreads until the desired data volume was reached).
Notethatdifferentaveragereadlengthsmayresultindifferingreadnumbersatthesamedepthwhencomparing
between technologies but this is, in our opinion, the mNost valid comparison. For clarity, the figure only shows
resultsobtainedwithmetaMDBGonHiFidataandnanoMDBGonONTdata, thecombinationswhichconsis-
tently produced the best assemblies across experiments. Comprehensive results for all assemblers are provided
in Supplementary Figure 3.
On the Human gut sample, ONT data yielded more near-complete MAGs at lower sequencing depths (30
additional MAGs at 10 Gbp of data). At this low-depth, most HiFi MAGs were of high-quality. At sequencing
depths exceeding 20 Gbp, HiFi consistently recovered more near-complete MAGs compared to ONT data, with
36morenear-completeMAGsat50GIbp(with12moreassingle-contigs). FortheZymoFecalReferencedataset,
more near-complete MAGs were reconstructed from HiFi technologies at all depths, with 294 MAGs recovered
fromHiFicomparedto255froRmONTat200Gbp. ONTdataproducedmorescMAGsatlowerdepth(14more
scMAGsat100Gbp)butmAorewereproducedfromHiFidataat200Gbp(5morescMAGs). IntheSoilsample
dataset, thenumberofnear-completeMAGswascomparablebetweenthetechnologies, with165near-complete
MAGs from HiFi and 166 from ONT at 250 Gbp. Nonetheless, HiFi consistently produced more scMAGs, with
one additional scMAG at 50 Gbp and 32 more at 250 Gbp.
To evaluate differences between near-complete MAGs generated by the two technologies, we used the
dRep [22] tool to identify shared and specific MAGs and assessed their coverage and contiguity (see Figure 4b).
A total of 333 near-complete MAGs were shared between the technologies, while 221 and 126 MAGs were
unique to HiFi and ONT, respectively. Shared MAGs generally exhibited higher coverage (median ≈35x versus
≈17x for MAGs specific to HiFi and ≈25x for MAGs specific to ONT) and were reconstructed in fewer contigs
compared to those unique to a single technology. Unshared MAGs, therefore, represent the most challenging to
assemble.
Supplementary Figure 3 explores the recovery of circular viruses and plasmids across assemblers (see also
Supplementary Table 5). For the Human gut sample, the number of phages and plasmids recovered was
comparable between technologies (at 50 Gbp; 32 phages with HiFi using hifiasm-meta and 27 with ONT using
metaFlye; 42 plasmids with HiFi using metaMDBG and 36 with ONT using nanoMDBG). In the Zymo Fecal
Referencedataset,ONTconsistentlyrecoveredmorephagesandplasmidsatallsequencingdepths(At200Gbp,
121phagesand90plasmidswererecoveredfromONTdatacomparedto93phagesand83plasmidsfromHiFi).
FortheSoildataset,HiFirecoveredmorevirusesatalldepths(572withHiFiusingmetaMDBGversus418with
ONT using nanoMDBG). The recovery of plasmids was also higher from HiFi data (20 plasmids compared to
13). Notably, metaMDBG and nanoMDBG were the most effective assemblers for recovering circular plasmids
on HiFi and ONT data, respectively. For circular phages, metaMDBG and hifiasm-meta excelled with HiFi
data, while metaFlye or nanoMDBG performed better on ONT data, depending on the sample complexity.
Evaluation of metagenomics assembly errors
Due to the complexity of metagenomic data, assemblies are inevitably prone to various types of errors. On real
data sets where the ground truth is not known it is standard to assess assemblies in terms of MAGs recovered
aswehavedoneabove. However,itisalsoimportanttoevaluateassemblersintermsofthefrequenciesoflikely
artefacts as has been recently suggested [23]. In this section we compare assemblers across the ONT and HiFi
PacBio data sets in terms of four such artefacts:
• Long contaminated contigs: the level of contamination for contigs longer than 1 Mbp and the proportion
ofcontaminatedcontigswithCheckM2contaminationexceeding5%. Itispossiblethatsomesuchcontigs
may represent biological novelty but in general contamination at this level likely indicates misassembly
generating a chimeric contig from more than one genome.
• Clipping events: locations within the assembly supported by at least 10 reads where 100% of the reads
are clipped. This indicates a region where assembly has incorrectly joined distant sequences.
• Zero-coverage regions: regions longer than 1,000 bp within the assembly with no apparent read support.
These are unambiguous errors.
• Frameshift errors: we used the program IDEEL [24] on contigs longer than 5 kb and with coverage over
10x to quantify the fraction of predicted proteins which were ≥S90% the length of their best-matching
known protein (higher fractions being better). Insertion-deletEion errors from sequencing noise can lead to
predicted proteins that are shorter than expected due to frameshifts leading to mismatched amino acids
and stop codons. Some of these may be genuine pseudogenes but again in general these will be errors.
We benchmarked three metagenome assemblers: nanoMDBG/metaMDBG (v1.2), metaFlye (v2.9.3-b1797),
and hifiasm-meta (v0.3-r063.2), using both NanoporeNand HiFi data. The datasets used included: 50 Gbp from
the Human gut sample, 100 Gbp from the Zymo Fecal Reference sample, and 250 Gbp from the Soil sample.
We did not use the full Zymo Fecal Reference dataset, as hifiasm-meta repeatedly crashed on the full dataset.
We used the scripts “anvi-script-find-misassemEbly” from the Anvi’o platform [25] for measuring clipping event
and zero-coverage regions (using minimap2 [26] to map reads back to the assemblies with the same commands
as described in [23]). The commands that were used are provided in Supplementary Table 1 and all results are
summarized in Supplementary Tables 8 and 9.
Figure 5a shows that long contigsIfrom all assemblers generally had low contamination (median contamina-
tion ≈0.35%). On ONT data, metaFlye produced the fewest contaminated long contigs: 2 out of 326 (0.61%),
comparedto13outof1,006(1R.29%)fornanoMDBG.OnHiFidata,metaFlyeagainshowedthelowestcontam-
ination(1outof362longcAontigs; 0.27%),followedbymetaMDBG(9outof1239; 0.72%)andhifiasm-meta(13
out of 667; 1.94%). We did not observe strong trends based on sequencing technology or sample complexity;
assemblers that produced more long contigs tended to also have a higher number of contaminated ones. Using
metaMDBG on uncorrected ONT data usually leads to more contamination and errors overall.
For clipping events and zero-coverage regions, hifiasm-meta produced the cleanest assemblies, with fewer
than 1 clipping event per 100 Mbp on all datasets and at most 3.5 zero-coverage regions per 100 Mbp in the
Soil dataset (see Figure 5b–c). NanoMDBG and metaMDBG performed better than metaFlye. On ONT data,
nanoMDBG had up to 5 clipping events per 100 Mbp, whereas metaFlye exhibited 12 to 31 clipping events per
100 Mbp, with the highest count on the Zymo Fecal Reference dataset. For HiFi data, both metaMDBG and
metaFlyeproducedfewerclippingevents(upto3and8.6per100Mbp,respectively). Intermsofzero-coverage
regionsonONTdata,nanoMDBGproduced5.3regionsper100MbpintheHumangutsampleandupto29in
the Soil sample, compared to 7.4 and 44 for metaFlye, respectively. On HiFi data, metaMDBG and metaFlye
showed similar performance for the Human gut and Zymo datasets (≈20.5 regions per 100 Mbp), but metaFlye
had substantially more such regions in the Soil sample (120 versus 51 for metaMDBG).
IDEELresultsindicatedthatnanoMDBGassembliescontainedahigherfractionoffull-lengthproteinsthan
metaFlye (see Figure 5d), particularly on the Soil dataset (88.7% vs 86.0% in the Human gut, 87.4% vs 83.1%
in Zymo Fecal Reference, and 70.0% vs 56.8% in Soil). On HiFi data, all assemblers performed similarly
on the Human gut and Zymo Fecal Reference datasets (≈88% full-length proteins). However, metaMDBG
outperformed the others on the Soil sample (76.4% vs 70.4% for hifiasm-meta and 68.6% for metaFlye). HiFi
assemblies generally yielded more full-length proteins compared to ONT data, particularly on the Soil sample
(70.0% from ONT using nanoMDBG vs 76.4% from HiFi using metaMDBG).

Methods
Figure 1 summarises the steps of nanoMDBG, which we describe in more detail below.

Preliminaries
We start with a lexicon of some terms and concepts related to MDBGs, assembly and sequence alignment.
Minimizer In order to scale-up large datasets, our method uses only aSfraction of the k-mers present in the
reads. Toselectk-mers,weusethewell-knownconceptofminimizers,morespecificallyuniversalminimizers[14].
Given a hash function f that maps k-mers to [0,H], we select a k-mer m as a minimizer if f(m) < dH where
d controls the fraction (or density) of selected k-mers.

Minimizer-space read (mRead) Prior to any treatmePnt, reads are scanned and transformed into their
ordered list of minimizers, that we call a mRead. We record the positions and orientations (forward/reverse) of
minimizers in each read.

Seed-and-chaining The seed-and-chaining str ategy is a heuristic to quickly localize potential alignments
between two sequences [26, 33, 34]. Read mapEping tools usually rely on this technique prior to applying a more
costly base-level alignment algorithm (henLce the full technique is named “seed-chaining-extend”). Seed-and-
chaining is also the core procedure of nanoMDBG (but the extend step will be different).
It starts by collecting a subset of k-mers (the seeds) from the sequences to compare. In our case, we use all
universal minimizers in reads as seeds. The chaining step then finds exact seed matches between the sequences
(i.e. anchors) and identifies sets of colinear anchors (the chains). For identifying the optimal chains, we use the
same scoring function and banded dynamic programming algorithm as defined in skani [33].

Minimizer-space alignment The seed-and-chaining procedure is usually a preliminary step prior to base-
levelalignment. Howeverhere,theentirealignmentstepwilloperateinminimizer-spaceandwewillnotgoback
to base-space. We refer to this as minimizer-space alignment. Sequence divergence can be directly estimated
from the results of chaining as follows [35]:
(cid:16)n(cid:17)1/k
e= (1)
m
where n is the number of matching seeds, m is the number of seeds in the query and k is the minimizer length.
Toseethedifferencewithbase-levelalignment,weexplainherehowalignmentisperformedwithminimizers
as units (see figure 1-B) instead of bases. For a pair of mReads and matching minimizers (anchors), the other
minimizerspresentbetweentheseanchorsareexaminedtoderiveasequenceofmatches,mismatches,insertions
and deletions of minimizers. Such a sequence is the minimizer-space alignment. It is crucially different from
standard alignment algorithms, which use minimizers for chaining and do not further try and align the other
minimizers not involved in chains: they then align all bases, which is more computationally expensive. Here,
we perform an alignment on all minimizers.

Algorithmic challenges and motivations
At the core of metaMDBG is a variant of the de-Brujin graph tailored for long reads, the minimizer-space
de-Brujingraph(MDBG).Thisapproachconsidersonlyasmallsubset(around0.5%)ofk-mersfromeachread,
selected uniformly using a minimizer-based sampling technique. The minimizers are then linked into chains of
size k′ and further extended using a de Bruijn graph-like framework. In order to exploit the full potential of
long reads, metaMDBG creates long and specific chains (k’ ≈ 100). However, in the presence of sequencing
errors, such long chains inevitably end up containing erroneous k-mers which break contiguity (see [14] for a
detailed study of how sequencing errors in base-space propagate to minimizer-space). In this study, we aim to
extend metaMDBG to support ONT data assembly through read correction, while preserving computational
efficiency to enable the assembly of large metagenomes.
Self-correctionoflongreadstypicallyreliesonmultiplesequencealignment. Thisapproachinvolvesselecting
eachreadasatargetforcorrection,recruitingandaligningsimilarreadstoit,andidentifyingcorrectorerroneous
basesthroughreadpileuporpartialorderalignment(POA)graph. Thiscorrectionprocedureinvolvesaninitial
all-vs-all read mapping and base-level multiple sequence alignment, which are both computationally expensive.
Our goal is to make this process more efficient.
Ourfirstingredienttospeedupreadcomparisonsistobypassbase-levelalignment,performingcorrectionat
thelevelof minimizersonly. Thisapproach mirrorsthe principlesof base-level correction, with mReads aligned
with fast seed-and-chaining procedure instead, and minimizers stacked up to produce consensus. The corrected
mReads are then assembled in minimizer-space using metaMDBG, with base-level correction deferred to the
final non-redudant contigs.
Despite this efficient approach, the all-vs-all step remains computationally demanding due to the high
densityofminimizersrequiredforaccuratereadrecruitment. Toaddressthis,weintroduceasecondingredient,
a two-step alignment strategy tailored for read correction, based on three key observations:
1. ONT read self-correction in minimizer-space can achieve high accuracy with moderate coverage (around
20x) of recruited reads.
2. For a given target read, recruiting candidate reads thatRare likely to belong to same species and to the
same genome location can be done quickly using the following heuristics. 1) Longer alignments are more
likely to be correct than shorter alignments. 2) Alignments bounds can be estimated quickly using seed-
and-chaining with low-density of minimizers.
3. ConfirmingthatthecandidaterecruitedreadsIareindeedlocation-andspecies-compatiblewiththetarget
necessitates more effort as it requires to es timate sequence divergence. This step requires high-density
minimizers, especially on shorter reads [E33].
Our correction strategy, therefore, proceeds as follows. For each target read, we recruit the most similar reads
in(sparse)minimizer-space(uptoapproximately20xcoverage)usingseed-and-chainingwithavery-lowdensity
of minimizers. We then remove divergent reads using a higher density of minimizers. This strategy restricts
the more costly divergence analysis to a subset of reads, all read vs approximately 20x coverage, rather than
requiring it for all reads. The following section describe our correction strategy and heuristics in more details.

Read correction in minimizer-space
Thefollowingstepsdescribethenovelcorrectionstepspecifically(seefigure1-C).Inanutshell,wefirstperform
a fast all-vs-all read mapping using a low density of minimizers (0.5% by default). The output of this step
is, for each read, the list of their best read matches (up to 20x reads). For a given target read to correct, we
recompute its alignments using a higher density of minimizers (2.5% by default), and filtered recruited reads
more accurately. We then pileup the minimizers using a variation graph and determine the most supported
path as consensus. We describe each of these steps more precisely in the following.
Converting to minimizer-space. We start by converting the reads into their ordered list of universal
minimizers, that we call mReads. We extract two sets of mReads. One with a low-density of minimizers (0.5%
by default), used to quickly perform all-vs-all read mapping, and another with a high-density of minimizers
(2.5% by default), used to accurately correct mReads. We note that certain minimizers can be extremely
abundant, which significantly impacts mapping performance. To address this, we filter out the top 0.0001
fraction of repetitive minimizers from both correction and assembly.
Ultra-fast mRead recruitement using low-density minimizer-space alignment. In this step, we per-
form all-vs-all read seed-and-chaining in order to find for each read, its best matching reads that will be used
for correction. In order to speed-up this costly process, we use a low density of minimizers (0.5% by default).
We use the following simple formula to rank alignments : score = number of minimizer matches - number of
differences, where the number of differences is the sum of missmatching miminizers, deletions and insertions.
For each read, we recruit up to 20x reads. Other alignments are discarded.
Accuratehigh-densityalignmentfiltering. Inthisstep,weperformseed-and-chainingalignmentbetween
the target mRead and the recruited mReads using their full minimizer representation (2.5% by default). Align-
ments with more than 4% divergence are discarded in order to discard reads that belong to other species while
allowing for a certain error rate. Additionally, we discard alignments smaller than 1000 bps or with over hang
longer than 2000 bps, in order to correctly recruit reads in genomic repeats.
Piling up minimizer with minimizer-space variation graph from high-density alignments. After
accurate alignment filtering, we start the construction of a variation graph from the remaining alignment (see
figure C-3). We initiate the graph with the minimizers of the target mRead. We then process sequentially
each high-density alignment computed in the previous step. A node is added to the graph when a mismatch or
an insertion occur (see respectively node m7 and node m8 in figure 3-C). We also record the support of each
minimizer transition.
Wenotethatthisisasimplerapproachthanapartialordergraph(POA),asalignmentsareperformedonly
against the target mRead, rather than against a graph as in the case of POA. We found that the correction
with this strategy was good enough for assembly. It has the advantages of being faster than the POA and the
resulting variation graph does not depend on the order in which sequences are added to the graph.
IfaFASTQfileisprovidedasinput,weweightedgesbyqualityscoresinsteadofrawtransitioncounts. The
quality score of a minimizer is defined as the minimum quality score among its constituent bases. Each edge in
the graph is then weighted by the sum of qualities of its source and deSstination minimizers.
Generating consensus Afterallthealignmentshavebeenaddedtothevariationgraph,weextractthemost
supported path from the graph as a consensus using the following procedure. We begin by initializing a hash
table S that maps each node v in the graph to its support score, with all values initially set to zero: S[v]=0.
Next, we perform a topological sort of the graph’s nodes and iterate through them in the sorted order. For
eachnodev,weidentifythehighest-supportedincomingedgee=(u,v)withweightw ,andupdateitssupport
e
score as S[v] = S[u]+w . The node u is recordedIas the parent of v for path reconstruction. The consensus
e
path is then constructed by identifying the node v with the highest support score S[v], and backtracking
max
through its parent nodes until a node with no parent is reached.

Assembling corrected minimiCzer-space reads
The corrected mReads are assembled using metaMDBG [9], with only minor modifications for the conversion
of contigs from minimizer-space to base-space. A brief summary of the methods of MetaMDBG is provided
here for completeness. MetaMDBG uses the minimizer de-Bruijn graph as a core structure. It assembles
mReads using an efficientAmulti-k strategy in minimizer-space for handling uneven species coverage. At each
iteration of the multi-k algorithm, an abundance-based algorithm termed ‘local progressive abundance filter’ is
applied to remove potential inter-genomic repeats, strain variability and complex error patterns. The resulting
minimizer-space contigs (mContigs) are finally converted into their base-space representation. Previously, our
method for reconstructing mContig sequences was designed for highly accurate reads. However, for ONT data,
wedevelopedamoreadaptableapproach. Wemaptherawreads(inminimizerrepresentation)tothemContigs
usingtheseed-and-chainingalgorithmthatwedevelopedforourcorrectionmodule. Wethenidentifythelongest
alignmentsandextractthesequencesfromtherawreadsthatarespannedbytheminimizerstoreconstructthe
mContig sequences. The final draft assembly is polished using a racon-like [36] approach.
Modifications to metaMDBG to improve assembly quality.
To enhance assembly quality, we adjusted some parameters and improved the polishing workflow. To reduce
assembly errors, we configured the assembler to be less aggressive at the cost of fewer near-complete contigs
comparedtopreviousversions. Specifically,weincreasedtheminimizersizefrom13to15,andloweredthelocal
abundance threshold used to filter out variability and inter-genomic repeats in the assembly graph from 0.5 to
0.25 (parameter β in the original metaMDBG study). For example, for a species with 50x coverage, we now
filter out its neighboring contigs in the graph with abundance ≤12.5 (50 * 0.25). We also addressed an issue
in our racon-like polishing implementation that was a source of clipping events in the final assembly. The 500
bp polished contig windows could be too short due to a trimming step. We improved contig dereplication by
enablingthebase-levelalignmentmodeofminimap2duringall-vs-allcontigmapping. Thismodewaspreviously
disabled to reduce memory usage, but doing so could result in missed overlaps and duplication. Finally, we
added a second round of contig polishing to improve base-level accuracy.

Metagenome sequencing data generation
DetailedprotocolsforDNAextractionandplatformspecificlibrarypreparationsareavailableat(https://www.
protocols.io/view/soil-metagenome-pacbio-djga4jse.htmlandhttps://www.protocols.io/view/soil-
metagenome-ont-dhrm3546.html). A summary of sample collection, DNA extraction and library preparations
are provided here.
Sample collection and storage All samples were collected and stored in Zymo DNA/RNA shield and left
to incubate at 4 oC for three hours prior to snap freezing and storage at -80 oC prior to DNA extraction.
Soil was collected using a sterile soil corer and passed through a sterile soil sieve to homogenise the sample.
A total of 10g of homogenised soil sample was mixed with 100 ml of Zymo DNA/RNA shield and distributed
into 1 ml aliquots containing 100mg/ml of soil.
Faecal samples were provided as part of an ongoing human study “QIB colon model study”. The study was
approvedbythelocalQuadramInstituteBioscienceHumanResearchGovernancecommittee(IFR01/2015)and
by the London-Westminster Research Ethics Committee (15/LO/2169). The trial was registered at clinical-
trials.gov (NCT02653001). All participants provided signed informed consent prior to donating samples. The
study was conducted in accordance with the Declaration of Helsinki.
Faecalsamplesareprovidedbyvolunteersofbothgenders,agedbetweeSn25and54. Allparticipantsdeclare
to be in good health, have no diagnosed chronic gastrointestinal health problems, and have not consumed
antibiotics for at least six months prior to stool donation. Donors are not instructed to follow any diets or
consume any specific foods, in accordance with the terms of the ethical approval.
Atotalof10goffreshfaecalmaterialwasprocessedinastoRmacherwithatotalof100mlofanaerobicPBS.
Faecal slurry was then collected and distributed into 1 ml aliquots. Aliquots were centrifuged for 10 min at
10,000 x g at Room temperature and the supernatant discarded. Samples were then homogenised with 1 ml of
Zymo DNA/RNA shield and stored at -80 oC prior toNDNA extraction.
DNA Extraction DNA was extracted from all samples using the MPBio Fast DNA spin kit for soil with
slightmodificationsfromthemanufacturerproEtocol. Modificationsincludeprocessingsamplesonice,areduced
homogenisation time of 2 x 10 sec at 5.0 m/s with 5 min of cooling between runs. DNA was eluted first into
100 ml of DES elution buffer at 56 oC with the addition of 1 ml of RNaseA. Samples then undertook at 0.5 X
SPRI clean up prior to both PacBio and ONT library preparations.
PacBioLibrarypreparationandsequencing PacBiolibrarieswereconstructedusingSPK2.0kitandthe
overhangSMRTbelladapter. Samplesweresizeselectedusinga3.7XdiluteSPRIcleanuppriortosequencing.
Libraries were sequenced Ausing the PacBio Revio device and high-density flow cells and ran for a total of 24
hour movie time with a hifi q score cut off at q20.
Nanopore Library preparation and sequencing NanoporelibrarieswereconstructedusingLSK-114and
processed using the long fragment buffer workflow. Libraries were sequenced on a P2 solo device using R 10.4.1
flowcells(Flo-Pro114M).Samplesweresequencedforatotalof96hourswithascorecutoffatq10. Sequences
were base called and trimmed using Dorado SUP V5 basecalling model in post processing.
Assembling data sets, mapping reads and binning contigs
Weranallassemblerswith32CPUthreadsonamachineequippedwitha2.60GHzIntel(R)Xeon(R)Gold6132
CPU with 112 cores and 2,038 GB of memory. All programs were installed via Bioconda. We ran metaMDBG
with option ‘--in-ont’ for ONT data (to activate nanoMDBG method) and with the option ‘--in-hifi’ for
HiFi. We ran metaFlye with the options ‘--meta’ and ‘--nano-hq’ for Nanopore data sets and with option
‘--pacbio-hifi’ for HiFi data sets. We ran hifiasm-meta with default parameters. We only used the hifiasm-
meta primary assembly of polished contigs (p ctg.gfa), as adding alternate contigs reduced the overall MAG
quality. We used the command ‘/usr/bin/time -v’ to obtain wall-clock runtime and peak memory usage. All
tools that were used and the complete command line instructions are available in Suplementary Table 1.
We mapped reads to contigs using ‘minimap2’ with option ‘-x map-ont’ for ONT reads and with option
‘-x map-hifi’ for HiFi reads. We filtered out reads in which all of the alignments were shorter than 80% of
its length, and we assigned each remaining read to a unique contig through its longest alignment (breaking
ties arbitrarily). We performed contig binning using SemiBin2 [4] (v.2.1.0), using ‘single easy bin’ command
withoptions‘--sequencing-type=long read’,‘--self-supervised’andafixedseed(--random-seed 42)for
reproducibility.
Quality assessment of assemblies
We used CheckM2 (v.1.0.1) to assess the quality of all MAGs. We used Barrnap (https://github.com/
tseemann/barrnap), and Infernal [37] to predict, respectively, rRNA and tRNA genes from near-complete
MAGs. WefilteredoutannotationswithE-valuesover0.01. Wedenotednear-completeMAGasRNAcomplete
iftheyhaveatleastonefull-lengthcopyforallthreetypesofrRNAs,andatleast18full-lengthcopiesoftRNAs.
We used geNomad (v.1.8.0) using command ‘end-to-end’ with options ‘--conservative’ to identify strongly
supported plasmids and viruses in each assembly. We used CheckV [19] (v.1.0.3) to assess the quality of viral
contigs.
Quality assessment of assemblies on the Zymo mock community
WeuseMetaQUAST(v.5.2.0)withoptions‘--unique-mapping’and‘--reuse-combined-alignments’toassess
assemblyqualityagainstmockreferencegenomes. Metricsreportedincludecompleteness, contiguity(auNGA),
thenumberofmismatchesandindelsper100kbp,andthenumberofmisassemblies. Wealsoreporteda‘status’
metric, defined as ‘circular’ if a circular contig covers at least 99% of the reference genome, or ‘single-contig’ if
the contig is linear.
Taxonomic classification of MAGs recovered from the ONT soil sample
The phylogenetic tree of Figure 2c was built using fasttree [38] frEom the output alignment of gtdbtk version
2.1.0[39]onnear-completequalityMAGsofallthreeassemblersfortheanaerobicdigesterdataset. Concurrent
diversity coverage between the different assemblers was explored at different taxonomic levels from genus to
domain. Todoso,itisnecessarytofirstaddressMAGsforwhichnoannotationisavailableatagiventaxonomic
rank. A pair of unannotated MAGs may or may not shar e the same taxa. A first pass based on tree topology
allows us to select neighbouring MAGs as candidates for sharing the same unknown taxa. As a second step we
compute the Relative Evolutionary Distance using tIhe R library Castor version 1.7.3 [40]. Following guidelines
from gtdb, we use their median RED values for each taxa in order to decide on grouping together unknown
MAGs. We then find the best ancestor for each unknown MAG in terms of their RED being nearest to the
corresponding taxa median RED. If they sLhare the same best ancestor, we group them together otherwise we
split them into distinct unknown taxa.CTree manipulation and representation is carried out using the library
ggtree version 2.4.1 [41], treeio version 1.14.3 [42] and ggtreeExtra version 1.0.2 [43].
Identification of Single-Copy Core Genes (SCGs)
In order to assess the specAies diversity of the assemblies and hence quantify our ability to capture this diversity
as MAGs, single copy core genes (SCGs) were identified from contigs. First, open reading frames (ORFs)
were called using prodigal [44] (v2.6.3) with option -p meta. A set of 36 different SCGs were annotated using
RPS-BLAST (version 2.16.0) [45] using the pssm formatted COG database [46], which is made available by
the CDD [47]. Annotation was performed with a best hit strategy, filtering out any hit with an e-value higher
than 1e-10 and for which the alignment represents less than 50% of the subject length. Resulting hits were
de-replicatedat97%usingvsearch(2.29.1)[48]withoption-cluster smallmem. Speciesdiversitywasestimated
as the median abundance across all SCGs.
A custom Python (version 3.10) script was used to link previously computed contig coverages, inclusion of
contig in a MAG, related MAG quality and SCGs. Coverage of SCGs from the same cluster were summed and
when multiple MAG qualities could be found, the higher one was taken. This resulted in a table stating for
each SCG cluster, its coverage and if it was found included in a MAG as well as related quality. This table was
plotted using the ggplot2 [49] package from R (version 4.3.3) to give Figure 3.
Identification of errors in assemblies
We used CheckM2 (v1.0.1) to assess contamination in contigs longer than 1 Mb. Clipping events and zero-
coverage regions were identified using the anvi-script-find-misassembly program from the Anvi’o platform [25],
following the protocol described in [23]. This script relies on read mapping to the assemblies using minimap2
with the options -a -p 1 --secondary-seq. It extracts clipping events (hard or soft clipping) directly from
the CIGAR strings of the resulting BAM file. We reported clipping events within the assemblies that were
supported by at least 10 reads, with 100% of those reads exhibiting clipping. We used IDEEL [24] with default
parametersoncontigslongerthan5kbandwithcoverageover10xtoquantifythefractionofpredictedproteins
which were ≥90% the length of their best-matching known protein.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript introduces nanoMDBG, a long-read metagenome assembly algorithm for Oxford Nanopore sequencing data. The authors benchmark it on a range of datasets and show that it can produce more complete assemblies than other long-read metagenome assemblers while using fewer computational resources. I have only minor comments, which are listed below.\nIn the Data availability section, it states: \"The sequencing read data generated for this study are available at ENA bio project PRJEB88618; accession numbers are given in Table 1.\" However, Table 1 does not currently include any accessions. While I could see that project on ENA, I was unable to find any associated read sets. Please ensure all read sets are publicly available and add their accessions to Table 1.\nThe topic of strain mixtures and their impact on metagenome assembly is only mentioned briefly: in the Zymo mock community results (\"No assembler could correctly resolve all of the E. coli strain diversity...\") and in the discussion of the soil metagenome (\"...it may be strain variation...\"). I think this topic merits further discussion. For example, when faced with a strain mixture, should a long-read metagenome assembler attempt to reconstruct each strain separately, even if that leads to fragmentation (as metaFlye may be doing)? Or should it favour longer contigs at the cost of collapsing diversity (as nanoMDBG may be doing)? Since strain mixtures are very common in metagenomes, a paragraph exploring this trade-off would be a valuable addition to the Discussion.\nWhen introducing nanoMDBG, it should be made clearer that it is a new algorithm/mode within metaMDBG, not a standalone tool. For example, the phrase \"introduce an application, nanoMDBG\" suggests it is separate. This distinction is clarified only in the Code availability section, but it would help readers to explain this earlier in the manuscript.\nThe sentence \"The initial step in metagenomic analysis is the assembly of sequencing reads into longer contiguous fragments or contigs\" may overstate the generality of assembly in metagenomics. Many analyses use read-based taxonomic profiling without performing assembly. A more accurate phrasing might be: \"The initial step in many metagenomic analyses is...\"\nThe minimap2 preset for HiFi reads is given as \"-map-hifi\", but it should be \"-x map-hifi\".\nThroughout the manuscript, command-line options are printed with a single dash instead of a double hyphen (--), possibly due to Latex formatting. Consider using a monospace font (e.g. \\texttt{--in-ont}) to preserve formatting.\nThe Code availability section states that nanoMDBG is invoked using --in-nano, but it should be --in-ont.\n(Remarks on code availability)\nWhile I have not attempted to reproduce the specific results presented in the manuscript, I have run metaMDBG with the new nanoMDBG algorithm on my own datasets, including both metagenomes and isolate genomes, and it worked well.\nInstallation was straightforward using conda. The metaMDBG GitHub repository includes a README that covers the basics (installation, usage and output) but it is somewhat brief and contains typos. The documentation would benefit from additional detail and a link to a small demo dataset to help new users get started. Also, the Advanced usage and Low-memory contig polisher sections are currently empty, so the README seems to be incomplete.", "reviewer_2": "The authors propose an extension of their previous minimizer space based metagenomic assembly (metaMDBG) to less accurate Nanopore reads, called nanoMDBG. The core improvement (and novelty) is an efficient error correction method that operates in minimizer space. This is done through a two step method via seed-chaining: For each read, first a lower density of minimizers are used to detect top 20 \"similar\" reads that are from the same species and same genomic region. Then, all minimizers on the selected reads are used for multiple sequence alignment on minimizer space to construct a variation graph and derive the consensus sequence as the most supported path in this graph. Rest of the assembly process is similar to metaMDBG, where a minimizer space DBG is constructed and progressive abundance filter (LPAF) is iteratively applied to suppress variance (either due to strain variation or errors). nanoMDBG with and without error correction is benchmarked against metaFlye and hifiasm-meta (in the case of HiFi reads). Authors also benchmark nanoMDBG and other assemblers after applying two base-level read correction methods. The data used for this benchmarking analysis contains mock and real datasets from different environments. nanoMDBG shows improvements in terms of number of single contig, near-complete and high quality MAGs recovered.\nOverall, the manuscript is an important contribution as it makes a substantial improvement over the state-of-the-art methods for nanopore metagenomic assembly, and has potential to become a popular tool. We have a few specific requests:\n1. Conceptually, it is currently unclear how exactly metaMDBG (assembly algorithm) benefits from corrected reads. What is the exact issue if metaMDGB is applied to uncorrected reads? If raw reads can be aligned during the error correction stage, why couldn't the same alignment be used to construct the assembly graph? Although the answers might be obvious for an assembly expert, the paper might benefit from making these points explicit.\n2. What is the exact algorithm for generating consensus from the variant graph? Greedily selecting the most supported path at every junction may not yield the most supported overall path.\n3. This sentence is a bit confusing: “Such a sequence is the minimizer-space alignment. It is crucially different from standard alignment algorithms, which use minimizers but only focus on anchors and do not further try and align the other minimizers present between the anchors, they align the bases, which is more computationally expensive“. Do authors imply that pairwise minimizer alignment is different from minimizer chaining? If yes - how exactly? In what case an optimal (matched) minimizers chain will not be equivalent to an optimal pairwise alignment?\n4. Benchmarking in terms of MAG recovery is a good strategy for real datasets, but it does not specifically address misassemblies, contiguity and sequence-level quality. For datasets where reference is available (i.e., Zymo Mock), metaQUASTcould be used to generate these metrics.\n5. For real datasets, an indirect measure of misassmeblies could be the number of MAGs with high completion, but elevated contamination (e.g. 10%+) - perhaps this metric could be added for real datasets comparison?\n6. Base-level consensus quality has been a concern for nanopore assemblies. For real datasets (when references are not available), one could use tools like IDEEL (https://github.com/mw55309/ideel) to indirectly estimate the number of frameshift errors.\n7. It would be interesting to include species similarity analysis in different datasets. Strain similarity can also explain the differences in complexity and assembly quality for the datasets used in the paper.\nMinor:\n8. In Hifi vs Nanopore comparison (Figure 4), Is there any explanation as to why for Human gut, Hifi performs better with increasing coverage but Nanopore and Hifi perform similarly for Zymo Fecal and Soil samples. This analysis is not very conclusive overall.\n9. Does the error rate in minimizer space correspond to base-level error rate for different reads? Although it’s expected - since the primary focus of the paper is error correction, this analysis might be important.\n10. Zymo mock results description is inconsistent: “In this case, nanoMDBG and metaFlye both obtained five circularized genomes and metaMDBG obtained four. [...] In contrast, metaFlye produced only fragmented genomes.” Seems to be a contradiction with metaFlye results.\n11. Runtime and peak memory values in the supplementary data do not seem consistent. For ONT Human Gut (50Gb) why does metaMBG take longer than nanoMDBG where nanoMDBG applies error correction on top of the general assembly steps that metaMDBG follows? For ONT Zymo Fecal Reference (200Gb), it’s surprising to see metaFlye the fastest? These results require some more explanation.\n(Remarks on code availability)\nThe repository is in a good shape.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\n(Remarks on code availability)", "reviewer_4": "Benoit et al present nanoMDBG, a substantial modification of the metaMDBG algorithm to enable it to assemble ONT readsets. The benchmarks as presented are very impressive, and while I quibble about such strong self-endorsement such as suggesting the algorithm “could prove transformative to metagenomics research”, I do think nanoMDBG has the potential to be the go-to assembler for ONT reads. These are increasingly common in metagenomics, and therefore nanoMDBG appears to be an extremely useful resource for the community. Initial application of the tool to my own datasets confirms the minimal RAM requirements (and ease of installation through bioconda) even for large and complex communities (assembly and binning is on-going so I cannot comment on those aspects).\nMy two main criticisms of the manuscript lie with benchmarking the accuracy of the contigs produced. I would argue this is imperative for metagenome assemblers to present, but it is especially pertinent given a recent prepublication which finds several error modes find their way into metaMDBG assemblies (https://www.biorxiv.org/content/10.1101/2025.04.22.649783v2). While that manuscript is not passed peer review, it highlights the paucity of contig accuracy benchmarks from this nanoMDBG manuscript. The tools developed in that preprint may be helpful for analysis here.\nBeyond overall benchmarks of per-base accuracy, chimeras, and other error modes, there is another issue with the approach which should be at least discussed in the manuscript, or even better analysed in detail. The issue is around the strain-specificity of the contigs produced. Conceptually, if a read from a non-dominant strain is “corrected” by the method proposed, then it may be “corrected” to the genotype of the dominant strain/genotype if that genotype shares regions which are sufficiently similar, especially since only a 20x coverage limit is applied in the initial step, precluding some analysis which might differentiate sequencing error from real but low-abundance variation. On the other hand, if that were to happen then it might artificially inflate the coverage of the dominant strain, aiding in its eventual genome recovery when it might otherwise be too sparsely covered. Perhaps these considerations also apply to other assemblers, but I suggest that they are non-trivial enough that they should be at least mentioned for reviewer context.\nMinor comments.\nPlease include line numbers in future versions of the manuscript to assist reviewers — it greatly aids clarity and precision in feedback.\nIn general referencing in the introduction could do with some more care. For instance, the statement “MAGs constructed from short reads frequently suffer from incompleteness, contamination and high Fragmentation.” is unreferenced, and the first mention of metaMDBG is not referenced, etc.\n“ntigs into metagenome-assembled genomes (MAGs) [1],” - this [1] should be replaced by reference(s) which better represent the state of the field, rather than a manuscript by the same corresponding author.\n“In contrast, metaFlye produced only fragmented Genomes” - this section could be written more clearly. It isn’t clear which genomes are being referred to here, for instance. Perhaps it would read better if the E. coli strains and low abundance species were discussed separately, rather than everything at once. It would also be clearer if the true abundance table was given, so that the precise meaning of “low abundance” is immediately available to the reader, rather than asking them to chase this down through the Zymo website (an option which may also be not available in the long term).\n“Significantly” - word should be replaced as no statistical analysis is present (nor should there be).\nCapitalisation of bioniformatic tool names should be made more carefully. For instance the first C of CheckM2 is capitalised (there were several variants in the ms), and the tool name is CheckM2, not CheckM.\nThe words “near complete” and “complete” are used to define categories of MAG reconstruction accuracy. However, these words have precise meaning as defined in the MIMAG standard, and they should not be changed to suit a particular study. They also incorporate genome characters beyond CheckM2-based completeness and contamination statistics. For instance, requirements to include rRNA operons. One option might be to use one threshold of contamination (say 5%), and then the categories names can reflect different levels of completeness. This way more subtle trends than high quality vs medium quality can be showcased, while not confusing the precise categorisations set up in the MIMAG standard.\n“Not every read will be represented in the assembly, .. “ - I did not understand the logic for excluding unassembled reads from this percentage. I suggest rephrasing or removing this interpretation.\n“We note that we ..” over-use of “we” here. There is an argument also that is true more generally in the manuscript, but perhaps that is a matter of taste.\n“Varying sequencing depths”: On first read I worried about non-equal read quantities being used, which might bias the results. Further reading allayed my concerns on this, but perhaps this sentence could be clearer on that point up front.\n“The improved MAG recovery by nanoMDBG translates into a more representative picture of microbial diversity at all levels of evolutionary divergence.” While this is likely true, it cannot be concluded so confidently, because the true diversity remains unknown.\n“Specific” vs “shared” - are these the same meaning? If so only 1 should be used to avoid confusion.\n“Gut” or “human gut”? Only one descriptor should be used, to avoid confusion.\n“Proved essential”. It may not have been essential, even if the manuscript shows it helps assembly quality. Suggest rephrase.\n“he latest ONT sequencing technology now produces results comparable to PacBio HiFi sequencing at equivalent sequencing depth, despite the remaining differences in raw read accuracy.” - this is true only if the per-base accuracy of the contigs is comparable, which has not been established.\n“read Metagenomicss” - error\n“adapt our algorithm to a streaming assembler, “ - grammatical mistake. This sentence is also too long.\n“The most similar reads (up to approximately 20x coverage)”. This should be qualified to mean that they are the most similar in (sparse) minimizer space, which is not the same thing.\n“Filitered” - grammatical mistake.\n“And”: should not start a sentence.\n“Perform against” - grammar mistake.\n“Generating consensus” - this section needs more detail.\n“At each multi-k” - is this multi-k, or just each k?\nA description of the hardware architecture used should be given, if walltime is the main output of the benchmark. How was it compiled? With optimisations for the specific hardware or a general-purpose build for e.g. conda?\n“-map-hifi” is not an option of minimap2\n“checkV” - which version?\n“Assessment of completeness..” Does this section refer only to the Zymo community? If so, it would be clearer to say that directly.\nEmdashes - in several places it appears “--” has been replaced by an emdash, which is incorrect if they refer to command line options.\nFigure 4 “showcase” - Consider replacing ‘showcase’ with a more precise verb like ‘illustrates’ or ‘demonstrates’ to maintain academic tone.\n“The highest sequencing depth available” - this was unclear to me - 50, 200 and 250G respectively? Or the full depth available for each method for each assembler?\nFigure S3 “assemblers” should be singular. Y-axis Virus should be plural. Gbps - the s is not needed.\nReviewed by Ben Woodcroft\n(Remarks on code availability)\nI was able to run the code without issue."}, "author_response": "RESPONSE TO REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThis manuscript introduces nanoMDBG, a long-read metagenome assembly\nalgorithm for Oxford Nanopore sequencing data. The authors benchmark it on\na range of datasets and show that it can produce more complete assemblies\nthan other long-read metagenome assemblers while using fewer\ncomputational resources. I have only minor comments, which are listed below.\nIn the Data availability section, it states: \"The sequencing read data generated\nfor this study are available at ENA bio project PRJEB88618; accession\nnumbers are given in Table 1.\" However, Table 1 does not currently include\nany accessions. While I could see that project on ENA, I was unable to find\nany associated read sets. Please ensure all read sets are publicly available\nand add their accessions to Table 1.\nWe have added the accessions for the newly sequenced datasets in Table 1.\nThese are all now publicly available for download.\nThe topic of strain mixtures and their impact on metagenome assembly is only\nmentioned briefly: in the Zymo mock community results (\"No assembler could\ncorrectly resolve all of the E. coli strain diversity...\") and in the discussion of\nthe soil metagenome (\"...it may be strain variation...\"). I think this topic merits\nfurther discussion. For example, when faced with a strain mixture, should a\nlong-read metagenome assembler attempt to reconstruct each strain\nseparately, even if that leads to fragmentation (as metaFlye may be doing)?\nOr should it favour longer contigs at the cost of collapsing diversity (as\nnanoMDBG may be doing)? Since strain mixtures are very common in\nmetagenomes, a paragraph exploring this trade-off would be a valuable\naddition to the Discussion.\nWe agree with the reviewer that this interesting topic deserves further\ndiscussion. We have added both additional sentences to the Introduction\n(lines 59-62) and a paragraph to the Discussion:\n“355-360: In general, the philosophy behind nanoMDBG is to try to obtain as\nmany species or sub-species consensus genomes as possible. This is\nperhaps different to metaFlye which may be attempting to reconstruct each\nstrain separately…”\nWhen introducing nanoMDBG, it should be made clearer that it is a new\nalgorithm/mode within metaMDBG, not a standalone tool. For example, the\nphrase \"introduce an application, nanoMDBG\" suggests it is separate. This\ndistinction is clarified only in the Code availability section, but it would help\nreaders to explain this earlier in the manuscript.\nWe have modified the following sentences in the introduction, in the overview\nsection and in the discussion:\n“We introduce nanoMDBG, a novel algorithm integrated into the\nmetaMDBG software, which revisits the concept of read correction in\nminimizer-space to address errors in ONT data.”\n“We present nanoMDBG, a metagenome assembly method within\nmetaMDBG, designed for Oxford Nanopore Technologies (ONT) long-\nread data.”\n“We have introduced nanoMDBG, a new metagenome assembly\nmethod within metaMDBG designed for ONT long reads.”\nThe sentence \"The initial step in metagenomic analysis is the assembly of\nsequencing reads into longer contiguous fragments or contigs\" may overstate\nthe generality of assembly in metagenomics. Many analyses use read-based\ntaxonomic profiling without performing assembly. A more accurate phrasing\nmight be: \"The initial step in many metagenomic analyses is...\"\nThis has been changed according to the suggestion of the reviewer (Line 31).\nThe minimap2 preset for HiFi reads is given as \"-map-hifi\", but it should be \"-x\nmap-hifi\".\nThroughout the manuscript, command-line options are printed with a single\ndash instead of a double hyphen (--), possibly due to Latex formatting.\nConsider using a monospace font (e.g. \\texttt{--in-ont}) to preserve formatting.\nThe Code availability section states that nanoMDBG is invoked using --in-\nnano, but it should be --in-ont.\nWe have fixed the above formatting and issues with precise parameter\nspecification.\nReviewer #1 (Remarks on code availability):\nWhile I have not attempted to reproduce the specific results presented in the\nmanuscript, I have run metaMDBG with the new nanoMDBG algorithm on my\nown datasets, including both metagenomes and isolate genomes, and it\nworked well.\nInstallation was straightforward using conda. The metaMDBG GitHub\nrepository includes a README that covers the basics (installation, usage and\noutput) but it is somewhat brief and contains typos. The documentation would\nbenefit from additional detail and a link to a small demo dataset to help new\nusers get started. Also, the Advanced usage and Low-memory contig polisher\nsections are currently empty, so the README seems to be incomplete.\nWe have updated the usage instructions on the GitHub README, and revised\ntypos, and completed the Advanced Usage section.\nReviewer #2 (Remarks to the Author):\n…\nOverall, the manuscript is an important contribution as it makes a substantial\nimprovement over the state-of-the-art methods for nanopore metagenomic\nassembly, and has potential to become a popular tool. We have a few specific\nrequests:\n1. Conceptually, it is currently unclear how exactly metaMDBG (assembly\nalgorithm) benefits from corrected reads. What is the exact issue if\nmetaMDGB is applied to uncorrected reads? If raw reads can be aligned during the error correction stage, why couldn't the same alignment be used to\nconstruct the assembly graph? Although the answers might be obvious for an\nassembly expert, the paper might benefit from making these points explicit.\nMetaMDBG builds its assembly graph using a de Bruijn graph-like structure in\nminimizer-space, based purely on exact k-mer matches. Unlike string-graph-\nbased assemblers (e.g. hifiasm-meta) that rely on full base-level read\nalignments, our assembly procedure does not perform any form of alignment.\nThis exact matching strategy allows for extremely fast and memory-efficient\ngraph construction. However, it is also highly sensitive to sequencing errors. A\nsingle base error can break a k-mer match and disrupt graph connectivity,\nmuch like in classical de Bruijn graph assemblers. While our error correction\nstrategy\ndoes rely on alignments, these are performed in sparse minimizer-space and\nare much less computationally intensive than full-read base-level alignments.\nThe fourth paragraph of the Introduction already discusses these limitations\nbut we now present them more explicitly in the first paragraph of the\n\"Algorithmic Challenges and Motivations\" as follows:\n“Lines 412-420 : At the core of metaMDBG is a variant of the de-Brujin\ngraph tailored for long reads, the minimizer-space de-Brujin graph\n(MDBG). This approach considers only a small subset (around 0.5%)\nof k-mers from each read, selected uniformly using a minimizer-based\nsampling technique. The minimizers are then linked into chains of size\nk′ and further extended using a de Bruijn graph-like framework. In order\nto exploit the full potential of long reads, metaMDBG creates long and\nspecific chains (k’ ≈ 100). However, in the presence of sequencing\nerrors, such long chains inevitably end up containing erroneous k-mers\nwhich break contiguity (see [10] for a detailed study of how sequencing\nerrors in base-space propagate to minimizer-space). In this study, we\naim to extend metaMDBG to support ONT data assembly through read\ncorrection, while preserving computational efficiency to enable the\nassembly of large metagenomes.”\n2. What is the exact algorithm for generating consensus from the variant\ngraph? Greedily selecting the most supported path at every junction may not\nyield the most supported overall path.\nWe do not greedily select the most supported edge but do indeed find the\nmost supported overall path which is straightforward given a directed acyclic\ngraph. We have added a detailed description of the method used to extract\nthe most supported path in this section.\n“Lines 485-492: After all alignments have been added to the variation\ngraph. We extract the most supported path from the graph as a\nconsensus using the following procedure. We begin by initializing a\nhash table S that maps each node v in the graph to its support score,\nwith all values initially set to zero: S[v] = 0. Next, we perform a\ntopological sort of the graph’s nodes and iterate through them in the\nsorted order. For each node v, we identify the highest-supported\nincoming edge e = (u, v) with weight we, and update its support score\nas S[v] = S[u] + we. The node u is recorded as the parent of v for path\nreconstruction. The consensus path is then constructed by identifying\nthe node vmax with the highest support score S[v], and backtracking\nthrough its parent nodes until a node with no parent is reached.”\n3. This sentence is a bit confusing: “Such a sequence is the minimizer-space alignment. It is crucially different from standard alignment algorithms, which use minimizers for chaining and do not further try and align the other minimizers not involved in chains: they then align all bases, which is more computationally expensive. Here, we perform an alignment on all minimizers.”\nYes, our minimizer alignment is different from minimizer chaining. The key\ndifference is that all minimizers are sought to be aligned, whereas in chaining,\na colinear chain is sought, possibly discarding some minimizers, thus it is not\nan alignment. We have revised the paragraph as follows:\n“Lines 407-410 : Such a sequence is the minimizer-space alignment. It\nis crucially different from standard alignment algorithms, which use\nminimizers for chaining and do not further try and align the other\nminimizers not involved in chains: they then align all bases, which is\nmore computationally expensive. Here, we perform an alignment on all\nminimizers.”\n4. Benchmarking in terms of MAG recovery is a good strategy for real\ndatasets, but it does not specifically address misassemblies, contiguity and\nsequence-level quality. For datasets where reference is available (i.e., Zymo\nMock), metaQUAST could be used to generate these metrics.\nThis is a good suggestion. We used MetaQUAST between references and\nmapped contigs to measure completeness, contiguity (auNGA), the number of\nmismatches and indels per 100~kbp, and the number of misassemblies.\nThese results have been added as a new paragraph at the start of the section\n‘Evaluation of bacterial genome reconstruction.’ We have also updated the\nmethods in the “Quality assessment of assemblies on the Zymo mock\ncommunity” section accordingly.\n5. For real datasets, an indirect measure of misassmeblies could be the\nnumber of MAGs with high completion, but elevated contamination (e.g.\n10%+) - perhaps this metric could be added for real datasets comparison?\nThis is also a good suggestion, contamination is a useful way to assess real\ndata sets, but we felt that contamination at the MAG level could be biased, as\nmodern binning tools such as SemiBin2 use SCGs during the clustering\nprocess. Since assemblers do not optimize for SCGs, we chose instead to\nevaluate contamination at the contig level. Specifically, we examined long\ncontigs (>1 Mb) and identified those with over 5% contamination, treating\nthese as chimeric. The results of this analysis have been included in the new\nsection ‘Evaluation of metagenomics assembly errors’ of the revised\nmanuscript.\n6. Base-level consensus quality has been a concern for nanopore assemblies.\nFor real datasets (when references are not available), one could use tools like\nIDEEL (https://github.com/mw55309/ideel) to indirectly estimate the number of\nframeshift errors.\nAs suggested by the reviewer we applied IDEEL to contigs with coverage\ngreater than 10x in order to assess the importance of frameshift errors and\nhence indirectly the base-level consensus quality of each assembler. The\nresults of this analysis have been included in the revised manuscript (Figure\n5d). Interestingly, for the R10.4 ONT sequences used here there is no\nnoticeable difference in quality between HiFi PacBio and ONT except in the\nlow coverage Soil data sets. Soil is also where we see an advantage for\nnanoMDBG over metaFlye in terms of a higher rate of predicted proteins\naligning to the database.\n7. It would be interesting to include species similarity analysis in different\ndatasets. Strain similarity can also explain the differences in complexity and\nassembly quality for the datasets used in the paper.\nWe agree that potentially differences in assembly quality may derive from\nlevels of strain variation. However, without actually resolving strains it is hard\nto quantify this. Our experience is that simply looking at frequencies of\nvariants on contigs is not informative perhaps because of challenges in\nvariant calling for metagenomics data. So whilst we have added some\ndiscussion of strain resolution to the Discussion section of the manuscript we\ndecided not to add this additional analysis.\nMinor:\n8. In Hifi vs Nanopore comparison (Figure 4), Is there any explanation as to\nwhy for Human gut, Hifi performs better with increasing coverage but\nNanopore and Hifi perform similarly for Zymo Fecal and Soil samples. This\nanalysis is not very conclusive overall.\nThe results in Figure 4 have changed somewhat as the result of more\nstringent error correction in nano/metaMDBG. Now we do see an advantage\nfor HiFi in soil in terms of near-complete contigs. The better overall relative\nperformance of HiFi on the gut data sets at high depth remains though but we\nhave no obvious explanation for this.\n9. Does the error rate in minimizer space correspond to base-level error rate\nfor different reads? Although it’s expected - since the primary focus of the\npaper is error correction, this analysis might be important.\nThis relationship has been thoroughly analyzed in the original paper on the\nminimizer de Bruijn graph\n(https://www.sciencedirect.com/science/article/pii/S240547122100332X, see\nsection “How sequencing errors in base-space propagate to minimizer-\nspace”). Base-level errors have a pronounced impact in minimizer-space: for\nexample, a base-space error rate of 5% results in a 65.1% error rate in\nminimizer-space, while even a low base-space error rate of 0.1% leads to a\n2.3% error rate in minimizer-space.\n10. Zymo mock results description is inconsistent: “In this case, nanoMDBG\nand metaFlye both obtained five circularized genomes and metaMDBG\nobtained four. [...] In contrast, metaFlye produced only fragmented genomes.”\nSeems to be a contradiction with metaFlye results.\nThis was an error that has been corrected, and in fact this whole section has\nbeen rewritten as a result of including the MetaQUAST results (see above).\n11. Runtime and peak memory values in the supplementary data do not seem\nconsistent. For ONT Human Gut (50Gb) why does metaMBG take longer than\nnanoMDBG where nanoMDBG applies error correction on top of the general\nassembly steps that metaMDBG follows? For ONT Zymo Fecal Reference\n(200Gb), it’s surprising to see metaFlye the fastest? These results require\nsome more explanation.\nThe original version of MetaMDBG (v1.1) could be very slow on uncorrected\ndata, because errors create massive and complex assembly graphs, requiring\na longer cleaning process (particularly the superbubble detection step).\nHowever, in the new 1.2 version (described above), the cleaning\nimplementation has been parallelized so this is now less of an issue.\nMetaFlye can still be faster in some instances, for instance the Zymo Fecal\nReference is a dataset with low species complexity but very high coverage\nwhich is the worst case for an algorithm that relies on all-vs-all read mapping\nas nanoMDBG does in the error correction step.\nReviewer #4 (Remarks to the Author):\nBenoit et al present nanoMDBG, a substantial modification of the metaMDBG\nalgorithm to enable it to assemble ONT readsets. The benchmarks as\npresented are very impressive, and while I quibble about such strong self-\nendorsement such as suggesting the algorithm “could prove transformative to\nmetagenomics research”, I do think nanoMDBG has the potential to be the\ngo-to assembler for ONT reads. These are increasingly common in\nmetagenomics, and therefore nanoMDBG appears to be an extremely useful\nresource for the community. Initial application of the tool to my own datasets\nconfirms the minimal RAM requirements (and ease of installation through\nbioconda) even for large and complex communities (assembly and binning is\non-going so I cannot comment on those aspects).\nMy two main criticisms of the manuscript lie with benchmarking the accuracy\nof the contigs produced. I would argue this is imperative for metagenome\nassemblers to present, but it is especially pertinent given a recent\nprepublication which finds several error modes find their way into metaMDBG\nassemblies (https://www.biorxiv.org/content/10.1101/2025.04.22.649783v2).\nWhile that manuscript is not passed peer review, it highlights the paucity of\ncontig accuracy benchmarks from this nanoMDBG manuscript. The tools\ndeveloped in that preprint may be helpful for analysis here.\nAs we mention above, we have in response to points 5 and 6 raised by\nReviewer 2 added a whole new section ‘Evaluation of metagenomics\nassembly errors’ which is a comprehensive evaluation of likely artefacts and\nassembly errors. This includes the results of the tools referred to by the\nreviewer from https://www.biorxiv.org/content/10.1101/2025.04.22.649783v2\nbut also single contig contamination and the usage of IDEEL to detect\nframeshift errors as recommended by Reviewer 2. We thank both reviewers\nfor their suggestions. In addition, as we explain in our preamble to these\nresponses, the error rates for the meta/nanoMDBG v1.1 that was used in the\ninitial version of this manuscript were relatively high compared to the other\nassemblers. This inspired the changes described in the new Methods\nsubsection ‘Modifications to metaMDBG to improve assembly quality’\ncorresponding to the current v1.2.\nFollowing these changes the metaMDBG error rates are comparable to the\nother state-of-the-art assemblers. So again we thank the reviewers for their\nsuggestions which resulted in improvements to our underlying algorithms.\nBeyond overall benchmarks of per-base accuracy, chimeras, and other error\nmodes, there is another issue with the approach which should be at least\ndiscussed in the manuscript, or even better analysed in detail. The issue is\naround the strain-specificity of the contigs produced. Conceptually, if a read\nfrom a non-dominant strain is “corrected” by the method proposed, then it may\nbe “corrected” to the genotype of the dominant strain/genotype if that\ngenotype shares regions which are sufficiently similar, especially since only a\n20x coverage limit is applied in the initial step, precluding some analysis\nwhich might differentiate sequencing error from real but low-abundance\nvariation. On the other hand, if that were to happen then it might artificially\ninflate the coverage of the dominant strain, aiding in its eventual genome\nrecovery when it might otherwise be too sparsely covered. Perhaps these\nconsiderations also apply to other assemblers, but I suggest that they are\nnon-trivial enough that they should be at least mentioned for reviewer context.\nOur assembler is designed to produce contigs that are consensus genomes at\nthe species or at best sub-species level. This is implicit in our use of a\nrelatively low density (0.5%) of short minimisers (kmer length 13). Strain-\nvariation between these minimisers will be ignored. In our experience this\nlimits resolution to about 1% average nucleotide identity (ANI). Consequently,\nthe fact that the ONT error correction step may remove low-coverage variants\nas the reviewer suggests is not an issue. Since we are not attempting to\nassemble these strains in any case. That metaMDBG is not attempting strain\nresolution is an important point and in response to Reviewer 1 we have added\na new paragraph to the Discussion ‘“In general, the philosophy behind\nnanoMDBG is to try to obtain as many species or sub-species consensus\ngenomes as possible….” which should help clarify this for readers.\nMinor comments.\nPlease include line numbers in future versions of the manuscript to assist\nreviewers — it greatly aids clarity and precision in feedback.\nWe have added line numbers to the Manuscript.\nIn general referencing in the introduction could do with some more care. For\ninstance, the statement “MAGs constructed from short reads frequently suffer\nfrom incompleteness, contamination and high\nFragmentation.” is unreferenced, and the first mention of metaMDBG is not\nreferenced, etc.\nWe have addressed this, adding a relevant reference regarding short-read\nMAG construction and referencing metaMDBG.\n“ntigs into metagenome-assembled genomes (MAGs) [1],” - this [1] should be\nreplaced by reference(s) which better represent the state of the field, rather\nthan a manuscript by the same corresponding author.\nWe have added references to two recent binners, SemiBin2 and ComeBin.\n“In contrast, metaFlye produced only fragmented Genomes” - this section\ncould be written more clearly. It isn’t clear which genomes are being referred\nto here, for instance. Perhaps it would read better if the E. coli strains and low\nabundance species were discussed separately, rather than everything at\nonce. It would also be clearer if the true abundance table was given, so that\nthe precise meaning of “low abundance” is immediately available to the\nreader, rather than asking them to chase this down through the Zymo website\n(an option which may also be not available in the long term).\nWe have revised this section for clarity, now discussing E. coli strains and\nlow-abundance species separately. The results are also reformatted in\nSupplementary Table 2 on a per-species basis, so comparisons across\nassemblers is easier. We also have added the relative abundance of species\nin this table.\n“Significantly” - word should be replaced as no statistical analysis is present\n(nor should there be).\nIt is not clear which use of the word significantly the reviewer objects to. In\nthose contexts where confusion about whether a test was performed might\noccur we have removed it, e.g:\n“On this dataset, nanoMDBG substantially outperformed state-of-the-art”\n“For all three tested real communities, nanoMDBG reconstructed appreciably\nmore“\n“By performing the correction step in minimizer-space, our novel method,\nnanoMDBG, naturally extends metaMDBG to ONT data without impacting\noverall performance.”\n“In contrast, DeChat substantially enhanced assembly outcomes: it improved\nthe recovery of scMAGs by metaMDBG”\nWe also just deleted the word significantly in some other contexts. Otherwise\nwe believe it is clear and valid english.\nCapitalisation of bioniformatic tool names should be made more carefully. For\ninstance the first C of CheckM2 is capitalised (there were several variants in\nthe ms), and the tool name is CheckM2, not CheckM.\nThis is now corrected for CheckM2, CheckV and geNomad. We believe other\ncapitalisations are correct.\nThe words “near complete” and “complete” are used to define categories of\nMAG reconstruction accuracy. However, these words have precise meaning\nas defined in the MIMAG standard, and they should not be changed to suit a\nparticular study. They also incorporate genome characters beyond CheckM2-\nbased completeness and contamination statistics. For instance, requirements\nto include rRNA operons. One option might be to use one threshold of\ncontamination (say 5%), and then the categories names can reflect different\nlevels of completeness. This way more subtle trends than high quality vs\nmedium quality can be showcased, while not confusing the precise\ncategorisations set up in the MIMAG standard.\nWe respectfully disagree with the reviewer here. We are not obliged to use\nthe MIMAG definitions and many papers in the assembly/binning literature do\nnot. In fact there does not appear to be a definition of near-complete in\nMIMAG but I assume the reviewer is referring to high-quality draft. We would\nsuggest that as long as we are clear in our definitions then it is a fair\ncomparison across assemblers. We do though now use a single\ncontamination category of 5% so that our definitions only reflect completeness\nas the reviewer suggests. We give the proportions of near-complete MAGs\nthat contain a full complement of RNA genes elsewhere (it is around 90%)\nwhich makes it clear that this is not used in our completeness definition.\n“Not every read will be represented in the assembly, .. “ - I did not understand\nthe logic for excluding unassembled reads from this percentage. I suggest\nrephrasing or removing this interpretation.\nWe have rephrased this paragraph lines 157 - 162 to better explain why\ncomparing reads mapping to MAGs to reads mapping to the assembly is\nuseful. Hopefully this is clearer now.\n“We note that we ..” over-use of “we” here. There is an argument also that is\ntrue more generally in the manuscript, but perhaps that is a matter of taste.\nWe removed the two phrases ‘We note that we’ but otherwise we feel that ‘we’\nis acceptable usage in a multi-author scientific manuscript.\n“Varying sequencing depths”: On first read I worried about non-equal read\nquantities being used, which might bias the results. Further reading allayed\nmy concerns on this, but perhaps this sentence could be clearer on that point\nup front.\nWell we see the reviewers point but equal read numbers would be less valid\nin our opinion. Since it would favour longer reads. We have added a\nclarification about this though.\n‘Note that different average read lengths may result in differing read numbers\nat the same depth when comparing between technologies but this in our\nopinion is the most valid comparison.’\n“The improved MAG recovery by nanoMDBG translates into a more\nrepresentative picture of microbial diversity at all levels of evolutionary\ndivergence.” While this is likely true, it cannot be concluded so confidently,\nbecause the true diversity remains unknown.\nWe have now clarified that this is ‘compared to the other assemblers tested.’\n“Specific” vs “shared” - are these the same meaning? If so only 1 should be\nused to avoid confusion.\nNo, \"shared\" refers to MAGs that are recovered by both HiFi and Nanopore\ntechnologies, while \"specific\" (or unshared) refers to MAGs that are recovered\nby only one of the two technologies.\n“Gut” or “human gut”? Only one descriptor should be used, to avoid confusion.\nWe have replaced Gut with Human gut.\n“Proved essential”. It may not have been essential, even if the manuscript\nshows it helps assembly quality. Suggest rephrase.\nWe have rephrased this, removing ‘proved essential’ to give “Our new\ncorrection module greatly improved the number of reconstructed MAGs,\nphages and plasmids compared to metaMDBG.”\n“The latest ONT sequencing technology now produces results comparable to\nPacBio HiFi sequencing at equivalent sequencing depth, despite the\nremaining differences in raw read accuracy.” - this is true only if the per-base\naccuracy of the contigs is comparable, which has not been established.\nThis is a fair point in the new section “Evaluating metagenomics assembly\nerrors” we show that for soil at least that there are still more frame shift errors\nin ONT contigs than HiFi we have adjusted this statement accordingly. To\ngive:\n“Abstract: As a result of these advances, we show that the latest ONT\ntechnology can now produce comparable MAG construction results as those\nobtained using PacBio HiFi sequencing at the same sequencing depth.”\n“Line 86: .. we demonstrate that nanoMDBG can now deliver ONT-derived\nresults comparable in MAG number to those achieved with HiFi sequencing.”\n“read Metagenomicss” - error\nFixed.\n“adapt our algorithm to a streaming assembler, “ - grammatical mistake. This\nsentence is also too long.\nWe have reworded this:\n“Line 364: In future, it may prove useful to implement a version of our\nalgorithm that can perform streaming assembly, assembling reads …”\n“The most similar reads (up to approximately 20x coverage)”. This should be\nqualified to mean that they are the most similar in (sparse) minimizer space,\nwhich is not the same thing.\nWe have updated this sentence as follows:\n“Line 444: For each target read, we recruit the most similar reads in (sparse)\nminimizer-space (up to approximately 20x coverage) using seed-and-chaining\nwith a very-low density of minimizers.”\n“Filitered” - grammatical mistake.\nWe could not find this error in the MS.\n“And”: should not start a sentence.\nFixed.\n“Perform against” - grammar mistake.\nFixed.\n“Generating consensus” - this section needs more detail.\nWe have added a detailed description of the method used to extract the most\nsupported path in this section.\n“Line 485: … After all the alignments have been added to the variation graph,\nwe extract the most supported path from the graph as a consensus using the\nfollowing procedure. We begin by initializing a hash table S that maps each\nnode v in the graph to its support score, with all values initially set to zero: S[v]\n= 0. Next, we perform a topological sort of the graph’s nodes and iterate\nthrough them in the sorted order. For each node v, we identify the highest-\nsupported incoming edge e = (u, v) with weight we, and update its support\nscore as S[v] = S[u] + we. The node u is recorded as the parent of v for path\nreconstruction. The consensus path is then constructed by identifying the\nnode vmax with the highest support score S[v], and backtracking through its\nparent nodes until a node with no parent is reached.”\n“At each multi-k” - is this multi-k, or just each k?\nWe have revised this sentence for clarity as follows: At each iteration of the\nmulti-k algorithm\nA description of the hardware architecture used should be given, if walltime is\nthe main output of the benchmark. How was it compiled? With optimisations\nfor the specific hardware or a general-purpose build for e.g. conda?\nWe have added information about the system architecture and software in the\nMethods section as follows:\n“Line 555: We ran all assemblers with 32 CPU threads on a machine\nequipped with a 2.60 GHz Intel(R) Xeon(R) Gold 6132 CPU with 112 cores\nand 2,038 GB of memory. All programs were installed via Bioconda.”\n“-map-hifi” is not an option of minimap2\nFixed: -x map-hifi\n“checkV” - which version?\nFixed: checkV (v.1.0.3)\n“Assessment of completeness..” Does this section refer only to the Zymo\ncommunity? If so, it would be clearer to say that directly.\nWe have renamed this section to “Quality assessment of assemblies on the\nZymo mock community”\nEmdashes - in several places it appears “--” has been replaced by an\nemdash, which is incorrect if they refer to command line options.\nWe have fixed this formatting issues.\nFigure 4 “showcase” - Consider replacing ‘showcase’ with a more precise\nverb like ‘illustrates’ or ‘demonstrates’ to maintain academic tone.\nFixed.\n“The highest sequencing depth available” - this was unclear to me - 50, 200\nand 250G respectively? Or the full depth available for each method for each\nassembler?\nWe used 50 Gb of Human Gut, 200 Gb of Zymo Fecal Reference and 250 Gb\nof Soil. We have added this information in the legend:\n“We de-replicated MAGs per dataset using the highest sequencing depth\navailable for each dataset (50 Gb, 200 Gb and 250 Gb respectively).”\nFigure S3 “assemblers” should be singular. Y-axis Virus should be plural.\nGbps - the s is not needed.\nFixed.\nWe thank the reviewers for reading the revised version of our manuscript.\nThis time there were relatively minor comments to address but we did repeat\nthe IDEEL analysis with a cut-off of 5 kb as recommended by Reviewer 4 and\nadd some additional description of the methods used in the error analysis as\nrequested by them.\nWe include a point-by-point response to the reviewers below with our\nresponses highlighted in blue. We hope that our manuscript is now suitable\nfor publication.\nYours sincerely,\nDr Christopher Quince\nGroup Leader\nEarlham Institute"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed the comments from my previous review of this manuscript.\nOne minor typo in the Introduction: 'The initial step in many metagenomic analysis' should be 'The initial step in many metagenomic analyses'.\n(Remarks on code availability)", "reviewer_2": "The authors addressed our comments and made the appropriate changes to the text where necessary, including additional discussions, explanations and analyses. We have only a couple minor comments below that do not require re-review.\nProportion of read correction time for the same dataset in lines 82 and 322 (10% and 6%) are not consistent.\nWe appreciated the expanded discussion on the issue of strain assemblies. The authors mentioned that metaFlye attempts to reconstruct strain genomes, but the more recent work from this group (https://pubmed.ncbi.nlm.nih.gov/39327484/) might be more relevant in this context.\n(Remarks on code availability)", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.\n(Remarks on code availability)", "reviewer_4": "In responding to these reviews, the authors have made substantial improvements that greatly benefit the manuscript.\nIn the IDEEL comparison, I am curious what the effect of a more fragmented assembly might have on the results. In particular, if many contigs are short, then ORFs on the ends of contigs might be substantial in number. I note that the -c parameter of prodigal is not used in IDEEL (maybe it should be, though of course that is not the tool being assessed here). The authors might choose to restrict the IDEEL analysis to contigs over a certain length, or even compare between different assemblers just those contigs in length bins e.g. 5-10kb, 2-5kb, etc. It is particularly a problem if one assembler uses a different minimum output contig length threshold - I have not looked into this.\nThe methods for the next section appear to be incomplete. For instance, it was not explained how clipping counts were determined.\n273: It was not clear from the text what datasets were used for the first long contigs benchmark. I suppose it is the union of all contigs from all datasets assembled separately?\n228-9: Should be past tense.\nReviewed by Ben Woodcroft\n(Remarks on code availability)"}, "author_response": ""}]

Decision letter