Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs per-sample, the rRNA-operon collapse that fails short-read MAGs, and strain resolution with inStrain. Use when reconstructing genomes from a microbiome, soil, ocean, or gut community, recovering MAGs, or resolving strain-level variation.
Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs per-sample, the rRNA-operon collapse that fails short-read MAGs, and strain resolution with inStrain. Use when reconstructing genomes from a microbiome, soil, ocean, or gut community, recovering MAGs, or resolving strain-level variation.
Before using code patterns, verify installed versions match. If versions differ:
CLI: <tool> --version then <tool> --help to confirm flags
Python: pip show <package> then help(module.function) to check signatures
GTDB-Tk results track the reference-package RELEASE (e.g. R214 vs R220); the DB release MUST match the GTDB-Tk binary or classification silently fails. CheckM2 and GUNC each download their own DIAMOND DB. SemiBin2's pretrained models are versioned. If code throws an error, introspect the installed tool and adapt rather than retrying.
--environment
Metagenome Assembly
"Assemble genomes from my metagenome" -> Co-assemble a community at uneven, strain-mixed coverage, then bin the contigs into a set of consensus population genomes (MAGs) and QC each against MIMAG. The deliverable is MAGs, not a single assembly.
The Single Most Important Modern Insight -- A Metagenome Is Not a Genome; the Assembler Cannot Assume Uniform Coverage
Every isolate assembler is built on the premise that the true sequence sits at roughly one depth, so a coverage drop or spike signals a repeat or an error. In a community that premise is false by construction: an abundant species at 500x and a rare one at 3x are both real. Running plain SPAdes/Unicycler or single-genome Flye on a community treats the abundance spread and strain bubbles as errors to "fix" and produces garbage. Use --meta modes. Three consequences cascade:
A MAG is a population consensus, not an organism's genome. Co-occurring strains differing by <1% ANI become bubbles the assembler collapses into one consensus path -- a sequence that may match no actual cell in the sample. A 99%-complete circular MAG is still the consensus of the dominant strain; minority-strain accessory genome is averaged away. Treat every per-strain, per-allele, or pangenome claim from a single consensus MAG as suspect until read-level microdiversity (inStrain) or strain-aware assembly confirms it.
The deliverable is a community of MAGs, not one assembly -- a community has no N50. N50 is dominated by whichever few abundant genomes assembled well and says nothing about the community; a "better N50" assembly can have recovered fewer genomes. Report MAG count split by MIMAG tier (HQ/medium/low) and community fraction binned. Bigger total assembly size is not better -- it can mean more chimeras and strain-fragmentation.
Modern practice is multi-binner -> consolidate -> CheckM2 + GUNC -> GTDB-Tk. Never trust one binner; run several (each weights composition vs coverage differently and recovers a partially-different genome set), reconcile with DAS_Tool, then QC every bin with CheckM2 AND GUNC (completeness lies about chimeras) before classifying against the MIMAG 90%/5% bar. HiFi/long reads are the single biggest quality jump: they span the conserved rRNA operons and strain bubbles short reads shred, yielding complete, circular, genuinely-HQ MAGs.
assembly is blind below the abundance-detection limit
Reads not QC'd / host-contaminated
-> long-read-sequencing/long-read-qc
remove host reads vs a T2T reference before assembly
MAG contamination forensics
-> contamination-detection
detailed CheckM2/GUNC interpretation
metaFlye (ONT / Long Reads)
flye --meta --nano-hq ont.fastq.gz --out-dir flye_out -t 32
# --meta uneven-coverage metagenome mode (REQUIRED for communities)# read-type flag (mutually exclusive): --nano-hq (Guppy5+/Q20) | --nano-raw (older) |# --pacbio-hifi | --pacbio-raw (CLR)# outputs: assembly.fasta, assembly_graph.gfa, assembly_info.txt (circularity flag in col 'circ.')
ONT contigs are contiguous but error-prone (indels in homopolymers); polish before downstream use (-> assembly-polishing; medaka needs the matching basecaller model). HiFi usually needs no polishing.
metaSPAdes / MEGAHIT (Illumina)
# metaSPAdes -- contiguity priority; exactly ONE paired library
spades.py --meta -1 R1.fastq.gz -2 R2.fastq.gz -o spades_out -t 32 -m 500
# -m memory cap in GB (SPAdes aborts if exceeded); -k auto by default# outputs: contigs.fasta, scaffolds.fasta# MEGAHIT -- huge/low-RAM; accepts comma-separated multiple libraries
megahit -1 a1.fq.gz,b1.fq.gz -2 a2.fq.gz,b2.fq.gz -o megahit_out -t 32 \
--presets meta-sensitive --min-contig-len 1000
# --presets meta-sensitive | meta-large (huge complex); raise --min-contig-len to ~1000 for binning
metaSPAdes --meta supports exactly ONE paired-end library -- a real constraint people miss; concatenate libraries first or use MEGAHIT for many. metaSPAdes is heavier on RAM/time and chokes on soil-scale co-assembly; MEGAHIT assembled a 252 Gbp soil set on one node at the cost of somewhat more fragmentation.
# Map reads back to the assembly -- per sample for differential coverage
minimap2 -ax map-ont -t 32 contigs.fa reads.fq.gz | samtools sort -@ 32 -o s1.sorted.bam -
samtools index s1.sorted.bam
jgi_summarize_bam_contig_depths --outputDepth depth.txt s1.sorted.bam s2.sorted.bam # all samples
metabat2 -i contigs.fa -a depth.txt -o metabat/bin -m 1500 -t 32 # -m 1500 = min contig (do not go below ~1000)
run_MaxBin.pl -contig contigs.fa -abund abund1.txt -out maxbin/bin -thread 32 -min_contig_length 1000
SemiBin2 single_easy_bin -i contigs.fa -b s1.sorted.bam -o semibin_out # add --environment human_gut for a pretrained model
CONCOCT is a 4-step pipeline (cut_up_fasta.py -> concoct_coverage_table.py -> concoct -> merge_cutup_clustering.py -> extract_fasta_bins.py), not one command. Differential-coverage binning needs MULTIPLE samples with abundance variation; with one sample binning collapses to weak composition-only signal -- more samples, not more tuning.
Consolidate (DAS_Tool), then QC
# Convert each binner's output to contig2bin tables, then aggregate-and-score
Fasta_to_Contig2Bin.sh -i metabat/ -e fa > metabat.tsv
Fasta_to_Contig2Bin.sh -i maxbin/ -e fasta > maxbin.tsv # MaxBin emits .fasta
gunzip -k semibin_out/output_bins/*.gz 2>/dev/null || true# SemiBin2 bins are gzipped
Fasta_to_Contig2Bin.sh -i semibin_out/output_bins/ -e fa > semibin.tsv
DAS_Tool -i metabat.tsv,maxbin.tsv,semibin.tsv -l metabat,maxbin,semibin \
-c contigs.fa -o dastool/DAS --write_bins -t 32
# DAS_Tool assumes the SAME contig set across binners -- feeding bins from different assemblies is a silent error
checkm2 predict --input dastool/DAS_DASTool_bins/ -x fa --output-directory checkm2_out -t 32
gunc run --input_dir dastool/DAS_DASTool_bins/ --file_suffix .fa --out_dir gunc_out --threads 32
gtdbtk classify_wf --genome_dir dastool/DAS_DASTool_bins/ -x fa --out_dir gtdbtk_out --cpus 32
Run CheckM2 AND GUNC: CheckM2 counts marker copy number (completeness/contamination), GUNC tests whether a genome's genes share one lineage (chimerism). A bin made of two half-genomes can score high completeness, low contamination, and still be a chimera -- GUNC is the orthogonal catch. Dereplicate MAGs across samples (dRep ~95% ANI species, ~99% strain) before reporting counts.
Trigger: plain spades.py/flye (no --meta) on community reads. Mechanism: uniform-coverage assumption deletes rare-taxon contigs and mis-resolves strain bubbles as errors. Symptom: few short contigs, missing abundant taxa. Fix:--meta mode for every meta-assembler.
Single-binner pipeline
Trigger: "we used SemiBin2 because it's SOTA," one binner. Mechanism: each binner recovers a partially-different genome set. Symptom: real MAGs left on the table; lower count than peers. Fix: run >=2-3 binners -> DAS_Tool consolidation.
Single-sample differential-coverage expectation
Trigger: MetaBAT2/CONCOCT on one sample, surprised bins are bad. Mechanism: one coverage value -> composition (TNF) only, which is weak (related genera share TNF). Symptom: poor, split bins. Fix: more samples with abundance variation, then map all back; do not retune.
Short-read MAG reported HQ on 90/5 alone
Trigger: calling a 95%-complete/2%-contam short-read MAG "high-quality." Mechanism: conserved + multi-copy rRNA operon tangles the short-read graph and stays unbinned. Symptom: MAG fails MIMAG HQ for missing 16S/23S/5S despite great completeness. Fix: check the FULL MIMAG HQ definition (rRNA + tRNA); use HiFi/long reads to span the operon.
High CheckM2 completeness read as quality
Trigger: trusting completeness/contamination alone. Mechanism: non-overlapping markers from two half-genomes score high completeness, low contamination. Symptom: "clean" MAG that is a chimera. Fix: always pair CheckM2 with GUNC -> contamination-detection.
Strain claims from a consensus MAG
Trigger: per-allele/per-strain interpretation of one MAG. Mechanism: the assembler collapsed strains to consensus. Symptom: strain findings that read-level data contradict. Fix: inStrain popANI or strain-aware (hifiasm-meta) assembly.
Reading MAG richness as community richness
Trigger: "200 MAGs cover 60% of reads" treated as the whole community. Mechanism: the rare biosphere never crosses the assembly detection limit. Symptom: species richness massively undercounted. Fix: complement with read-based profiling -> metagenomics/kraken-classification.
shorter contigs have unreliable TNF/coverage -> noise/chimeras
Differential-coverage binning needs N >= ~3-5 samples with abundance variation
binning practice
one coverage value cannot separate co-abundant genomes
Assembly detection limit ~3-5x coverage
de Bruijn requirement
below this the rare biosphere does not assemble at all
MAG dereplication 95% ANI (species), 99% (strain)
dRep/ANI convention
collapse redundant per-sample MAGs before counting
metaSPAdes input: exactly ONE paired library
--meta constraint
merge libraries or use MEGAHIT for many
N50 / contiguity
not a community metric
report MAG count + MIMAG tiers instead
Common Errors
Error / symptom
Cause
Solution
Few short contigs, missing abundant taxa
isolate mode on a community
add --meta
metaSPAdes rejects multiple libraries
--meta supports one paired library
concatenate, or use MEGAHIT
metaSPAdes aborts / out of memory
RAM cap hit on a complex co-assembly
raise -m, or switch to MEGAHIT
Poor bins from one sample
no differential-coverage signal
add samples; map all back for depth
"HQ MAG" lacks rRNA
rRNA-operon collapse in short reads
full MIMAG check; HiFi/long reads
Clean CheckM2 but suspect bin
chimera invisible to marker counts
run GUNC alongside CheckM2
GTDB-Tk classify_wf errors
DB release != binary version
match the GTDB reference-package release
Strain finding not reproducible
consensus MAG blurs strains
inStrain popANI / strain-aware assembly
References
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. 2017. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27:824-834.
Li D, Liu CM, Luo R, Sadakane K, Lam TW. 2015. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31:1674-1676.
Kolmogorov M, et al. 2020. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nat Methods 17:1103-1110.
Feng X, Cheng H, Portik D, Li H. 2022. Metagenome assembly of high-fidelity long reads with hifiasm-meta. Nat Methods 19:671-674.
Benoit G, et al. 2024. High-quality metagenome assembly from long accurate reads with metaMDBG. Nat Biotechnol 42:1378-1383.
Bertrand D, et al. 2019. Hybrid metagenomic assembly enables high-resolution analysis of resistance determinants and mobile elements in human microbiomes (OPERA-MS). Nat Biotechnol 37:937-944.
Kang DD, et al. 2019. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359.
Wu YW, Simmons BA, Singer SW. 2016. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:605-607.
Alneberg J, et al. 2014. Binning metagenomic contigs by coverage and composition (CONCOCT). Nat Methods 11:1144-1146.
Pan S, Zhao XM, Coelho LP. 2023. SemiBin2: self-supervised contrastive learning leads to better MAGs for short- and long-read sequencing. Bioinformatics 39:i21-i29.
Nissen JN, et al. 2021. Improved metagenome binning and assembly using deep variational autoencoders (VAMB). Nat Biotechnol 39:555-560.
Sieber CMK, et al. 2018. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy (DAS_Tool). Nat Microbiol 3:836-843.
Chklovski A, et al. 2023. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nat Methods 20:1203-1212.
Orakov A, et al. 2021. GUNC: detection of chimerism and contamination in prokaryotic genomes. Genome Biol 22:178.
Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. 2020. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36:1925-1927.
Bowers RM, et al. 2017. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol 35:725-731.
Olm MR, et al. 2021. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nat Biotechnol 39:727-736.
Related Skills
contamination-detection - CheckM2/GUNC interpretation and chimerism forensics for MAGs
assembly-qc - Isolate-assembly QC; the uniform-coverage assumption metagenomes abandon
assembly-polishing - Polish ONT/CLR meta-contigs before binning (HiFi usually needs none)
metagenomics/kraken-classification - Read-based taxonomy; recovers the rare biosphere assembly cannot
metagenomics/abundance-estimation - Community abundance downstream of recovered MAGs
metagenomics/functional-profiling - Functional potential complementary to genome recovery
long-read-sequencing/long-read-qc - Read-level QC and host removal before assembly
