Assemble genomes, not just gene lists.
Short-read metagenomics profiles communities efficiently but fragments assemblies into thousands of partial contigs. Long reads span repeats, plasmids, and mobile elements, enabling near-complete — sometimes circular — genome recovery directly from community DNA.
Long-Read Shotgun Metagenomics at Nucleome Informatics
Shotgun metagenomics sequences all DNA in a sample, capturing species composition, strain variation, functional gene content, and — with long reads — whole genomes of uncultured organisms. Nucleome Informatics offers long-read shotgun metagenomics on:
- Host-depleted, quality-filtered long reads (FASTQ/BAM) with QC metrics
- Metagenomic assembly (metaFlye or hifiasm-meta) and assembly QC
- Binned MAGs with completeness/contamination estimates (CheckM2) against MIMAG standards
- Taxonomic classification of reads and MAGs (Kraken2/Centrifuge; GTDB-Tk)
- Functional annotation: gene prediction, pathway mapping (HUMAnN-style), AMR gene and virulence screening
- Comparative reports: abundance tables, diversity analyses, MAG–taxonomy–function linkage
Why long reads for metagenomics?
- Complete genomes: long reads span the repeats that break short-read assemblies, enabling circularised MAGs and full-length operons.
- Strain resolution: long reads link mobile genetic elements (plasmids, phages, AMR cassettes) to their host genomes — short reads usually cannot.
- Fewer blind spots: repetitive CRISPR arrays, rRNA operons, and biosynthetic gene clusters assemble correctly rather than collapsing.
From first principles to expert-level analysis
New here? Start with Foundations. Planning a study or reviewing results? Jump to Practitioner or Expert. Each level builds on the one before it, so you can read this page as a short course or dip in where you need to.
Start here if you are new to this service — six ideas that explain what it is and why it matters.
What is shotgun metagenomics?
Sequencing all DNA extracted from a community (gut, soil, water, biofilm) without targeted PCR. Every genome present contributes reads in proportion to its abundance.
What is a MAG?
A metagenome-assembled genome: the set of contigs belonging to one organism, binned from the assembly by composition and coverage. A high-quality MAG is a near-complete draft genome of an organism you may never have cultured.
16S vs shotgun — which do I need?
16S answers "who is there, roughly" cheaply. Shotgun adds "what can they do" (genes, pathways, AMR) and strain-level detail, at higher cost per sample. Many projects use 16S for screening and shotgun for key samples.
What does "host depletion" mean?
For host-associated samples (human, animal, plant tissue), host DNA can dominate the library. We deplete host reads computationally and, for ONT, optionally in real time with adaptive sampling.
How deep do we sequence?
Depth depends on community complexity and your MAG goals. Low-complexity communities may yield complete MAGs at modest depth; highly diverse soil communities need far more data for the same recovery. We scope this with you honestly before starting.
What samples work best?
Any purified DNA from microbial communities: stool, oral, soil, sediment, water filters, rumen, bioreactors, food fermentations.
For scientists planning a project or reading a report: how each step works and how it can mislead.
Library and sequencing design
High-molecular-weight DNA extraction is critical — long reads are only as long as your input DNA. We advise on extraction (or accept HMW DNA) and sequence on Revio (HiFi) or PromethION (ONT) at depths agreed in advance.
Assembly and binning
metaFlye or hifiasm-meta assembles reads into contigs; MetaBAT2/VAMB-style binners group contigs by tetranucleotide composition and coverage across samples. Co-assembly and per-sample assembly strategies are chosen per project.
MAG quality control
CheckM2 estimates completeness and contamination from marker genes and domain knowledge. We report MIMAG tiers and flag low-quality bins rather than overselling them.
Taxonomy: reads vs genomes
Read-based classifiers (Kraken2, Centrifuge) profile known organisms fast; GTDB-Tk classifies MAGs against the genome tree. We use both and reconcile discrepancies explicitly.
Functional annotation
Predicted genes are mapped to KEGG/eggNOG/GO pathways; stratified functional profiles (HUMAnN-style) link gene families to organisms where binning permits.
AMR and virulence screening
AMR genes are detected against curated databases (CARD, ResFinder, AMRFinderPlus). With long reads, we can often assign an AMR gene to its host MAG or plasmid — a key advantage for surveillance and food-safety work.
For study design: the decisions that determine whether the data answers your question.
Depth planning for MAG recovery
MAG yield scales with sequencing depth and organism abundance. As a planning heuristic, recovering a genome at 1% relative abundance to high-quality standard typically requires substantially more data than profiling alone; we model expected recovery from pilot data where needed.
Time-course and multi-omics designs
Strain-resolved longitudinal designs benefit from co-binning across time points; pairing metagenomics with metatranscriptomics (RNA co-extraction) links potential to activity.
Circularity and end-to-end validation
Circular MAGs are validated by read coverage across the join and by assembly graph structure; we report circularity evidence rather than asserting it.
Contamination control
Low-biomass samples are vulnerable to kitome contamination. Include extraction blanks; we screen for recurring contaminant taxa and report them.
Integrating with 16S and culture
Full-length 16S (see our dedicated page) provides economical compositional screening; MAGs can guide targeted cultivation of novel organisms from your community.
Data and reporting standards
We follow MIMAG and MIxS reporting norms: assembler versions, binning parameters, database versions, and completeness/contamination for every MAG.
- MAG
- metagenome-assembled genome.
- MIMAG
- Minimum Information about a Metagenome-Assembled Genome; community quality standards (e.g. high-quality: ≥90% complete, <5% contaminated).
- Adaptive sampling
- ONT's real-time software that ejects unwanted DNA molecules (e.g. host) mid-read.
- Co-assembly
- assembling reads from multiple related samples together to improve MAG recovery.
- AMR
- antimicrobial resistance.
- HMW DNA
- high-molecular-weight DNA, required for long-read libraries.
How it works, in two figures
HiFi metagenomic assembly has demonstrated near-complete, single-contig recovery of bacterial genomes from complex communities (e.g. the human gut), and MIMAG defines the community standards we apply to every bin. See references below.
Built for the questions you are asking
The platforms, pipelines and standards behind this service.
PacBio HiFi metagenomics
Per-molecule consensus accuracy ≥99.9% at read lengths of 15–20+ kb — the combination that makes near-complete, single-contig MAG recovery routine in many communities.
Oxford Nanopore ultra-long reads
PromethION flow cells deliver multi-Gb outputs with read lengths that can exceed 100 kb, resolving the longest repeats and plasmid networks; adaptive sampling depletes host reads in real time.
metaFlye / hifiasm-meta assembly
Long-read metagenomic assemblers purpose-built for community data, with repeat resolution and polishing tuned for HiFi and ONT read profiles.
MIMAG-standard MAG QC
Every MAG is reported with completeness, contamination, and quality tier (high-quality: ≥90% completeness, <5% contamination, plus rRNA/tRNA presence per MIMAG criteria) — no unqualified "genome bins".
GTDB-Tk classification
Genome-informed taxonomy via marker-gene placement in the Genome Taxonomy Database, resolving the misclassifications that plague marker-gene-only approaches.
Functional and AMR profiling
Gene prediction and pathway annotation (HUMAnN-style stratified profiles), plus AMR screening against CARD/ResFinder/AMRFinderPlus and virulence factor databases — with long reads linking AMR genes to host contigs. ---
What this service does and does not cover
Service highlights
What you can expect from our team in Hyderabad.
Both long-read platforms in-house
PacBio Revio/Sequel IIe and ONT PromethION — platform chosen to fit your community and budget, not our instrument schedule.
MAGs to MIMAG standards
Every genome binned, QC'd, and classified — with honest quality tiers.
AMR-to-host linkage
Long reads connect resistance genes to the organisms and plasmids carrying them.
India-focused expertise
Gut, soil, rumen, aquaculture, and food microbiomes from Indian and tropical settings.
Full analysis, not just data
From raw reads to publication-ready figures and methods text.
Pilot-friendly
Start with a small pilot to calibrate depth and MAG yield before scaling. ---
Frequently asked, in plain language
From the basics to the finer points of design and interpretation.
basicHow is this different from 16S amplicon sequencing?
basicWhat do I get at the end?
basicCan you recover genomes of uncultured bacteria?
intermediatePacBio HiFi or Oxford Nanopore — which should I choose?
intermediateHow much sequencing depth do I need?
intermediateCan you identify antimicrobial resistance genes?
advancedWhat quality standard do you apply to MAGs?
advancedCan you do strain-resolved analysis?
advancedCan metatranscriptomics be added?
advancedDo you deliver raw data?
Key literature & resources
Selected sources behind the methods and reference values on this page. Figures labelled "schematic" or "illustrative" are generated for this page, not client data.
- Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new versatile metagenomic assembler. Genome Research. 2017;27(5):824–834. (metaFlye: Kolmogorov M, et al. metaFlye: scalable long-read metagenome assembly using repeat graphs. Nature Methods. 2020;17(11):1103–1110.)
- Bowers RM, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nature Biotechnology. 2017;35(8):725–731.
- Chklovski A, et al. CheckM2 — an improved method to assess genomic completeness and contamination of metagenome-assembled genomes. Nature Biotechnology. 2023;41(12):1633–1638.
- Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36(6):1925–1926.
- Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biology. 2019;20(1):257.
- Almeida A, et al. A new genomic blueprint of the human gut microbiota. Nature. 2019;568(7753):499–504.
- Bhatt AS, et al. — see also: Lakin SM, et al. Metagenomic assembly of antimicrobial resistance genes. (AMR databases: Alcock BP, et al. CARD 2023. Nucleic Acids Research. 2023;51(D1):D690–D709.)









