# Metagenomic assembly of complex ecosystems with highly accurate long-reads

Nicolas Maurice, Claire Lemaitre, Clémence Frioux, Riccardo Vicedomini

<figure><img src="image_1.png" /></figure>

To cite this version:

Nicolas Maurice, Claire Lemaitre, Clémence Frioux, Riccardo Vicedomini. Metagenomic assembly of complex ecosystems with highly accurate long-reads. Journées 2024 du PEPR Agroécologie et Numérique, Jan 2024, Rennes, France. pp.1-1, 2024. [hal-04425626](https://hal-04425626v1)

HAL Id: hal-04425626

https://inria.hal.science/hal-04425626v1

Submitted on 30 Jan 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Metagenomic assembly of complex ecosystems with highly accurate long-reads

<figure><img src="image_3.png" /></figure>

Nicolas Maurice<fnref n="1" />,<fnref n="2" />, Claire Lemaitre<fnref n="1" />, Clémence Frioux<fnref n="2" />, Riccardo Vicedomini<fnref n="1" />

<figure><img src="image_4.png" /></figure>

UMR

IRISA

<figure><img src="image_5.png" /></figure>

Université
de Rennes

nicolas.maurice@inria.fr

<figure><img src="image_6.png" /></figure>

FRANCE

<figure><img src="image_7.png" /></figure>

PROGRAMME
DE RÉCHERCHE

AGROÉCOLOGIE
ET NUMÉRIQUE

<figure><img src="image_8.png" /></figure>

PLEIADE

## Context

<figure><img src="image_9.png" /></figure>

### Plant microbiota

Plants can recruit micro-organisms from the millions ($10^3-10^8/g$) found in soil, to regulate key functions such as :

* Nutrition and growth,

* Abiotic stress,

* Biotic stress.

Still, both ecosystems are poorly characterised, as culturomics, transcriptomics and short-read metagenomics cannot recover enough complete genomes needed to modelise and characterise those communities.

### MISTIC Project : project.inria.fr/mistic

Methodological development for microbial community dynamics modelling

* WP1 : Top down approach from large-scale data on natural communities,

* WP2 : Bottom up approach, building dynamic models of increased complexity under controlled conditions.

<figure><img src="image_10.png" /></figure>

## Motivations

<figure><img src="image_11.png" /></figure>

### Emerging sequencing technologies

<table><thead><tr><td></td><td>Read type</td><td>Read length (bp)</td><td>Error rate</td></tr></thead><tbody><tr><td>Illumina</td><td>Short reads</td><td>151 bp</td><td>~0.1</td></tr><tr><td>ONT R9.4</td><td rowspan="2">Long reads</td><td>~20kb</td><td>~10%</td></tr><tr><td>PacBio CLR</td><td>~10kb</td><td>~10%</td></tr><tr><td>ONT R10.4</td><td rowspan="2">Highly accurate long reads</td><td>~20kb</td><td>~1%</td></tr><tr><td>PacBio Hi-Fi</td><td>~10kb</td><td>~1%</td></tr></tbody></table>

Higher quality reads lead to higher quality assemblies.

### How to exploit those highly accurate long-reads to recover high-quality genomes from complex environments such as soil and rhizosphere ?

## Mapler : gitlab.inria.fr/mistic/mapler

<figure><img src="image_12.png" /></figure>

### Pipeline

Built with Snakemake and Slurm integration.

Benchmarks three highly accurate long reads assemblers :

* Metaflye<fnref n="1" />,

* Hifiasm-meta<fnref n="2" />,

* MetaMDBG³.

### Datasets

<table><thead><tr><td></td><td>Total size (Gb)</td><td>Median size (Kb)</td><td>Taxonomic complexity</td></tr></thead><tbody><tr><td>Zymo D6331</td><td>18.0</td><td>8.1</td><td>Mock community</td></tr><tr><td>Human gut</td><td>15.2</td><td>8.4</td><td>+</td></tr><tr><td>Rhizosphere</td><td>19.5</td><td>5.8</td><td>++</td></tr><tr><td>Soil</td><td>13.4</td><td>6.0</td><td>+++</td></tr></tbody></table>

### Evaluation metrics

* Aligning contigs on reference genomes, if aviable : NGA50 and genome fraction,

* Aligning reads on contigs : counting reads that map to the assembly,

* Evaluating the bins completeness and contamination with marker genes, then categorizing bins by level of quality.

## Mapler : Results

### Assembler comparison
On the soil sample

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

MetaMDBG captures :
* A greater proportion of reads,
* More high-quality bins,
* More medium-quality bins.

### Sample comparaison
Using metaMDBG

<figure><img src="image_15.png" /></figure>

As taxonomic complexity increases :
* Quality of bins decreases,
* Proportion of captured taxa decreases.

## Methodological avenues of improvement

### Read partitioning

<figure><img src="image_16.png" /></figure>

By partitioning the reads into smaller sets via :

* Taxonomic assignation,

* Composition-based clustering,

* Abundance-based clustering.

We could reduce the complexity of each subset, simplifying the assemblies.

### Read recovery

<figure><img src="image_17.png" /></figure>

By mapping the reads on contigs, we could extract unassembled reads, and assemble those separately with reduced complexity.

### Hybrid assembly or binning

Highly accurate long reads could be complemented by a higher coverage of short reads, which could be used to better assess the abundance of each long-read, leading to a better assembly or binning.

[1] M.Kolmogorov et Al. metaFlye: scalable long-read metagenome assembly using repeat graphs, 2020

[2] X. Feng et Al. Metagenome assembly of high-fidelity long reads with hifiasm-meta, 2022

[3] G. Benoit et Al. High-quality metagenome assembly from long accurate reads with metaMDBG, 2024

<footnote n="1">¹ Inria/IRISA Genscale, Campus de Beaulieu, 35042 Rennes, France</footnote>

<footnote n="2">² Inria/INRAE Pleiade, Campus de Talence, 33405 Bordeaux, France</footnote>