# Unlocking the Soil Microbiome: Unraveling Soil Microbial Complexity using Long-Read Metagenomics

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

## Context

Plants can recruit micro-organisms from the millions (10³-10⁸/g) found in soil, to regulate key functions such as [1]:

* Nutrition and growth,

* Resistance to biotic and abiotic stress.

Still, soil microbiomes are poorly characterized [2], as culturomics, transcriptomics and short-read metagenomics cannot recover enough complete genomes needed to infer their functions and model those communities.

We produced both short-reads (SR) and HiFi long-reads (LR) metagenomes from tunnel culture soils, with the aim to reconstructing metagenome-assembled genomes (MAGs).

We hypothesize that HiFi long-read sequencing technologies could enhance metagenomics by improving genome reconstruction. We compared several methods to reconstruct metagenomes from HiFi long reads and compared the results to deep short-read sequencing.

<figure><img src="image_2.png" /><figcaption>Bioinformatic processing</figcaption></figure>

MetaMDBG[3] captures a greater proportion of reads and higher quality bins.

NC: 1 contig, >99% completeness, <1% contamination; HQ: >90% completeness, <5% contamination; MQ: >50% completeness, <10% contamination; LQ: other bins.

Compared to a mock community, more complex real communities yield less complete assemblies.

## Evaluation of existing highly accurate long-read metagenome assemblers

### Mapler

A pipeline built for evaluating Hi-Fi long read metagenome assemblers.

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### Assembler comparison

On the soil of a salad culture in PACA region (1SMRT Cell)

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

### Sample comparaison

Using metaMDBG

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## Long-reads versus short-reads technologies comparison

### Soil metagenomes summary

<table><thead><tr><th></th><th></th><th>Short-reads</th><th>Long-reads</th></tr></thead><tbody><tr><td rowspan="3">Contigs</td><td>Read nb. (N50)</td><td>2 x 991M (151)</td><td>2,9M (7,6 kb)</td></tr><tr><td>Contig nb. (N50)</td><td>31M (620)</td><td>493 030 (7,6 kb)</td></tr><tr><td>Predicted proteins (% complete)</td><td>41 72 9705 (39%)</td><td>1 379 489 (97%)</td></tr><tr><td rowspan="4">Bins</td><td>Average depth</td><td>6X[0:763 551]</td><td>3X [0:37]</td></tr><tr><td>Bin nb.</td><td>228</td><td>55</td></tr><tr><td>MAGs nb.</td><td>139</td><td>13</td></tr><tr><td>Average depth</td><td>48X</td><td>3X</td></tr></tbody></table>

Unassembled HiFi LR surpass the contig length of SR contigs. Although SR predicted 30 times more proteins, LR predictions are less redundant and almost complete, with more than 60% of prediction from SR being fragmented.

### Example of a Metagenome-Assembled genome

Actinoplanes phage phiAsp2, a tiny genome of 58.6kb

Fully reconstructed genome achieved through HiFi LR metagenomic assembly.

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

Highly fragmented genome achieved through SR metagenomic assembly.

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

LR provides higher contiguity of reconstructed genomes, yielding more suitable data for their structural and functional analysis.

### More reliable taxonomic assignment

Dozens of MAGs were obtained from LR after assembly, binning and dereplication, encompassing bacterial, archaeal, and viral genomes from terrestrial environments taxonomically annotated with high accuracy.

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

Shorter sequences result in a significant amount of mis-annotation.

## Lack of captured diversity

### By the metaMDBG assembly

Taxonomic assignation comparison

full read set

k-mer occurrences comparison

unmapped read set

Assembly fail at capturing some or the soil's biodiversity, as reads that failed to map to the assembly are evidence that they aren't properly assembled :

* Rare taxa are more represented in unmapped reads,
* Some k-mers, especially rare ones, belong to unmapped reads.

### By sequencing

Assessment of the genetic diversity captured by sequencing technologies via Virtual metabarcoding

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

Genetic diversity captured by sequencing technologies assessed by k-mer occurrence analysis

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

No sequencing technology seems to have successfully captured all the soil's biodiversity yet

## Conclusion

LR metagenomics promises a more comprehensive insights into the genomic structure, functional potential of soil microbial genomes and their taxonomic assignment. Since none of the existing technologies appear to offer an optimal representation of the terrestrial microbiome, the integration of both technology into a hybrid assembly and/or adapting assembly algorithms for low coverage HiFi LR could enhance metagenome recovery.