Inês Mendes

MRamirez Lab - iMM

@ines_cim

cimendes

XIV CAML · IV NeurULisboa PhD Students Meeting

5th to 7th May, 2021

Metagenomics

Random "shotgun" sequencing of microbial DNA, without selecting a particular gene or species.

Promising methodology for obtaining fast results for the identification of pathogens and their virulence and antimicrobial resistance properties without the need for culture.

 | The motivation

Metagenomics

 | The motivation

de novo Assembly

The assembly methods provide longer sequences that are more informative than shorter sequencing data and can provide a more complete picture of the microbial community in a given sample.

Metagenomics

 | An assembly challenge

  • Results are highly dependent on the tools chosen for the analysis - Lack of standardization and proper benchmark.

Major issues

Reads

Contigs

Genomes

  • Highlights the potential and the limitations of shotgun metagenomics as a diagnostic tool - Lack of reproducibility

LMAS

 | de novo Assembly Benchmark

https://github.com/cimendes/LMAS

https://lmas.readthedocs.io/

LMAS

 | de novo Assembly Benchmark

Git, Nextflow (java) and a container engine (Docker, singularity, shifter...).

apt-get install git
curl -s https://get.nextflow.io | bash 
apt-install docker-ce

Clone

git clone https://github.com/cimendes/LMAS.git

Run LMAS

nextflow run LMAS.nf

LMAS

 | de novo Assembly Benchmark

The input data is assembled in parallel by the set of genomic and metagenomic de novo assemblers in LMAS.

The global and per reference metrics are grouped in the interactive LMAS report for exploration

The resulting assembled sequences are processed and assembly quality metrics are computed,

LMAS

 | ZymoBIOMICS Standards

The resulting LMAS report is available at

https://lmas-demo.herokuapp.com

Eight bacterial genomes and four plasmids of the ZymoBIOMICS Microbial Community Standards were used as the triple reference.

Sample Distribution Error Model Read Pairs (M)
ENN Even None 8.6
EHS Even Illumina HiSeq 8.6
ERR2984773 Even Real Sample 8.6
LNN Log None 47.5
LHS Log Illumina HiSeq 47.5
ERR2935805 Log Real Sample 47.5

Special thanks to Pedro Vila-Cerqueira, Rafael Maria Mamede and Mário Ramirez.

Thank you for your attention

FCT PhD Grant SFRH/BD/129483/2017

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