Microbial (meta)genomics

Shotgun Sequencing Data

• Direct Read Classification Methods

• Assembly-Based Methods

• Marker Gene Detection

• Taxonomical Assignment

• Abundance estimation

• Sequence binning

• Abundance estimation

• Computational expensive
• Lacks context

• De novo metagenomic assemby

• De novo metagenomic co-assemby

• Consensus metagenomic assemby

• Provides contextual information
• Normalizes information

Microbial (meta)genomics

| 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.

Assembly

consensus

Microbial (meta)genomics

| Assembly

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

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

Reads

Contigs

Genomes

Microbial (meta)genomics

| Assembly

• State-of-the-art software assessment for short-read metagenomic de novo assembly (Task 1)

• Evaluation of the influence the choice of assembler has on quality, reproducibility and resource requirements for metagenomic analyisis

• Development of a reproducible, user-friendly workflow for the assessment of assembly success (Task 2)

Reads

Contigs

Genomes

Microbial (meta)genomics

| Assembly

• Limited to short-read paired-end sequencing data

Reads

Contigs

Genomes

• Limited to de novo assembly software

• Lack of flexibility for the introduction of new tools

7 months ago...

Compiled a collection of de novo assembly tools, including Overlap, Layout and Consensus (OLC) and De Bruijn graph (dBg) assembly algorithms, with both single k-mer and multiple k-mer value approaches, and hybrid assemblers. The collection includes both genomic and metagenomic assemblers.

7 months ago...

Initial development of global and reference specific performance metrics for the assessment of de novo assembly quality.

7 months ago...

https://github.com/cimendes/metagenomic-assembler-comparison

Reference Dataset (Complete Bacterial Genomes)

In silico mock sample (even)

In silico mock sample (log)

Zymos standard (even)

Zymos standard (log)

3.7 M read pairs

8.8 M read pairs

47.8 M read pairs

Basic Assembly Workflow

Assembly Quality Assessment

LMAS

| Last Metagenomic Assembler Standing

Automated workflow enabling the benchmarking of genomic and metagenomic prokaryotic de novo assembly software using defined mock communities.

LMAS

| Last Metagenomic Assembler Standing

LMAS

| Last Metagenomic Assembler Standing

LMAS

| Last Metagenomic Assembler Standing

LMAS

| Assembly Quality Metrics

The tabular presentation allows direct comparison of exact values between assemblies, and the interactive plots allow for an intuitive overview and easy exploration of results.

LMAS

| Report

LMAS

| Report

LMAS

| ZymoBIOMICS Microbial Community Standards

LMAS

| ZymoBIOMICS Microbial Community Standards

LMAS

| ZymoBIOMICS Microbial Community Standards

LMAS

| 3 LMAS runs

https://github.com/cimendes/LMAS_Zymos_Resuls

LMAS

| 3 LMAS runs

There’s a disparity in usage for the evenly and logarithmically distributed samples, with the latter having more resource-intensive requirements. The resource usage also varied greatly by assembler, with multiple k-mer dBG having overall higher resource usage. 

LMAS

| 3 LMAS runs

Only GATBMiniaPipeline, IDBA-UD and Minia assemblers produced inconsistent contigs (0.22%, 0.04% and 0.12% of the total contigs produced by the assemblers, respectively)

LMAS

| 3 LMAS runs

• The logarithmically distributed samples showed greater variation than the evenly distributed counterparts, and the real samples showed greater variation than the mock samples.

• The greatest difference observed was in the total basepairs produced by the assemblers but it didn’t impact the contiguity of the assemblies (N50) or the maximum contig size obtained.

• After filtering for the minimum contig length of 1000 basepairs, the effect of the variation in assembly length is mitigated, with all assemblers showing increased robustness in the number of contigs and total basepairs assembled.

LMAS

| Know issues

Generate mock dataset with matching read number and sample abundance distribution https://github.com/HadrienG/InSilicoSeq/issues/208

LMAS

| Know issues

LMAS

| Nice to have

Allow for long and hybrid assembly https://github.com/cimendes/LMAS/tree/dev_long_read

LMAS

| Nice to have

Allow for long and hybrid assembly https://github.com/cimendes/LMAS/tree/dev_long_read