News about FROGS

What’s new and what’s coming soon

Olivier Rué

MaIAGE - Migale

Gabryelle Agoutin

GenPhySE

Lucas Auer

IAM

Maria Bernard

GABI - SIGENAE

Géraldine Pascal

GenPhySE

15 novembre 2024

What is FROGS

• Metabarcoding data analysis tool
• Active project since 2015
• Several trainings every year (Toulouse webinars, Jouy-en-Josas on site)
• More than 750 citations, >28,500 downloads

• Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, et al. FROGS: Find, Rapidly, OTUs with Galaxy Solution [1]
• Bernard M, Rué O, Mariadassou M, Pascal G. FROGS: a powerful tool to analyse the diversity of fungi with special management of internal transcribed spacers [2]

FROGS team

What FROGS offers:

• An up-to-date software that keeps pace with advances in the field

What FROGS offers:

• An up-to-date tool that keeps pace with advances in the field
• A tool accessible to everyone
  • Galaxy
  • Command line
• Personalized help in case of difficulties
  • Understanding mistakes
  • Exceptional case management (need lots of resources)
• Tutorials, trainings and support to become autonomous
• Formatting and access to databanks in the tool

FROGS new functionalities (2023-2024)

New functionalities

• FROGS 4.1.0 [2023-03]
  • FROGSfunc tools, Pacbio reads management…
  • Changes in vocabulary: OTU → ASV [3]
• FROGS v5.0.0 [2024-06]
  • Only available in command line for now!
  • DADA2 [4] integration in a new tool called denoising, fusion of preprocess and clustering
  • Companion tools for generating descriptive statistics (cluster_stats, affiliation_stats) integrated into tools

Hakimzadeh A, Abdala Asbun A, Albanese D, Bernard M, Buchner D, Callahan B, et al. A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses [3]

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data [4]

Tools reorganization

Why integrate a part of DADA2?

• User demand and sometimes from reviewers to justify why we use swarm and not DADA2 [4], “considered to be the gold standard”
• However, our benchmarks and experience do not always support DADA2 [5] …
  • Technical problems during sequencing considered as biological variation [6]
• … but have some advantages
  • Able to detect differences of 1 or 2 nucleotides [5]

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data [4]

Rué O, Coton M, Dugat-Bony E, Howell K, Irlinger F, Legras J-L, et al. Comparison of metabarcoding taxonomic markers to describe fungal communities in fermented foods [5]

Irlinger F, Mariadassou M, Dugat-Bony E, Rué O, Neuvéglise C, Renault P, et al. A comprehensive, large-scale analysis of « terroir » cheese and milk microbiota reveals profiles strongly shaped by both geographical and human factors [6]

Better resolution for DADA2 compared to clustering methods

• Better resolution for very close sequences [7]

MacIntyre DA, Chandiramani M, Lee YS, Kindinger L, Smith A, Angelopoulos N, et al. The vaginal microbiome during pregnancy and the postpartum period in a European population. Scientific reports [7]

Better resolution for DADA2 compared to swarm

METABARFOOD project

• ITS1, ITS2, D1/D2 and RPB2 markers in fermented food environments [5]

Rué O, Coton M, Dugat-Bony E, Howell K, Irlinger F, Legras J-L, et al. Comparison of metabarcoding taxonomic markers to describe fungal communities in fermented foods [5]

Why not choose DADA2 only?

• The combination of DADA2 and FROGS shows better results, especially for retrieving exact sequences [5]
• FROGS and DADA2+FROGS are best at finding exact expected sequences (D)
• DADA2+FROGS offers efficient tools for merging pairs (vsearch, pear), remove chimera (vsearch and FROGS sample cross-validation)
• Similar results between DADA2+FROGS and FROGS for most datasets
• We offer FROGS v5 users the opportunity to choose and/or compare the approach they prefer

Rué O, Coton M, Dugat-Bony E, Howell K, Irlinger F, Legras J-L, et al. Comparison of metabarcoding taxonomic markers to describe fungal communities in fermented foods [5]

Future work on FROGS

• Integrate version 5 into Galaxy
  • reduce wrapper numbers to avoid conflicts in dependencies during installation
• Test and allow to deal with AVITI sequencing data

FROGS databanks

FROGS databanks

• Databanks used at the taxonomic affiliation step
• Some are public, some are “personal”, some are home-made
• Questions
  • Which one to use?
  • What’s the difference between two versions?
  • What is its content and diversity?

>Slackia_heliotrinireducens_AF101241_TS Root;k__Bacteria [id: 1];p__Actinobacteria [id: 2];c__Coriobacteriia [id: 3];o__Coriobacteriales [id: 4];f__Coriobacteriaceae [id: 5];g__Slackia [id: 6];s__Slackia_heliotrinireducens [id: 7]
GATGAACGCTGGCGGCGCGCCTAACACATGCAAGTCGAACG...

Build a website dedicated to FROGS databanks

• Based on Quarto dashboards
• Hosted on the ForgeMIA
• CI/CD
• Automatic creation of dashboards from a list of databanks

More than 80 databanks available

Ratio of ambiguous taxonomies

Some descriptive data

A lot of usefull information

• Which species of the genus Lactococcus are present?

A lot of usefull information

• Which taxa are the most represented?

Comparison between versions

Future work on databanks

• Finalize the website
• Use the genomic sequence
• Be able to find a taxa over the whole website

Thanks for your attention

- https://frogs.toulouse.inrae.fr - frogs@inrae.fr / frogs-support@inrae.fr

References

1. Escudié F, Auer L, Bernard M, Mariadassou M, Cauquil L, Vidal K, et al. FROGS: Find, Rapidly, OTUs with Galaxy Solution. Bioinformatics. 2018;34:1287‑94. doi:10.1093/bioinformatics/btx791.

2. Bernard M, Rué O, Mariadassou M, Pascal G. FROGS: a powerful tool to analyse the diversity of fungi with special management of internal transcribed spacers. Briefings in Bioinformatics. 2021;22. doi:10.1093/bib/bbab318.

3. Hakimzadeh A, Abdala Asbun A, Albanese D, Bernard M, Buchner D, Callahan B, et al. A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses. Molecular Ecology Resources. 2023.

4. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nature methods. 2016;13:581.

5. Rué O, Coton M, Dugat-Bony E, Howell K, Irlinger F, Legras J-L, et al. Comparison of metabarcoding taxonomic markers to describe fungal communities in fermented foods. Peer Community Journal. 2023;3. doi:10.24072/pcjournal.321.

6. Irlinger F, Mariadassou M, Dugat-Bony E, Rué O, Neuvéglise C, Renault P, et al. A comprehensive, large-scale analysis of « terroir » cheese and milk microbiota reveals profiles strongly shaped by both geographical and human factors. ISME Communications. 2024;ycae095. doi:10.1093/ismeco/ycae095.

7. MacIntyre DA, Chandiramani M, Lee YS, Kindinger L, Smith A, Angelopoulos N, et al. The vaginal microbiome during pregnancy and the postpartum period in a European population. Scientific reports. 2015;5:8988.