Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG
Abstract
Building Metagenome–Assembled Genomes (MAGs) from highly complex metagenomics datasets encompasses a series of steps covering from cleaning the sequences, assembling them to finally group them into bins. Along the process, multiple tools aimed to assess the quality and integrity of each MAG are implemented. Nonetheless, even when incorporated within end–to–end pipelines, the outputs of these pieces of software must be visualized and analyzed manually lacking integration in a complete framework. We developed a Nextflow pipeline (MAGFlow) for estimating the quality of MAGs through a wide variety of approaches (BUSCO, CheckM2, GUNC and QUAST), as well as for annotating taxonomically the metagenomes using GTDB-Tk2. MAGFlow is coupled to a Python–Dash application (BIgMAG) that displays the concatenated outcomes from the tools included by MAGFlow, highlighting the most important metrics in a single interactive environment along with a comparison/clustering of the input data. By using MAGFlow/BIgMAG, the user will be able to benchmark the MAGs obtained through different workflows or establish the quality of the MAGs belonging to different samples following the divide and rule methodology. MAGFlow/BIgMAG represents a unique tool that integrates state-of-the-art tools to study different quality metrics and extract visually as much information as possible from a wide range of genome features.
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www.sib.swiss Metagenome quality metrics and taxonomical annotation visualization through the integration of MAGFlow and BIgMAG Jeferyd Yepes-García, Laurent Falquet University of Fribourg and Swiss Institute of Bioinformatics, Switzerland Contact: [email protected] Methods Background References •Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2022). GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics , 38 (23), 5315–5316. •Chklovski, A., Parks, D. H., Woodcroft, B. J., & Tyson, G. W. (2023). CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nature Methods , 20 (8), 1203–1212. •Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology , 35 (4), 316–319. •Gurevich, A., Saveliev, V., Vyahhi, N., & Tesler, G. (2013). QUAST:quality assessment tool for genome assemblies. Bioinformatics , 29 (8), 1072–1075. •Manni, M., Berkeley, M. R., Seppey, M., & Zdobnov, E. M. (2021). BUSCO:Assessing Genomic Data Quality and Beyond. Current Protocols , 1 (12), e323. •Orakov, A., Fullam, A., Coelho, L. P., Khedkar, S., Szklarczyk, D., Mende, D. R., Schmidt, T. S. B., & Bork, P. (2021). GUNC:detection of chimerism and contamination in prokaryotic genomes. Genome Biology , 22 (1), 1–19. Application example Remarks • BIgMAG complements MAGFlow by generating high-quality automatic plots for all the encompassed tools, allowing ahigh degree of interactivity and customization according to the user needs. • MAGFlow/BIgMAG represents aunique tool to our knowledge that integrates the execution of the software with avisualization module to extract MAG quality information and taxonomical features, making it interestingly useful when targeting comparisons among different metagenomics pipelines or tools to bin contigs. • MAGFlow/BIgMAG provides aconvenient support during exploratory analyses that involve establishing general differences across samples. Motivation: •Many metagenomics pipelines or methodologies only include one or two tools to measure the quality of the MAGs. •The visualization and/or analysis of the quality data relies entirely on the user who should be familiar with the type of generated files and how to display the information in apleasant manner. •The users must develop manually their own methodology to perform this important step during the metagenome assembly, increasing the risk of lack of reproducibility and the difficulty to test and validate the results. Opportunity: •Workflows aiming to measure the quality of the MAGs coupled to visualization modules are required to carry out comparisons among metagenomics pipelines, or software designed to bin contigs, in terms of their performance and accuracy to recover MAGs. Furthermore, this kind of tools can allow exploratory analysis of highly complex datasets such as marine or soil samples. ! BIgMAG Board InteGrating Metagenome-Assembled Genomes Documentation: " Yepes-García J and Falquet L. (2024) Summary ATLAS, n = 11 DATMA, n = 10 MetaWRAP, n = 14 SnakeMAGs, n = 12 nf_core_mag_hybrid, n = 12 nf_core_mag_short, n = 11 MUFFIN, n = 28 0 20 40 60 80 100 Percentages: % of annotated MAGs at Genus level (GTDB-Tk2) % of unique annotated MAGs (GTDB-Tk2) % of mid-quality MAGs (CheckM2) % of high-quality MAGs (CheckM2) % of bins passing GUNC Sample/Pipeline Percentage (%) ATLAS DATMA MUFFIN MetaWRAP SnakeMAGs nf_core_mag_hybrid nf_core_mag_short nf_core_mag_short nf_core_mag_hybrid SnakeMAGs MetaWRAP MUFFIN DATMA ATLAS Kruskal-Wallis p-value = 0.01771 p-value matrix of a Duncan Test 0 0.2 0.4 0.6 0.8 1 P-value Sample/Pipeline Sample/Pipeline QUAST Select parameter: ATLAS DATMA MetaWRAP SnakeMAGs nf_core_mag_hybrid nf_core_mag_short MUFFIN 30 40 50 60 70 Sample/Pipeline GC (%) p-value Welch ANOVA test: 0.7442 GC (%) CheckM2 0% 100% 0 5 10 20 40 60 80 100 0.22 1.7 3.17 4.64 6.11 Sample/Pipeline: ATLAS DATMA MetaWRAP SnakeMAGs nf_core_mag_hybrid nf_core_mag_short MUFFIN Contamination (%) Completeness (%) Genome size (Mbp) GUNC Select parameter: ATLAS DATMA MetaWRAP SnakeMAGs nf_core_mag_hybrid nf_core_mag_short MUFFIN 0 0.2 0.4 0.6 0.8 1 Bins passed GUNC: False True Sample/Pipeline clade_separation_score clade_separation_score BUSCO 0% 100% 0 2 4 6 20 40 60 80 100 0.22 1.7 3.17 4.64 6.11 Sample/Pipeline: ATLAS DATMA MetaWRAP SnakeMAGs nf_core_mag_hybrid nf_core_mag_short MUFFIN Duplicated SCO (%) Complete SCO (%) Genome size (Mbp) GTDB-Tk2 Select taxonomic level: Staphylococcus Streptococcus Cereibacter_A Porphyromonas Clostridium Pseudomonas Bacillus_A Escherichia Unclassified Acinetobacter Cutibacterium Helicobacter Neisseria ATLAS DATMA MetaWRAP SnakeMAGs nf_core_mag_hybrid nf_core_mag_short MUFFIN Absent taxa Present taxa Samples/Pipelines One sample All samples 1 7 Genus Clustering of samples/pipelines Completeness (CheckM2) Contamination (CheckM2) Complete SCO (BUSCO) Single SCO (BUSCO) Duplicated SCO (BUSCO) Fragmented SCO (BUSCO) Missing SCO (BUSCO) proportion_genes_retained_in_major_clades (GUNC) genes_retained_index (GUNC) clade_separation_score (GUNC) contamination_portion (GUNC) n_effective_surplus_clades (GUNC) mean_hit_identity (GUNC) reference_representation_score (GUNC) Proportion of bins passing the filter (GUNC) Proportion of annotated Genus (GTDB-Tk2) DATMA MUFFIN nf_core_mag_short SnakeMAGs nf_core_mag_hybrid ATLAS MetaWRAP −0.5 −0.4 −0.3 −0.2 −0.1 0 0.1 0.2 0.3 0.4 Explore your data / 5 sample Bin Dataset Complete Single Duplicated Fragmented Missing n_markers Scaffold N50 Contigs N50 Percent gaps Number of scaffolds ×ATLAS SRR8359173_metabat_01 bacteria_odb10 3.2 3.2 04.8 92 124 2008 2008 0.00% 191 ×ATLAS SRR8359173_metabat_02 bacteria_odb10 35.5 35.5 010.5 54 124 2009 2009 0.00% 469 ×ATLAS SRR8359173_metabat_03 bacteria_odb10 12.9 12.9 013.7 73.4 124 2027 2027 0.00% 528 ×ATLAS SRR8359173_metabat_04 bacteria_odb10 7.3 7.3 0 4 88.7 124 2199 2199 0.00% 149 ×ATLAS SRR8359173_metabat_08 bacteria_odb10 91.9 91.1 0.8 08.1 124 63590 63590 0.00% 113 ×DATMA round_0_b70_clameBin_0_contigs bacteria_odb10 1.6 1.6 03.2 95.2 124 1035 1035 0.00% 200 ×DATMA round_1_b50_clameBin_0_contigs bacteria_odb10 0 0 0 0.8 99.2 124 918 918 0.00% 178 ×DATMA round_2_b30_clameBin_0_contigs bacteria_odb10 1.6 1.6 03.2 95.2 124 1235 1235 0.00% 150 ×DATMA round_2_b30_clameBin_1_contigs bacteria_odb10 23.4 20.2 3.2 25.8 50.8 124 1235 1235 0.00% 2412 ×DATMA round_2_b30_clameBin_3_contigs bacteria_odb10 3.2 3.2 03.2 93.6 124 1369 1369 0.00% 291 ×DATMA round_2_b30_clameBin_4_contigs bacteria_odb10 0 0 0 0 100 124 1642 1642 0.00% 35 ×DATMA round_2_b30_clameBin_5_contigs bacteria_odb10 0.8 0.8 03.2 96 124 1310 1310 0.00% 206 ×MetaWRAP bin_2 bacteria_odb10 50.8 47.6 3.2 10.5 38.7 124 7184 7184 0.00% 310 ×MetaWRAP bin_3 bacteria_odb10 91.9 91.1 0.8 08.1 124 109682 109682 0.00% 57 ×MetaWRAP bin_4 bacteria_odb10 77.4 76.6 0.8 14.5 8.1 124 6351 6351 0.00% 1067 ×MetaWRAP bin_8 bacteria_odb10 67.7 66.9 0.8 5.6 26.7 124 6396 6396 0.00% 1084 ×MetaWRAP bin_unbinned bacteria_odb10 42.8 19.4 23.4 11.3 45.9 124 3104 3104 0.00% 954 ×SnakeMAGs bin_4 bacteria_odb10 90.3 90.3 06.5 3.2 124 5432 5432 0.00% 1218 ×SnakeMAGs bin_9 bacteria_odb10 58.1 57.3 0.8 16.1 25.8 124 4997 4997 0.00% 1230 ×nf_core_mag_hybrid SPAdesHybrid-MetaBAT2-mock_7 bacteria_odb10 99.2 99.2 0 0 0.8 124 150828 118931 0.01% 113 filter data... filter data... filter data... filter data...filter data...filter data... filter data... filter data...filter data...filter data... filter data... filter data... filter data... 1 1 Value 10 Value 10 0% 100% Value 10 0% 100% Value 10