eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale

Cantalapiedra, Carlos Pérez; Hernández-Plaza, Ana; Letunić, Ivica; Bork, Peer; Huerta-Cepas, Jaime

doi:10.1101/2021.06.03.446934

Cited by 434 publications

(219 citation statements)

References 18 publications

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“…Rather than relying on single MAGs where the presence of specific genes can be questioned, mOTUpan can robustly quantify this presence as long as highly similar MAGs are available (which is often the case in mediumto-large scale metagenomic project). Notably, it can be used with a variety of genome-encoded traits, and the currently available version has parsers available for: Roary, PPanGGoLiN, eggNOGmapper [22], and mmseqs2 [21], with possibly more to be included later.…”

Section: Benchmarking Motupan Against Ppanggolin For a Prochlorococcus A Genomesetmentioning

confidence: 99%

mOTUpan: a robust Bayesian approach to leverage metagenome assembled genomes for core-genome estimation

Buck

Mehrshad

Bertilsson

2021

Preprint

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Recent advances in sequencing and bioinformatics have expanded the tree of life by providing genomes for uncultured environmentally relevant clades, either through metagenome assembled genomes (MAGs) or single-cell assembled genomes (SAGs). While this expanded diversity can provide novel insights about microbial population structure, most tools available for core-genome estimation are overly sensitive to genome completeness. Consequently a major portion of the huge phylogenetic diversity uncovered by environmental genomics approaches remain excluded from such analyses. We present mOTUpan, a novel iterative Bayesian method for computing the core-genome for sets of genomes of highly diverse completeness range. The likelihood for each gene-cluster to belong to core- or accessory-genome is estimated by computing the probability of its presence/absence pattern in the target set of genomes. The core-genome prediction is computationally efficient and can be scaled up to thousands of genomes. It has shown comparable estimates as state-of-the-art tools Roary and PPanGGoLiN for high-quality genomes and outperforms them at lower completeness thresholds. mOTUpan wraps a bootstrapping procedure to estimate the quality of a specific core-genome prediction, as the accuracy of each run will depend on the specific completeness distribution and the number of genomes in the dataset under scrutiny. mOTUpan is implemented in the mOTUlizer software package, and available at github.com/moritzbuck/mOTUlizer, under GPL 3.0 license.

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Section: Benchmarking Motupan Against Ppanggolin For a Prochlorococcus A Genomesetmentioning

confidence: 99%

mOTUpan: a robust Bayesian approach to leverage metagenome assembled genomes for core-genome estimation

Buck

Mehrshad

Bertilsson

2021

Preprint

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“…In order to investigate if other annotation methods provide better functional predictions, we scanned the sequences annotated to EC 1.1.3.15 with HAMAP and EggNOG predictors (S1 File). HAMAP [46] classifies and annotates proteins using a collection of expert-curated protein family signatures and annotation rules, while EggNOG [22] is a tool based on fast orthology assignments using precomputed clusters and phylogenies. Both methods provided predictions for only a portion of the input sequences (74% HAMAP, 59% EggNOG), indicating that for some of the sequences there was no evidence for either EC 1.1.3.15, or any other functional prediction.…”

Section: Investigation Of Alternative Functional Predictions For Sequences Annotated To Ec 11315 In the Brenda Databasementioning

confidence: 99%

“…Even more proteins with functions yet to be discovered might be hidden among the PLOS COMPUTATIONAL BIOLOGY misannotated sequences. The fields of systems biology [57], metabolic and enzyme engineering [58,59] also rely on accurate annotations, and improved methods for functional annotation are constantly being developed to meet their needs [20,22,60].…”

Section: Plos Computational Biologymentioning

confidence: 99%

“…In order to improve functional annotations and predict novel protein subtypes, more refined methods are constantly being developed. They exploit signatures of protein families and domains, orthology, patterns of functional divergence, or a mixture of all the approaches [19][20][21][22]. Still, the quality of functional annotations is considered far from perfect [23].…”

Section: Introductionmentioning

confidence: 99%

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Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class

Rembeza

Engqvist

2021

PLoS Comput Biol

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Only a small fraction of genes deposited to databases have been experimentally characterised. The majority of proteins have their function assigned automatically, which can result in erroneous annotations. The reliability of current annotations in public databases is largely unknown; experimental attempts to validate the accuracy within individual enzyme classes are lacking. In this study we performed an overview of functional annotations to the BRENDA enzyme database. We first applied a high-throughput experimental platform to verify functional annotations to an enzyme class of S-2-hydroxyacid oxidases (EC 1.1.3.15). We chose 122 representative sequences of the class and screened them for their predicted function. Based on the experimental results, predicted domain architecture and similarity to previously characterised S-2-hydroxyacid oxidases, we inferred that at least 78% of sequences in the enzyme class are misannotated. We experimentally confirmed four alternative activities among the misannotated sequences and showed that misannotation in the enzyme class increased over time. Finally, we performed a computational analysis of annotations to all enzyme classes in the BRENDA database, and showed that nearly 18% of all sequences are annotated to an enzyme class while sharing no similarity or domain architecture to experimentally characterised representatives. We showed that even well-studied enzyme classes of industrial relevance are affected by the problem of functional misannotation.

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“…The genomes were annotated in Prokka, after which the genes were organized using MCL algorithm into core, shell, and singleton clusters (Distance: Euclidean; Linkage: Ward). The core, shell, and singleton genes were separately annotated by BLASTp against the NCBI COG database using eggNOG-mapper (Cantalapiedra et al, 2021). Heatmap based on the annotated COG functions of the core and singleton gene clusters was then plotted in R. The Tettelin best-fit curves of the core-and pan-genomes were constructed using OMCL v1.4 implemented in GET_ HOMOLOGUES pipeline (Contreras-Moreira and Vinuesa, 2013).…”

Section: Pan-genome Analysismentioning

confidence: 99%

Expanding Characterized Diversity and the Pool of Complete Genome Sequences of Methylococcus Species, the Bacteria of High Environmental and Biotechnological Relevance

et al. 2021

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The bacterial genus Methylococcus, which comprises aerobic thermotolerant methanotrophic cocci, was described half-a-century ago. Over the years, a member of this genus, Methylococcus capsulatus Bath, has become a major model organism to study genomic and metabolic basis of obligate methanotrophy. High biotechnological potential of fast-growing Methylococcus species, mainly as a promising source of feed protein, has also been recognized. Despite this big research attention, the currently cultured Methylococcus diversity is represented by members of the two species, M. capsulatus and M. geothermalis, while finished genome sequences are available only for two strains of these methanotrophs. This study extends the pool of phenotypically characterized Methylococcus strains with good-quality genome sequences by contributing four novel isolates of these bacteria from activated sludge, landfill cover soil, and freshwater sediments. The determined genome sizes of novel isolates varied between 3.2 and 4.0Mb. As revealed by the phylogenomic analysis, strains IO1, BH, and KN2 affiliate with M. capsulatus, while strain Mc7 may potentially represent a novel species. Highest temperature optima (45–50°C) and highest growth rates in bioreactor cultures (up to 0.3h−1) were recorded for strains obtained from activated sludge. The comparative analysis of all complete genomes of Methylococcus species revealed 4,485 gene clusters. Of these, pan-genome core comprised 2,331 genes (on average 51.9% of each genome), with the accessory genome containing 846 and 1,308 genes in the shell and the cloud, respectively. Independently of the isolation source, all strains of M. capsulatus displayed surprisingly high genome synteny and a striking similarity in gene content. Strain Mc7 from a landfill cover soil differed from other isolates by the high content of mobile genetic elements in the genome and a number of genome-encoded features missing in M. capsulatus, such as sucrose biosynthesis and the ability to scavenge phosphorus and sulfur from the environment.

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eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale

Cited by 434 publications

References 18 publications

mOTUpan: a robust Bayesian approach to leverage metagenome assembled genomes for core-genome estimation

mOTUpan: a robust Bayesian approach to leverage metagenome assembled genomes for core-genome estimation

Experimental and computational investigation of enzyme functional annotations uncovers misannotation in the EC 1.1.3.15 enzyme class

Expanding Characterized Diversity and the Pool of Complete Genome Sequences of Methylococcus Species, the Bacteria of High Environmental and Biotechnological Relevance

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