The Quest for Orthologs orthology benchmark service in 2022

Cosentino, Salvatore; Ebersberger, Ingo; Gabaldón, Toni; Glover, Natasha; Guijarro-Clarke, Cristina; Hickman, Mark J.; Jones, Tamsin E. M.; Jyothi, Dushyanth; Kress, Arnaud; Linard, Benjamin; Marcet‐Houben, Marina; Nevers, Yannis; Persson, Emma; Piližota, Ivana; Poidevin, Laetitia; Sonhammer, Erik; Thomas, Paul D.; Vandepoele, Klaas; Vlasova, Anna; Yates, Bethan; Capella-Gutiérrez, Salvador; Codó, Laia; Ferret, Meritxell; Gonzalez-Uriarte, Asier; Garrayo-Ventas, Javier; Portell-Silva, Laura; Repchevsky, Dmitry; Sundesha, Vicky; Iwasaki, Wataru; Lecompte, Odile; Sonnhammer, Erik L. L.; Roos, David S.; Thybert, David; Thomas, Paul D.; Hu, Yanhui; Emms, David; Bruford, Elspeth A.; Capella-Gutierrez, Salvador; Martin, Maria Jesus; Dessimoz, Christophe; Altenhoff, Adrian M.

doi:10.1093/nar/gkac330

Cited by 45 publications

(44 citation statements)

References 37 publications

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“…Though there are benchmarks available for optimizing and comparing methods of orthology inference, the metrics are calculated over a set of reference genomes which are sparsely sampled over a broad taxonomic range, so it is unclear if they are informative for method designed to yield robust inferences when the genomes are highly related [45]. Furthermore, the heterogeneity of genome architectures and annotations may require quality assurance methods tailored to each set of genomes.…”

Section: Discussionmentioning

confidence: 99%

Leveraging genomic redundancy to improve inference and alignment of orthologous proteins

Singleton

2023

Preprint

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Identifying protein sequences with common ancestry is a core task in bioinformatics and evolutionary biology. However, methods for inferring and aligning such sequences in annotated genomes have not kept pace with the increasing scale and complexity of the available data. Thus, in this work we implemented several improvements to the traditional methodology that more fully leverage the redundancy of closely related genomes and the organization of their annotations. Two highlights include the application of the more flexible k-clique percolation algorithm for identifying clusters of orthologous proteins and the development of a novel technique for removing poorly supported regions of alignments with a phylogenetic HMM. In making the latter, we also wrote a fully documented Python package Homomorph that implements standard HMM algorithms and created a set of tutorials to promote its use by a wide audience. We applied the resulting pipeline to a set of 33 annotated Drosophila genomes, generating 22,813 orthologous groups and 8,566 high-quality alignments.

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Section: Discussionmentioning

confidence: 99%

Leveraging genomic redundancy to improve inference and alignment of orthologous proteins

Singleton

2023

Preprint

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“…To address this issue, we used DeepLoc 2.0, a sequence-based deep learning algorithm that can predict cellular localization of proteins with high accuracy (Thumuluri et al, 2022) (Figure 1A) . We subjected the current zebrafish reference proteome (UP000000437) to DeepLoc 2.0-based prediction (Nevers et al, 2022). Majority of proteins were predicted to be cytoplasmic ( n = 12186; 26%), nuclear ( n = 7905; 17%) and multilocalizing ( n = 12211; 26%) (Figure 1-C, Figure 1 – figure supplement 1B & Supplementary file 1) .…”

Section: Resultsmentioning

confidence: 99%

A ligand-receptor interactome atlas of the zebrafish

Chodkowski

Zieleziński

Anbalagan

2022

Preprint

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Studies in zebrafish can unravel the functions of cellular communication and thus identify novel bench-to-bedside drugs targeting cellular communication signalling molecules. Due to the incomplete annotation of zebrafish proteome, the knowledge of zebrafish receptome, secretome and tools to explore their interactome is limited. To address this gap, we de novo predicted the cellular localization of zebrafish reference proteome using deep learning algorithm. We combined the predicted and existing annotations on cellular localization of zebrafish proteins, and created repositories of zebrafish secretome, receptome, and interactome as well as associated diseases and targeting drugs. Unlike other tools, our interactome atlas is primarily based on physical interaction data of zebrafish proteome. The resources are available as R and Python scripts (https://github.com/DanioTalk). DanioTalk provides a novel resource for researchers interested in targeting cellular communication in zebrafish, as we demonstrate in applications studying synapse and axo-glial interactome.

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“…The inferred homologs, IDs, and gene annotations were collected from worldwide resources. Specifically, HGD integrates predictions from five of the top-performing methods, using the most recent assessment from Quest for ortholog benchmarking ( 33 ), including eggNOG (version 5.0, http://eggnog5.embl.de/#/app/home ), Panther (version 17.0, http://www.pantherdb.org ), TreeFam (version 4.5.1, http://www.treefam.org ), Hieranoid (version 2, https://hieranoid.sbc.su.se ) and InParanoid (version 8, https://inparanoid.sbc.su.se/cgi-bin/index.cgi ). Currently, the inclusion criterion is performing at a higher number of predicted homology relationships and a higher rate of positive predictive values.…”

Section: Methodsmentioning

confidence: 99%

HGD: an integrated homologous gene database across multiple species

Duan

Chen

et al. 2022

Nucleic Acids Research

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Homology is fundamental to infer genes’ evolutionary processes and relationships with shared ancestry. Existing homolog gene resources vary in terms of inferring methods, homologous relationship and identifiers, posing inevitable difficulties for choosing and mapping homology results from one to another. Here, we present HGD (Homologous Gene Database, https://ngdc.cncb.ac.cn/hgd), a comprehensive homologs resource integrating multi-species, multi-resources and multi-omics, as a complement to existing resources providing public and one-stop data service. Currently, HGD houses a total of 112 383 644 homologous pairs for 37 species, including 19 animals, 16 plants and 2 microorganisms. Meanwhile, HGD integrates various annotations from public resources, including 16 909 homologs with traits, 276 670 homologs with variants, 398 573 homologs with expression and 536 852 homologs with gene ontology (GO) annotations. HGD provides a wide range of omics gene function annotations to help users gain a deeper understanding of gene function.

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The Quest for Orthologs orthology benchmark service in 2022

Cited by 45 publications

References 37 publications

Leveraging genomic redundancy to improve inference and alignment of orthologous proteins

Leveraging genomic redundancy to improve inference and alignment of orthologous proteins

A ligand-receptor interactome atlas of the zebrafish

HGD: an integrated homologous gene database across multiple species

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