CrowdGO: machine learning and semantic similarity guided consensus Gene Ontology annotation

Reijnders, Maarten J.M.F.; Waterhouse, Robert M.

doi:10.1101/731596

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…As none of the species in this study have available GO annotations, we annotated all the proteomes using the program CrowdGO ( 54 ). CrowdGO is a consensus-based GO term metapredictor that employs machine-learning models combined with GO term semantic similarities and information contents to leverage strengths of individual predictors and produce comprehensive and accurate gene-functional annotations.…”

Section: Methodsmentioning

confidence: 99%

Convergent evolution of venom gland transcriptomes across Metazoa

Zancolli

Reijnders

Waterhouse

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Animals have repeatedly evolved specialized organs and anatomical structures to produce and deliver a mixture of potent bioactive molecules to subdue prey or predators—venom. This makes it one of the most widespread, convergent functions in the animal kingdom. Whether animals have adopted the same genetic toolkit to evolved venom systems is a fascinating question that still eludes us. Here, we performed a comparative analysis of venom gland transcriptomes from 20 venomous species spanning the main Metazoan lineages to test whether different animals have independently adopted similar molecular mechanisms to perform the same function. We found a strong convergence in gene expression profiles, with venom glands being more similar to each other than to any other tissue from the same species, and their differences closely mirroring the species phylogeny. Although venom glands secrete some of the fastest evolving molecules (toxins), their gene expression does not evolve faster than evolutionarily older tissues. We found 15 venom gland–specific gene modules enriched in endoplasmic reticulum stress and unfolded protein response pathways, indicating that animals have independently adopted stress response mechanisms to cope with mass production of toxins. This, in turn, activates regulatory networks for epithelial development, cell turnover, and maintenance, which seem composed of both convergent and lineage-specific factors, possibly reflecting the different developmental origins of venom glands. This study represents a first step toward an understanding of the molecular mechanisms underlying the repeated evolution of one of the most successful adaptive traits in the animal kingdom.

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Section: Methodsmentioning

confidence: 99%

Convergent evolution of venom gland transcriptomes across Metazoa

Zancolli

Reijnders

Waterhouse

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…As none of the species in this study have available GO annotations, we annotated all the proteomes using the program CrowdGO (54). CrowdGO is a consensus-based GO term meta-predictor that employs machine learning models combined with GO term semantic similarities and information contents to leverage strengths of individual predictors and produce comprehensive and accurate gene functional annotations.…”

Section: Species-level Differential Expression Analysismentioning

confidence: 99%

Convergent evolution of venom gland transcriptomes across Metazoa

Zancolli

Reijnders

Waterhouse

et al. 2021

Preprint

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Animals have repeatedly evolved specialized organs and anatomical structures to produce and deliver a cocktail of potent bioactive molecules to subdue prey or predators: venom. This makes it one of the most widespread convergent functions in the animal kingdom. Whether animals have adopted the same genetic toolkit to evolved venom systems is a fascinating question that still eludes us. Here, we performed the first comparative analysis of venom gland transcriptomes from 20 venomous species spanning the main Metazoan lineages, to test whether different animals have independently adopted similar molecular mechanisms to perform the same function. We found a strong convergence in gene expression profiles, with venom glands being more similar to each other than to any other tissue from the same species, and their differences closely mirroring the species phylogeny. Although venom glands secrete some of the fastest evolving molecules (toxins), their gene expression does not evolve faster than evolutionarily older tissues. We found 15 venom gland specific gene modules enriched in endoplasmic reticulum stress and unfolded protein response pathways, indicating that animals have independently adopted stress response mechanisms to cope with mass production of toxins. This, in turns, activates regulatory networks for epithelial development, cell turnover and maintenance which seem composed of both convergent and lineage-specific factors, possibly reflecting the different developmental origins of venom glands. This study represents the first step towards an understanding of the molecular mechanisms underlying the repeated evolution of one of the most successful adaptive traits in the animal kingdom.

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“…As a bonus, local versions are often open-source and provide flexibility in the usage of the tool by advanced users, e.g. by changing databases or utilizing a meta-predictor [3]. As shown in Table 1, there is a need for accurate prediction tools which provide the options that come with a local installation.…”

Section: Motivationmentioning

confidence: 99%

“…As a bonus, these versions are often open-source and provide flexibility in the usage of the tool by advanced users, e.g. by utilizing a meta-predictor [5]. As such, there is a need for accurate open-source and locally installable prediction tools.…”

Section: Introductionmentioning

confidence: 99%

Wei2GO: weighted sequence similarity-based protein function prediction

2020

Preprint

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MotivationProtein function prediction is an important part of bioinformatics and genomics studies. There are many different predictors available, however most of these are in the form of web-servers instead of open-source locally installable versions. Such local versions are necessary to perform large scale genomics studies due to the absence of any limitations imposed by web servers such as queues, prediction speed, and updatability of reference data. ResultsThis paper describes Wei2GO: a weighted sequence similarity and python-based opensource protein function prediction software. It is compared against the Argot2 and Argot2.5 web servers, which use a similar concept. Wei2GO shows an increase in performance according to precision and recall curves, Fmax scores, and computational time. Wei2GO is further assessed on its biological relevance by annotating part of the Chlamydomonas reinhardtii triacylglycerol pathway, Anopheles gambiae Toll signaling pathway, and Pseudomonas putida Twin Arginine Targeting (TAT) complex. These annotations show Wei2GO is able to accurately annotate relevant data with high precision and recall. Availability and implementationWei2GO can be found at https://gitlab.com/mreijnders/Wei2GO

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CrowdGO: machine learning and semantic similarity guided consensus Gene Ontology annotation

Cited by 4 publications

References 39 publications

Convergent evolution of venom gland transcriptomes across Metazoa

Convergent evolution of venom gland transcriptomes across Metazoa

Convergent evolution of venom gland transcriptomes across Metazoa

Wei2GO: weighted sequence similarity-based protein function prediction

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