scholarly journals Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper

2017 ◽  
Vol 34 (8) ◽  
pp. 2115-2122 ◽  
Author(s):  
Jaime Huerta-Cepas ◽  
Kristoffer Forslund ◽  
Luis Pedro Coelho ◽  
Damian Szklarczyk ◽  
Lars Juhl Jensen ◽  
...  
2016 ◽  
Author(s):  
Jaime Huerta-Cepas ◽  
Kristoffer Forslund ◽  
Damian Szklarczyk ◽  
Lars Juhl Jensen ◽  
Christian von Mering ◽  
...  

AbstractOrthology assignment is ideally suited for functional inference. However, because predicting orthology is computationally intensive at large scale, and most pipelines relatively in accessible, less precise homology-based functional transfer is still the default for (meta-)genome annotation. We therefore developed eggNOG-mapper, a tool for functional annotation of large sets of sequences based on fast orthology assignments using precomputed clusters and phylogenies from eggNOG. To validate our method, we benchmarked Gene Ontology predictions against two widely used homology-based approaches: BLAST and InterProScan. Compared to BLAST, eggNOG-mapper reduced by 7% the rate of false positive assignments, and increased by 19% the ratio of curated terms recovered over all terms assigned per protein. Compared to InterProScan, eggNOG-mapper achieved similar proteome coverage and precision, while predicting on average 32 more terms per protein and increasing by 26% the rate of curated terms recovered over total term assignments per protein. Through strict orthology assignments, eggNOG-mapper further renders more specific annotations than possible from domain similarity only (e.g. predicting gene family names). eggNOG-mapper runs ~15x than BLAST and at least 2.5x faster than InterProScan. The tool is available standalone or as an online service at http://eggnog-mapper.embl.de.


The Prostate ◽  
2010 ◽  
Vol 71 (9) ◽  
pp. 955-963 ◽  
Author(s):  
Yizhen Lu ◽  
Zheng Zhang ◽  
Hongjie Yu ◽  
S. Lily Zheng ◽  
William B. Isaacs ◽  
...  

2020 ◽  
Author(s):  
Yanjiao Jin ◽  
Jie Yang ◽  
Shuyue Zhang ◽  
Jin Li ◽  
Songlin Wang

Abstract Background: Oral diseases impact the majority of the world’s population. The following traits are common in oral inflammatory diseases: mouth ulcers, painful gums, bleeding gums, loose teeth, and toothache. Despite the prevalence of genome-wide association studies, the associations between these traits and common genomic variants, and whether pleiotropic loci are shared by some of these traits remain poorly understood. Methods: In this work, we conducted multi-trait joint analyses based on the summary statistics of genome-wide association studies of these five oral inflammatory traits from the UK Biobank, each of which is comprised of over 10,000 cases and over 300,000 controls. We estimated the genetic correlations between the five traits. We conducted fine-mapping and functional annotation based on multi-omics data to better understand the biological functions of the potential causal variants at each locus. To identify the pathways in which the candidate genes were mainly involved, we applied gene-set enrichment analysis, and further performed protein-protein interaction (PPI) analyses.Results: We identified 39 association signals that surpassed genome-wide significance, including three that were shared between two or more oral inflammatory traits, consistent with a strong correlation. Among these genome-wide significant loci, two were novel for both painful gums and toothache. We performed fine-mapping and identified causal variants at each novel locus. Further functional annotation based on multi-omics data suggested IL10 and IL12A/TRIM59 as potential candidate genes at the novel pleiotropic loci, respectively. Subsequent analyses of pathway enrichment and protein-protein interaction networks suggested the involvement of candidate genes at genome-wide significant loci in immune regulation.Conclusions: Our results highlighted the importance of immune regulation in the pathogenesis of oral inflammatory diseases. Some common immune-related pleiotropic loci or genetic variants are shared by multiple oral inflammatory traits. These findings will be beneficial for risk prediction, prevention, and therapy of oral inflammatory diseases.


2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Naihui Zhou ◽  
Yuxiang Jiang ◽  
Timothy R. Bergquist ◽  
Alexandra J. Lee ◽  
Balint Z. Kacsoh ◽  
...  

Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.


2020 ◽  
Vol 36 (18) ◽  
pp. 4749-4756 ◽  
Author(s):  
Alexey A Shadrin ◽  
Oleksandr Frei ◽  
Olav B Smeland ◽  
Francesco Bettella ◽  
Kevin S O'Connell ◽  
...  

Abstract Motivation Determining the relative contributions of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here, we present Annotation Informed-MiXeR, a likelihood-based method for estimating the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. Results Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes. Specifically, the exons of protein-coding genes harbor more than 90% of variants influencing type 2 diabetes and inflammatory bowel disease, making them good candidates for whole-exome studies. In contrast, <10% of the causal variants for schizophrenia, bipolar disorder and attention-deficit/hyperactivity disorder are located in protein-coding exons, indicating a more substantial role of regulatory mechanisms in the pathogenesis of these disorders. Availability and implementation The software is available at: https://github.com/precimed/mixer. Supplementary information Supplementary data are available at Bioinformatics online.


2016 ◽  
Vol 35 (7) ◽  
pp. 1389-1400 ◽  
Author(s):  
Ankita Sharma ◽  
Sarika Sahu ◽  
Pooja Kumari ◽  
Soundhara Rajan Gopi ◽  
Rajesh Malhotra ◽  
...  

Genomics Data ◽  
2016 ◽  
Vol 8 ◽  
pp. 67-69 ◽  
Author(s):  
Omar Darwish ◽  
Shuxian Li ◽  
Benjamin Matthews ◽  
Nadim Alkharouf

2014 ◽  
Vol 113 (4) ◽  
pp. 1269-1281 ◽  
Author(s):  
Qi Liao ◽  
Jia Shen ◽  
Jianfa Liu ◽  
Xi Sun ◽  
Guoguang Zhao ◽  
...  

Author(s):  
Akinobu Fukuzaki ◽  
Takeshi Nagashima ◽  
Kaori Ide ◽  
Fumikazu Konishi ◽  
Mariko Hatakeyama ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document