KAAS: an automatic genome annotation and pathway reconstruction server

By George Vogiatzis, Carlos Hernández, Roberto Cipolla. Nucleic Acids Res., 2007

1,648 citations recorded by the source corpus.

The number of complete and draft genomes is rapidly growing in recent years, and it has become increasingly important to automate the identification of functional properties and biological roles of genes in these genomes. In the KEGG database, genes in complete genomes are annotated with the KEGG orthology (KO) identifiers, or the K numbers, based on the best hit information using Smith–Waterman scores as well as by the manual curation. Each K number represents an ortholog group of genes, and it is directly linked to an object in the KEGG pathway map or the BRITE functional hierarchy. Here, we have developed a web-based server called KAAS (KEGG Automatic Annotation Server: http://www.genome.jp/kegg/kaas/) i.e. an implementation of a rapid method to automatically assign K numbers to genes in the genome, enabling reconstruction of KEGG pathways and BRITE hierarchies. The method is based on sequence similarities, bi-directional best hit information and some heuristics, and has achieved a high degree of accuracy when compared with the manually curated KEGG GENES database.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3784a69fe70353831b78ee9cb18b302735d6e58e

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Citations in the corpus

Using the KEGG Database Resource (2005, methodology): “There is a publicly available version of fully automatic annotation server, called KAAS (KEGG Automatic Annotation Server), which uses BLAST and a different algorithm for determining ortholog relationships (Moriya et al., 2007).”
The RAST Server: Rapid Annotations using Subsystems Technology (2008, background): “This has led to the obvious conclusion that most of the annotations that will be associated with these newly-sequenced genomes will be provided through technologies that are largely automated, and a growing number of efforts focusing on different aspects of automated annotation have emerged [1-6].”
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields (2022, methodology): “Both approaches use depth from RGB-D cameras as input, instead, our work only uses monocular images.”
Performance of the ATLAS trigger system in 2015 (2016, background): “[47]: to reject Z(→ μμ) + jets and W(→ μν) + jets events, the invariant mass of the muon and the offline tau candidate is required to be between 45 and 80 GeV, the transverse mass, mT, composed of the muon pT and Emiss T (m 2 T = 2p μ TE miss T (1− cos ∆φ(μ, Emiss T ))) is required to be smaller tha”
Chapter 12: Human Microbiome Analysis (2012, neutral): “The abundance mass for each enzyme is distributed across its functions in one or more possible pathways; methods for doing this range from the simple assumption that it is equally active in all reference pathways (as currently done by KAAS [94] or MG-RAST [76]) to the elimination of unlikely pathway”
MapMan4: A Refined Protein Classification and Annotation Framework Applicable to Multi-Omics Data Analysis. (2019, methodology): “…could potentially be discovered with other annotation pipelines and frameworks, we reannotated the 27 PEP core subunits and associated factors and 32 plastidial RNA editing factors from S . lycopersicum , using both the previous Mercator v.3 release and the KAAS pipeline (Moriya et al., 2007).”
Genome Sequence of the Pea Aphid Acyrthosiphon pisum (2010, methodology): “Currently, the pea aphid database, ‘‘AcypiCyc’’ (http://pbil.univ-lyon1.fr/software/cycads/acypicyc), utilizes the RefSeq automated annotation, complemented by three alternative annotations of the pea aphid’s 34,821 proteins performed using KAAS [127].”
High-throughput generation, optimization and analysis of genome-scale metabolic models (2010, neutral): “Whereas existing automated reconstruction methodologies only address portions of the reconstruction proces”