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PubMed2003

MaXML: mouse annotation XML.

Although the sequencing of the human genome and several model organisms is almost complete, the number of genes in the human is still in debate. cDNA (complementary DNA) is generated from mRNA that is transcribed from the genome and can be regarded as a gene itself; therefore, decoding cDNA sequences is important in characterizing genes. Recently, biologists have been able to describe more knowledge about genes in order to characterize them, and this information is generally called 'annotation.' Furthermore, annotation is important in understanding the systems of organisms in various fields of research. We therefore constructed the MaXML (Mouse annotation XML) format with which mouse cDNA annotation data can be exchanged and shared between laboratories more efficiently. Defining strict data types for annotations is difficult, but we consider XML a feasible format for describing them. We have used the MaXML format to express mouse annotation data in FANTOM DB. We have also developed tools and systems utilizing these MaXML data, including a parser and a server to provide data on-the-fly.

Animals↗

PubMed2004

Automated clinical annotation of tissue bank specimens.

Modern, molecular bio-medicine is driving a growing demand for extensively annotated tissue bank specimens. With careful clinical, pathologic and outcomes annotation, samples can be better matched to the research question at hand and experimental results better understood and verified. However, the difficulty and expense of detailed specimen annotation is well beyond the capability of most banks and has made access to well documented tissue a major limitation in medical re-search. In this context, we have implemented automated annotation of banked tissue by integrating data from three clinical systems--the cancer registry, the pathology LIS and the tissue bank inventory system--through a classical data warehouse environment. The project required modification of clinical systems, development of methods to identify patients between and map data elements across systems and the creation of de-identified data in data marts for use by researchers. The result has been much more extensive and accurate initial tissue annotation with less effort in the tissue bank, as well as dynamic ongoing annotation as the cancer registry follows patients over time.

Clinical Laboratory Information Systems↗

PubMed2004

Gene ontology application to genomic functional annotation, statistical analysis and knowledge mining.

While a massive amount of biomolecular information is increasingly accumulating in different databanks, on the other hand high-throughput technologies are generating a great quantity of data that need to be annotated with the genomic information available, and interpreted. To this aim, the use of specific ontologies can greatly help either in integrating different information stored within heterogeneous databanks, or in identifying and clustering sequence data sharing common characteristics. In the molecular biology domain, the Gene Ontology (GO) is the most developed and widely used ontology. To demonstrate its great utility in the annotation and biological interpretation of gene sets obtained by means of high-throughput experiments, we implemented the web application here described. It enables functional annotations of a given gene set on a genomic scale and across different species. Within our application the annotations provided by the GO vocabulary allow either to easily bind several information from different resources, or to cluster annotated genes according to their biological characteristics. Through the GO structure it is also possible to represent biological concepts with different specificity levels, from very general to very precise concepts. Furthermore, the statistical evaluation of the categorizations provided by the GO annotations enables to highlight the most significant biological characteristics of a gene set, and therefore to mine knowledge from data. Our created tool meets the need to manage a vast quantity of biological data with a simple user interface adapt also for users with limited informatics knowledge, leading them to evaluate the functional significance of experiment's results with graphical views and statistical indexes in a well-known web browser user interface.

Genomics↗

PubMed2005

Microarray annotation and biological information on function.

OBJECTIVES: Many methods for statistical analysis of gene expression studies by DNA microarrays produce lists of genes as output. To understand gene lists in terms of traditional biology, e.g. which pathways may be affected, it is necessary to get appropriate annotations for the probes on an array. METHODS: Problems arise with the different sources that have been used by manufacturers to design microarray probes, and their association to biological entities like genes, transcripts and proteins. Function annotation is of crucial importance, and systems like Gene Ontology can be used for this purpose. It arranges annotation terms in a hierarchical manner and thus makes annotations in a gene list amenable to automated analysis. RESULTS: Several methods for analyses of gene function are described. The hierarchical nature of systems like Gene Ontology particularly suggests using methods from graph theory. CONCLUSIONS: The main problem in annotating microarray probes and inferring affected functional modules is the incompleteness and degree of error in current biological databases. Initial approaches to make use of functional annotation exist, but have to be extended, in particular with respect to estimating the statistical significance of results.

Computational Biology↗

PubMed1997

Automatic annotation for biological sequences by extraction of keywords from MEDLINE abstracts. Development of a prototype system.

We have developed a prototype for the automatic annotation of functional characteristics in protein families. The system is able to extract biological information directly from scientific literature in the form of MEDLINE abstracts. The criterion for selecting relevant keywords is the difference between their frequency in the abstracts associated with the protein family under study and its frequency in other unrelated protein families. The concept of functional information associated to protein families is the key feature of our system and gathers evolutionary information into the problem of functional annotation of biological sequences. The system has been tested in two different scenarios: first, a large set of protein families with a small number of abstract per family and second, selected protein families with large number of abstracts attached to each one. In both cases the performances are compared with annotations provided by human experts showing a clear relation between the amount of information provided to the system and the quality of the annotations. The automatic annotations are in many cases of similar quality to the ones contained in current data bases. The possibilities and difficulties to be encountered during the development of a full system for automatic annotation are discussed.

Abstracting and Indexing↗

PubMed2026

A novel deep learning-driven framework for improving lncRNA comprehensive annotation with LncADeep 2.0.

MOTIVATION: Long non-coding RNAs (lncRNAs) have emerged as crucial players in diverse physiological and pathological processes, yet the biological mechanisms of the vast majority of lncRNAs remain elusive. To fill this gap, it is necessary to improve the accuracy of lncRNA identification and functional annotation. RESULTS: Here, we introduce LncADeep 2.0, an integrated deep learning framework designed to meet these needs. In the identification module, LncADeep 2.0 incorporated novel peptide features along with sequence and structural information, demonstrating superior performance over our previous LncADeep and other existing tools on both annotated transcripts from GENCODE and RNA-seq data. For functional annotation, LncADeep 2.0 leveraged lncRNA-centric interaction networks and gene ontology terms through the transfer learning strategy to achieve robust annotation performance with limited functional data. Compared to LncADeep, LncADeep 2.0 could accurately elucidate the general functions of given lncRNA sequences, predict tissue- or cell-type-specific functions from bulk and single-cell RNA-seq data, and establish connections between tumor-associated lncRNAs and genomic markers. Overall, LncADeep 2.0 stands out as an efficient and reliable tool for lncRNA identification and functional annotation across a wide spectrum of biological processes. AVAILABILITY AND IMPLEMENTATION: LncADeep 2.0 is available for use at https://github.com/Jefferson-Chou/LncADeep2 and https://doi.org/10.5281/zenodo.17164767.

RNA, Long Noncoding↗

PubMed2026

AniAnn's: alignment-free annotation of tandem repeat arrays using fast average nucleotide identity estimates.

MOTIVATION: Satellite DNA has long posed challenges for genome assembly and analysis due to its low sequence complexity and poor mappability. These large heterochromatic arrays of tandem repeats are ubiquitous across eukaryotic genomes, yet remain understudied. Current methods for annotating satellite regions, and other classes of tandem repeat arrays, are limited in their ability to annotate divergent or novel sequences. RESULTS: In this work, we introduce AniAnn's, an algorithm for annotating large blocks of tandemly repeating DNAs. AniAnn's exploits the high Average Nucleotide Identity (ANI) shared between repeat units of the same array to quickly and accurately infer the boundaries of such arrays. We show that AniAnn's improves the annotation of satellites and other tandem repeats within a variety of plant and animal genomes, while requiring only a fraction of the runtime compared to previous approaches. We conclude by exploring several use cases of AniAnn's as a lightweight method for masking repeats prior to whole-genome alignment as well as the de novo annotation and classification of satellite repeats. AVAILABILITY: AniAnn's is open source software and available at github.com/marbl/anianns.

Algorithms↗

PubMed2026

Microbiome Datahub: an open-access platform integrating environmental metadata, taxonomy, and functional annotation for comprehensive metagenome-assembled genome datasets.

BACKGROUND: Metagenome-assembled genomes (MAGs) provide crucial insights into the genomic diversity of uncultured microbes. However, MAG datasets deposited in public repositories such as INSDC are often difficult to reuse due to heterogeneous quality, inconsistent taxonomic and functional annotations, and insufficiently curated environmental metadata. While secondary MAG databases such as MGnify, IMG/M, and SPIRE provide standardized resources, they reconstruct MAGs de novo from public metagenomic reads and therefore do not represent the original MAGs reported in publications. RESULTS: To address this gap, we developed Microbiome Datahub, an open-access platform that systematically aggregates and re-annotates original MAGs from INSDC. We collected 214,427 MAGs, predicted genes by DFAST, performed quality assessment with CheckM, standardized taxonomic assignments with GTDB-Tk, inferred 27 phenotypic traits using Bac2Feature, assigned proteins to MBGD ortholog clusters and KEGG Orthology IDs using PZLAST, and annotated environmental metadata with the Metagenome and Microbes Environmental Ontology. Across these MAGs, the average completeness was 80.5% and contamination 1.8%; notably, the most frequent values were >95% completeness and <1% contamination, indicating that the majority of MAGs are of high quality. Comparative analyses showed that Microbiome Datahub provides phylogenetically and environmentally diverse MAGs: while the majority originated from vertebrate gut environments, a substantial number were also recovered from other habitats such as groundwater, including nearly 10,000 MAGs from the Patescibacteria. Inference of 27 phenotypic traits, including optimum growth temperature, further revealed ecological differentiation across phyla. Protein clustering revealed 56 million identity 40% clusters, with the majority unique compared with MGnify and GlobDB, and ~19% of proteins unassigned to MBGD ortholog clusters, underscoring their novelty. CONCLUSIONS: Microbiome Datahub integrates MAG genome sequences, gene and protein predictions, quality metrics, environmental and taxonomic annotations, ortholog cluster assignments, and phenotype predictions, all accessible via a web interface, API, and bulk downloads. By combining original MAGs with curated metadata and functional annotations, Microbiome Datahub constitutes a comprehensive and reusable resource that will accelerate microbiome and microbial genomics research. Video Abstract.

Metagenome↗

PubMed2025

Expanding kinetoplastid genome annotation through protein structure comparison.

Kinetoplastids belong to the Discoba supergroup, an early divergent eukaryotic clade. Although the amount of genomic information on these parasites has grown substantially, assigning gene functions through traditional sequence-based homology methods remains challenging. Recently, significant advancements have been made in in-silico protein structure prediction and algorithms for rapid and precise large-scale protein structure comparisons. In this work, we developed a protein structure-based homology search pipeline (ASC, Annotation by Structural Comparisons) and applied it to transfer biological information to all kinetoplastid proteins available in TriTrypDB, the reference database for this lineage. Our pipeline enabled the assignment of structural similarity to a substantial portion of kinetoplastid proteins, improving current knowledge through annotation transfer. Additionally, we identified structural homologs for representatives of 6,700 uncharacterized proteins across 33 kinetoplastid species, proteins that could not be annotated using existing sequence-based tools and databases. As a result, this approach allowed us to infer potential biological information for a considerable number of kinetoplastid proteins. Among these, we identified structural homologs to ubiquitous eukaryotic proteins that are challenging to detect in kinetoplastid genomes through standard genome annotation pipelines. The results (KASC, Kinetoplastid Annotation by Structural Comparison) are openly accessible to the community at kasc.fcien.edu.uy through a user-friendly, gene-by-gene interface that enables visual inspection of the data.

Kinetoplastida↗

PubMed2026

Large-scale benchmarking of prokaryotic annotation tools across thousands of species.

BACKGROUND: Genome annotation is an important step in deriving functional meaning from prokaryotic sequencing data, yet systematic evaluations guiding tool selection are lacking. We present the first large-scale investigation of four prominent open-source annotation tools (Prokka, Bakta, EggNOG-mapper, and PGAP) across 156,033 diverse genomes. This includes Escherichia coli strains for baseline performance, thousands of archaea and bacteria genomes, as well as frameshifted and metagenome-assembled genomes. RESULTS: Bakta excels in annotating high-quality bacterial genomes, while PGAP was better for archaeal genomes and challenging bacterial assemblies, including metagenome-assembled, fragmented, or contaminated samples. For Gene Ontology annotation, PGAP consistently provides broader term coverage, whereas EggNOG-mapper offers more terms per feature. CONCLUSIONS: Our findings highlight tool-specific strengths crucial for selecting optimal solutions based on genome quality, taxonomy, and origin (e.g. MAGs). This study provides an evidence-based guide for users and informs future tool development.

Molecular Sequence Annotation↗

PubMed2005

PolyMAPr: programs for polymorphism database mining, annotation, and functional analysis.

Pharmacogenomic and disease-association studies rely on identifying a comprehensive set of polymorphisms within candidate genes. Public SNP databases are a rich source of polymorphism data, but mining them effectively requires overcoming at least four challenges: ensuring accurate annotations for genes and polymorphisms, eliminating both inter- and intra-database redundancy, integrating data from multiple public sources with data generated locally, and prioritizing the variants for further study. PolyMAPr (Polymorphism Mining and Annotation Programs)' was developed to overcome these challenges and to improve the efficiency of database mining and polymorphism annotation. PolyMAPr takes as input a file containing a list of genes to be processed and files containing each annotated gene sequence. Polymorphic sequences obtained from public databases (dbSNP, CGAP, and JSNP) or through local SNP discovery efforts, as well as oligonucleotide sequences (e.g., PCR primers), are mapped to the annotated gene sequences and named according to suggested nomenclature guidelines. The functional effects of nonsynonymous coding-region SNPs (cSNPs) and any variants that might alter exon splicing enhancer (ESE) sites, putative transcription factor binding sites, or intron-exon splice sites are predicted. The output files are accessible though a browser interface. In addition, the results are also provided in Extensible Markup Language (XML) format to facilitate uploading them into a local relational database. PolyMAPr increases the efficiency of mining public databases for genetic variants within candidate genes and provides a mechanism by which data from multiple sources (both public and private) can be uniformly integrated, thereby significantly reducing the effort required to obtain a comprehensive set of polymorphisms for pharmacogenomic and disease-association studies. PolyMAPr can be obtained from http://pharmacogenomics.wustl.edu.

Databases, Nucleic Acid↗

PubMed2004

The RESID Database of Protein Modifications as a resource and annotation tool.

The RESID Database of Protein Modifications is a comprehensive collection of annotations and structures for protein modifications and cross-links including pre-, co-, and post-translational modifications. The database provides: systematic and alternate names, atomic formulas and masses, enzymatic activities that generate the modifications, keywords, literature citations, Gene Ontology (GO) cross-references, protein sequence database feature table annotations, structure diagrams, and molecular models. This database is freely accessible on the Internet through resources provided by the European Bioinformatics Institute (http://www.ebi.ac.uk/RESID), and by the National Cancer Institute--Frederick Advanced Biomedical Computing Center (http://www.ncifcrf.gov/RESID). Each RESID Database entry presents a chemically unique modification and shows how that modification is currently annotated in the protein sequence databases, Swiss-Prot and the Protein Information Resource (PIR). The RESID Database provides a table of corresponding equivalent feature annotations that is used in the UniProt project, an international effort to combine the resources of the Swiss-Prot, TrEMBL and PIR. As an annotation tool, the RESID Database is used in standardizing and enhancing modification descriptions in the feature tables of Swiss-Prot entries. As an Internet resource, the RESID Database assists researchers in high-throughput proteomics to search monoisotopic masses and mass differences and identify known and predicted protein modifications.

Databases, Factual↗

PubMed2005

Automatic annotation of matrix-assisted laser desorption/ionization N-glycan spectra.

Matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) is the pre-eminent technique for mass mapping of glycans. In order to make this technique practical for high-throughput screening, reliable automatic methods of annotating peaks must be devised. We describe an algorithm called Cartoonist that labels peaks in MALDI spectra of permethylated N-glycans with cartoons which represent the most plausible glycans consistent with the peak masses and the types of glycans being analyzed. There are three main parts to Cartoonist. (i) It selects annotations from a library of biosynthetically plausible cartoons. The library we currently use has about 2800 cartoons, but was constructed using only about 300 archetype cartoons entered by hand. (ii) It determines the precision and calibration of the machine used to generate the spectrum. It does this automatically based on the spectrum itself. (iii) It assigns a confidence score to each annotation. In particular, rather than making a binary yes/no decision when annotating a peak, it makes all plausible annotations and associates them with scores indicating the probability that they are correct.

Algorithms↗

PubMed2005

How well are protein structures annotated in secondary databases?

We investigated to what extent Protein Data Bank (PDB) entries are annotated with second-party information based on existing cross-references between PDB and 15 other databases. We report 2 interesting findings. First, there is a clear "annotation gap" for structures less than 7 years old for secondary databases that are manually curated. Second, the examined databases overlap with each other quite well, dividing the PDB into 2 well-annotated thirds and one poorly annotated third. Both observations should be taken into account in any study depending on the selection of protein structures by their annotation.

Amino Acid Sequence↗

PubMed2005

Knowledge-based voting algorithm for automated protein functional annotation.

Automated annotation of high-throughput genome sequences is one of the earliest steps toward a comprehensive understanding of the dynamic behavior of living organisms. However, the step is often error-prone because of its underlying algorithms, which rely mainly on a simple similarity analysis, and lack of guidance from biological rules. We present herein a knowledge-based protein annotation algorithm. Our objectives are to reduce errors and to improve annotation confidences. This algorithm consists of two major components: a knowledge system, called "RuleMiner," and a voting procedure. The knowledge system, which includes biological rules and functional profiles for each function, provides a platform for seamless integration of multiple sequence analysis tools and guidance for function annotation. The voting procedure, which relies on the knowledge system, is designed to make (possibly) unbiased judgments in functional assignments among complicated, sometimes conflicting, information. We have applied this algorithm to 10 prokaryotic bacterial genomes and observed a significant improvement in annotation confidences. We also discuss the current limitations of the algorithm and the potential for future improvement.

Algorithms↗

PubMed2006

Gene Ontology annotation status of the fission yeast genome: preliminary coverage approaches 100%.

In this review, we present an overview of the Gene Ontology (GO) structure and describe how the GO is implemented for Sz. pombe and made available via Sz. pombe GeneDB (http://www.genedb.org/genedb/pombe/). We give a detailed progress report of Sz. pombe GO annotation, providing the current status of both manual and automatic annotations. Fission yeast has at least one GO annotation for 98.3% of its genes (excluding annotations to 'unknown' terms), greater than the current percentage coverage for any other organism. Approximately 65% (3225 gene products) have at least one annotation to each of the three ontologies (biological process, cellular component and molecular function). Approximately 30% (1443 gene products) have GO terms derived directly from small-scale experiments in fission yeast, supporting the validity of fission yeast as a model eukaryote and a reference organism.

Computational Biology↗

PubMed1999

Benchmarking PSI-BLAST in genome annotation.

The recognition of remote protein homologies is a major aspect of the structural and functional annotation of newly determined genomes. Here we benchmark the coverage and error rate of genome annotation using the widely used homology-searching program PSI-BLAST (position-specific iterated basic local alignment search tool). This study evaluates the one-to-many success rate for recognition, as often there are several homologues in the database and only one needs to be identified for annotating the sequence. In contrast, previous benchmarks considered one-to-one recognition in which a single query was required to find a particular target. The benchmark constructs a model genome from the full sequences of the structural classification of protein (SCOP) database and searches against a target library of remote homologous domains (<20 % identity). The structural benchmark provides a reliable list of correct and false homology assignments. PSI-BLAST successfully annotated 40 % of the domains in the model genome that had at least one homologue in the target library. This coverage is more than three times that if one-to-one recognition is evaluated (11 % coverage of domains). Although a structural benchmark was used, the results equally apply to just sequence homology searches. Accordingly, structural and sequence assignments were made to the sequences of Mycoplasma genitalium and Mycobacterium tuberculosis (see http://www.bmm.icnet. uk). The extent of missed assignments and of new superfamilies can be estimated for these genomes for both structural and functional annotations.

Algorithms↗

PubMed2004

Inappropriate annotation of a key defence marker in Arabidopsis: will the real PR-1 please stand up?

PR-1 has been extensively used as a marker for salicylic acid (SA)-mediated defence and systemic and local acquired resistance. The Arabidopsis Genome Project annotates At2g19990 as PR-1. This gene is also identified as PR-1 in two "full genome" Arabidopsis microarrays, and TAIR cites approximately 60 articles to describe its patterns of expression. However, most of these citations are incorrect; the probes used were not At2g19990, but a homologous gene At2g14610, which is annotated as "PR-1-like". Because of the potential for confusion, we analyzed the expression of both genes in Arabidopsis thaliana (L.) Heynh. At2g14610 (PR-1-like) showed the archetypal patterns of SA-responsive expression: mRNA levels increased following SA-treatment, inoculation with an avirulent (but not a virulent) strain of Pseudomonas syringae, and in wild-type (but not NahG) Arabidopsis infected with cauliflower mosaic virus (CaMV). In cpr5 mutants it was expressed constitutively. In contrast, expression of At2g19990 (annotated as PR-1) was detectable in neither SA-treated Col-0 nor in cpr5. Infection by virulent and avirulent isolates of P. syringae up-regulated expression, but to a similar level, and infection by CaMV induced a modest increase in expression in both the wild type and NahG. At2g19990, although pathogen responsive, does not show the SA-dependent patterns of expression expected from a member of the PR-1 regulon, and its annotation as " PR-1" is inappropriate. The annotations should identify At2g14610 as the authentic PR-1.

Arabidopsis↗