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Results for “annotation contribution score”

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At least 19 recordsLinked to original sources

PubMed2026

Beyond exons: Linking noncoding heritability and polygenicity across complex human traits and disorders.

The genetic architecture of complex traits spans a continuum of polygenicity, yet it remains unclear how differences in polygenicity relate to the functional localization of SNP heritability across the genome. We use a MiXeR-based framework to partition heritability across 74 functional annotations covering exonic, intronic, and intergenic regions for 34 complex traits and introduce a likelihood-based annotation contribution score that quantifies annotation-specific impact on heritability. Exons account for a minority of heritability, and their contribution decreases with increasing polygenicity, from an average of 22% in less-polygenic somatic diseases and biomarkers to 13% in highly polygenic psychiatric and cognitive phenotypes. Intergenic fractions show the opposite trend, whereas intronic fractions remain relatively stable. Analysis of the broader set of functional annotations also reveals systematic differences along the polygenicity axis: highly polygenic traits show stronger contributions from comparative genomics and variant-effect scores, whereas less-polygenic traits show stronger contributions from promoter, transcription, and chromatin annotations. Together, these results indicate that the functional partitioning of heritability systematically varies with polygenicity, shifting from gene-proximal regulatory architectures to architectures shaped by numerous dispersed regulatory effects.

MiXeR↗

PubMed2026

Beyond Exons: Linking Noncoding Heritability and Polygenicity across Complex Human Traits and Disorders.

The genetic architecture of complex traits spans a continuum of polygenicity, yet it remains unclear how differences in polygenicity relate to the functional localization of SNP heritability across the genome. We use a MiXeR-based framework to partition heritability across exonic, intronic, and intergenic regions for 34 traits and introduce a likelihood-based annotation contribution score that quantifies annotation-specific impact on heritability. Exons explain a minority of heritability, and their contribution decreases with increasing polygenicity, from an average of 22% in less polygenic somatic diseases and biomarkers to 13% in highly polygenic psychiatric and cognitive phenotypes. Intergenic fractions show the opposite trend, whereas intronic fractions remain relatively stable. Analysis of a broader set of functional annotations reveals systematic differences along the polygenicity axis: highly polygenic traits show stronger contributions from comparative genomics and variant-effect scores, whereas less polygenic traits show stronger contributions in promoter, transcription, and chromatin annotations. Together, these results indicate that the functional partitioning of heritability systematically varies with polygenicity, pointing to a shift from gene-proximal regulatory architectures to architectures shaped by numerous dispersed regulatory effects as a key determinant of differences in polygenicity across traits.

Journal Article↗

PubMed2003

Combining NLP and probabilistic categorisation for document and term selection for Swiss-Prot medical annotation.

MOTIVATION: Searching relevant publications for manual database annotation is a tedious task. In this paper, we apply a combination of Natural Language Processing (NLP) and probabilistic classification to re-rank documents returned by PubMed according to their relevance to Swiss-Prot annotation, and to identify significant terms in the documents. RESULTS: With a Probabilistic Latent Categoriser (PLC) we obtained 69% recall and 59% precision for relevant documents in a representative query. As the PLC technique provides the relative contribution of each term to the final document score, we used the Kullback-Leibler symmetric divergence to determine the most discriminating words for Swiss-Prot medical annotation. This information should allow curators to understand classification results better. It also has great value for fine-tuning the linguistic pre-processing of documents, which in turn can improve the overall classifier performance.

Abstracting and Indexing↗

PubMed2024

Rare variant contribution to the heritability of coronary artery disease.

Whole genome sequences (WGS) enable discovery of rare variants which may contribute to missing heritability of coronary artery disease (CAD). To measure their contribution, we apply the GREML-LDMS-I approach to WGS of 4949 cases and 17,494 controls of European ancestry from the NHLBI TOPMed program. We estimate CAD heritability at 34.3% assuming a prevalence of 8.2%. Ultra-rare (minor allele frequency ≤ 0.1%) variants with low linkage disequilibrium (LD) score contribute ~50% of the heritability. We also investigate CAD heritability enrichment using a diverse set of functional annotations: i) constraint; ii) predicted protein-altering impact; iii) cis-regulatory elements from a cell-specific chromatin atlas of the human coronary; and iv) annotation principal components representing a wide range of functional processes. We observe marked enrichment of CAD heritability for most functional annotations. These results reveal the predominant role of ultra-rare variants in low LD on the heritability of CAD. Moreover, they highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

Humans↗

PubMed2005

Bio-medical entity extraction using support vector machines.

OBJECTIVE: Support vector machines (SVMs) have achieved state-of-the-art performance in several classification tasks. In this article we apply them to the identification and semantic annotation of scientific and technical terminology in the domain of molecular biology. This illustrates the extensibility of the traditional named entity task to special domains with large-scale terminologies such as those in medicine and related disciplines. METHODS AND MATERIALS: The foundation for the model is a sample of text annotated by a domain expert according to an ontology of concepts, properties and relations. The model then learns to annotate unseen terms in new texts and contexts. The results can be used for a variety of intelligent language processing applications. We illustrate SVMs capabilities using a sample of 100 journal abstracts texts taken from the {human, blood cell, transcription factor} domain of MEDLINE. RESULTS: Approximately 3400 terms are annotated and the model performs at about 74% F-score on cross-validation tests. A detailed analysis based on empirical evidence shows the contribution of various feature sets to performance. CONCLUSION: Our experiments indicate a relationship between feature window size and the amount of training data and that a combination of surface words, orthographic features and head noun features achieve the best performance among the feature sets tested.

Algorithms↗

PubMed2000

Website review: interPro (the integrated resource of protein domains and functional sites).

The family and motif databases, PROSITE, PRINTS, Pfam and ProDom, have been integrated into a powerful resource for protein secondary annotation. As of June 2000, InterPro had processed 384 572 proteins in SWISS-PROT and TrEMBL. Because the contributing databases have different clustering principles and scoring sensitivities, the combined assignments compliment each other for grouping protein families and delineating domains. The graphic displays of all matches above the scoring thresholds enables judgements to be made on the concordances or differences between the assignments. The website links can be used to analyse novel sequences and for queries across the proteomes of 32 organisms, including the partial human set, by domain and/or protein family. An analysis of selected HtrA/DegQ proteases demonstrates the utility of this website for detailed comparative genomics. Further information on the project can be found at the European Bioinformatics Institute at http://www.ebi.ac.uk/interpro/

Amino Acid Motifs↗

PubMed2006

Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array.

BACKGROUND: Alternative splicing is a mechanism for increasing protein diversity by excluding or including exons during post-transcriptional processing. Alternatively spliced proteins are particularly relevant in oncology since they may contribute to the etiology of cancer, provide selective drug targets, or serve as a marker set for cancer diagnosis. While conventional identification of splice variants generally targets individual genes, we present here a new exon-centric array (GeneChip Human Exon 1.0 ST) that allows genome-wide identification of differential splice variation, and concurrently provides a flexible and inclusive analysis of gene expression. RESULTS: We analyzed 20 paired tumor-normal colon cancer samples using a microarray designed to detect over one million putative exons that can be virtually assembled into potential gene-level transcripts according to various levels of prior supporting evidence. Analysis of high confidence (empirically supported) transcripts identified 160 differentially expressed genes, with 42 genes occupying a network impacting cell proliferation and another twenty nine genes with unknown functions. A more speculative analysis, including transcripts based solely on computational prediction, produced another 160 differentially expressed genes, three-fourths of which have no previous annotation. We also present a comparison of gene signal estimations from the Exon 1.0 ST and the U133 Plus 2.0 arrays. Novel splicing events were predicted by experimental algorithms that compare the relative contribution of each exon to the cognate transcript intensity in each tissue. The resulting candidate splice variants were validated with RT-PCR. We found nine genes that were differentially spliced between colon tumors and normal colon tissues, several of which have not been previously implicated in cancer. Top scoring candidates from our analysis were also found to substantially overlap with EST-based bioinformatic predictions of alternative splicing in cancer. CONCLUSION: Differential expression of high confidence transcripts correlated extremely well with known cancer genes and pathways, suggesting that the more speculative transcripts, largely based solely on computational prediction and mostly with no previous annotation, might be novel targets in colon cancer. Five of the identified splicing events affect mediators of cytoskeletal organization (ACTN1, VCL, CALD1, CTTN, TPM1), two affect extracellular matrix proteins (FN1, COL6A3) and another participates in integrin signaling (SLC3A2). Altogether they form a pattern of colon-cancer specific alterations that may particularly impact cell motility.

Algorithms↗

PubMed2004

Comparison of character-level and part of speech features for name recognition in biomedical texts.

The immense volume of data which is now available from experiments in molecular biology has led to an explosion in reported results most of which are available only in unstructured text format. For this reason there has been great interest in the task of text mining to aid in fact extraction, document screening, citation analysis, and linkage with large gene and gene-product databases. In particular there has been an intensive investigation into the named entity (NE) task as a core technology in all of these tasks which has been driven by the availability of high volume training sets such as the GENIA v3.02 corpus. Despite such large training sets accuracy for biology NE has proven to be consistently far below the high levels of performance in the news domain where F scores above 90 are commonly reported which can be considered near to human performance. We argue that it is crucial that more rigorous analysis of the factors that contribute to the model's performance be applied to discover where the underlying limitations are and what our future research direction should be. Our investigation in this paper reports on variations of two widely used feature types, part of speech (POS) tags and character-level orthographic features, and makes a comparison of how these variations influence performance. We base our experiments on a proven state-of-the-art model, support vector machines using a high quality subset of 100 annotated MEDLINE abstracts. Experiments reveal that the best performing features are orthographic features with F score of 72.6. Although the Brill tagger trained in-domain on the GENIA v3.02p POS corpus gives the best overall performance of any POS tagger, at an F score of 68.6, this is still significantly below the orthographic features. In combination these two features types appear to interfere with each other and degrade performance slightly to an F score of 72.3.

Abbreviations as Topic↗

PubMed2004

CSTminer: a web tool for the identification of coding and noncoding conserved sequence tags through cross-species genome comparison.

The identification and characterization of genome tracts that are highly conserved across species during evolution may contribute significantly to the functional annotation of whole-genome sequences. Indeed, such sequences are likely to correspond to known or unknown coding exons or regulatory motifs. Here, we present a web server implementing a previously developed algorithm that, by comparing user-submitted genome sequences, is able to identify statistically significant conserved blocks and assess their coding or noncoding nature through the measure of a coding potential score. The web tool, available at http://www.caspur.it/CSTminer/, is dynamically interconnected with the Ensembl genome resources and produces a graphical output showing a map of detected conserved sequences and annotated gene features.

Animals↗

PubMed2025

Comparative Analysis of Primary Sarcopenia and End-Stage Renal Disease-Related Muscle Wasting Using Multi-Omics Approaches.

BACKGROUND: Age-related primary sarcopenia and end-stage renal disease (ESRD)-related muscle wasting are discrete entities; however, both manifest as a decline in skeletal muscle mass and strength. The etiological pathways differ, with aging factors implicated in sarcopenia and a combination of uremic factors, including haemodialysis, contributing to ESRD-related muscle wasting. Understanding these molecular nuances is imperative for targeted interventions, and the integration of proteomic and metabolomic data elucidate these intricate processes. METHODS: We generated detailed clinical data and multi-omics data (plasma proteomics and metabolomics) for 78 participants to characterise sarcopenia (n = 28; mean age, 72.6 ± 7.0 years) or ESRD (n = 22; 61.6 ± 5.5 years) compared with controls (n = 28; 69.3 ± 5.7 years). Muscle mass was measured using bioelectrical impedance analysis and handgrip strength. Five-times sit-to-stand test performance was measured for all participants. Sarcopenia was diagnosed in accordance with the 2019 Consensus Guidelines from the Asian Working Group for Sarcopenia. An abundance of 234 metabolites and 722 protein groups was quantified in all plasma samples using liquid chromatography with tandem mass spectrometry. RESULTS: Muscle mass, handgrip strength and lower limb muscle function significantly lower in the sarcopenia group and the ESRD group compared with those in the control group. Metabolomics revealed altered metabolites, highlighting exclusive differences in ESRD-related muscle wasting. Metabolite set enrichment analysis revealed the involvement of numerous metabolic intermediates associated with urea cycle, amino acid metabolism and nucleic acid metabolism. Catecholamines, including epinephrine, dopamine and serotonin, are significantly elevated in the plasma of patients within the ESRD group. Proteomics data exhibited a clearer distinction among the three groups compared with the metabolomics data, particularly in distinguishing the control group from the sarcopenia group. The ciliary neurotrophic factor receptor was top-ranked in terms of the variable importance of projection scores. Plasma AHNAK protein levels was higher in the sarcopenia group but was lower in the ESRD group. Proteomic set enrichment analysis revealed enrichment of several pathways related to sarcopenia, such as hemopexin, defence response and cell differentiation, in sarcopenia group. Multi-omic integration analysis revealed associations between relevant metabolites, including catecholamines, and a group of annotated proteins in extracellular exosomes. CONCLUSIONS: We identified distinct multi-omic signatures in individuals with ESRD or sarcopenia, providing new insights into the mechanisms underlying ESRD-related muscle wasting, which differ from primary sarcopenia. These findings may support interventions for context-dependent muscle loss and contribute to the development of targeted treatments and preventive strategies for muscle wasting.

Humans↗

PubMed2026

Can Psychiatric Genetics Advance Without Incorporating a Life Course Perspective?

Psychiatric disorders unfold over the life course; however, genomic studies of these conditions overwhelmingly rely on phenotypes collected at a single time point, often in adulthood. Therefore, genome-wide association studies (GWASs) of psychiatric conditions may miss genetic variants with time-varying relevance to etiology, prevention, and treatment, such as those that influence trajectories of symptoms and behaviors, age at onset, course of treatment response, and the co-evolution of comorbidities. With recent advances in longitudinal biobanks and analytic tools, we posit that incorporating a life course perspective in psychiatric genetics will enable critically relevant insights into each of these areas of investigation. We propose that the current inconsistent portability of polygenic scores across age groups can be reconciled through the design of carefully considered longitudinal GWASs in age-diverse samples. Pioneering longitudinal GWASs in psychiatry have revealed novel genomic signals associated with time-dependent phenotypes that are distinct from those influencing lifetime diagnosis, suggesting that the study of longitudinal phenotypes will complement cross-sectional approaches and empower biological and therapeutic discoveries. Advances in post-GWAS functional annotation resources and analytic approaches now enable us to contextualize the genetic contributions to psychiatric disorders as dynamic age- and exposure-dependent processes. Although longitudinal GWASs pose unique challenges with regard to data availability, selection bias, and missing data, integrating temporality into psychiatric genetics at scale is now attainable and promises to reveal novel biology and therapeutic opportunities for psychiatric conditions.

Cohort study↗

PubMed1999

Crystallographic threading.

Crystallographic studies play a major role in current efforts towards protein structure determination. Despite recent advances in computational tools for molecular modeling and graphics, the construction of a three-dimensional protein backbone model from crystallographic data remains complex and time-consuming. This paper describes a unique contribution to an automated approach to protein model construction and evaluation, where a model is represented as an annotated trace (or partial trace) of a structure. Candidate models are derived through a topological analysis of the electron density map of a protein. Using sequence alignment techniques, we determine an optimal threading of the known sequence onto the candidate protein structure models. In this threading, connected nodes on the model are associated with adjacent amino acids in the sequence and a fitness score is assigned based on features extracted from the electron density map for the protein. Experimental results demonstrate that crystallographic threading provides an effective means for evaluating the "goodness" of experimentally derived protein models.

Algorithms↗

PubMed2026

Integrating genetic predictors into subsequent breast cancer risk prediction in survivors of childhood cancer.

PURPOSE: Female survivors of childhood cancer are at high risk for developing breast cancer. The contributions of most general population primary breast cancer genetic predictors to this risk have not been explored. METHODS: Analyses included females who survived ≥5 years after their childhood cancer diagnosis with available array (N = 2096, subsequent breast cancer [SBC]=218) or whole-genome sequencing (WGS; N = 3292, SBC=101) data from the Childhood Cancer Survivor Study and St. Jude Lifetime Cohort. We computed 99 externally-validated primary breast cancer polygenic risk scores (PRS). Using deep-coverage WGS, ClinVar-annotated pathogenic/likely pathogenic (P/LP) variants in breast cancer susceptibility genes were identified. Cox proportional hazards models assessed associations with SBC risk, adjusting for treatments and genetic ancestry. RESULTS: Among 5388 female survivors (genetic ancestry, European: N = 4,752; African: N = 444; East Asian: N = 192), 319 developed SBC. Most (90.9%) PRSs were nominally associated with SBC risk (P < 0.05), but effect sizes varied substantially. PRSs with superior discriminatory ability had greater genome-wide coverage (e.g., 6.4 million-variant PRS, HR per SD = 1.71, 95% CI = 1.43 to 2.05; P = 4.2x10-9) and 7.7-fold higher odds (P = 7.0x10-4) of including variants in multiple DNA damage repair pathways compared with PRSs with weaker risk associations. Among survivors with WGS, 1.6% carried P/LP variants in clinical testing panel genes, which was associated with a 7.4-fold greater risk (95% CI = 3.16 to 17.19). Including genetic factors improved SBC risk prediction by age 40 (P < 0.001) compared to treatment exposures alone. CONCLUSIONS: Externally-validated primary breast cancer genetic susceptibility predictors are relevant for SBC risk prediction and should be prioritized for risk stratification in survivors.

Journal Article↗

PubMed2026

Genome-Wide Association Analyses of Bitter Food Preferences Link Genetic Loci to Sensory and Metabolic Pathways.

BACKGROUND: Genetic variation is implicated in individual preferences for bitter-tasting foods. However, previous studies have focused on candidate genes and limited varieties of bitter-tasting foods and have treated food preference scale responses as continuous data. OBJECTIVES: The present investigation aimed to identify genetic variants associated with preferences for bitter-tasting foods using ordinal multinomial regression models in genome-wide association studies (GWAS). In addition, post-GWAS functional annotation and mapping, genetic correlations, and associations with dietary intake were examined. METHODS: Food preference and genome-wide genotyping data were used from the UK Biobank (n = 125,578). Preference data from Likert scale rankings (from 1 to 9) for 12 individual foods were analyzed using ordinal multinomial regression GWAS. In addition, 1 composite continuous variable was created for preference for cruciferous vegetables as a group and analyzed using a linear mixed-model GWAS to enable the calculation of a polygenic score (PGS) for cruciferous vegetable preference. Convergent validity of GWAS results was assessed with dietary intake data for the same food items in the CARTaGENE cohort (n = 8176). Post-GWAS gene-level and pathway-level association analyses were conducted in MAGMA (Multimarker Analysis of GenoMic Annotation). RESULTS: Forty-six single-nucleotide polymorphisms (SNPs) were identified for preferences for 11 bitter-tasting foods at a genome-wide significance level (P < 7.14 × 10-9). Gene-set analysis for enrichment identified pathways related to caffeine metabolism and bitter taste perception for preference of coffee without sugar and grapefruit, respectively. Genes with higher expression in brain tissues showed stronger genetic associations with cruciferous vegetable preference. The PGS for cruciferous vegetable preference was weakly correlated with intake (r = 0.05, P < 0.0001), but individual SNPs were not associated with intake in a consistent manner. CONCLUSIONS: Genetic variation contributes to preferences for bitter-tasting foods among adults, and some links with food intake are detectable. Nevertheless, effect sizes are small and inconsistent, reflecting the multifactorial complexity of food intake.

bitter taste↗

PubMed2026

Genome-wide detection of human 5' UTR variants that impact protein translation.

The 5' untranslated region (5' UTR) of messenger RNAs (mRNAs) plays a central role in regulating protein synthesis initiation, particularly through the Kozak sequence and upstream open reading frames (uORFs). Genetic variants within these regulatory elements could affect translation, altering gene expression and contributing to clinical phenotypes in humans. We developed a computational method called 5ULTRA (5' Untranslated Region Annotation) for analysis of whole-exome sequencing and whole-genome sequencing data to detect, annotate, and prioritize 5' UTR variants with potential translation impact. 5ULTRA identifies single-nucleotide variants, indels, and splicing variants that affect uORFs by creating or disrupting start/stop codons and that alter Kozak sequence strength of either the uORFs or the main coding sequence. 5ULTRA incorporates recent uORF databases and provides comprehensive annotations. 5ULTRA implements a machine-learning score to prioritize candidate variants with predicted effects on translation and also provides specific mechanistic predictions. The score correlates strongly with experimentally measured protein-level effects of 5' UTR variants. We applied 5ULTRA to multiple genetics datasets across diverse disease contexts, identifying candidate variants including potential cancer-driving somatic mutations predicted to decrease ABI1 level or increase NRAS abundance; common variants associated with traits such as multiple sclerosis, lung function, and cardiovascular function, by altering protein levels of TAGAP, VRTN, and SPAAR, respectively; and rare germline variants in our cohort, including a splicing variant of RPSA leading to 5' UTR sequence alteration that causes congenital asplenia and a variant of TNF that could predispose to tuberculosis.

Humans↗

PubMed2003

Genome-wide linkage reveals a locus for human essential (primary) hypertension on chromosome 12p.

Essential (primary) hypertension is an important risk factor for cardiovascular morbidity and mortality. Blood pressure is largely heritable; however, the genetic factors contributing to essential hypertension are mostly unknown. We examined a large Chinese kindred (n=387) and selected a subset of 94 individuals for genotyping. An additional 32 Chinese nuclear families with essential hypertension were also recruited. Genome-wide parametric linkage analysis identified a new locus for primary hypertension on chromosome 12p (parametric LOD score 3.44). This locus overlaps with the assigned locus that causes severe autosomal-dominant hypertension and brachydactyly, the only form of monogenic hypertension known to date that resembles primary hypertension. We suggest that this genomic region, spanning 18 annotated genes, will be of great relevance in elucidating new mechanisms for primary hypertension.

Adult↗

PubMed2005

The predictive power of the CluSTr database.

SUMMARY: The CluSTr database employs a fully automatic single-linkage hierarchical clustering method based on a similarity matrix. In order to compute the matrix, first all-against-all pair-wise comparisons between protein sequences are computed using the Smith-Waterman algorithm. The statistical significance of the similarity scores is then assessed using a Monte Carlo analysis, yielding Z-values, which are used to populate the matrix. This paper describes automated annotation experiments that quantify the predictive power and hence the biological relevance of the CluSTr data. The experiments utilized the UniProt data-mining framework to derive annotation predictions using combinations of InterPro and CluSTr. We show that this combination of data sources greatly increases the precision of predictions made by the data-mining framework, compared with the use of InterPro data alone. We conclude that the CluSTr approach to clustering proteins makes a valuable contribution to traditional protein classifications. AVAILABILITY: http://www.ebi.ac.uk/clustr/.

Algorithms↗

PubMed2026

Genomes of the ex-type strains of Elsinoë mangiferae and E. perseae, the causal agents of scab on mango and avocado.

Elsinoë species are slow-growing, hemibiotrophic to necrotrophic fungi that cause scab diseases on economically important fruit crops. Genome resources for many host-specific species remain limited. We report high-quality draft genome assemblies for the ex-type strains of Elsinoë mangiferae (CBS 226.50) and E. perseae (CBS 406.34), causal agents of mango and avocado scab, respectively. Among 5 approaches tested, a Nanopore-only NextDenovo assembly produced the most contiguous genomes, yielding 24.5 Mb (E. mangiferae) and 25.1 Mb (E. perseae) assemblies with 13 and 18 contigs, respectively, BUSCO completeness scores of ∼94%, and multiple putative telomere-to-telomere chromosomes. Gene prediction identified 9,134 and 9,243 genes, respectively. Functional annotation revealed enrichment of metabolic and regulatory pathways, including those involved in posttranslational modification, protein transport, and secondary metabolism. Carbohydrate-active enzyme repertoires were small but conserved, consistent with stealth pathogenicity strategies and low plant cell wall degradation. Both genomes encoded large secretomes (>850 proteins), diverse protease repertoires (>300 proteins), Ecp2-like effector proteins, and multiple biosynthetic gene clusters, including clusters with similarity to those associated with elsinochrome and ACT-toxin II biosynthesis, some of which may contribute to host-pathogen interactions and disease development. A large fraction of genes lacked functional characterization, suggesting incomplete databases and/or the presence of lineage-specific genes potentially involved in virulence or host adaptation. These genome resources fill critical gaps for underrepresented Elsinoë species and provide taxonomically anchored references essential for diagnostics, comparative genomics, and research into the molecular basis of host specificity and pathogenicity in scab-causing fungi.

Persea↗