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PubMed2025

The chromosome-level genome assembly and annotation of the silver-lipped pearl oyster, Pinctada maxima.

The silver-lipped pearl oyster (Pinctada maxima) is a valuable tropical aquaculture species, playing a crucial economic role in the global pearl industry. However, the lack of genomic reference limits our in-depth understanding of this species in genome-based breeding, conservation, evolution and adaptation. Here, annotated chromosome-level reference genome for P. maxima was generated by integrating PacBio long-read sequencing, Illumina short-read sequencing, and Hi-C sequencing data. The total genome size is 1,264.93&#x2009;Mb, with contig N50 and scaffold N50 of 649&#x2009;kb and 89.19&#x2009;Mb, respectively. The majority (97.94%) of the assembled genome was anchored to the 14 chromosomes by Hi-C analysis. The relatively high genome completeness was observed, with 97.38% (metazoa_odb10 database) and 95.26% (mollusca_odb10 database) in BUSCO analysis. Genome annotation revealed approximately 65.46% of the repeat sequences and 26,315 protein-coding genes. Comparative genome analysis revealed 28 expanded and 48 contracted families (p&#x2009;<&#x2009;0.05) in P. maxima, with 3.2% of genes (894) being species-specific. This chromosome-level genome serves as an essential resource for research in evolutionary genomics, phylogenetics, and biomineralization.

Animals↗
PubMed2006

Binding site-based classification of coronaviral papain-like proteases.

The coronavirus replicase gene encodes one or two papain-like proteases (termed PL1pro and PL2pro) implicated in the N-terminal processing of the replicase polyprotein and thus contributing to the formation of the viral replicase complex that mediates genome replication. Using consensus fold recognition with the 3D-JURY meta-predictor followed by model building and refinement, we developed a structural model for the single PLpro present in the severe acute respiratory syndrome coronavirus (SCoV) genome, based on significant structural relationships to the catalytic core domain of HAUSP, a ubiquitin-specific protease (USP). By combining the SCoV PLpro model with comparative sequence analyses we show that all currently known coronaviral PLpros can be classified into two groups according to their binding site architectures. One group includes all PL2pros and some of the PL1pros, which are characterized by a restricted USP-like binding site. This group is designated the R-group. The remaining PL1pros from some of the coronaviruses form the other group, featuring a more open papain-like binding site, and is referred to as the O-group. This two-group, binding site-based classification is consistent with experimental data accumulated to date for the specificity of PLpro-mediated polyprotein processing and PLpro inhibition. It also provides an independent evaluation of the similarity-based annotation of PLpro-mediated cleavage sites, as well as a basis for comparison with previous groupings based on phylogenetic analyses.

Amino Acid Sequence↗
PubMed2006

Phylogenetic supermatrix analysis of GenBank sequences from 2228 papilionoid legumes.

A comprehensive phylogeny of papilionoid legumes was inferred from sequences of 2228 taxa in GenBank release 147. A semiautomated analysis pipeline was constructed to download, parse, assemble, align, combine, and build trees from a pool of 11,881 sequences. Initial steps included all-against-all BLAST similarity searches coupled with assembly, using a novel strategy for building length-homogeneous primary sequence clusters. This was followed by a combination of global and local alignment protocols to build larger secondary clusters of locally aligned sequences, thus taking into account the dramatic differences in length of the heterogeneous coding and noncoding sequence data present in GenBank. Next, clusters were checked for the presence of duplicate genes and other potentially misleading sequences and examined for combinability with other clusters on the basis of taxon overlap. Finally, two supermatrices were constructed: a "sparse" matrix based on the primary clusters alone (1794 taxa x 53,977 characters), and a somewhat more "dense" matrix based on the secondary clusters (2228 taxa x 33,168 characters). Both matrices were very sparse, with 95% of their cells containing gaps or question marks. These were subjected to extensive heuristic parsimony analyses using deterministic and stochastic heuristics, including bootstrap analyses. A "reduced consensus" bootstrap analysis was also performed to detect cryptic signal in a subtree of the data set corresponding to a "backbone" phylogeny proposed in previous studies. Overall, the dense supermatrix appeared to provide much more satisfying results, indicated by better resolution of the bootstrap tree, excellent agreement with the backbone papilionoid tree in the reduced bootstrap consensus analysis, few problematic large polytomies in the strict consensus, and less fragmentation of conventionally recognized genera. Nevertheless, at lower taxonomic levels several problems were identified and diagnosed. A large number of methodological issues in supermatrix construction at this scale are discussed, including detection of annotation errors in GenBank sequences; the shortage of effective algorithms and software for local multiple sequence alignment; the difficulty of overcoming effects of fragmentation of data into nearly disjoint blocks in sparse supermatrices; and the lack of informative tools to assess confidence limits in very large trees.

Algorithms↗
PubMed2025

Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder lacking objective biomarkers for early diagnosis. DNA methylation is a promising epigenetic marker, and machine learning offers a data-driven classification approach. However, few studies have examined whole-blood, genome-wide DNA methylation profiles for ASD diagnosis in school-aged children. METHODS: We analyzed genome-wide DNA methylation data from GEO dataset GSE113967, including 52 children with ASD and 48 typically developing (TD) controls. Differentially methylated positions (DMPs) were identified, and feature selection was performed using support vector machine-recursive feature elimination with cross-validation (SVM-RFECV). Classification models were developed using random forest (RF), extreme gradient boosting (XGBoost), and decision tree (DT) classifiers. A nomogram visualized feature contributions. RESULTS: A total of 138 DMPs differentiated ASD from TD children. Eleven CpG sites selected by SVM-RFECV formed the basis for model construction. RF and XGBoost achieved the highest accuracy (75%), with DT reaching 70%. Functional annotation indicated enrichment in cell adhesion and immune-related pathways. CONCLUSIONS: This exploratory study demonstrates the feasibility of integrating peripheral blood DNA methylation data with machine learning to distinguish children with ASD. While limited by sample size and moderate accuracy, this study provides methodological insights into the feasibility of integrating epigenetic and computational approaches for ASD-related biomarker exploration.

Humans↗
PubMed2025

A chromosome-level genome assembly and annotation of Cercis chuniana (Fabaceae).

The genus Cercis L., at the base of the subfamily Cercidoideae of Fabaceae, is known for its ecological adaptability and significant medicinal, ornamental, and economic value. However, the lack of a high-quality genome hinders the understanding of the evolution of Cercis and Fabaceae. In this study, we present a chromosome-level genome of Cercis chuniana by combining Illumina short reads, PacBio HiFi long reads, and Hi-C data. The final genome size is 355.53&#x2009;Mb, consisting of 12 contigs with a N50 of 42.34&#x2009;Mb. Notably, 344.24&#x2009;Mb, corresponding to 96.82% of the genome, was anchored to seven chromosomes. The assembly comprises 24.83% repetitive sequences, including 19.32% long terminal repeats. Additionally, a total of 33,837 protein-coding genes were predicted in the genome, with 32,709 (96.67%) genes successfully annotated. The high-quality genome assembly of C. chuniana not only bridges the existing gap in genomic data and offers important resources for molecular studies of this species, but also provides essential insights for future studies on speciation, functional and comparative genomics within the Fabaceae family.

Genome, Plant↗
PubMed1993

Transmembrane segment prediction from protein sequence data.

We consider the automated identification of transmembrane domains in membrane protein sequences. 324 proteins (containing 1585 segments) were examined, representing every protein in the PIR database having the transmembrane domain feature annotation. Machine learning techniques were used to evaluate the efficacy of alternative hydrophobicity measures and windowing techniques. We describe a simpler measure of hydrophobicity and a new variable window size concept. We demonstrate that these techniques are superior to some previous techniques in minimizing the segment error rate. Using these new techniques, we describe an algorithm that has a 7.9% segment error rate on the sampled proteins, while classifying 16.7% of the amino acid residues as transmembrane.

Algorithms↗
PubMed2026

Exome sequencing and large-scale analysis of electronic medical record-linked biobank data identify candidate deafness genes.

INTRODUCTION: Rapid advances in whole-exome sequencing (WES) have enabled large-scale detection of pathogenic variants. Although hundreds of genes are implicated in hearing loss, up to half of inherited cases remain unsolved, limiting eligibility for gene therapy trials that require genetic diagnosis. Biobanks and electronic medical records (EMRs) offer opportunities to integrate genomic and clinical data at scale and expand the spectrum of hearing loss genes. Despite clinical value, EMRs often lack key information such as inheritance patterns, posing challenges for accurate interpretation. METHODS: WES was performed on DNA samples from 1038 hearing-impaired patients enrolled in the Maccabi Research and Innovation Center Tipa Biobank. Clinical data were extracted from EMRs. Audiograms were available for all cases, although data on age of onset, family history and mode of inheritance were mostly unavailable. We applied a scalable bioinformatics analysis strategy for high-throughput annotation, filtering and prioritisation of WES variants across more than 1000 patients, designed to accommodate incomplete and heterogeneous clinical records. RESULTS: Using this approach, 15% of cases were solved or potentially solved through known or novel variants in established deafness genes. Homozygous variants in novel candidate genes were identified in 3% of cases. Functional characterisation was performed for promising candidate genes to validate their role in the ear. CONCLUSION: These findings demonstrate that WES can determine disease aetiology in large, genetically heterogeneous populations, even in the context of incomplete clinical data. This approach supports large-scale genetic screening and provides a framework for identifying patients who may benefit from emerging gene-based therapies.

Genetic Testing↗
PubMed2003

European data format 'plus' (EDF+), an EDF alike standard format for the exchange of physiological data.

The European data format (EDF) is a widely accepted standard for exchange of electroencephalogram and polysomnogram data between different equipment and labs. But it hardly accommodates other investigations. EDF+ is a more flexible but still simple format which is compatible to EDF except that an EDF+ file may contain interrupted recordings. Also, EDF+ supports time-stamped annotations for the storage of events such as text annotations, stimuli, averaged signals, electrocardiogram parameters, apnoeas and so on. When compared to EDF, EDF+ can not only store annotations but also electromyography, evoked potentials, electroneurography, electrocardiography and many more types of investigations. Further improvements over EDF include the use of standard electrode names. EDF+ is so much like EDF that existing EDF viewers still display the signals in EDF+ files. Software development is limited mainly to implementing the annotations. EDF+ offers a format for a wide range of neurophysiological investigations which can become a standard within a few years.

Data Interpretation, Statistical↗
PubMed2001

Consistent integration of non-reliable heterogeneous information resources applied to the annotation of transmembrane proteins.

Information agents integrate multiple distributed heterogeneous information sources. The challenging yet unsolved problem that remains, is to ensure the semantic consistency of the integrated data. In this paper we set out to develop a general approach to inconsistency management for information agents. It is implemented as part of the EDITtoTrEMBL system and applied on a large real-world problem in the domain of bioinformatics.

Amino Acid Sequence↗
PubMed1998

The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1998.

SWISS-PROT (http://www.expasy.ch/) is a curated protein sequence database which strives to provide a high level of annotations (such as the description of the function of a protein, its domains structure, post-translational modifications, variants, etc.), a minimal level of redundancy and high level of integration with other databases. Recent developments of the database include: an increase in the number and scope of model organisms; cross-references to two additional databases; a variety of new documentation files and improvements to TrEMBL, a computer annotated supplement to SWISS-PROT. TrEMBL consists of entries in SWISS-PROT-like format derived from the translation of all coding sequences (CDS) in the EMBL nucleotide sequence database, except the CDS already included in SWISS-PROT.

Amino Acid Sequence↗
PubMed2004

The Mycobacterium tuberculosis Rv1099c gene encodes a GlpX-like class II fructose 1,6-bisphosphatase.

There are now abundant data indicating that Mycobacterium tuberculosis uses fatty acids as a carbon source in vivo. A key enzyme in gluconeogenesis, missing in the original annotation of the M. tuberculosis genome, is fructose 1,6-bisphosphatase (FBPase; EC 3.1.3.11). The authors have shown that M. tuberculosis Rv1099c, a glpX homologue, can complement Escherichia coli mutants lacking FBPase. The protein encoded by Rv1099c was shown to have FBPase activity. Rv1099c was expressed at significant levels in M. tuberculosis, and may encode the major FBPase of this pathogen.

Escherichia coli↗
PubMed2004

Biomedical literature mining: challenges and solutions in the 'omics' era.

It is now obvious that the rate-limiting step in high throughput experimentation is neither data acquisition nor analysis, but rather our ability to interpret data on a genome-wide scale. Indeed, the explosion of data sampling capacity combined with increasing publication rates greatly impairs our ability to find meaning in vast collections of data. In order to support data interpretation, bioinformatic tools are needed to identify critical information contained in large bodies of literature. However, extracting knowledge embedded in free text is an arduous task, compounded in the biomedical field by an inconsistent gene nomenclature, domain-specific language and restricted access to full text articles. This paper presents a selection of currently available biomedical literature mining software. These tools rely on statistic and, more recently, semantic analyses (Natural Language Processing) to automatically extract information from the literature. In addition, a literature mining strategy has been developed to explore patterns of term occurrences in abstracts. This method automatically identifies relevant keywords in collections of abstracts, and uses a pattern discovery algorithm to generate a visual interface for exploring functional associations among genes. Term occurrence heatmaps can also be combined with gene expression profiles to provide valuable functional annotations. Furthermore, as demonstrated with tumor cell line literature profiling results, this approach can be applied to a variety of themes beyond genomic data analysis. Altogether, these examples illustrate how literature analysis can be employed to support knowledge discovery in biomedical research.

Algorithms↗
PubMed2026

Coordinated inflammatory macrophage and vascular smooth muscle cell remodeling signatures in human atherosclerosis: An integrative single-cell and bulk transcriptomic analysis.

Atherosclerotic plaque progression is shaped by coordinated inflammatory and remodeling programs involving immune cells and vascular wall cells. Inflammatory macrophage activation and vascular smooth muscle cell (VSMC) phenotypic remodeling are central features of human atherosclerosis, but their transcriptomic relationships during plaque progression remain incompletely characterized. This study integrated single-cell and bulk transcriptomic datasets to examine highly inflammatory macrophage states, VSMC remodeling-related transcriptional programs, and candidate ligand-receptor expression patterns in human atherosclerotic plaques. Human atherosclerotic plaque single-cell RNA sequencing data from GSE260657 and bulk transcriptomic data from GSE28829 were analyzed. After quality control, 7628 cells were retained for single-cell analysis. Major cell types were annotated using canonical markers, followed by reclustering of macrophages and VSMC-related cells. Functional module scoring, differential expression analysis, Gene Ontology biological process enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed to characterize macrophage transcriptional states. Slingshot was applied to infer VSMC pseudotime ordering. CellChat and NicheNet were used to prioritize candidate ligand-receptor expression patterns and ligand-associated VSMC target gene programs. External bulk transcriptomic analysis was performed to examine whether single-cell-derived inflammatory and remodeling signatures were represented at the tissue-transcriptome level during plaque progression. Macrophage reclustering identified a highly inflammatory macrophage state characterized by prominent inflammatory activation, cytokine-response, and stress-response features. Genes upregulated in this population were enriched in pathways related to tumor necrosis factor (TNF) response, nuclear factor kappa B signaling, leukocyte activation, cytokine signaling, lipid and atherosclerosis, toll-like receptor signaling, and inflammasome-associated inflammation. VSMC reclustering revealed contractile VSMCs, PTHLH+&#xa0;synthetic VSMCs, KRT7+ VSMC-like cells, interferon-responsive VSMCs, pericyte-like mural cells, and osteogenic/modulated VSMCs. Pseudotime analysis showed a broad contractile-to-osteogenic/modulated transcriptional continuum accompanied by increased expression of remodeling-associated genes and selected inflammatory or remodeling-associated receptor genes. CellChat and NicheNet analyses prioritized candidate ligand-receptor and ligand-associated target gene expression patterns involving SPP1-CD44, TNF-TNFRSF1A, IL1B-IL1R1/IL1RAP, MIF-ACKR3, PDGFB-PDGFRB, and FN1-SDC1/ITGB1. In GSE28829, inflammatory macrophage-, osteogenic/modulated VSMC-, candidate ligand-receptor expression-, SPP1-CD44 candidate axis-, and NicheNet-prioritized target program-related signatures were more prominent in advanced plaques and were positively correlated with each other. This integrative transcriptomic analysis identified a highly inflammatory macrophage state and a VSMC remodeling continuum in human atherosclerotic plaques. Candidate ligand-receptor and ligand-associated target gene expression patterns linked inflammatory macrophage activation with osteogenic/modulated VSMC remodeling at the computational level. External bulk data further showed coordinated enrichment of inflammatory and remodeling signatures in advanced plaques. These findings provide a descriptive and hypothesis-generating transcriptomic framework for understanding inflammatory macrophage activation and VSMC remodeling in human atherosclerosis.

atherosclerosis↗
PubMed2005

VAMPIRE microarray suite: a web-based platform for the interpretation of gene expression data.

Microarrays are invaluable high-throughput tools used to snapshot the gene expression profiles of cells and tissues. Among the most basic and fundamental questions asked of microarray data is whether individual genes are significantly activated or repressed by a particular stimulus. We have previously presented two Bayesian statistical methods for this level of analysis, collectively known as variance-modeled posterior inference with regional exponentials (VAMPIRE). These methods each require a sophisticated modeling step followed by integration of a posterior probability density. We present here a publicly available, web-based platform that allows users to easily load data, associate related samples and identify differentially expressed features using the VAMPIRE statistical framework. In addition, this suite of tools seamlessly integrates a novel gene annotation tool, known as GOby, which identifies statistically overrepresented gene groups. Unlike other tools in this genre, GOby can localize enrichment while respecting the hierarchical structure of annotation systems like Gene Ontology (GO). By identifying statistically significant enrichment of GO terms, Kyoto Encyclopedia of Genes and Genomes pathways, and TRANSFAC transcription factor binding sites, users can gain substantial insight into the physiological significance of sets of differentially expressed genes. The VAMPIRE microarray suite can be accessed at http://genome.ucsd.edu/microarray.

Bayes Theorem↗
PubMed2006

A hidden Markov model approach for determining expression from genomic tiling micro arrays.

BACKGROUND: Genomic tiling micro arrays have great potential for identifying previously undiscovered coding as well as non-coding transcription. To-date, however, analyses of these data have been performed in an ad hoc fashion. RESULTS: We present a probabilistic procedure, ExpressHMM, that adaptively models tiling data prior to predicting expression on genomic sequence. A hidden Markov model (HMM) is used to model the distributions of tiling array probe scores in expressed and non-expressed regions. The HMM is trained on sets of probes mapped to regions of annotated expression and non-expression. Subsequently, prediction of transcribed fragments is made on tiled genomic sequence. The prediction is accompanied by an expression probability curve for visual inspection of the supporting evidence. We test ExpressHMM on data from the Cheng et al. (2005) tiling array experiments on ten Human chromosomes. Results can be downloaded and viewed from our web site. CONCLUSION: The value of adaptive modelling of fluorescence scores prior to categorisation into expressed and non-expressed probes is demonstrated. Our results indicate that our adaptive approach is superior to the previous analysis in terms of nucleotide sensitivity and transfrag specificity.

Algorithms↗
PubMed2000

Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genome-scale pathway analysis.

The annotated full DNA sequence is becoming available for a growing number of organisms. This information along with additional biochemical and strain-specific data can be used to define metabolic genotypes and reconstruct cellular metabolic networks. The first free-living organism for which the entire genomic sequence was established was Haemophilus influenzae. Its metabolic network is reconstructed herein and contains 461 reactions operating on 367 intracellular and 84 extracellular metabolites. With the metabolic reaction network established, it becomes necessary to determine its underlying pathway structure as defined by the set of extreme pathways. The H. influenzae metabolic network was subdivided into six subsystems and the extreme pathways determined for each subsystem based on stoichiometric, thermodynamic, and systems-specific constraints. Positive linear combinations of these pathways can be taken to determine the extreme pathways for the complete system. Since these pathways span the capabilities of the full system, they could be used to address a number of important physiological questions. First, they were used to reconcile and curate the sequence annotation by identifying reactions whose function was not supported in any of the extreme pathways. Second, they were used to predict gene products that should be co-regulated and perhaps co-expressed. Third, they were used to determine the composition of the minimal substrate requirements needed to support the production of 51 required metabolic products such as amino acids, nucleotides, phospholipids, etc. Fourth, sets of critical gene deletions from core metabolism were determined in the presence of the minimal substrate conditions and in more complete conditions reflecting the environmental niche of H. influenzae in the human host. In the former case, 11 genes were determined to be critical while six remained critical under the latter conditions. This study represents an important milestone in theoretical biology, namely the establishment of the first extreme pathway structure of a whole genome.

Genome, Bacterial↗
PubMed2026

Whole-genome sequencing and characterization of Pseudomonas stutzeri P1 endophyte isolated from potato unveils plant growth-promoting and other traits.

Endophytic bacteria play an important role in plant growth promotion and stress tolerance, offering sustainable alternatives to chemical inputs in agriculture. In this study, an endophytic bacterial strain P1 was isolated and identified as Pseudomonas stutzeri, a plant-associated bacterium exhibiting multiple plant growth-promoting traits (PGPTs). Biochemical (qualitative and quantitative) and in vitro analyses demonstrated nitrogen fixation, phosphate solubilization, ammonia production, indole-3-acetic acid (IAA) production, biofilm formation, and tolerance to abiotic stresses, including salinity and drought. Furthermore, the P1 strain displayed strong biocontrol activity against the fungal pathogen Fusarium oxysporum f. sp. cumini, indicating its potential to mitigate biotic stress. Whole-genome sequencing generated a high-quality complete genome of 4,758,235 bp. Functional annotation showed enrichment of metabolic pathways associated with plant-microbe interactions and environmental adaptation. Further analyses using KEGG and PGPT-pred data confirmed the presence of genes associated with direct and indirect PGPT, such as nitrogen fixation, phosphate solubilization, biofilm formation, and stress tolerance. The genome also contained genes related to CAZymes, adhesion, and motility, highlighting a strong plant association, whereas the genome lacked major virulence factors and antimicrobial traits, supporting the non-pathogenic nature of the P1 strain. Overall, these findings demonstrate the potential of P1 as a promising bioinoculant candidate for sustainable agriculture in the potato sector.

PGPT-associated genes↗