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PubMed2002

Reliability of transmembrane predictions in whole-genome data.

Transmembrane prediction methods are generally benchmarked on a set of proteins with experimentally verified topology. We have investigated if the accuracy measured on such datasets can be expected in an unbiased genomic analysis, or if there is a bias towards 'easily predictable' proteins in the benchmark datasets. As a measurement of accuracy, the concordance of the results from five different prediction methods was used (TMHMM, PHD, HMMTOP, MEMSAT, and TOPPRED). The benchmark dataset showed significantly higher levels (up to five times) of agreement between different methods than in 10 tested genomes. We have also analyzed which programs are most prone to make mispredictions by measuring the frequency of one-out-of-five disagreeing predictions.

Algorithms↗
PubMed2003

Estimation of the inbreeding coefficient through use of genomic data.

Many linkage studies are performed in inbred populations, either small isolated populations or large populations with a long tradition of marriages between relatives. In such populations, there exist very complex genealogies with unknown loops. Therefore, the true inbreeding coefficient of an individual is often unknown. Good estimators of the inbreeding coefficient (f) are important, since it has been shown that underestimation of f may lead to false linkage conclusions. When an individual is genotyped for markers spanning the whole genome, it should be possible to use this genomic information to estimate that individual's f. To do so, we propose a maximum-likelihood method that takes marker dependencies into account through a hidden Markov model. This methodology also allows us to infer the full probability distribution of the identity-by-descent (IBD) status of the two alleles of an individual at each marker along the genome (posterior IBD probabilities) and provides a variance for the estimates. We simulate a full genome scan mimicking the true autosomal genome for (1) a first-cousin pedigree and (2) a quadruple-second-cousin pedigree. In both cases, we find that our method accurately estimates f for different marker maps. We also find that the proportion of genome IBD in an individual with a given genealogy is very variable. The approach is illustrated with data from a study of demyelinating autosomal recessive Charcot-Marie-Tooth disease.

Charcot-Marie-Tooth Disease↗
PubMed2006

Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier.

MOTIVATION: Most computational methodologies for microRNA gene prediction utilize techniques based on sequence conservation and/or structural similarity. In this study we describe a new technique, which is applicable across several species, for predicting miRNA genes. This technique is based on machine learning, using the Naive Bayes classifier. It automatically generates a model from the training data, which consists of sequence and structure information of known miRNAs from a variety of species. RESULTS: Our study shows that the application of machine learning techniques, along with the integration of data from multiple species is a useful and general approach for miRNA gene prediction. Based on our experiments, we believe that this new technique is applicable to an extensive range of eukaryotes' genomes. Specific structure and sequence features are first used to identify miRNAs followed by a comparative analysis to decrease the number of false positives (FPs). The resulting algorithm exhibits higher specificity and similar sensitivity compared to currently used algorithms that rely on conserved genomic regions to decrease the rate of FPs.

Algorithms↗
PubMed2006

Clinical genomics data standards for pharmacogenetics and pharmacogenomics.

This special report concerns a talk on data standards given at a workshop entitled 'An International Perspective on Pharmacogenetics: The Intersections between Innovation, Regulation and Health Delivery', which was held by the Organization for Economic Co-operation and Development (OECD) on October 17-19, 2005, in Rome, Italy. The worlds of healthcare and life sciences (HCLS) are extremely fragmented in terms of their underlying information technology, making it difficult to semantically exchange information between disparate entities. While we have reached the point where functional interoperability is ubiquitous, we are still far from achieving true semantic interoperability where a receiving system can use incoming data as though it was created internally. The critical enablers of semantic interoperability are information standards dedicated to HCLS data, spanning all the way from biological research data to clinical research and clinical trials, and finally to healthcare clinical data. The challenge lies in integrating various data standards based on predetermined goals, thereby improving the quality of care provided to patients.

Clinical Medicine↗
PubMed2006

Identifying neuropeptide and protein hormone receptors in Drosophila melanogaster by exploiting genomic data.

Most neuropeptide and protein hormone receptors belong to the large superfamily of G-protein-coupled receptors (GPCRs). These cell membrane proteins steer many important processes such as development, reproduction, homeostasis and behaviour when activated by their corresponding ligands. The first insect genome, that of the fruitfly Drosophila melanogaster, was sequenced in 2000, and about 200 GPCRs have been annnotated in this model insect. About 50 of these receptors were predicted to have neuropeptides or protein hormones as their ligands. Since 2000, the cDNAs of most of these candidate receptors have been cloned and for many receptors the endogenous ligand has been identified. In this review, we will give an update about the current knowledge of all Drosophila neuropeptide and protein hormone receptors, and discuss their phylogenetic relationships.

Animals↗
PubMed2003

Comparative analysis of mitochondrial genome data for Necator americanus from two endemic regions reveals substantial genetic variation.

Necator americanus is a blood-sucking, intestinal nematode of major human health importance in many tropical and subtropical regions of the world. The aim of the present study was to compare the complete mitochondrial genome sequence from one N. americanus individual from Togo with another from China, in order to estimate the magnitude of genetic variability for different mitochondrial genes and non-coding regions. For the 12 protein genes, this comparison revealed sequence differences at both the nucleotide (3-7%) and amino acid (1-7%) levels. The most conserved of these was the nad4L gene, whereas the nad1 gene was least conserved at both the nucleotide and amino acid levels. Nucleotide differences were also detected in 14 of the 22 transfer RNAs (trns) (1-13%), the AT-rich region ( approximately 8%), non-coding regions (8-25%) and in the small (rrnS) and large (rrnL) subunits of mitochondrial ribosomal RNA (rrn) ( approximately 1%). Comparison of the rrnL sequences among multiple individual worms revealed nine unequivocal nucleotide differences between N. americanus from the two countries. Consistent with previous studies, these findings provide evidence for substantial genetic variation within N. americanus, which may have implications for the transmission and control of hookworm disease.

Amino Acid Sequence↗
PubMed2026

Unraveling critical role of photosynthetic bacteria in sustaining aquatic microbial community stability and function through large-scale genomic data analyses.

The application of photosynthetic bacteria (PB) in water remediation has demonstrated exceptional advantages in terms of high efficiency and low-carbon benefits. However, the limited understanding of PB across natural aquatic environments has constrained the rational development of this strategy. Here, we analyzed 3198 genomic sequencing samples from seven types of natural aquatic ecosystems to investigate the distribution and functions of 42 PB genera commonly used in water remediation. The results showed that the average abundance of the targeted PB reached 9.83 %, with the highest value of 14.93 % observed in River, while Lake harbored the greatest PB genus diversity. PB genera exhibited high sensitivity to salinity, with Rhodoferax dominating freshwater habitats, whereas Rhodovulum was predominant in marine environments. Notably, co-occurrence network analysis revealed that PB were closely associated with microbial community stability and optimized interspecific interactions. Aquatic microbial communities with high PB abundance were characterized by efficient division-of-labor modules, accompanied by enhanced PB-associated functional potential for carbon fixation, denitrification, and sulfur oxidation. In summary, this study systematically elucidates the regional biogeographical patterns and ecological roles of PB in natural aquatic environments, providing a comprehensive scientific basis and theoretical guidance for the development and practical application of PB-based water remediation technologies.

Bacteria↗
PubMed2003

Improving reliability of gene selection from microarray functional genomics data.

Constructing a classifier based on microarray gene expression data has recently emerged as an important problem for cancer classification. Recent results have suggested the feasibility of constructing such a classifier with reasonable predictive accuracy under the circumstance where only a small number of cancer tissue samples of known type are available. Difficulty arises from the fact that each sample contains the expression data of a vast number of genes and these genes may interact with one another. Selection of a small number of critical genes is fundamental to correctly analyze the otherwise overwhelming data. It is essential to use a multivariate approach for capturing the correlated structure in the data. However, the curse of dimensionality leads to the concern about the reliability of selected genes. Here, we present a new gene selection method in which error and repeatability of selected genes are assessed within the context of M-fold cross-validation. In particular, we show that the method is able to identify source variables underlying data generation.

Algorithms↗
PubMed2005

First genome data from uncultured upland soil cluster alpha methanotrophs provide further evidence for a close phylogenetic relationship to Methylocapsa acidiphila B2 and for high-affinity methanotrophy involving particulate methane monooxygenase.

Members of upland soil cluster alpha (USC alpha) are assumed to be methanotrophic bacteria (MB) adapted to the trace level of atmospheric methane. So far, these MB have eluded all cultivation attempts. While the 16S rRNA phylogeny of USC alpha members is still not known, phylogenies constructed for the active-site polypeptide (encoded by pmoA) of particulate methane monooxygenase (pMMO) placed USC alpha next to the alphaproteobacterial Methylocapsa acidiphila B2. To assess whether the pmoA tree reflects the evolutionary identity of USC alpha, a 42-kb genomic contig of a USC alpha representative was obtained from acidic forest soil by screening a metagenomic fosmid library of 250,000 clones using pmoA-targeted PCR. For comparison, a 101-kb genomic contig from M. acidiphila was analyzed, including the pmo operon. The following three lines of evidence confirmed a close phylogenetic relationship between USC alpha and M. acidiphila: (i) tetranucleotide frequency patterns of 5-kb genomic subfragments, (ii) annotation and comparative analysis of the genomic fragments against all completely sequenced genomes available in public domain databases, and (iii) three single gene phylogenies constructed using the deduced amino acid sequences of a putative prephenate dehydratase, a staphylococcal-like nuclease, and a putative zinc metalloprotease. A comparative analysis of the pmo operons of USC alpha and M. acidiphila corroborated previous reports that both the pmo operon structure and the predicted secondary structure of deduced pMMO are highly conserved among all MB.

Bacteria↗
PubMed2004

Genomic data for alternate production strategies. I. Identification of major contaminating species for Cobalt(+2) immobilized metal affinity chromatography.

Recent advances in technology have allowed for the identification of complex protein mixtures in a rapid fashion. This report highlights the use of 2D gel electrophoresis, mass spectrometry, and database analysis to determine contaminating species of the Escherichia coli genome that are present during immobilized metal affinity chromatography (IMAC), highlighting Co(2+) as the affinity ligand. Four proteins (triosephosphate isomerase, alpha galactosidase, Hsp90, and glucosamine 6-phosphate synthase) constitute the majority of E. coli proteins that bind and potentially may coelute during chromatography. Results are discussed within the context of changes that when implemented could lead to an increase in IMAC efficiency, not by altering column conditions, but rather by changing the nature of the nuisance proteins that principally reduce column capacity and extend processing times. Such a study illustrates the use of proteome data to aid in bioprocess design.

Amino Acid Sequence↗
PubMed2026

Machine learning-based drug susceptibility prediction from Candida genomic data.

OBJECTIVES: Invasive Candida infection is an increasing clinical concern, with antifungal resistance rising across multiple species. However, rapid and accurate antifungal susceptibility testing (AFST) remains limited in routine practice. The study evaluated species distribution and antifungal susceptibility of invasive Candida isolates in China and assessed the feasibility of combining whole-genome sequencing (WGS) with machine learning to predict minimum inhibitory concentrations (MICs). METHODS: Consecutive non-repetitive isolates were collected from 20 hospitals in 13 provinces during 2022-2023. MICs of nine antifungal agents were determined by broth microdilution, and WGS was performed for species accounting for >5% of the total isolates. Genomic 11-mer features were extracted and used to train random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models, followed by optimization of the best-performing algorithm. RESULTS: A total of 337 isolates were obtained from blood (n = 232) and sterile body fluids (n = 105), comprising C. albicans (n = 103), C. tropicalis (n = 71), C. parapsilosis (n = 67), and C. glabrata (n = 63). Non-albicans Candida showed higher azole and echinocandin resistance, with C. tropicalis notably resistant to azoles and C. glabrata to echinocandins. Among the three models, RF demonstrated the best performance on 304 sequenced isolates. The optimized RF model was evaluated by the receiver operating characteristic (ROC) curve analysis and achieved an average area under the ROC curve (AUC) of 0.979 (95% CI: 0.974-0.984), essential agreement over 90.1%, and categorical agreement over 93.2% across species. CONCLUSIONS: These findings underscore the clinical challenge posed by non-albicans Candida resistance, and indicate that WGS-based MIC prediction may offer a highly accurate reference for earlier antifungal therapy.

Antifungal Agents↗
PubMed2004

Using comparative genomic data to test for fast-X evolution.

Genes may acquire nonsynonymous substitutions more rapidly when X-linked than when autosomal, but evidence for "fast-X evolution" has been elusive. Fast-X evolution could explain the disproportionate contribution of X-linked genes to hybrid sterility and other traits. Here, we use a comparative genomic approach, with sequences of 30-110 genes in four Drosophila species, to test for fast-X evolution. Specifically, the 3L autosome arm in D. melanogaster and D. simulans is homologous to the right arm of the X chromosome in D. pseudoobscura and D. miranda. We executed two paired comparisons to determine how often genes on this chromosome arm exhibit higher rates of nonsynonymous substitution in the D. pseudoobscura species group, as predicted by fast-X evolution. We found a statistically significant pattern consistent with fast-X evolution in one comparison and a similar trend in the other comparison. Variation in functional constraints across genes may have masked the signature of fast-X evolution in some previous studies, and we conclude paired comparisons are more powerful for examining rates of evolution of genes when X-linked over autosomal.

Animals↗
PubMed2005

Merging protein, gene and genomic data: the evolution of the MDR-ADH family.

Multiple members of the MDR-ADH (MDR: Medium-chain dehydrogenases/reductases; ADH: alcohol dehydrogenase) family are found in vertebrates, although the enzymes that belong to this family have also been isolated from bacteria, yeast, plant and animal sources. Initial understanding of the physiological roles and evolution of the family relied on biochemical studies, protein alignments and protein structure comparisons. Subsequently, studies at the genetic level yielded new information: the expression pattern, exon-intron distribution, in silico-derived protein sequences and murine knockout phenotypes. More recently, genomic and EST databases have revealed new family members and the chromosomal location and position in the cluster of both the first and new forms. The data now available provide a comprehensive scenario, from which a reliable picture of the evolutionary history of this family can be made.

Alcohol Dehydrogenase↗
PubMed2026

Predicting risk of ischemic stroke: A transformer model using genomic data.

BACKGROUND AND OBJECTIVE: Ischemic stroke is a leading cause of mortality and long-term disability worldwide. Genetic factors contribute to IS susceptibility, yet conventional polygenic risk score approaches are primarily based on additive effects and may not fully capture non-linear relationships or positional context and interactions among genetic variants. This study aimed to develop and evaluate a transformer-based genomic model incorporating position-wise genotype embedding for IS risk prediction. METHODS: We conducted a genome-wide association study using the UK Biobank dataset to identify IS-associated loci. Gene prioritisation was subsequently performed using tissue-specific expression quantitative trait locus-based Mendelian randomisation and colocalization analyses in whole blood and brain cortex. We then developed a transformer-based model that encoded genotype and SNP-position information using a position-wise embedding layer. Model performance was evaluated across three UK Biobank control definitions and externally assessed in the independent All of Us cohort. Performance metrics included the area under the receiver operating characteristic curve (AUROC), precision, recall, and F1 score. RESULTS: Across the three UK Biobank control definitions, the proposed method achieved the numerically highest discrimination among the evaluated models, with AUROCs of 0.8109, 0.7843, and 0.7468 using MRF-negative, combined, and MRF-positive controls, respectively. In the external All of Us cohort, the proposed method achieved an AUROC of 0.7251 and retained the highest AUROC among the evaluated models. In a separate incident-stroke survival analysis, medium- and high-score groups had hazard ratios of 1.13 and 1.21, respectively, relative to the low-score group. A total of 18 IS-associated loci were identified. Among the tissue-specific MR results, EDEM2 in the brain cortex remained significant after Bonferroni correction, while DCHS2 showed a nominal association. CONCLUSIONS: The proposed transformer-based framework provides a genomic modelling approach that achieved the highest discrimination among the evaluated models in this study and retained comparative performance in an independent external cohort. In further applications, integrating this genomic framework with conventional clinical, lifestyle, and environmental risk factors may support more comprehensive and personalised IS risk assessment. Prospective, population-representative, and multi-ancestry validation will be important to establish its potential role in future prevention-oriented risk management.

Genomics and bioinformatics↗