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PubMed2002

Substance abuse vulnerability loci: converging genome scanning data.

Classical genetic studies suggest strong complex genetic contributions to a predisposition to abuse multiple addictive substances. Until recently, there were no reproducible genome scanning data identifying chromosomal positions likely to contain allelic variants that predispose the carrier to illegal substance addiction. Nominal results of linkage-based genome scanning studies for ethanol and nicotine addictions failed to display much agreement. Our recent data from association-based genome scans for illegal addictions, and reanalyses of previous results now provide a substantial body of converging results. The 15 reproducible chromosomal loci identified here are good candidates to harbor allelic variants that alter human substance abuse vulnerabilities. We discuss several approaches to identifying the specific gene variants that underlie these convergent association and linkage observations, and the impact that these convergent observations should have on understanding important human addictive disorders.

Chromosome Mapping↗
PubMed2002

Fast phylogenetic methods for the analysis of genome rearrangement data: an empirical study.

Evolution operates on whole genomes through mutations that change the order and strandedness of genes within the genomes. Thus analyses of gene-order data present new opportunities for discoveries about deep evolutionary events, provided that sufficiently accurate methods can be developed to reconstruct evolutionary trees. In this paper we present two new methods of character coding for parsimony-based analysis of genomic rearrangements: one called MPBE-2, and a new parsimony-based method which we call MPME (based on an encoding of Bryant), both variants of the MPBE method. We then conduct computer simulations to compare this class of methods to distance-based methods (NJ under various distance measures). Our empirical results show that two of our new methods return highly accurate estimates of the true tree, outperforming the other methods significantly, especially when close to saturation.

Animals↗
PubMed2022

A deep learning approach to real-time HIV outbreak detection using genetic data.

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

Humans↗
PubMed2025

Extracting and calibrating evidence of variant pathogenicity from population biobank data.

Genomic medicine requires a robust evidence base of variant phenotypic impacts, which remains incomplete even in extensively studied genes with monogenic disease associations. Here, we evaluated the broad potential of using population cohort data to identify evidence that can be used in variant assessment. Across 41 genes related to 18 clinically actionable monogenic phenotypes, we calculated variant-level odds ratios of disease enrichment using data from 469,803 UK Biobank participants. We found significant differences in odds ratio values between ClinVar-labeled pathogenic and benign variants in 11 phenotypes, spanning both common and rare disorders. To facilitate clinical translation, we calibrated the strength of evidence provided by variant-level odds ratios to align with American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) interpretation guidelines (PS4 criterion) and found that odds ratios may reach "moderate," "strong," or "very strong" evidence, varying by phenotype and gene. Overall, we found that 2.6% (N = 12,350) of participants harbor a rare variant of uncertain significance (VUS) with at least moderate evidence of pathogenicity-an indication of potentially unrecognized disease risk. Finally, by incorporating computational and functional data alongside population-based odds ratios, we identified variants that met the criteria for clinical reclassification. Notably, using this approach, we identified that 12.4% of rare VUSs in LDLR seen in participants meet diagnostic criteria to be classified as likely pathogenic, demonstrating its potential to scale the reclassification of VUSs.

Humans↗
PubMed2003

GIMS: an integrated data storage and analysis environment for genomic and functional data.

Effective analyses in functional genomics require access to many kinds of biological data. For example, the analysis of upregulated genes in a microarray experiment might be aided by information concerning protein interactions or proteins' cellular locations. However, such information is often stored in different formats at different sites, in ways that may not be amenable to integrated analysis. The Genome Information Management System (GIMS) is an object database that integrates genomic data with data on the transcriptome, protein-protein interactions, metabolic pathways and annotations, such as gene ontology terms and identifiers. The resulting system supports the running of analyses over this integrated data resource, and provides comprehensive facilities for handling and interrelating the results of these analyses. GIMS has been used to store Saccharomyces cerevisiae data, and we demonstrate how the integrated storage of diverse types of data can be beneficial for analysis, using combinations of complex queries. As an example, we describe how GIMS has been used to analyse a collection of aryl alcohol dehydrogenase gene deletion mutants. The GIMS database can be accessed remotely using a Java application that can be downloaded from http://img.cs.man.ac.uk/gims.

Computational Biology↗
PubMed2003

Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction.

Genomic data, particularly genome-scale measures of gene expression derived from DNA microarray studies, has the potential for adding enormous information to the analysis of biological phenotypes. Perhaps the most successful application of this data has been in the characterization of human cancers, including the ability to predict clinical outcomes. Nevertheless, most analyses have used gene expression profiles to define broad group distinctions, similar to the use of traditional clinical risk factors. As a result, there remains considerable heterogeneity within the broadly defined groups and thus predictions fall short of providing accurate predictions for individual patients. One strategy to resolve this heterogeneity is to make use of multiple gene expression patterns that are more powerful in defining individual characteristics and predicting outcomes than any single gene expression pattern. Statistical tree-based classification systems provide a framework for assessing multiple patterns, that we term metagenes, selecting those that are most capable of resolving the biological heterogeneity. Moreover, this framework provides a mechanism to combine multiple forms of data, both genomic and clinical, to most effectively characterize individual patients and achieve the goal of personalized predictions of clinical outcomes.

Breast Neoplasms↗
PubMed2005

Denoising array-based comparative genomic hybridization data using wavelets.

Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number simultaneously at thousands of genomic loci. Typically, these measurements are reported and displayed linearly on chromosome maps, and gains and losses are detected as deviations from normal diploid cells. We propose that one may consider denoising the data to uncover the true copy number changes before drawing inferences on the patterns of aberrations in the samples. Nonparametric techniques are particularly suitable for data denoising as they do not impose a parametric model in finding structures in the data. In this paper, we employ wavelets to denoise the data as wavelets have sound theoretical properties and a fast computational algorithm, and are particularly well suited for handling the abrupt changes seen in array-CGH data. A simulation study shows that denoising data prior to testing can achieve greater power in detecting the aberrant spot than using the raw data without denoising. Finally, we illustrate the method on two array-CGH data sets.

Breast Neoplasms↗
PubMed2004

A new approach that allows identification of intron-split peptides from mass spectrometric data in genomic databases.

We present a new approach that allows the identification of intron-split peptides from mass spectrometric data in genomic databases. Our algorithm uses small regions of peptide sequence information which are automatically deduced from de novo amino acid sequence predictions together with the molecular mass information of the precursor ion. The sequence predictions are based on selected collision-induced mass spectrometric fragmentation spectra. Fragments of the predicted amino acid sequence are aligned with each of the six frames of the translated genome and the precursor mass information is used to assemble the corresponding tryptic peptides using the sequence as a matrix. Hereby, intron-split peptides can be gathered and in turn verified by mass spectrometric data interpretation tools such as Sequest.

Algorithms↗
PubMed2005

Localization of human X chromosomal mental retardation (MRX) genes in chicken and comparison with the chicken genome sequence data.

In an ongoing study human X chromosomal mental retardation genes (MRX) were mapped in the chicken genome. Up to now the homologs of 13 genes were localized by FISH techniques. Four genes from HSAXp (TM4SF2, RSK2/RPS6KA3, NLGN4, ARX) map to GGA1q13-->q31, and seven genes from HSAXq (OPHN1, AGTR2, ARHGEF6, PAK3, FACL4/ACS4, FMR2, ATRX) to GGA4p. The gene-rich region of HSAXq28 proved to be much less conserved. GDI1 localized to GGA1pter and SLC6A8 to a mid-sized microchromosome. The order of the genes was determined from the newly available genome sequence data from chicken, which reveals exact colinearity between the genes in HSAXp and GGA1q13-->q31, but completely scrambled gene order between the genes with common synteny from HSAXq and GGA4p. This result supports the hypothesis that the human X chromosome is a real ancient autosomal linkage group.

Animals↗
PubMed2004

Information assessment on predicting protein-protein interactions.

BACKGROUND: Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. RESULTS: Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. CONCLUSIONS: In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO) functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the Bayesian methods decreased classification performance.

Artificial Intelligence↗
PubMed2001

Rates of movement and distribution of transposable elements in Drosophila melanogaster: in situ hybridization vs Southern blotting data.

Genomic copy numbers and the rates of movement of nine families of transposable elements (TEs) of Drosophila melanogaster were estimated in two sets of mutation accumulation lines: Beltsville and Madrid. Southern blotting was used to screen a large number of samples from both genetic backgrounds for TEs. The Madrid lines were also screened by in situ hybridization of TEs to polytene chromosomes, in order to obtain more detailed information about the behaviour of TEs in the euchromatin. Southern blotting data provided evidence of insertions and excision events in both genetic backgrounds, occurring at rates of approximately 10(-5) and 10(-6) per element copy per generation, respectively. In contrast, in situ data from the Madrid background presented a completely different picture, with no evidence for excisions, and a significantly higher rate of transposition (1.01 x 10(-4)). Direct comparison of the two data sets suggests that the Southern blotting technique had serious deficiencies: (i) it underestimated element abundance; (ii) it revealed less than 30% of the new insertions detected by in situ hybridization; and (iii) changes in the size of restriction fragments from any source were spuriously identified as simultaneous insertion-excision events. Our in situ data are consistent with previous studies, and suggest that selection is the main force controlling element spread by transposition.

Animals↗
PubMed2026

Whole genome sequence data set of methicillin-resistant Staphylococcus aureus isolated from a milkman associated with cows with subclinical mastitis in Kiruhura district, Uganda.

The whole-genome sequence data set for methicillin-resistant Staphylococcus aureus, which was isolated from a milkman associated with cows with subclinical mastitis in the Kiruhura district of Uganda, is presented here. The assembled genome size was 2822,509 bp, with a 33% GC, 2 Contigs, a Contig N50 of 2818,424, and 1 Contig L50. You can access the genome sequence and related metadata at https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_056782255.1/. This dataset can be used again for resistance gene mapping, comparing genomic analysis, and comprehending genetic diversity among MRSA isolates from Ugandan milkmen.

Antimicrobial-resistant genes Staphylococcus aureu↗
PubMed2004

Integrative analysis of genome-scale data by using pseudoinverse projection predicts novel correlation between DNA replication and RNA transcription.

We describe an integrative data-driven mathematical framework that formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the "basis" set. By using pseudoinverse projection, the molecular biological profiles of the data samples are least-squares-approximated as superpositions of the basis profiles. Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis and gives a global picture of the correlations and possibly also causal coordination of these two sets of states. We illustrate this framework with an integration of yeast genome-scale proteins' DNA-binding data with cell cycle mRNA expression time course data. Novel correlation between DNA replication initiation and RNA transcription during the yeast cell cycle, which might be due to a previously unknown mechanism of regulation, is predicted.

Cell Cycle↗
PubMed2026

An alignment-free strategy for circulating tumor DNA detection and tumor fraction estimation from whole-genome sequencing data.

Circulating tumor DNA (ctDNA) is emerging as a promising biomarker for postoperative monitoring of cancer patients. Precise estimation of circulating tumor fraction is crucial for evaluating treatment effects and timely detection of disease recurrence. All current ctDNA detection methods that utilize whole-genome sequencing (WGS) data rely on the reference genome alignment of sequencing reads and often apply separate tools for detecting different variant types. However, various bioinformatic analysis confounders and the application of external variant calling tools could be avoided by analyzing k-mers from unaligned sequencing reads. While k-mer-based methods have successfully been applied for somatic variant validation and detection, the potential of k-mer-based ctDNA detection is unexplored. We have developed a tumor-informed alignment-free ctDNA detection tool called ctDNAmer that detects tumor-specific somatic variation directly from unaligned sequencing data by identifying k-mers unique to the tumor DNA. ctDNAmer detects variant information across the genome by comparing the primary tumor and germline WGS data and accounts for sample-specific germline variability and technical noise in the same framework. We tested the utility of ctDNAmer for tumor fraction estimation on postoperative plasma cfDNA WGS data (mean sequencing depth ~ 28x) from 90 stage III colorectal cancer patients with three years of follow-up. The tumor fraction (TF) estimates agreed with the available clinical information and ctDNA was detected in 77% (17/22) of recurring patients with a median lead time of 8 months compared to radiological imaging. We further validated ctDNAmer's tumor fraction estimates based on a comparison with the mean cfDNA allele frequencies of somatic clonal SNVs identified from aligned primary tumor sequencing data. The TF estimates showed a strong Pearson correlation of 0.897 with the mean allele frequencies and improved ctDNA detection results across samples with an AUC of 0.79 compared to 0.75 if the mean allele frequency of clonal mutations is used.

Circulating Tumor DNA↗
PubMed2026

Menopause in the All of Us Research Program: a descriptive summary of electronic health record and survey response across sociodemographic characteristics.

OBJECTIVES: Menopause is a significant physiological transition with implications for health outcomes (eg, cardiometabolic disease), yet gaps remain in understanding this transition, including how menopause timing and type influence health outcomes. Large-scale cohort studies in midlife (age=40-60) females, including the All of Us Research Program (AoURP), provide opportunities to study menopause across diverse populations and data modalities. We characterized menopause-related data in AoURP, focusing on age distributions and concordance between electronic health record (EHR) diagnosis codes and survey responses. METHODS: We analyzed menopause-related surveys, EHR diagnostic codes, and genomic data among ~396,000 AoURP female participants. We summarized menopause-related variables across data sources, evaluated overlap between survey, EHR, and genomic data sets, and described age distributions overall and across sociodemographic characteristics. RESULTS: Among ~396,000 females, survey responses captured ~193,000 menopause observations, nearly seven times more than EHR diagnoses (~28,000), suggesting under-ascertainment in EHR data. Nearly all females (~99%) with an EHR menopause diagnosis reported menopause in the survey. Approximately 22,000 participants had overlapping menopause-related EHR, survey, and genomic data. Survey age patterns matched expectations, with participants predominantly <40 years reporting premenopausal status and those >60 years reporting postmenopausal status. A small subset with age >70 years (N&#x2248;1,700; 4%) reported no menopause, suggesting response or recall bias. EHR menopause codes were concentrated after age 45 years, with a notable spike at age 65. Modest differences in survey-based menopause age distributions were observed across sociodemographic characteristics (eg, race and ancestry). CONCLUSIONS: These findings inform sampling strategies, power calculations, phenotype definition, and study design for menopause research using AoURP data.

Age↗
PubMed2026

Nuclear single-copy orthologous genes as phylogenomic markers for resolving the closely related firefly genera Pteroptyx, Medeopteryx, and Trisinuata (Coleoptera: Lampyridae: Luciolinae).

Fireflies (Lampyridae) are bioluminescent beetles with broad ecological roles across temperate and tropical ecosystems, occupying diverse habitats including forests, wetlands, grasslands, mangroves, and riverine systems. The subfamily Luciolinae is primarily distributed across Asia and the Indo-Pacific. Phylogenetic relationships among three closely related Luciolinae genera - Medeopteryx, Pteroptyx, and Trisinuata - remain unresolved using mitochondrial genome data alone. This study used nuclear genome data to resolve relationships among these genera and identify a lighter-weight nuclear marker panel for expanding taxon sampling. Draft genomes were reconstructed for fifteen firefly species, eight from the focal genera, and analyzed with five published firefly genomes. Using BUSCO and OrthoFinder, 1,011 nuclear single-copy orthologs (SCOs) were identified for phylogenomic inference. Discordance between concatenation- and coalescence-based phylogenies indicated incomplete lineage sorting (ILS). The coalescence-based phylogeny recoveredPteroptyxas monophyletic and sister to a (Medeopteryx,Trisinuata) clade, with Trisinuata nested within a non-monophyletic Medeopteryx; however, quartet support at the base of Pteroptyx, particularly at Pt. valida, was low.Filtering for compositional homogeneity, clock-likeness, and species-tree concordance yielded 103 SCOs with a significantly higher proportion of parsimony-informative sites than non-selected loci, retaining the backbone topology with higher gene concordance support at scored clades, while ILS-driven discordance at Pt. valida persists - confirming that the reduced panel retains phylogenetic resolving power for future taxon sampling. These findings demonstrate a practical framework for using nuclear SCOs to resolve close phylogenetic relationships within Luciolinae. Future work should expand taxon sampling - especially forTrisinuata - alongside long-read assemblies, for a more robust phylogenomic framework.

Fireflies↗