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At least 343 records · Page 19Linked to original sources
PubMed2004

Functional genomics tools for the analysis of zebrafish pigment.

Genetic model organisms are increasingly valuable in the post-genomics era to provide a basis for comparative analysis of the human genome. For higher order processes of vertebrate pigment cell biology and development, the mouse has historically been the model of choice. A complementary organism, the zebrafish (Danio rerio), shares many of the signaling and biological processes of vertebrates, e.g. neural crest development. The zebrafish has a number of characteristics that make it an especially valuable model for the study of pigment cell biology and disease. Large-scale genetic screens have identified a collection of pigmentation mutants that have already made valuable contributions to pigment research. An increasing repertoire of genomic resources such as an expressed sequence tag-based Gene Index (The Institute for Genomic Research) and improving methods of mutagenesis, transgenesis, and gene targeting make zebrafish a particularly attractive model. Morpholino phosphorodiamidate oligonucleotide (MO) 'knockdown' of pigment gene expression provides a non-conventional antisense tool for the analysis of genes involved in pigment cell biology and disease. In addition, an ongoing, reverse-genetic, MO-based screen for the rapid identification of gene function promises to be a valuable complement to other high-throughput microarray and proteomic approaches for understanding pigment cell biology. Novel reagents for zebrafish transgenesis, such as the Sleeping Beauty transposon system, continue to improve the capacity for genetic analysis in this system and ensure that the zebrafish will be a valuable genetic model for understanding a variety of biological processes and human diseases for years to come.

Animals↗
PubMedDate not supplied

Computational systems analysis of developmental toxicity: design, development and implementation of a Birth Defects Systems Manager (BDSM).

Birth defects and developmental disabilities remain an important public health issue worldwide. With the availability of genomic sequences from a growing number of human and model organisms and the rapid expansion of the public repositories holding large-scale gene expression datasets, a computational systems analysis of developmental toxicology can incorporate this vast digital information toward the realization of predictive models for complex disease. Here we describe the initial design, development and implementation of a Birth Defects Systems Manager (BDSM). The project was motivated by the need for a computational-bioinformatics infrastructure to manage vast digital information from functional genomics and for a new knowledge environment specifically engineered for the analysis of developmental processes and toxicities. Proof-of-concept tested BDSM using meta-analysis of gene expression data collected from different laboratories, technology platforms, and study models. The composite dataset incorporated 232 microarray comparisons of RNA samples by single or dual microarray platforms, cDNA or oligonucleotide based probes, and human or mouse sequence information. Preliminary results identified system-level features in the embryonic transcriptome as it reacted to various developmental-teratological stimuli. BDSM is open access through the worldwide web (http://systemsanalysis.louisville.edu/) and can be integrated with other bioinformatics tools and resources to advance the pace of discovery in birth defects research.

Animals↗
PubMedDate not supplied

The macro-ethics of genomics to health: the physiome project.

Advances in pharmacology and genomics, and their intervention in human biology are beyond our abilities to understand their consequences. Therapeutic intervention in highly complex, non-linear, adaptive biological systems results in some unforeseen and undesirable consequences. To do the most good with the least harm, the information on biological systems should be gathered into databases, and into comprehensive quantitative models that can help to predict the long-range effects of proposed interventions. This is a societal or professional macro-ethical imperative. The Physiome Project helps to meet this imperative via databasing and creating models and tools for large-scale integration.

Computational Biology↗
PubMed2005

MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes.

BACKGROUND: Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity. RESULTS: We describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method. CONCLUSION: The search engine, available at http://mapper.chip.org, allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation.

Algorithms↗
PubMed2025

Leveraging protein language models for cross-variant CRISPR/Cas9 sgRNA activity prediction.

MOTIVATION: Accurate prediction of single-guide RNA (sgRNA) activity is crucial for optimizing the CRISPR/Cas9 gene-editing system, as it directly influences the efficiency and accuracy of genome modifications. However, existing prediction methods mainly rely on large-scale experimental data of a single Cas9 variant to construct Cas9 protein (variants)-specific sgRNA activity prediction models, which limits their generalization ability and prediction performance across different Cas9 protein (variants), as well as their scalability to the continuously discovered new variants. RESULTS: In this study, we proposed PLM-CRISPR, a novel deep learning-based model that leverages protein language models to capture Cas9 protein (variants) representations for cross-variant sgRNA activity prediction. PLM-CRISPR uses tailored feature extraction modules for both sgRNA and protein sequences, incorporating a cross-variant training strategy and a dynamic feature fusion mechanism to effectively model their interactions. Extensive experiments demonstrate that PLM-CRISPR outperforms existing methods across datasets spanning seven Cas9 protein (variants) in three real-world scenarios, demonstrating its superior performance in handling data-scarce situations, including cases with few or no samples for novel variants. Comparative analyses with traditional machine learning and deep learning models further confirm the effectiveness of PLM-CRISPR. Additionally, motif analysis reveals that PLM-CRISPR accurately identifies high-activity sgRNA sequence patterns across diverse Cas9 protein (variants). Overall, PLM-CRISPR provides a robust, scalable, and generalizable solution for sgRNA activity prediction across diverse Cas9 protein (variants). AVAILABILITY AND IMPLEMENTATION: The source code can be obtained from https://github.com/CSUBioGroup/PLM-CRISPR.

CRISPR-Cas Systems↗
PubMed2006

Multilocus linkage disequilibrium mapping of epistatic quantitative trait loci that regulate HIV dynamics: a simulation approach.

The time-dependent change of HIV particle load, i.e. HIV dynamics, is likely to be controlled by a multitude of quantitative trait loci (QTL) that interact with each other as well as with various developmental and environmental factors in a coordinated manner. In this article, we have derived a new statistical model for mapping the epistatic QTL responsible for HIV dynamics in a natural human population. This model, constructed on the integrated theme of functional mapping and linkage disequilibrium (LD) mapping, can make use of information from multiple markers genotyped from the human genome. It allows for the test and estimation of genetic actions and interactions involved in the control of HIV progression and provides a general platform to identify the detailed genetic architecture of resistance or susceptibility of humans to HIV on a dynamic scale. We have generalized this model to accommodate various complicated clincal designs for AIDS studies. Simulation studies with different scenarios are performed to examine the statistical behaviour of the model. The genetic and statistical extensions of this mapping model to HIV/AIDS genomic research are discussed.

Computer Simulation↗
PubMed2006

Knockout mouse models to study Wnt signal transduction.

Wnt signal transduction is crucial for maintaining the balance between proliferation and differentiation throughout embryogenesis and postnatal life. Here, we provide a comprehensive overview of the conventional knockout mouse studies of both the canonical and the noncanonical Wnt pathways during mammalian development. Many of these knockout mice display early embryonic lethality, underscoring the fundamental importance of Wnt signal transduction, but precluding functional analyses at later stages. Use of conditional or inducible mouse models will enable us to study the role of Wnt signaling during later stages of development and adult life. Furthermore, genomic-scale approaches and advanced imaging techniques could provide a means to start dissecting the mechanism behind the observed phenotypes.

Alleles↗
PubMed2006

Additive genetic variation and the distribution of QTN effects among sites.

Early quantitative genetic theories emphasize the distribution of gene effects at individual loci or the distribution of mutant effects in maintaining quantitative genetic variation, but infrequently stress the distribution of gene effects among loci. In this study, we analyse the effects of the distribution of quantitative trait nucleotides (QTN) effects among sites under artificial and stabilizing selection. Wright's formula is applied to describing the density distribution of allele frequencies of multiple diallelic QTN at the equilibrium of mutation-drift-selection. Our results demonstrate that the distribution of QTN effects among sites can affect additive genetic variation in terms of total additive variance, average gene diversity, per-class contribution of QTN effects and per-QTN contribution. When the distribution of QTN effects among sites is changed from L-shaped to bell-shaped or to be a flatter, both the total additive variance and the average gene diversity are changed. Per-class and per-QTN contributions exhibit different distribution patterns. The L-shaped distribution indicates the predominant role of the aggregative effects from the QTN of small finite effects. The bell-shaped or flatter distributions indicate the predominance of the QTN of intermediate and large effects. These predictions highlight the significance of the distribution of QTN effects among sites in interpreting the maintenance of quantitative genetic variation at the fine genome scale.

Animals↗
PubMed2025

In silico generation of synthetic cancer genomes using generative AI.

Understanding how genomic alterations drive cancer is key to advancing precision oncology. To detect these alterations, accurate algorithms are used; however, due to privacy concerns, few deeply sequenced cancer genomes can be shared, limiting benchmarking and representing a major obstacle to the improvement of analytic tools. To address this, we developed OncoGAN, a generative AI model combining adversarial networks and variational autoencoders to create realistic synthetic cancer genomes. Trained on large-scale genomic datasets, OncoGAN accurately reproduces somatic mutations, copy number alterations, and structural variants across cancer types while preserving donors' privacy. The synthetic genomes reflect tumor-specific mutational signatures and positional mutation patterns. Using DeepTumour, we validated the synthetic data's fidelity, showing high concordance between generated and predicted tumors. Moreover, augmenting the training data with synthetic genomes improved DeepTumour's accuracy, underscoring OncoGAN's potential to generate shareable datasets with known ground truths for benchmarking and enhancement of cancer genome analysis tools.

Humans↗
PubMed2026

Genetic screening of children for familial hypercholesterolaemia: the VRONI study.

BACKGROUND AND AIMS: The role of genetic testing as part of universal screening programmes for familial hypercholesterolaemia (FH) in children is not well defined. Here, a two-step approach to identify children carrying FH-causing variants was investigated. METHODS: In this study from Southern Germany, paediatricians were invited to offer FH screening to all children aged 4.8-14.9 years at routine paediatric examinations. The FH screening programme began in September 2020 in Bavaria and has involved up to 480 paediatricians. It included biochemical and genetic testing using 0.2 mL of blood taken from a fingertip. In case of low-density lipoprotein cholesterol (LDL-C) serum concentration ≥3.36 mmol/L (≥130 mg/dL), FH-causing variants were determined in the same sample with a focused panel covering most frequent variants (n = 48) and sequencing of relevant genes. RESULTS: Out of 25 431 children screened so far, 1689 children had an LDL-C ≥ 3.36 mmol/L (>130 mg/dL), which defined this concentration as the 93rd percentile. Pathogenic variants were identified by the focused panel in 157 and by next-generation sequencing in 283 children, respectively. While 17% (283/1670) of all genetically analysed children tested positive, the fraction of individuals with FH-causing variants increased across the spectrum of LDL-C serum concentrations from 4.7% (23/492) at 3.36-3.49 mmol/L (130-135 mg/dL) to 78.6% (81/103) above 5.17 mmol/L (200 mg/dL). Overall, the prevalence of FH-causing variants was high (1:90). One reason was a founder variant (n = 63) within the LDLR gene, found 40 times more frequent than European average. The analysis of recruitment data revealed significant ascertainment bias, with lower recruitment rate practices exhibiting higher prevalence. After adjustment for the bias using a generalized linear mixed model, the predicted prevalence was 1 in 163 (0.61%), which is highly consistent with large-scale genomic benchmarks as gnomAD (1:165, n = 622 057) and the UK Biobank (1:176, n = 48 741). CONCLUSIONS: The prevalence of FH determined in this study is significantly higher than previously published estimates (∼1:250), highlighting the importance of this condition for public health and supporting calls for a national paediatric screening programme, given the availability of effective treatment options. For children between 5 and 15 years, biochemical screening is an effective way to select patients for genetic testing, with sequencing of candidate genes being superior to variant screening. In summary, the VRONI study demonstrates the feasibility and efficacy of a combined biochemical and genetic screening for FH in children.

Humans↗
PubMed2026

A pangenome framework uncovers the role of deletions in repeated evolution of cave-derived traits.

Structural variants (SVs) are increasingly recognized as key contributors to adaptive evolution, yet they remain underexplored compared with single-nucleotide variation. To understand how large-scale genomic changes shape repeated evolution, we leveraged multiple levels of sequence data across the powerful evolutionary model system of the Mexican tetra fish (Astyanax mexicanus). We constructed one of the first pangenome graphs from a naturally evolving vertebrate, enabling comprehensive discovery of SVs among 120 fish from 11 populations. We discover substantial amounts of structural variation and explore the roles of genomic biases and selection in shaping the distribution of these variants. More than 2400 high-confidence cave-specific deletions are enriched in biological pathways involved in vision, metabolism, and behavior and cluster nonrandomly in quantitative trait loci linked to cavefish traits. Additionally, 67 genes harbor unique deletions between independent cavefish lineages. These reused genes show evidence of population-specific selection (99% contain selective sweeps compared with 8%-15% in genes lacking SVs), indicating that deletions likely rose in frequency through repeated positive selection rather than drift. Together, these results reveal that recurrent deletion events have repeatedly contributed to the evolution of cave-adapted phenotypes and highlight deletions as underexplored contributors of adaptive evolution in extreme environments.

Animals↗
PubMed2001

Molecular evolution of receptor-like kinase genes in hexaploid wheat. Independent evolution of orthologs after polyploidization and mechanisms of local rearrangements at paralogous loci.

Hexaploid wheat is a young polyploid species and represents a good model to study mechanisms of gene evolution after polyploidization. Recent studies at the scale of the whole genome have suggested rapid genomic changes after polyploidization but so far the rearrangements that have occurred in terms of gene content and organization have not been analyzed at the microlevel in wheat. Here, we have isolated members of a receptor kinase (Lrk) gene family in hexaploid and diploid wheat, Aegilops tauschii, and barley (Hordeum vulgare). Phylogenetic analysis has allowed us to establish evolutionary relationships (orthology versus paralogy) between the different members of this gene family in wheat as well as with Lrk genes from barley. It also demonstrated that the sequences of the homoeologous Lrk genes evolved independently after polyploidization. In addition, we found evidence for gene loss during the evolution of wheat and barley. Analysis of large genomic fragments isolated from nonorthologous Lrk loci showed a high conservation of the gene content and gene organization at these loci on the homoeologous group 1 chromosomes of wheat and barley. Finally, sequence comparison of two paralogous fragments of chromosome 1B showed a large number of local events (sequence duplications, deletions, and insertions), which reveal rearrangements and mechanisms for genome enlargement at the microlevel.

Base Sequence↗
PubMed2004

Reconstructing genetic networks from time ordered gene expression data using Bayesian method with global search algorithm.

Different genes of an organism are expressed to different levels at different times during the life cycle and in response to various environmental stresses. Elucidating the network of gene-gene interactions responsible for the expression helps understand living processes. Microarray technology allows concurrent genomic scale measurement of an organism's mRNA levels. We describe a power-law formalism to model the combinatorial effect of regulators on gene transcription. The dynamic model allows delayed transcription. We employ a principled network reconstruction approach that accounts for the high noise and low replicate characteristics of present day microarray data. An important feature of our approach is that the detail of the reconstructed network is limited to the noise level of the data. We apply the methodology to a microarray dataset of yeast cells grown in glucose and experiencing a diauxic transition upon glucose depletion. The reconstructed transcriptional regulations of yeast glycolytic genes are consistent with published findings.

Algorithms↗
PubMed2006

Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN) models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array) with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes) are explicitly disfavored in one network module (G2), relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of MBF sites in G1 class genes.

Artificial Intelligence↗
PubMed2000

Large-scale methylation analysis of human genomic DNA reveals tissue-specific differences between the methylation profiles of genes and pseudogenes.

Cytosine in CpG dinucleotides is frequently found to be methylated in the DNA of higher eukaryotes and differential methylation has been proposed to be a key element in the organization of gene expression in man. To address this question systematically, we used bisulfite genomic sequencing to study the methylation patterns of three X-linked genes and one autosomal pseudogene in two adult individuals and across nine different tissues. Two of the genes, SLC6A8 and MSSK1, are tissue-specifically expressed. CDM is expressed ubiquitously. The pseudogene, psi SLC6A8, is exclusively expressed in the testis. The promoter regions of the SLC6A8, MSSK1 and CDM genes were found to be essentially unmethylated in all tissues, regardless of their relative expression level. In contrast, the pseudogene psi SLC6A8 shows high methylation of the CpG islands in all somatic tissues but complete demethylation in testis. Methylation profiles in different tissues are similar in shape but not identical. The data for the two investigated individuals suggest that methylation profiles of individual genes are tissue specific. Taken together, our findings support a model in which the bodies of the genes are predominantly methylated and thus insulated from the interaction with DNA-binding proteins. Only unmethylated promoter regions are accessible for binding and interaction. Based on this model we propose to use DNA methylation studies in conjunction with large-scale sequencing approaches as a tool for the prediction of cis-acting genomic regions, for the identification of cryptic and potentially active CpG islands and for the preliminary distinction of genes and pseudogenes.

Aged↗
PubMed2026

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs.

MOTIVATION: Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. RESULTS: We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. AVAILABILITY AND IMPLEMENTATION: Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Genome-Wide Association Study↗
PubMed2004

Molecular networks in model systems.

Model organisms, especially the budding yeast, are leading systems in the transformation of biology into an information science. With the availability of genome sequences and genome-scale data generation technologies, the extraction of biological insight from complex integrated molecular networks has become a major area of research. Here I examine key concepts and review research developments. I propose specific areas of research effort to drive network analysis in directions that will promote modeling with increasing predictive power.

Computational Biology↗
PubMed2001

Shortcuts in genome-scale cancer pharmacology research from multivariate analysis of the National Cancer Institute gene expression database.

Application of a soft multivariate statistical procedure, called PLS, partial least squares modelling in latent variables or projections to latent structures, allows extensive exploitation of the enormous amount of information embedded in the National Cancer Institute gene expression and antitumour screen databases. Interpretation of the statistical results provides new significant biological insights such as classification of human tumour cell lines based on their gene expression patterns, evaluation of the influence of gene transcripts on drug efficacy and assessment of their selectivity for classes of compounds which act by the same mechanism, and identification of uncharacterized gene expression targets involved in cancer chemotherapy. Among them, the transcripts GC11121, GC17689, and GC18564 (unknown gene products extremely selective for RNA/DNA antimetabolites) are indicated by the present work as deserving high priority in future molecular studies.

Databases, Factual↗