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At least 19 recordsLinked to original sources

Expression of factor VII by muscle cells in vitro and in vivo following direct gene transfer: modelling gene therapy for haemophilia.

Direct injection of plasmid DNA into skeletal muscle has been proposed as a method of effecting somatic gene therapy. This article describes the construction and testing of a plasmid derived expression cassette believed to confer skeletal muscle specific expression. Expression constructs were designed containing the full-length cDNAs for both coagulation factor VIII and factor VII. The engineered genes were flanked by two muscle specific regulatory elements from different myosin isoforms and by an artificial polyadenylation signal sequence. In vitro transfection of C2-myoblasts led to expression of the factor VIII gene, shown by reverse transcription and polymerase chain reaction, upon differentiation of the myoblasts. The expression of the FVII construct was tested in a C2 cell culture system and also when injected directly into mouse muscle. It was found that in cell culture the level of factor VII antigen outside the cell, ie in the cell culture medium was two- to three-fold higher than inside the cell, ie in the cell lysate. This level of expression was found to continue for the duration of cell culture maintenance and a fully functional protein was produced. In vivo transfection experiments in mice showed a substantial increase in factor VII antigen compared with the background level 4-5 days after injection. An anti-human factor VII antibody was detected 7-10 days after injection. We conclude that muscle cells in vitro secrete and efficiently carry out post-translational modifications of the engineered gene product and in vivo secrete the gene product resulting in elevation of systemic levels. The data provide the basis for the use of muscle cells as an in vivo expression system for coagulation proteins in the treatment of inherited haemostatic and thrombotic disorders.

Animals↗

Modeling gene expression networks using fuzzy logic.

Gene regulatory networks model regulation in living organisms. Fuzzy logic can effectively model gene regulation and interaction to accurately reflect the underlying biology. A new multiscale fuzzy clustering method allows genes to interact between regulatory pathways and across different conditions at different levels of detail. Fuzzy cluster centers can be used to quickly discover causal relationships between groups of coregulated genes. Fuzzy measures weight expert knowledge and help quantify uncertainty about the functions of genes using annotations and the gene ontology database to confirm some of the interactions. The method is illustrated using gene expression data from an experiment on carbohydrate metabolism in the model plant Arabidopsis thaliana. Key gene regulatory relationships were evaluated using information from the gene ontology database. A new regulatory relationship concerning trehalose regulation of carbohydrate metabolism was also discovered in the extracted network.

Animals↗

A sequence-based classifier distinguishes phenotype-associated genes from other gene models in plants.

Only a small fraction of annotated plant genes possess experimentally validated associations with specific phenotypes. Phenotype-associated genes have distinct structural, molecular, and evolutionary characteristics compared with nonvalidated gene models. Here, we develop a simple classifier that uses sequence and evolutionary features, which can be generated for any species with an annotated reference genome assembly, to accurately distinguish phenotype-associated genes from both the overall population of annotated gene models and a specific set of genes identified as being tolerant of premature stop mutations. A model trained solely on genes from maize (Zea mays) identifies and prioritizes rice (Oryza sativa) and Arabidopsis (Arabidopsis thaliana) genes that are highly enriched in genes with experimentally validated links to phenotypes in both of these evolutionarily distant species. Gene models predicted to have a higher probability of being linked to phenotypes display patterns consistent with known biological properties of phenotype-associated genes. Notably, the sets of genes predicted to have a high probability of being linked to phenotype variation do not consist exclusively of well-characterized gene families but included many uncharacterized gene families carrying domains of unknown function. The quantitative scores generated by this model offer a valuable resource for prioritizing and exploring the vast number of uncharacterized gene models in plants, reducing the risk of failure in future reverse genetic efforts and potentially accelerating gene discovery and functional annotation in crops.

Phenotype↗

ECgene: genome-based EST clustering and gene modeling for alternative splicing.

With the availability of the human genome map and fast algorithms for sequence alignment, genome-based EST clustering became a viable method for gene modeling. We developed a novel gene-modeling method, ECgene (Gene modeling by EST Clustering), which combines genome-based EST clustering and the transcript assembly procedure in a coherent and consistent fashion. Specifically, ECgene takes alternative splicing events into consideration. The position of splice sites (i.e., exon-intron boundaries) in the genome map is utilized as the critical information in the whole procedure. Sequences that share any splice sites are grouped together to define an EST cluster in a manner similar to that of the genome-based version of the UniGene algorithm. Transcript assembly is achieved using graph theory that represents the exon connectivity in each cluster as a directed acyclic graph (DAG). Distinct paths along exons correspond to possible gene models encompassing all alternative splicing events. EST sequences in each cluster are subclustered further according to the compatibility with gene structure of each splice variant, and they can be regarded as clone evidence for the corresponding isoform. The reliability of each isoform is assessed from the nature of cluster members and from the minimum number of clones required to reconstruct all exons in the transcript.

Algorithms↗

Single-gene models of epilepsy.

Single-gene models of epilepsy present valuable opportunities to isolate and experimentally reproduce gene mutations for human seizure disorders, to test molecular mechanisms of epileptogenesis, and to explore strategies to correct early hyperexcitability defects in the developing brain. Although not all inherited epilepsies are monogenic, analysis of epileptic phenotypes in spontaneous and transgenic mouse mutants is beginning to define the kinds of molecular defects favoring inherited aberrant synchronization in central neurons. The range of genes identified shows that rather than arising from a few superfamilies that regulate membrane excitability, the gene products are drawn from many categories involved in widely diverse functions of the cell. Although some primary defects directly alter membrane electrogenesis and neurotransmitter signaling at synapses, others are too far removed from these processes to allow one to visualize the steps by which they promote epileptogenesis. There is now clear evidence that several and probably most epilepsy genes entrain specific patterns of secondary cellular plasticity during brain development. It can be predicted that these downstream rearrangements may partially account for the delayed temporal onset and other progressive features of epilepsy syndromes. Experimental alterations that target the mutant gene product and patterns of secondary network plasticity provide a basis for future strategies to reverse the epileptogenic process.

Animals↗

Constructing gene models from accurately predicted exons: an application of dynamic programming.

This paper presents a computationally efficient algorithm, the Gene Assembly Program III (GAP III), for constructing gene models from a set of accurately-predicted 'exons'. The input to the algorithm is a set of clusters of exon candidates, generated by a new version of the GRAIL coding region recognition system. The exon candidates of a cluster differ in their presumed edges and occasionally in their reading frames. Each exon candidate has a numerical score representing its 'probability' of being an actual exon. GAP III uses a dynamic programming algorithm to construct a gene model, complete or partial, by optimizing a predefined objective function. The optimal gene models constructed by GAP III correspond very well with the structures of genes which have been determined experimentally and reported in the Genome Sequence Database (GSDB). On a test set of 137 human and mouse DNA sequences consisting of 954 true exons, GAP III constructed 137 gene models using 892 exons, among which 859 (859/954 = 90%) are true exons and 33 (33/892 = 3%) are false positive. Among the 859 true positives, 635 (74%) match the actual exons exactly, and 838 (98%) have at least one edge correct. GAP III is computationally efficient. If we use E and C to represent the total number of exon candidates in all clusters and the number of clusters, respectively, the running time of GAP III is proportional to (E x C).

Algorithms↗

Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models.

BACKGROUND: Many current gene prediction methods use only one model to represent protein-coding regions in a genome, and so are less likely to predict the location of genes that have an atypical sequence composition. It is likely that future improvements in gene finding will involve the development of methods that can adequately deal with intra-genomic compositional variation. RESULTS: This work explores a new approach to gene-prediction, based on the Self-Organizing Map, which has the ability to automatically identify multiple gene models within a genome. The current implementation, named RescueNet, uses relative synonymous codon usage as the indicator of protein-coding potential. CONCLUSIONS: While its raw accuracy rate can be less than other methods, RescueNet consistently identifies some genes that other methods do not, and should therefore be of interest to gene-prediction software developers and genome annotation teams alike. RescueNet is recommended for use in conjunction with, or as a complement to, other gene prediction methods.

Chromosome Mapping↗

Fishing for a reelGene: evaluating gene models with evolution and machine learning.

Assembled genomes and their associated annotations have transformed our study of gene function. However, each new annotated assembly generates new gene models. Inconsistencies between annotations likely arise from biological and technical causes, including pseudogene misclassification, transposon activity, and intron retention from sequencing of unspliced transcripts. To evaluate gene model predictions, we developed reelGene, a pipeline of machine learning models focused on (1) transcription boundaries, (2) mRNA integrity, and (3) protein structure. The first two models leverage sequence characteristics and evolutionary conservation across related taxa to learn the grammar of conserved transcription boundaries and mRNA sequences, while the third uses the conserved evolutionary grammar of protein sequences to predict whether a gene can produce a protein. Evaluating 1.8 million transcript models in Zea mays ssp. mays (maize), reelGene classified 28% as incorrectly annotated or non-functional. We find that reelGene classifies 92.2% of genes in the maize proteome and 99.2% of genes within the maize classical gene list as functional. reelGene also provides a way to further investigate genome biology- for instance, reelGene indicates that 10.3% of dispensable genes in B73 are functional, and within retained duplicate genes, reelGene identifies a 30% bias toward the retention of the M1 subgenome when one copy is functional and the other is non-functional. As an annotation-evaluating tool, reelGene is directly applicable to species of the Andropogoneae tribe, including other important crops like sorghum and miscanthus. As a community resource, reelGene has been integrated onto MaizeGDB both as a browser track and as an individual Shiny App, allowing researchers to evaluate gene model accuracy and further investigate genome biology.

Machine Learning↗

A two-gene model for schizophrenia with the possibility to detect carriers of the modifier gene.

A two-gene model for the hereditary transmission of schizophrenia is presented involving two pairs of autosomal alleles Ss and Pp. It is hypothesized that the recessive gene can produce schizophrenia in homozygous state with a penetrance of .40. In the presence of the gene P schizophrenia can occur in the heterozygote Ss, resulting in a more serious form of the disease and having a manifestation rate of .70. The population frequencies best fitting the available data are estimated to be approximately .03 for P and .07 or .08 for s. It is also hypothesized that the reproductive fitness is .80 in manifest schizophrenics with genotype ss pp and .50 in overt schizophrenics carrying the modifier gene P. The model is proposed to cover only the so-called "process" or "nuclear" type of schizophrenia. The remaining schizophrenics of "reactive" and other types may belong to different genotypes or consist in phenocopies. The model is discussed in relation to literature observations and our own previous studies. The possibilities for increased fitness of non-schizophrenic carriers of P and s are also discussed.

Capillaries↗

ASmodeler: gene modeling of alternative splicing from genomic alignment of mRNA, EST and protein sequences.

Alternative splicing is in important mechanism of modulating gene function and expression which greatly expands transcriptome diversity. ASmodeler is a novel web-based utility that finds gene models including alternative splicing events from genomic alignment of mRNA, EST and protein sequences. User-supplied sequences are aligned against the genome map using the BLAT and SIM4 programs. Resulting exon connectivity is analyzed by applying graph-theoretic methods to build all possible gene models including splice variants. The algorithm essentially combines the genome-based sequence clustering and transcript assembly procedures in a coherent fashion. In addition to the user-supplied sequences, UniGene clusters and many well-known gene predictions such as Genscan, Ensembl and Acembly may be included in gene modeling. The current implementation supports human, mouse and rat genomes. ASmodeler is available at http://genome.ewha.ac.kr/ECgene/ASmodeler/.

Algorithms↗

Cytogenetic and molecular characterization of heterochromatin gene models in Drosophila melanogaster.

In the past decade, genome-sequencing projects have yielded a great amount of information on DNA sequences in several organisms. The release of the Drosophila melanogaster heterochromatin sequence by the Drosophila Heterochromatin Genome Project (DHGP) has greatly facilitated studies of mapping, molecular organization, and function of genes located in pericentromeric heterochromatin. Surprisingly, genome annotation has predicted at least 450 heterochromatic gene models, a figure 10-fold above that defined by genetic analysis. To gain further insight into the locations and functions of D. melanogaster heterochromatic genes and genome organization, we have FISH mapped 41 gene models relative to the stained bands of mitotic chromosomes and the proximal divisions of polytene chromosomes. These genes are contained in eight large scaffolds, which together account for approximately 1.4 Mb of heterochromatic DNA sequence. Moreover, developmental Northern analysis showed that the expression of 15 heterochromatic gene models tested is similar to that of the vital heterochromatic gene Nipped-A, in that it is not limited to specific stages, but is present throughout all development, despite its location in a supposedly "silent" region of the genome. This result is consistent with the idea that genes resident in heterochromatin can encode essential functions.

Animals↗

The effects of increased phenotypic variance on the evolutionary outcomes of generalized major-gene models.

The study of generalized major-gene models has been extended to accommodate selective, assortative mating mechanisms. In this formulation the consequences of amplified phenotypic variance on the major gene frequency are investigated. It is shown that the rate of approach to the equilibrium state is slowed under attenuated assortative mating tendencies and/or with increased environmental noise. Also, an increased phenotypic variance induces more skewness in the nature of the gene frequencies, i.e. a less 'central' polymorphic expression. Where strong positive assortative mating occurs, a population fixation state results. Negative assortment generally facilitates the stability of a 'central' polymorphism.

Alleles↗

Consensus promoter identification in the human genome utilizing expressed gene markers and gene modeling.

Deciphering the human genome includes locating the promoters that initiate transcription and identifying the exons of genes. Many promoter prediction programs have been proposed, but when they are applied to extended regions of the genome, most of their predictions are false-positives. The extensive collection of gene transcript sequences is an important new source of information, which has not been used previously in promoter predictions. Our approach is to enhance the specificity of predictions by restricting the genomic regions that are searched using gene transcript alignments as anchors in the genome for gene modeling. We developed a consensus promoter prediction method combining previously developed algorithms with the GENSCAN gene modeling program. Our method, CONPRO (CONsensus PROmoter), identifies promoters with very high confidence, and the predicted promoters are guaranteed to be associated with genes. On our test data set, the method correctly detects promoters for approximately half of all human genes (37%-71%), and most predictions are true promoters (85%-90%). Applying our method to the human genome and human genes from the Unigene data set, we find the promoters for 13,744 genes. Of these, 6440 are genes with a functionally cloned mRNA, and 7304 are novel genes for which only expressed sequence tags (ESTs) are available. Candidate promoters for many novel genes will be a useful resource in elucidating complex biological response mechanisms.

5' Untranslated Regions↗

An improved system for exon recognition and gene modeling in human DNA sequences.

A new version of the GRAIL system (Uberbacher and Mural, 1991; Mural et al., 1992; Uberbacher et al., 1993), called GRAIL II, has recently been developed (Xu et al., 1994). GRAIL II is a hybrid AI system that supports a number of DNA sequence analysis tools including protein-coding region recognition, PolyA site and transcription promoter recognition, gene model construction, translation to protein, and DNA/protein database searching capabilities. This paper presents the core of GRAIL II, the coding exon recognition and gene model construction algorithms. The exon recognition algorithm recognizes coding exons by combining coding feature analysis and edge signal (acceptor/donor/translation-start sites) detection. Unlike the original GRAIL system (Uberbacher and Mural, 1991; Mural et al., 1992), this algorithm uses variable-length windows tailored to each potential exon candidate, making its performance almost exon length-independent. In this algorithm, the recognition process is divided into four steps. Initially a large number of possible coding exon candidates are generated. Then a rule-based prescreening algorithm eliminates the majority of the improbable candidates. As the kernel of the recognition algorithm, three neural networks are trained to evaluate the remaining candidates. The outputs of the neural networks are then divided into clusters of candidates, corresponding to presumed exons. The algorithm makes its final prediction by picking the best canadidate from each cluster. The gene construction algorithm (Xu, Mural and Uberbacher, 1994) uses a dynamic programming approach to build gene models by using as input the clusters predicted by the exon recognition algorithm. Extensive testing has been done on these two algorithms.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗

Modeling gene expression from microarray expression data with state-space equations.

We describe a new method to model gene expression from time-course gene expression data. The modelling is in terms of state-space descriptions of linear systems. A cell can be considered to be a system where the behaviours (responses) of the cell depend completely on the current internal state plus any external inputs. The gene expression levels in the cell provide information about the behaviours of the cell. In previously proposed methods, genes were viewed as internal state variables of a cellular system and their expression levels were the values of the intemal state variables. This viewpoint has suffered from the underestimation of the model parameters. Instead, we view genes as the observation variables, whose expression values depend on the current intemal state variables and any external input. Factor analysis is used to identify the internal state variables, and Bayesian Information Criterion (BIC) is used to determine the number of the internal state variables. By building dynamic equations of the internal state variables and the relationships between the internal state variables and the observation variables (gene expression profiles), we get state-space descriptions of gene expression model. In the present method, model parameters may be unambiguously identified from time-course gene expression data. We apply the method to two time-course gene expression datasets to illustrate it.

Algorithms↗

GeneExt: a gene model extension tool for enhanced single-cell RNA-seq analysis.

MOTIVATION: Incomplete gene models negatively impact single-cell gene expression quantification. This is particularly true in non-model species where often gene 3' ends are inaccurately annotated, while most scRNA-seq methods only capture the 3' transcript region. This results in many genes being incorrectly quantified or not detected. RESULTS: GeneExt leverages scRNA-seq data to refine gene annotations. We exemplify GeneExt usage and its impact on the gene expression quantification of eight non-model organism single-cell atlases. By extending and homogenizing gene annotations, our tool will help improve biological interpretation and cross-species comparisons of cell type expression atlases. AVAILABILITY: GeneExt is available at https://github.com/sebepedroslab/GeneExt (DOI: https://doi.org/10.5281/zenodo.18712940) under a GNU General Public license, together with test data and usage instructions.

Software↗