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At least 397 records · Page 22Linked to original sources
PubMed2003

A computational pipeline for protein structure prediction and analysis at genome scale.

MOTIVATION: Experimental techniques alone cannot keep up with the production rate of protein sequences, while computational techniques for protein structure predictions have matured to such a level to provide reliable structural characterization of proteins at large scale. Integration of multiple computational tools for protein structure prediction can complement experimental techniques. RESULTS: We present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is our threading-based protein structure prediction system PROSPECT. The pipeline consists of a dozen tools for identification of protein domains and signal peptide, protein triage to determine the protein type (membrane or globular), protein fold recognition, generation of atomic structural models, prediction result validation, etc. Different processing and prediction branches are determined automatically by a prediction pipeline manager based on identified characteristics of the protein. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. Genome-scale applications on Caenorhabditis elegans, Pyrococcus furiosus and three cyanobacterial genomes are presented. AVAILABILITY: The pipeline is available at http://compbio.ornl.gov/proteinpipeline/

Algorithms↗
PubMed2026

Deciphering the mosaic genome of sugarcane cultivars through polyploid admixture inference with AdmixPoly.

BACKGROUND: Characterizing population structure and admixture events between ancestral groups plays a key role in understanding the evolutionary history of species and crops. Most tools for inferring admixture have been developed for diploids and are not suitable for polyploids, in particular those with high and mixed ploidy such as Saccharum. RESULTS: Here we present AdmixPoly, an R-package designed to infer admixture in polyploid species both at the genome-wide scale and locally along chromosomes. We compare AdmixPoly with state-of-the-art methods using simulations, demonstrating its precision and computational efficiency. Notably, local admixture inference in complex scenarios, such as high ploidy levels, large numbers of ancestral groups and alleles per marker is enabled through efficient approximations of emission and transition probabilities within a hidden Markov model framework. We apply this approach to characterize the contributions of wild Saccharum species to the complex polyploid genome of modern sugarcane cultivars. A panel of wild and cultivated Saccharum accessions is genotyped for 80K genomic regions, each revealing approximately 50 read-scale haplotypes. CONCLUSIONS: The results reveal that most of the approximately 12 copies of each basic chromosome in modern cultivars are derived from the domesticated species Saccharum officinarum, with one to four copies typically contributed by distinct subgroups of the wild species Saccharum spontaneum. In addition, contributions from an unknown wild Saccharum group originating from the Pacific were identified in most cultivars. The conserved pattern of these introgressions suggests that they can be traced back to the early stages of sugarcane breeding approximately a century ago.

Saccharum↗
PubMed2000

Identification of two new Pmp22 mouse mutants using large-scale mutagenesis and a novel rapid mapping strategy.

Mouse mutants have a key role in discerning mammalian gene function and modelling human disease; however, at present mutants exist for only 1-2% of all mouse genes. In order to address this phenotype gap, we have embarked on a genome-wide, phenotype-driven, large-scale N-ethyl-N--nitrosourea (ENU) mutagenesis screen for dominant mutations of clinical and pharmacological interest in the mouse. Here we describe the identification of two similar neurological phenotypes and determination of the underlying mutations using a novel rapid mapping strategy incorporating speed back-crosses and high throughput genotyping. Two mutant mice were identified with marked resting tremor and further characterized using the SHIRPA behavioural and functional assessment protocol. Back-cross animals were generated using in vitro fertilization and genome scans performed utilizing DNA pools derived from multiple mutant mice. Both mutants were mapped to a region on chromosome 11 containing the peripheral myelin protein 22 gene (Pmp22). Sequence analysis revealed novel point mutations in Pmp22 in both lines. The first mutation, H12R, alters the same amino acid as in the severe human peripheral neuropathy Dejerine Sottas syndrome and Y153TER in the other mutant truncates the Pmp22 protein by seven amino acids. Histological analysis of both lines revealed hypo-myelination of peripheral nerves. This is the first report of the generation of a clinically relevant neurological mutant and its rapid genetic characterization from a large-scale mutagenesis screen for dominant phenotypes in the mouse, and validates the use of large-scale screens to generate desired clinical phenotypes in mice.

Animals↗
PubMed2002

The ups and downs of daily life: profiling circadian gene expression in Drosophila.

Circadian rhythms are responsible for 24-hour oscillations in diverse biological processes. While the central genes governing circadian pacemaker rhythmicity have largely been identified, clock-controlled output molecules responsible for regulating rhythmic behaviors remain largely unknown. Two recent reports from McDonald and Rosbash(1) and Claridge-Chang et al.2 address this issue. By identifying a large number of genes whose mRNA levels show circadian oscillations, the reports provide important new information on the biology of circadian rhythm. In addition, the reports illustrate both the power and limitations of microarray-based methods for profiling mRNA expression on a genomic scale.

Animals↗
PubMed2004

Arabidopsis to rice. Applying knowledge from a weed to enhance our understanding of a crop species.

Although Arabidopsis is well established as the premiere model species in plant biology, rice (Oryza sativa) is moving up fast as the second-best model organism. In addition to the availability of large sets of genetic, molecular, and genomic resources, two features make rice attractive as a model species: it represents the taxonomically distinct monocots and is a crop species. Plant structural genomics was pioneered on a genome-scale in Arabidopsis and the lessons learned from these efforts were not lost on rice. Indeed, the sequence and annotation of the rice genome has been greatly accelerated by method improvements made in Arabidopsis. For example, the value of full-length cDNA clones and deep expressed sequence tag resources, obtained in Arabidopsis primarily after release of the complete genome, has been recognized by the rice genomics community. For rice >250,000 expressed sequence tags and 28,000 full-length cDNA sequences are available prior to the completion of the genome sequence. With respect to tools for Arabidopsis functional genomics, deep sequence-tagged lines, inexpensive spotted oligonucleotide arrays, and a near-complete whole genome Affymetrix array are publicly available. The development of similar functional genomics resources for rice is in progress that for the most part has been more streamlined based on lessons learned from Arabidopsis. Genomic resource development has been essential to set the stage for hypothesis-driven research, and Arabidopsis continues to provide paradigms for testing in rice to assess function across taxonomic divisions and in a crop species.

Arabidopsis↗
PubMed2004

Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.

BACKGROUND: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data. RESULTS: We present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative. CONCLUSION: Our reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at http://bussemaker.bio.columbia.edu/papers/MA-Networker/.

Algorithms↗
PubMed2004

Kernel mixture survival models for identifying cancer subtypes, predicting patient's cancer types and survival probabilities.

One important application of microarray gene expression data is to study the relationship between the clinical phenotype of cancer patients and gene expression profiles on the whole-genome scale. The clinical phenotype includes several different types of cancers, survival times, relapse times, drug responses and so on. Under the situation that the subtypes of cancer have not been previously identified or known to exist, we develop a new kernel mixture modeling method that performs simultaneously identification of the subtype of cancer, prediction of the probabilities of both cancer type and patient's survival, and detection of a set of marker genes on which to base a diagnosis. The proposed method is successfully performed on real data analysis and simulation studies.

Artificial Intelligence↗
PubMed2005

A high frequency of overlapping gene expression in compacted eukaryotic genomes.

The gene density of eukaryotic nuclear genomes is generally low relative to prokaryotes, but several eukaryotic lineages (many parasites or endosymbionts) have independently evolved highly compacted, gene-dense genomes. The best studied of these are the microsporidia, highly adapted fungal parasites, and the nucleomorphs, relict nuclei of endosymbiotic algae found in cryptomonads and chlorarachniophytes. These systems are now models for the effects of compaction on the form and dynamics of the nuclear genome. Here we report a large-scale investigation of gene expression from compacted eukaryotic genomes. We have conducted EST surveys of the microsporidian Antonospora locustae and nucleomorphs of the cryptomonad Guillardia theta and the chlorarachniophyte Bigelowiella natans. In all three systems we find a high frequency of mRNA molecules that encode sequence from more than one gene. There is no bias for these genes to be on the same strand, so it is unlikely that these mRNAs represent operons. Instead, compaction appears to have reduced the intergenic regions to such an extent that control elements like promoters and terminators have been forced into or beyond adjacent genes, resulting in long untranslated regions that encode other genes. Normally, transcriptional overlap can interfere with expression of a gene, but these genomes cope with high frequencies of overlap and with termination signals within expressed genes. These findings also point to serious practical difficulties in studying expression in compacted genomes, because many techniques, such as arrays or serial analysis of gene expression will be misleading.

Animals↗
PubMed2002

High-throughput SELEX SAGE method for quantitative modeling of transcription-factor binding sites.

The ability to determine the location and relative strength of all transcription-factor binding sites in a genome is important both for a comprehensive understanding of gene regulation and for effective promoter engineering in biotechnological applications. Here we present a bioinformatically driven experimental method to accurately define the DNA-binding sequence specificity of transcription factors. A generalized profile was used as a predictive quantitative model for binding sites, and its parameters were estimated from in vitro-selected ligands using standard hidden Markov model training algorithms. Computer simulations showed that several thousand low- to medium-affinity sequences are required to generate a profile of desired accuracy. To produce data on this scale, we applied high-throughput genomics methods to the biochemical problem addressed here. A method combining systematic evolution of ligands by exponential enrichment (SELEX) and serial analysis of gene expression (SAGE) protocols was coupled to an automated quality-controlled sequence extraction procedure based on Phred quality scores. This allowed the sequencing of a database of more than 10,000 potential DNA ligands for the CTF/NFI transcription factor. The resulting binding-site model defines the sequence specificity of this protein with a high degree of accuracy not achieved earlier and thereby makes it possible to identify previously unknown regulatory sequences in genomic DNA. A covariance analysis of the selected sites revealed non-independent base preferences at different nucleotide positions, providing insight into the binding mechanism.

Base Sequence↗
PubMed1992

Base compositional structure of genomes.

We model the base compositional structure of the human and Escherichia coli genomes. Three particular properties are first quantified: (1) There is a significant tendency for any region of either genome to have a strand-symmetric base composition. (2) The variation in base composition from region to region, within each genome, is very much larger than expected from common homogeneous stochastic models. (3) A given local base composition tends to persist over a scale of at least kilobases (E. coli) or tens of kilobases (human). Multidomain stochastic models from the literature are reviewed and sharpened. In particular, quantitative measurements of the third property lead us to suggest a significant shift in the style of domain models, in which the variation of A+T content with position is modeled by a random walk with frequent small steps rather than with large quantum jumps. As an application, we suggest a way to reduce the amount of computation in the assembly of large sequences from sequences of randomly chosen fragments.

Escherichia coli↗
PubMed2004

Prediction of protein-protein interactions: the CAPRI experiment, its evaluation and implications.

Given the increasing interest in protein-protein interactions, the prediction of these interactions from sequence and structural information has become a booming activity. CAPRI, the community-wide experiment for assessing blind predictions of protein-protein interactions, is playing an important role in fostering progress in docking procedures. At the same time, novel methods are being derived for predicting regions of a protein that are likely to interact and for characterizing putative intermolecular contacts from sequence and structural data. Together with docking procedures, these methods provide an integrated computational approach that should be a valuable complement to genome-scale experimental studies of protein-protein interactions.

Algorithms↗
PubMed2004

Medaka genomics: a bridge between mutant phenotype and gene function.

Recent advances in medaka genetics have proven that the medakafish is an excellent model system for developmental and evolutionary biology studies and that it can complement similar studies in zebrafish. Large-scale mutagenesis projects are now being conducted by several groups in Japan and are delivering a vastly expanded pool of medaka mutant stocks. This growing availability of genomic resources will greatly accelerate progress in moving from mutant phenotypes to the elucidation of gene function. This phenotype-driven approach can be expected to lead to the identification and characterization of novel genes and pathways in vertebrate genomes. This review discusses the current state of medaka genomic resources, the state of medaka gene mapping and medaka genome sequencing projects.

Animals↗
PubMed2026

Extravascular coagulation stabilizes pro-fibrotic stromal states via tumor-intrinsic PAR1 signaling in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) exhibits a desmoplastic stroma with context-dependent tumor-restraining and tumor-promoting functions, highlighting the need to selectively reprogram stromal states. Extravascular coagulation is a prominent feature of the PDAC tumor microenvironment, yet whether it functions as an upstream regulator of fibrotic stromal states, rather than merely a byproduct of tumor-associated vascular dysfunction, has remained unclear. Here, we identify extravascular coagulation as a tumor-amplified regulatory module that stabilizes pro-fibrotic stromal states via tumor-intrinsic protease-activated receptor-1 (PAR1) signaling. To interrogate this axis mechanistically, we integrated human tumor bioinformatics with microphysiological tumor-stroma (MPTS) models that reconstruct tumor-stroma interactions under controlled coagulation exposure, followed by cross-scale validation in vivo. Analysis of The Cancer Genome Atlas (TCGA) revealed heterogeneous F2R (PAR1) expression across tumors, with elevated expression associated with fibrotic transcriptional programs and reduced survival. Consistently, thrombin induced coordinated pro-fibrotic programs in tumor cells and cancer-associated fibroblasts (CAFs), which were recapitulated in MPTS where tumor-intrinsic PAR1 was required for amplification of extracellular matrix deposition and CAF activation. Mechanistically, PAR1 signaling amplified tumor-stroma communication, in part through induction of TGF-β1-dependent pathways, establishing a reinforcing feedback loop that stabilizes fibrotic remodeling. Pharmacologic inhibition of PAR1 selectively suppressed the fibrotic transcriptional program within myofibroblastic CAFs while reducing the abundance of other CAF subtypes, reprogramming stromal states and attenuating tumor progression across MPTS and in vivo models. These findings establish a coagulation-PAR1 axis as an upstream organizer of PDAC stromal architecture and identify pharmacologic PAR1 inhibition as a mechanistically grounded strategy for selectively reprogramming the tumor-promoting stroma.

Journal Article↗
PubMed2004

Protein structure modeling in the proteomics era.

Structural proteomics aims to understand the structural basis of protein interactions and functions. A prerequisite for this is the availability of 3D protein structures that mediate the biochemical interactions. The explosion in the number of available gene sequences set the stage for the next step in genome-scale projects -- to obtain 3D structures for each protein. To achieve this ambitious goal, the slow and costly structure determination experiments are supplemented with theoretical approaches. The current state and recent advances in structure modeling approaches are reviewed here, with special emphasis on comparative protein structure modeling techniques.

Animals↗
PubMed2004

Tertiary structure predictions on a comprehensive benchmark of medium to large size proteins.

We evaluate tertiary structure predictions on medium to large size proteins by TASSER, a new algorithm that assembles protein structures through rearranging the rigid fragments from threading templates guided by a reduced Calpha and side-chain based potential consistent with threading based tertiary restraints. Predictions were generated for 745 proteins 201-300 residues in length that cover the Protein Data Bank (PDB) at the level of 35% sequence identity. With homologous proteins excluded, in 365 cases, the templates identified by our threading program, PROSPECTOR_3, have a root-mean-square deviation (RMSD) to native < 6.5 angstroms, with >70% alignment coverage. After TASSER assembly, in 408 cases the best of the top five full-length models has a RMSD < 6.5 angstroms. Among the 745 targets are 18 membrane proteins, with one-third having a predicted RMSD < 5.5 A. For all representative proteins less than or equal to 300 residues that have corresponding multiple NMR structures in the Protein Data Bank, approximately 20% of the models generated by TASSER are closer to the NMR structure centroid than the farthest individual NMR model. These results suggest that reasonable structure predictions for nonhomologous large size proteins can be automatically generated on a proteomic scale, and the application of this approach to structural as well as functional genomics represent promising applications of TASSER.

Algorithms↗
PubMed2026

Zebrafish relatives as models for functional comparative genetics and genomics.

Closely related species, such as danionin fishes of the Danio, Danionella and Devario genera, often differ in their biology despite their shared evolutionary history, providing a platform for defining the molecular basis for the divergence of phenotypic traits. Such an approach requires the availability of large-scale genomic data, which have been provided by recent reports detailing the genomes of several danionins. Facilitated by the large number of genetic tools that are available for manipulation of the most studied member of this subgroup - the zebrafish, Danio rerio - the danionins have emerged as a useful comparative model system. Here we review their phylogeny and outline the phenotypic traits that are distinct to individual species or genera. We highlight how functional genetic tools such as interspecies hybridization, mutagenesis and transgenesis, as well as the recently reported genome assemblies, have enabled new avenues for hypothesis-driven and technology-driven exploration that collectively establish danionins as important genetic models for understanding a wide range of evolutionary innovations.

Journal Article↗
PubMed2025

Widespread induction of SINE-RNA expression in the mouse brain following transient focal ischemia.

Ischemic stroke triggers rapid transcriptional changes in the brain, including the induction of noncoding RNAs, which are well-established regulators of post-stroke pathophysiology. Among the numerous classes of noncoding RNAs, short interspersed nuclear element RNAs (SINE-RNAs) are transcribed by Pol III and reported to be upregulated in various paradigms of cellular stress. In the ischemic brain, Pol III-driven gene expression is not well-studied and the expression of SINE-RNAs is virtually unmapped. In the current study, we used a mouse model of transient focal ischemia to evaluate for the first time post-stroke SINE-RNA expression in the cerebral cortex on a genome-wide scale. We observed SINE-RNA induction as early as 0 to 3 h of reperfusion and peak expression at 6&#xa0;h of reperfusion, with 335 SINE-RNAs induced at this timepoint as compared to sham controls. Many of these transcripts remained induced through later timepoints during the acute phase of reperfusion (24&#xa0;h). Fluorescence in situ hybridization against the SINE-RNAs, combined with immunohistochemistry for cell-type markers, revealed that these RNAs are localized to the nuclei of post-ischemic neurons and microglia in the ipsilateral cortex and hippocampus in both males and females. Further, we found that SINE-RNA expression was recapitulated in vitro following oxygen-glucose deprivation in HT22 hippocampal neurons, showing that they are reproducibly expressed in neurons in both in vivo and in vitro models of ischemia. Together, this is the first study to map genome-wide SINE-RNA expression in the post-ischemic brain and reveals a new layer of the noncoding transcriptome that may play a role in the post-stroke pathophysiology.

Animals↗
PubMed2002

Genetics of schizophrenia: from animal models to clinical studies.

Genetic epidemiological studies strongly suggest that additive and interactive genes, each with small effects, mediate the genetic vulnerability for schizophrenia. With the human genome working draft at hand, candidate gene (and ultimately large-scale genome-wide) association studies are gaining renewed interest in the effort to unravel the complex genetics of schizophrenia. In the absence of an unequivocally established biological theory for schizophrenia, identifying candidate genes to be tested in an association paradigm remains a challenging task. We maintain that it is possible to use animal models to map genes or loci involved in behavioural traits that are relevant to schizophrenia. The human genes (or syntenic loci) homologous to those identified in mice can subsequently be tested in patients with schizophrenia who have been carefully phenotyped for traits "isomorphic" to the ones modelled in mice. If confirmed in humans, these genes may be further analyzed in the animal model to identify their role and the biological network they are involved in. To tackle the complex and intimidating problem of the genetics of schizophrenia, it may be necessary to go from animal models to human studies and vice versa; this strategy has been proven to be efficient in less complicated, though complex, human diseases.

Animals↗