Protein refinement: a new challenge for CASP in its 10th anniversary.
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Algorithms↗
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Results for “Biological sequence analysis”
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GandrKB--ontological microarray annotation and visualization.
SUMMARY: The Gandr (gene annotation data representation) knowledgebase is an ontological framework for laboratory-specific gene annotation. Gandr uses Protege 2000 for editing, querying and visualizing microarray data and annotations. Genes can be annotated with provided, newly created or imported ontological concepts. Annotated genes can inherit assigned concept properties and can be related to each other. The resulting knowledgebase can be visualized as interactive network of nodes and edges representing genes and their functional relationships. This allows for immediate and associative gene context exploration. Ontological query techniques allow for powerful data access.
Algorithms↗
Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment.
MOTIVATION: The topology and function of gene regulation networks are commonly inferred from time series of gene expression levels in cell populations. This strategy is usually invalid if the gene expression in different cells of the population is not synchronous. A promising, though technically more demanding alternative is therefore to measure the gene expression levels in single cells individually. The inference of a gene regulation network requires knowledge of the gene expression levels at successive time points, at least before and after a network transition. However, owing to experimental limitations a complete determination of the precursor state is not possible. RESULTS: We investigate a strategy for the inference of gene regulatory networks from incomplete expression data based on dynamic Bayesian networks. This permits prediction of the number of experiments necessary for network inference depending on parameters including noise in the data, prior knowledge and limited attainability of initial states. Our strategy combines a gradual 'Partial Learning' approach based solely on true experimental observations for the network topology with expectation maximization for the network parameters. We illustrate our strategy by extensive computer simulations in a high-dimensional parameter space in a simulated single-cell-based example of hematopoietic stem cell commitment and in random networks of different sizes. We find that the feasibility of network inferences increases significantly with the experimental ability to force the system into different initial network states, with prior knowledge and with noise reduction. AVAILABILITY: Source code is available under: www.izbi.uni-leipzig.de/services/NetwPartLearn.html SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.
Algorithms↗
Using a state-space model with hidden variables to infer transcription factor activities.
MOTIVATION: In a gene regulatory network, genes are typically regulated by transcription factors (TFs). Transcription factor activity (TFA) is more difficult to measure than gene expression levels are. Other models have extracted information about TFA from gene expression data, but without explicitly modeling feedback from the genes. We present a state-space model (SSM) with hidden variables. The hidden variables include regulatory motifs in the gene network, such as feedback loops and auto-regulation, making SSM a useful complement to existing models. RESULTS: A gene regulatory network incorporating, for example, feed-forward loops, auto-regulation and multiple-inputs was constructed with an SSM model. First, the gene expression data were simulated by SSM and used to infer the TFAs. The ability of SSM to infer TFAs was evaluated by comparing the profiles of the inferred and simulated TFAs. Second, SSM was applied to gene expression data obtained from Escherichia coli K12 undergoing a carbon source transition and from the Saccharomyces cerevisiae cell cycle. The inferred activity profile for each TF was validated either by measurement or by activity information from the literature. The SSM model provides a probabilistic framework to simulate gene regulatory networks and to infer activity profiles of hidden variables. AVAILABILITY: Supplementary data and Matlab code will be made available at the URL below. SUPPLEMENTARY INFORMATION: http://www.chems.msu.edu/groups/chan/ssm.zip.
Algorithms↗
The MGED Ontology: a resource for semantics-based description of microarray experiments.
MOTIVATION: The generation of large amounts of microarray data and the need to share these data bring challenges for both data management and annotation and highlights the need for standards. MIAME specifies the minimum information needed to describe a microarray experiment and the Microarray Gene Expression Object Model (MAGE-OM) and resulting MAGE-ML provide a mechanism to standardize data representation for data exchange, however a common terminology for data annotation is needed to support these standards. RESULTS: Here we describe the MGED Ontology (MO) developed by the Ontology Working Group of the Microarray Gene Expression Data (MGED) Society. The MO provides terms for annotating all aspects of a microarray experiment from the design of the experiment and array layout, through to the preparation of the biological sample and the protocols used to hybridize the RNA and analyze the data. The MO was developed to provide terms for annotating experiments in line with the MIAME guidelines, i.e. to provide the semantics to describe a microarray experiment according to the concepts specified in MIAME. The MO does not attempt to incorporate terms from existing ontologies, e.g. those that deal with anatomical parts or developmental stages terms, but provides a framework to reference terms in other ontologies and therefore facilitates the use of ontologies in microarray data annotation. AVAILABILITY: The MGED Ontology version.1.2.0 is available as a file in both DAML and OWL formats at http://mged.sourceforge.net/ontologies/index.php. Release notes and annotation examples are provided. The MO is also provided via the NCICB's Enterprise Vocabulary System (http://nciterms.nci.nih.gov/NCIBrowser/Dictionary.do). CONTACT: Stoeckrt@pcbi.upenn.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Computational Biology↗
Branch and bound computation of exact p-values.
UNLABELLED: P-value computation is often used in bioinformatics to quantify the surprise, or significance, associated with a given observation. An implementation is provided that computes the exact p-value associated with any observed sample, against a null multinomial distribution, using the likelihood-ratio statistic. The efficient branch and bound code, far exceeding the full enumeration implemented by commercial packages, is especially useful with small sample, sparse data and rare events, common scenarios in bioinformatics, where approximations are often inaccurate and inappropriate. This code base can also be adapted to compute exact p-values of other statistics in diverse sampling scenarios. AVAILABILITY: Freely available at http://www.soe.ucsc.edu/~jill/src/.
Algorithms↗
Inferring gene regulatory networks from time series data using the minimum description length principle.
MOTIVATION: A central question in reverse engineering of genetic networks consists in determining the dependencies and regulating relationships among genes. This paper addresses the problem of inferring genetic regulatory networks from time-series gene-expression profiles. By adopting a probabilistic modeling framework compatible with the family of models represented by dynamic Bayesian networks and probabilistic Boolean networks, this paper proposes a network inference algorithm to recover not only the direct gene connectivity but also the regulating orientations. RESULTS: Based on the minimum description length principle, a novel network inference algorithm is proposed that greatly shrinks the search space for graphical solutions and achieves a good trade-off between modeling complexity and data fitting. Simulation results show that the algorithm achieves good performance in the case of synthetic networks. Compared with existing state-of-the-art results in the literature, the proposed algorithm exceptionally excels in efficiency, accuracy, robustness and scalability. Given a time-series dataset for Drosophila melanogaster, the paper proposes a genetic regulatory network involved in Drosophila's muscle development. AVAILABILITY: Available from the authors upon request.
Algorithms↗
Detecting functional modules in the yeast protein-protein interaction network.
MOTIVATION: Identification of functional modules in protein interaction networks is a first step in understanding the organization and dynamics of cell functions. To ensure that the identified modules are biologically meaningful, network-partitioning algorithms should take into account not only topological features but also functional relationships, and identified modules should be rigorously validated. RESULTS: In this study we first integrate proteomics and microarray datasets and represent the yeast protein-protein interaction network as a weighted graph. We then extend a betweenness-based partition algorithm, and use it to identify 266 functional modules in the yeast proteome network. For validation we show that the functional modules are indeed densely connected subgraphs. In addition, genes in the same functional module confer a similar phenotype. Furthermore, known protein complexes are largely contained in the functional modules in their entirety. We also analyze an example of a functional module and show that functional modules can be useful for gene annotation. CONTACT: yuan.33@osu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Algorithms↗
MARD: a new method to detect differential gene expression in treatment-control time courses.
MOTIVATION: Characterizing the dynamic regulation of gene expression by time course experiments is becoming more and more important. A common problem is to identify differentially expressed genes between the treatment and control time course. It is often difficult to compare expression patterns of a gene between two time courses for the following reasons: (1) the number of sampling time points may be different or hard to be aligned between the treatment and the control time courses; (2) estimation of the function that describes the expression of a gene in a time course is difficult and error-prone due to the limited number of time points. We propose a novel method to identify the differentially expressed genes between two time courses, which avoids direct comparison of gene expression patterns between the two time courses. RESULTS: Instead of attempting to 'align' and compare the two time courses directly, we first convert the treatment and control time courses into neighborhood systems that reflect the underlying relationships between genes. We then identify the differentially expressed genes by comparing the two gene relationship networks. To verify our method, we apply it to two treatment-control time course datasets. The results are consistent with the previous results and also give some new biologically meaningful findings. AVAILABILITY: The algorithm in this paper is coded in C++ and is available from http://leili-lab.cmb.usc.edu/yeastaging/projects/MARD/
Algorithms↗
BNArray: an R package for constructing gene regulatory networks from microarray data by using Bayesian network.
UNLABELLED: BNArray is a systemized tool developed in R. It facilitates the construction of gene regulatory networks from DNA microarray data by using Bayesian network. Significant sub-modules of regulatory networks with high confidence are reconstructed by using our extended sub-network mining algorithm of directed graphs. BNArray can handle microarray datasets with missing data. To evaluate the statistical features of generated Bayesian networks, re-sampling procedures are utilized to yield collections of candidate 1st-order network sets for mining dense coherent sub-networks. AVAILABILITY: The R package and the supplementary documentation are available at http://www.cls.zju.edu.cn/binfo/BNArray/.
Algorithms↗
Causality and pathway search in microarray time series experiment.
MOTIVATION: Interaction among time series can be explored in many ways. All the approach has the usual problem of low power and high dimensional model. Here we attempted to build a causality network among a set of time series. The causality has been established by Granger causality, and then constructing the pathway has been implemented by finding the Minimal Spanning Tree within each connected component of the inferred network. False discovery rate measurement has been used to identify the most significant causalities. RESULTS: Simulation shows good convergence and accuracy of the algorithm. Robustness of the procedure has been demonstrated by applying the algorithm in a non-stationary time series setup. Application of the algorithm in a real dataset identified many causalities, with some overlap with previously known ones. Assembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks.
Algorithms↗
Gene expression profiling of initiated epidermal cells with benign or malignant tumor fates.
High-density oligonucleotide array technology was used to search for gene expression changes that may predict or cause sporadic tumorigenesis. To reduce variation among samples, a non-transformed mouse epidermal keratinocyte clone was compared with its carcinoma-producing or papilloma-producing 7,12-dimethylbenz[a]anthracene-initiated cell derivative. The majority of the approximately 12 325 genes or expressed sequence tags (ESTs) analyzed remained unaltered reflective of the clonal nature of the model. Consistent gene expression changes among biological replicates included 69 in malignancy-prone initiated cells, 46 in papilloma-precursor initiated cells and an additional 45 with changes in both initiated cell lineages. The changes covered a broad spectrum of cellular activities, implying that multiple pathways cooperate at the initiation of carcinogenesis. There was a tendency in the malignancy-prone cells for differences in proliferation and apoptosis-related genes. The benign lineage tended toward aberrant expression of genes involved in differentiation and epidermal barrier function. Several independently confirmed gene expression changes revealed plausible effectors to bypass an activated ras protein in proliferation pathways and to compromise the wild-type p53 and other apoptosis pathways at initiation of carcinogenesis. The differential expression patterns of the initiated cells are consistent with the hypothesis that changes in gene expression at initiation may cooperate with subsequent oncogenic changes to determine malignant fate. The cloned epidermal cell lineages allowed detection of putative early cancer genes in a model that is conducive to testing their direct role in epithelial multistep carcinogenesis in vitro and in vivo.
Animals↗
Aberrant expression of PDGF ligands and receptors in the tumor prone ovary of follitropin receptor knockout (FORKO) mouse.
Although PDGF family members play a vital role in cell proliferation, motility and chemotaxis via activation of structurally similar alpha- and beta-receptors, little is known of their function in ovarian regulation and induction of tumorigenesis. Microarray analyses of ovaries from young follitropin receptor knockout (FORKO) mice that are prone to late ovarian tumors upon aging have revealed significant imbalances in PDGF ligands and receptors. We hypothesized that FSH/FSH-R signaling may exert effects partly by regulation of PDGF the family. To further understand their implications for ovarian tumorigenesis, we studied FORKO ovaries and hormonal regulation of the PDGF family members in normal mice, by using RT-PCR, Q-PCR, immunohistochemistry and western blotting. While PDGF-C and PDGFR-alpha increased, PDGFR-beta mRNA and protein decreased significantly in absence of FSH-R signaling. In the normal ovary, PDGFR-alpha was not affected by gonadotropin (eCG) stimulation but PDGF-C and PDGFR-beta decreased. Administration of estradiol decreased PDGF and their receptors. To further probe the differential regulation of PDGF family members by eCG and estradiol, we co-administered eCG with estrogen antagonist, ICI 182780. Increase in PDGFR-alpha in the absence of estradiol suggests direct effects of FSH signaling. During the estrous cycle in mice PDGF-C, PDGF-D and PDGFR-alpha mRNA levels were higher at the proestrous. By IHC, we report for the first time the localization of PDGF-C, PDGFR-alpha and PDGFR-beta protein in mouse ovarian compartments including the surface epithelium that is also altered in mutants. Immunostaining of PDGFRs increased as the follicle developed to preantral stage and declined thereafter. Thus, FSH modulates PDGF family members, partly via E2, suggesting that loss of FSH-R signaling causes an imbalance of PDGF family members predisposing the abnormal ovarian follicular environment for inducing tumorigenesis in aging FORKO mice.
Animals↗
cDNA microarray screening for taste-bud-specific genes.
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Animals↗
Regions of genomic instability on 22q11 and 11q23 as the etiology for the recurrent constitutional t(11;22).
The constitutional t(11;22)(q23;q11) is the only known recurrent, non-Robertsonian translocation. To analyze the genomic structure of the breakpoint, we have cloned the junction fragments from the der(11) and der(22) of a t(11;22) balanced carrier. On chromosome 11 the translocation occurs within a short, palindromic AT-rich region (ATRR). Likewise, the breakpoint on chromosome 22 has been localized within an ATRR that is part of a larger palindrome. Interestingly, the 22q11 breakpoint falls within one of the 'unclonable' gaps in the genomic sequence. Further, a sequenced chromosome 11 BAC clone, spanning the t(11;22) breakpoint in 11q23, is deleted within the palindromic ATRR, suggesting instability of this region in bacterial clones. Several unrelated t(11;22) families demonstrate similar breakpoints on both chromosomes, indicating that their translocations are within the same palindrome. It is likely that the palindromic ATRRs produce unstable DNA structures in 22q11 and 11q23 that are responsible for the recurrent t(11;22) translocation.
Base Sequence↗
Guidelines for conducting and reporting whole genome/large-scale association studies.
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Genetic Variation↗
Gene profiling of the red light signalling pathways in roots.
Red light, acting through the phytochromes, controls numerous aspects of plant development. Many of the signal transduction elements downstream of the phytochromes have been identified in the aerial portions of the plant; however, very few elements in red-light signalling have been identified specifically for roots. Gene profiling studies using microarrays and quantitative Real-Time PCR were performed to characterize gene expression changes in roots of Arabidopsis seedlings exposed to 1 h of red light. Several factors acting downstream of phytochromes in red-light signalling in roots were identified. Some of the genes found to be differentially expressed in this study have already been characterized in the red-light-signalling pathway for whole plants. For example, PHYTOCHROME KINASE 1 (PKS1), LONG HYPOCOTYL 5 (HY5), EARLY FLOWERING 4 (ELF4), and GIGANTEA (GI) were all significantly up-regulated in roots of seedlings exposed to 1 h of red light. The up-regulation of SUPPRESSOR OF PHYTOCHROME A RESPONSES 1 (SPA1) and CONSTITUTIVE PHOTOMORPHOGENIC 1-like (COP1-like) genes suggests that the PHYA-mediated pathway was attenuated by red light. In addition, genes involved in lateral root and root hair formation, root plastid development, phenylpropanoid metabolism, and hormone signalling were also regulated by exposure to red light. Interestingly, members of the RPT2/NPH3 (ROOT PHOTOTROPIC 2/NON PHOTOTROPIC HYPOCOTYL 3) family, which have been shown to mediate blue-light-induced phototropism, were also differentially regulated in roots in red light. Therefore, these results suggest that red and blue light pathways interact in roots of seedlings and that many elements involved in red-light-signalling found in the aerial portions of the plant are differentially expressed in roots within 1 h of red light exposure.
ATP-Binding Cassette Transporters↗
The coevolution of insect muscle TpnT and TpnI gene isoforms.
In bilaterians, the main regulator of muscle contraction is the troponin (Tpn) complex, comprising three closely interacting subunits (C, T, and I). To understand how evolutionary forces drive molecular change in protein complexes, we have compared the gene structures and expression patterns of Tpn genes in insects. In this class, while TpnC is encoded by multiple genes, TpnT and TpnI are encoded by single genes. Their isoform expression pattern is highly conserved within the Drosophilidae, and single orthologous genes were identified in the sequenced genomes of Drosophila pseudoobscura, Anopheles gambiae, and Apis mellifera. Apis expression patterns also support the equivalence of their exon organization throughout holometabolous insects. All TpnT genes include a previously unidentified indirect flight muscle (IFM)-specific exon (10A) that has evolved an expression pattern similar to that of exon 9 in TpnI. Thus, expression patterns, sequence evolution trends, and structural data indicate that Tpn genes and their isoforms have coevolved, building species- and muscle-specific troponin complexes. Furthermore, a clear case can be made for independent evolution of the IFM-specific isoforms containing alanine/proline-rich sequences. Dipteran genomes contain one tropomyosin gene that encodes one or two high-molecular weight isoforms (TmH) incorporating APPAEGA-rich sequences, specifically expressed in IFM. Corresponding exons do not exist in the Apis tropomyosin gene, but equivalent sequences occur in a high-molecular weight Apis IFM-specific TpnI isoform (TnH). Overall, our approach to comparatively analyze supramolecular complexes reveals coevolutionary trends not only in gene families but in isoforms generated by alternative splicing.
Amino Acid Sequence↗