Computer programs for single channel kinetic analysis

Programs are represented that permit us to solve the following three problems of single channel kinetic activity: 1) to fulfill a preliminary analysis of single channel activity, i.e., to estimate the minimum number of agonist molecules that bind with a channel structure, the minimum number of open and closed kinetic states of the channel, and possible connections between these states, to check the condition of thermodynamic equilibrium and adequacy of a Markovian assumption for modeling of the channel kinetics; 2) to select the model of channel gating: for a given number of kinetic states, the potential models that permit us to fit a list of dwell-times observed in an experiment are examined; the model is selected by a criterium of the maximum likelihood; 3) to simulate the response to a rapid change of the agonist concentration of single channels that demonstrate processes of adaptation or inactivation.     

A set of Macintosh computer programs for the design and analysis of synthetic genes

Computer programs that can be used for the design of syntheticgenes and that are run on an Apple Macintosh computer are described.These programs determine nucleic acid sequences encoding aminoacid sequences. They select DNA sequences based on codon usageas specified by the user, and determine the placement of basechanges that can be used to create restriction enzyme siteswithout altering the amino acid sequence. A new algorithm forfinding restriction sites by translating the restriction endonucleasetarget sequence in all three reading frames and then searchingthe given peptide or protein amino acid sequence with theseshort restriction enzyme peptide sequences is described. Examplesare given for the creation of synthetic DNA sequences for thebovine prethrombin-2 and ribonuclease A genes Received on October 18, 1988; accepted on December 9, 1988     

A modular framework for gene set analysis integrating multilevel omics data

Modern high-throughput methods allow the investigation of biological functions across multiple ‘omics’ levels. Levels include mRNA and protein expression profiling as well as additional knowledge on, for example, DNA methylation and microRNA regulation. The reason for this interest in multi-omics is that actual cellular responses to different conditions are best explained mechanistically when taking all omics levels into account. To map gene products to their biological functions, public ontologies like Gene Ontology are commonly used. Many methods have been developed to identify terms in an ontology, overrepresented within a set of genes. However, these methods are not able to appropriately deal with any combination of several data types. Here, we propose a new method to analyse integrated data across multiple omics-levels to simultaneously assess their biological meaning. We developed a model-based Bayesian method for inferring interpretable term probabilities in a modular framework. Our Multi-level ONtology Analysis (MONA) algorithm performed significantly better than conventional analyses of individual levels and yields best results even for sophisticated models including mRNA fine-tuning by microRNAs. The MONA framework is flexible enough to allow for different underlying regulatory motifs or ontologies. It is ready-to-use for applied researchers and is available as a standalone application from http://icb.helmholtz-muenchen.de/mona.     

Patient-oriented gene set analysis for cancer mutation data

Recent research has revealed complex heterogeneous genomic landscapes in human cancers. However, mutations tend to occur within a core group of pathways and biological processes that can be grouped into gene sets. To better understand the significance of these pathways, we have developed an approach that initially scores each gene set at the patient rather than the gene level. In mutation analysis, these patient-oriented methods are more transparent, interpretable, and statistically powerful than traditional gene-oriented methods.     

Computer programs for the analysis of cellular survival data

Four programs have been written to enable radiobiologists to build a computer data base of cellular dose-survival data, calculate cell survival with a correction for cell multiplicity at the time of irradiation, fit various survival models to the data by iteratively weighted least squares, and calculate the ratio of survival levels corresponding to specified doses or the ratio of doses that produce specified survival levels (e.g., oxygen enhancement ratio or relative biological effectiveness). The programs make plots of survival curves and data, and they calculate standard errors and confidence intervals of the fitted survival curve parameters and ratios. The programs calculate survival curves for the linear-quadratic, repair-saturation, single-hit multitarget, linear-multitarget, and repair-misrepair models of cell survival and have been designed to accommodate the addition of other survival models in the future. The programs can be used to compare the accuracy with which different models fit the data, determine if a difference in fit is statistically significant, and show how the estimated value of a survival curve parameter, such as the extrapolation number or the final slope, varies with the survival model. The repair of radiation-induced damage is analyzed in a novel way using these programs.     

Digitization of sedimentation equilibrium and velocity data for analysis by minicomputer

A procedure is described whereby phosphorylated seryl residues may be unequivocally identified during the sequential degradation of a polypeptide chain by the Edman technique. The phosphoseryl residue, Ser(P), was first converted by treatment with methylamine in dilute alkali to a β-methylaminoalanyl residue which was split from the polypeptide by the degradative procedure as the derived phenylthiohydantoin. This was identified by high-performance liquid chromatography. The procedure was highly effective when the Ser(P) occupied an isolated position in a polypeptide chain but was less so when grouped consecutively with other Ser(P).     

Differentiation between equilibrium and nonequilibrium kinetic systems by noise analysis.

Methods for the differentiation between equilibrium and nonequilibrium steady-state kinetic mechanisms based on fluctuation and noise analysis are discussed. Specifically, the "sharpening" in the auto noise power spectrum is shown to be a useful indicator in identifying a nonequilibrium steady state.     

Exact and user-friendly kinetic analysis of the two-step rapid equilibrium Michaelis-Menten mechanism

Most enzyme kinetic experiments are carried out under pseudo-first-order conditions, that is, when one of the reactant species (the enzyme or the substrate) is in a large excess of the other species. More accurate kinetic information about the system can be gained without the restrictions of the pseudo-first-order conditions. We present a practical and general method of analysis of the common two-step rapid equilibrium Michaelis-Menten mechanism. The formalism is exact in that it does not involve any other approximations such as the steady-state, limitations on the reactant concentrations or on reaction times. We apply this method to the global analysis of kinetic progress curves for bovine alkaline phosphatase assays carried out under both pseudo-first-order and pseudo-second-order conditions.     

Interactive computer programs in sequence data analysis

We present interactive computer programs for the analysis of nucleic acid sequences. In order to handle these programs, minimum computer experience is sufficient. The nucleotide sequence of the human gamma globin gene complex is used as an example to illustrate the data analysis.     

Comparison of different microarray data analysis programs and description of a database for microarray data management

Data analysis and management represent a major challenge for gene expression studies using microarrays. Here, we compare different methods of analysis and demonstrate the utility of a personal microarray database. Gene expression during HIV infection of cell lines was studied using Affymetrix U-133 A and B chips. The data were analyzed using Affymetrix Microarray Suite and Data Mining Tool, Silicon Genetics GeneSpring, and dChip from Harvard School of Public Health. A small-scale database was established with FileMaker Pro Developer to manage and analyze the data. There was great variability among the programs in the lists of significantly changed genes constructed from the same data. Similarly choices of different parameters for normalization, comparison, and standardization greatly affected the outcome. As many probe sets on the U133 chip target the same Unigene clusters, the Unigene information can be used as an internal control to confirm and interpret the probe set results. Algorithms used for the determination of changes in gene expression require further refinement and standardization. The use of a personal database powered with Unigene information can enhance the analysis of gene expression data.     

ddrage: A data set generator to evaluate ddRADseq analysis software

High‐throughput sequencing makes it possible to evaluate thousands of genetic markers across genomes and populations. Reduced‐representation sequencing approaches, like double‐digest restriction site‐associated DNA sequencing (ddRADseq), are frequently applied to screen for genetic variation. In particular in nonmodel organisms where whole‐genome sequencing is not yet feasible, ddRADseq has become popular as it allows genomewide assessment of variation patterns even in the absence of other genomic resources. However, while many tools are available for the analysis of ddRADseq data, few options exist to simulate ddRADseq data in order to evaluate the accuracy of downstream tools. The available tools either focus on the optimization of ddRAD experiment design or do not provide the information necessary for a detailed evaluation of different ddRAD analysis tools. For this task, a ground truth, that is, the underlying information of all effects in the data set, is required. Therefore, we here present ddrage , the ddRA D Data Set Ge nerator, that allows both developers and users to evaluate their ddRAD analysis software. ddrage allows the user to adjust many parameters such as coverage and rates of mutations, sequencing errors or allelic dropouts, in order to generate a realistic simulated ddRADseq data set for given experimental scenarios and organisms. The simulated reads can be easily processed with available analysis software such as stacks or pyrad and evaluated against the underlying parameters used to generate the data to gauge the impact of different parameter values used during downstream data processing.     

Biosensor-based estimation of kinetic and equilibrium constants

The steady-state affinity constant for the interaction of glutathione S-transferase (GST) with anti-GST immunoglobulin (IgG) was determined by solution-phase equilibrium analysis. A Biacore concentration assay for the determination of free anti-GST IgG was employed giving a Kd value of 6.83 x 10(-10) M. A simple 1:1 solution-phase, equilibrium model approximated the data well. Furthermore, saturation studies showed a maximum occupation of approximately 50%. The choice of affinity-capture ligand, used to anchor anti-GST IgG to the hydrogel, influenced the interaction curves, as evidenced by contact-time-dependent dissociation-phase curves. This was apparent when performing the analysis on anti-mouse Fc-coated surfaces. When the interaction was conducted on a protein A-coated CM5 sensor chip, the interaction conformed well to ideal behavior and was selected for kinetic analysis of the GST interaction.     

Protein folding: defining a "standard" set of experimental conditions and a preliminary kinetic data set of two-state proteins

Recent years have seen the publication of both empirical and theoretical relationships predicting the rates with which proteins fold. Our ability to test and refine these relationships has been limited, however, by a variety of difficulties associated with the comparison of folding and unfolding rates, thermodynamics, and structure across diverse sets of proteins. These difficulties include the wide, potentially confounding range of experimental conditions and methods employed to date and the difficulty of obtaining correct and complete sequence and structural details for the characterized constructs. The lack of a single approach to data analysis and error estimation, or even of a common set of units and reporting standards, further hinders comparative studies of folding. In an effort to overcome these problems, we define here a "consensus" set of experimental conditions (25 degrees C at pH 7.0, 50 mM buffer), data analysis methods, and data reporting standards that we hope will provide a benchmark for experimental studies. We take the first step in this initiative by describing the folding kinetics of 30 apparently two-state proteins or protein domains under the consensus conditions. The goal of our efforts is to set uniform standards for the experimental community and to initiate an accumulating, self-consistent data set that will aid ongoing efforts to understand the folding process.     

GeneticStudio: a suite of programs for spatial analysis of genetic-marker data

The analysis of genetic marker data is increasingly being conducted in the context of the spatial arrangement of strata (e.g. populations) necessitating a more flexible set of analysis tools. GeneticStudio consists of four interacting programs: (i) Geno a spreadsheet-like interface for the analysis of spatially explicit marker-based genetic variation; (ii) Graph software for the analysis of Population Graph and network topologies, (iii) Manteller, a general purpose for matrix analysis program; and (iv) SNPFinder, a program for identifying single nucleotide polymorphisms. The GeneticStudio suite is available as source code as well as binaries for OSX and Windows and is distributed under the GNU General Public License.