PubMed HealthSearch

PubMed · 8742720

The micrograph data processing program.

Abstract

MDPP is a fully featured general-purpose image processing package primarily written to support research in structural biology using data gathered by electron microscopy. It has focused on the analysis of images using Fourier techniques, particularly periodic plane-layer and helical structures, but has extensive tools for other processing options (e.g., point-counting, image quantitation, DNA sequencing, and display). Three-dimensional reconstruction methods are supported, including iterative deconvolution schemes for light micrographs. The basic program is command line-driven, but the user can choose to write sophisticated command scripts and use a menu-driven full-screen or Motif interface to call them. Color images are supported with both color palette and RGB options. Extensive documentation is accessible from within the program as online HELP and also as HTML. Care has been taken to support interfaces to other image processing packages, e.g., MacIntosh applications via the TIFF image format, and other EM-targeted image processing packages. The paper provides an overview of the program, its design, and implementation and outlines future plans for software development. Some general issues concerning image processing program design are discussed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

P R Smith, S M Gottesman. The micrograph data processing program.. https://doi.org/10.1006/jsbi.1996.0007

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation

The relationship between insulin bioactivity and structure in the NH2-terminal A-chain helix.

Studies of naturally occuring and chemically modified insulins have established that the NH2-terminal helix of the A-chain is important in conferring affinity in insulin-receptor interactions. Nevertheless, the three-dimensional structural basis for these observations has not previously been studied in detail. To correlate structure and function in this region of the molecule, we have used the solution structure of an engineered monomer (GluB1, GluB10, GluB16, GluB27, desB30)-insulin (4E insulin) as a template for design of A-chain mutants associated with enhanced or greatly diminished affinity for the insulin receptor. In the context of 4E insulin, the employed mutants, i.e. ThrA8-->His and ValA3-->Gly, result in species with 143% and 0.1% biological activity, respectively, relative to human insulin. The high-resolution NMR studies reveal two well-defined structures each resembling the template. However, significant structural differences are evident notably in residues A2-A8 and their immediate environment. In comparison with the template structure, the A8His mutation enhances the helical character of residues A2-A8. This structural change leads to additional exposure of a hydrophobic patch mainly consisting of species invariant residues. In contrast, the A3Gly mutation leads to stretching and disruption of the A2-A8 helix and changes both the dimensions and the access to the hydrophobic patch exposed in the more active insulins. We conclude that the mutations induce small, yet decisive structural changes that either mediate or inhibit the subtle conformational adjustments involved in the presentation of this part of the insulin pharmacophore to the receptor.

Computer Simulation