Development of a computational environment for MIT electromagnetic equation propagation simulator

Abstract Background The relentless continuing emergence of new genomic sequencing protocols and the resulting generation of ever larger datasets continue to challenge the meaningful summarization and visualization of the underlying signal generated to answer important qualitative and quantitative biological questions. As a result, the need for novel software able to reliably produce quick, comprehensive, and easily repeatable genomic signal visualizations in a user-friendly manner is rapidly re-emerging. Results recoup is a Bioconductor package for quick, flexible, versatile, and accurate visualization of genomic coverage profiles generated from Next Generation Sequencing data. Coupled with a database of precalculated genomic regions for multiple organisms, recoup offers processing mechanisms for quick, efficient, and multi-level data interrogation with minimal effort, while at the same time creating publication-quality visualizations. Special focus is given on plot reusability, reproducibility, and real-time exploration and formatting options, operations rarely supported in similar visualization tools in a profound way. recoup was assessed using several qualitative user metrics and found to balance the tradeoff between important package features, including speed, visualization quality, overall friendliness, and the reusability of the results with minimal additional calculations. Conclusion While some existing solutions for the comprehensive visualization of NGS data signal offer satisfying results, they are often compromised regarding issues such as effortless tracking of processing and preparation steps under a common computational environment, visualization quality and user friendliness. recoup is a unique package presenting a balanced tradeoff for a combination of assessment criteria while remaining fast and friendly.

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BASIC ELEMENTS OF BIOLOGICAL COMPUTATIONAL SYSTEMS

International Journal of Modern Physics C ◽

10.1142/s0129183194000386 ◽

1994 ◽

Vol 05 (02) ◽

pp. 313-315 ◽

Cited By ~ 20

Author(s):

CHARLES H. ANDERSON

Keyword(s):

Probability Density ◽

Probability Density Functions ◽

Density Functions ◽

Cortical Circuits ◽

Computational Environment ◽

Computational Systems

This paper explores some basic representational and implementation issues arising from the premise that cortical circuits operate on probability density functions to reason about analog quantities. Some insight is provided into why neurobiological systems can appear messy, while at the same time provide a rich and robust computational environment.

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