Search Results for "data capture framework"

polyglot framework using Python/C++/JavaScript to fast develop applications to analyze biological sequences

An open source framework for LC-MS based proteomics and metabolomics. OpenMS offers data structures and algorithms for the processing of mass spectrometry data. The library is written in C++. Our source code and wiki lives on GitHub (https://github.com/OpenMS/OpenMS).

The OpenGEREA is a open enrichment analysis framework for gene expression regulation data analysis.

CARGO is a high-level framework that can semi-automatically generate software systems optimized for the compressed storage of arbitrary types of large genomic data collections. Straightforward applications of CARGO methods to compress FASTQ and SAM format archives require only a few lines of code, produce solutions that match and sometimes outperform specialized format-tailored compressors, and scale well to multi-TB datasets.

...A nucleotide assembly-based strategy does not fare much better since metagenomic assemblies are typically very fragmented and also leave a large fraction of reads unassembled. We present a method for reconstructing complete protein sequences directly from NGS metagenomic data. Our framework is based on a novel Short Peptide Assembler (SPA) that assembles protein sequences from their constituent peptide fragments identified on short reads. We also present a new implementation of SPA based on suffix array (SFA-SPA) which runs significantly faster than SPA. Youngik Yang, Cuncong Zhong, and Shibu Yooseph* J. ...

Scipion is an image processing framework to obtain 3D models of macromolecular complexes using Electron Microscopy (3DEM). It integrates several software packages and presents an unified interface for both biologists and developers. Scipion allows to execute workflows combining different software tools, while taking care of formats and conversions. Additionally, all steps are tracked and can be reproduced later on. Xmipp is a well-known package in the EM image processing. It is integrated...

From Amat et al., Nature Methods, 2014*: "The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (1) generality, by reconstructing cell lineages in four-dimensional, terabyte-sized image data of fruit-fly, zebrafish and mouse embryos, acquired with three different types of fluorescence microscopes, (2) scalability, by analyzing advanced stages of development with up to 20,000 cells per time point, at 26,000 cells min-1 on a single computer workstation, and (3) ease of use, by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. ...

...The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems).

A collection of tools for working with the comparative data analysis ontology including import/export facilities for common phylogenetic file formats, and also a triple-store framework.

MIVF - Medical Imaging and Visualization Factory, is a framework for medical applications. It supplies a platform, in which image processing and 3D visualization algorithms can be employed as reusable components (functional modules or plugins).

4-Dimensional Cell Simulator (4DiCeS) is a framework on hybrid (stochastic and deterministic) modeling and simulation of (whole) cell environments in 4D. The framework may incorporate any reaction and diffusion algorithms applicable in a 4D grid layout.

CompuCell3D is a software framework for multimodel simulations of biocomplexity problems. It has been developed as an ongoing project for the Interdisciplinary Center for the Study of Biocomplexity at the university of Notre Dame.