Search Results for "level set methods"

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.

MSL is a C++ library that enables the computational study of macromolecules. The MSL library is not a program (although some applications are distributed) but a tool for scientist to code their own molecular modeling methods. Philosophy The main goal is to create a set of tools that enable the computational study of macromolecules with relative ease at all levels, from simple operations (for example, load a PDB and measure a distance or edit a dihedral) to complex applications (protein modeling or design). MSL is developed in the Senes Lab at the University of Wisconsin-Madison and by a team of developers/users from other research laboratories. ...

LiverSegm has been developed with the processing of liver images in mind. This software implements a level set based variational approach that incorporates shape priors and appearance models. It uses ITK Snap as interface.

BIL++ is a set of standalone C++ packages for data processing in Bioinformatics (Graph mining, Bayesian networks, Genetic algorithm, Discretization, Gene expression data analysis, Hypothesis testing).

A set of utilities to handle individual-level genotype data files

BigBang/Horizon is a proteomics data analysis pipeline with focus on the shotgun LC/MSMS workflow.

Cis-Regulatory analysis modules. Includes frequenc/weight matrices scanning at genome level, annotation by using DB access to ensembl, genekeydb, etc. and visualization (through Bioperl or as custom UCSC tracks). May include gene set analysis in future.

trimAl can consider several parameters, alone or in multiple combinations, in order to select the most-reliable positions in the alignment. These include the proportion of sequences with a gap, the level of residue similarity and, if several alignments for the same set of sequences are provided, the consistency level of columns among alignments. Moreover, trimAl allows to manually select a set of columns and sequences to be removed from the alignment. trimAl implements a series of automated algorithms that trim the alignment searching for optimum thresholds based on inherent characteristics of the input alignment, to be used so that the signal-to-noise ratio after alignment trimming phase is increased. ...