Search Results for "events"

ViReMa (Viral Recombination Mapper) detects and reports recombination or fusion events in virus genomes using deep sequencing datasets. Feb 2014 - Our paper (Open Access) is available at Nucleic Acids Research: "Discovery of functional genomic motifs in viruses with ViReMa–a Virus Recombination Mapper–for analysis of next-generation sequencing data" http://nar.oxfordjournals.org/content/42/2/e11 This is an on-going project and updates will be regularly posted.

Turku Event Extraction System (TEES) is a free and open source natural language processing system developed for the extraction of events and relations from biomedical text. It is written mostly in Python, and should work in generic Unix/Linux environments. Currently, the TEES source code repository still remains on GitHub at http://jbjorne.github.com/TEES/ where there is also a wiki with more information.

LGTmate is a simple and powerful bioinformatic software tool to identify lateral gene transfer events and contaminants in Eukaryotic proteomes. It is available as a GUI or command-line application, with no dependencies and no installation required.

.... -------- Eukaryotic pathogens have complicated and dynamic genomes. To facilitate analysis of copy number variations (CNV), single nucleotide polymorphisms (SNPs), and loss of heterozygosity (LOH) events in Candida albicans, the most common human fungal pathogen, we developed a pipeline for analyzing diverse genome-scale datasets from microarray, deep sequencing, and restriction site associated DNA sequence experiments for clinical and laboratory strains. The YMAP pipeline automatically illustrates genome-wide information in a single intuitive figure and is readily modified for the analysis of other categories of data and other pathogen species with small genomes.

This software detects homologous recombination events (HREs) from SNP data. Based on SNP alleles calls and locations, it breaks the genomes into locally colinear blocks, and looks for cases where SNPs do not agree with the vertical pattern of inheritance in a phylogeny. It applies a dynamic programming algorithm to model whether changes within a block are likely a result of mutations, sequencing errors, or HRE.

This software uses computer vision algorithms for mining sequence data from telemonitoring data with CBRs. We propose an approach which treats the detection of changes in behavior detected with a sensor/video fusion, which occur at radically different time-scales, through a CBR in two levels: low and high level. The system is always updating the database with the daily data.