Search Results for "factor"

...The software enables: creation of 1D scattering profiles from 2D detector images, standard data operations such as averaging and subtraction, analysis of radius of gyration (Rg) and molecular weight, and advanced analysis using GNOM and DAMMIF as well as electron density reconstructions using DENSS. It also allows easy processing of inline SEC-SAXS data and data deconvolution using the evolving factor analysis (EFA) or the regularized alternating least squares (REGALS) methods. Active source code is now maintained on github: https://github.com/jbhopkins/bioxtasraw To install: Check the instructions available at: http://bioxtas-raw.readthedocs.io/en/latest/install.html and in the Files tab. User guides: RAW guides are available at: http://bioxtas-raw.readthedocs.io/ and in the Files tab. ...

The only open source counter to count any items the simplest and easiest way with the keyboard, or the mouse specifically on images. After associating a key to each item, or a predefined graphical symbol for images, pressing the key or clicking on the image increments its associated counter, and displays (for the images) the symbol at the mouse's pointer location. Such a project is so simple a child could use it!

MutaNET comes with a next generation sequencing (NGS) pipeline that calls mutations based on paired-end NGS reads, an automated mutation analysis tool and various file converters and mergers. The mutation analysis feature considers the coding region, protein domains, regulation and transcription factor binding site information, and can be used to analyse the potential impact of mutations on antibiotic resistance.

Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. Complexation of transcription factors and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enable to infer controlled genes as well as the exerted regulatory mechanism. We developed DACO (domain-aware cohesiveness optimization), a novel algorithm that combines...

*** Please note this repository is now deprecated as of January 2018. All EFO development and source code has been moved to https://github.com/EBISPOT/efo *** The Experimental Factor Ontology (EFO) available from http://www.ebi.ac.uk/efo is an application focused ontology modeling the experimental factors in ArrayExpress and constructing mappings to multiple existing domain specific ontologies. To submit new terms, features or report bugs please submit a ticket at the following: http://www.ebi.ac.uk/panda/jira/secure/CreateIssue!...

Processing of smallRNA-Seq data to gather biologically relevant information requires application of multiple statistical and bioinformatics tools from different sources, each focusing on a specific step of the analysis pipeline. The analytical workflow can be challenging for the continuous interventions by the operator, a critical factor when large numbers of datasets need to be analyzed at once. To allow a flexible and comprehensive analysis of smallRNA-Seq data we designed a novel modular pipeline, called iMir, integrating multiple open source modules and resource in an automated workflow, devising different statistical approaches to analyze data rigorously. iMir comprises also a Graphical User Interface (GUI), so that the pipeline is particularly suited for biologist and early stage bioinformaticians and produces both graphics and text outputs.

Transcription Factor Analysis using SELEX with High-Throughput Sequencing (TFAST) is software developed by the Mobley lab at the University of Michigan designed to assist with transcription factor binding site discovery using data generated from aptamer-free SELEX-seq (afSELEX-seq).

This R package can be used to identify TRMs using experimental evidence from a single ChIP-seq experiment. It combines computational predicted transcription factor (TF) binding sites, gene expression and protein-protein interaction (PPI) data and use it to predict TRMs.

We present an improved ball and stick representation called HyperBalls. This type of depiction is particularly useful to represent dynamic phenomena, such as the evolution of non covalent bonds and takes advantage of GPU capabilities. HyperBalls are now fully integrated into the UnityMol software and are actively developed in that context. You will find the most up-to-date versions of the shaders in the http://unitymol.sourceforge.net project.

GEM is a computational method for identification of transcription factor modulators, using expression datasets. The method tests if the correlation between factor and target gene depends on the expression of the modulator.