Search Results for "profiles"

BioXTAS RAW is a program for analysis of Small-Angle X-ray Scattering (SAXS) data. 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. ...

...MToolBox includes an updated computational strategy to assemble mitochondrial genomes from Whole Exome and/or Genome Sequencing (PMID: 22669646) and an improved fragment-classify tool (PMID:22139932) for haplogroup assignment, functional and prioritization analysis of mitochondrial variants. MToolBox provides pathogenicity scores, profiles of genome variability and disease-associations for mitochondrial variants. MToolBox provides also a Variant Call Format file (version 4.0) featuring, for the first time, allele-specific heteroplasmy. Please, check out the most recent updates of the source code with the Github repository of MToolBox: https://github.com/mitoNGS/MToolBox or visit the Web version of MToolBox @ MSeqDR: https://mseqdr.org/mtoolbox.php

We developed a novel clustering-free method, microarray-associated motif analyzer (MAMA), to predict novel cis-acting elements based on weighted sequence similarities and gene expression profiles in microarray analyses. Simulation of gene expression was performed using a support vector machine and based on the presence of predicted motifs and motif pairs. The accuracy of simulated gene expression was used to evaluate the quality of prediction and to optimize the parameters used in this method. After optimization, MAMA accurately simulated more than 87% of gene expression. ...

This application allows observing genome rearrangements in prokaryotic genomes. It provides rearrangements frequencies profiles and genomes graph representation.

Genome coverage, the number of sequencing reads mapped to a position in a genome, is an insightful indicator of irregularities within sequencing experiments. While the average genome coverage is frequently used within algorithms in computational genomics, the complete information available in coverage profiles (i.e. histograms over all coverages) is currently not exploited to its full extent. Thus, biases such as fragmented or erroneous reference genomes often remain unaccounted for. Making this information accessible can improve the quality of sequencing experiments and quantitative analyses. fitGCP is a framework for fitting mixtures of probability distributions to genome coverage profiles. ...