Search Results for "chip"

...After data is available, statistical analysis, regression analysis, time series analysis, machine learning, and deep learning can be performed. For individual stocks, provide visual analysis of technical, fundamental, and chip levels. According to different strategies, back-test analysis is performed to provide performance, profit and loss, and stock selection targets of different strategy investment portfolios.

GDS3D is a cross-platform 3D hardware accelerated viewer for chip layouts. Read standalone GDS files or use the Cadence plugin for easy integration with your Virtuoso environment. Developed by PhDs of the IC-Design Group, University of Twente, The Netherlands The GDS3D project is also hosted on Github: https://github.com/icdut24/GDS3D, but currently not actively maintained by us. Forks can be found at https://github.com/trilomix/GDS3D and https://github.com/skuep/GDS3D

1. Added the ability to directly work with the chip (parameter "settings.ini" - Direct=1). 2. The possibility of cyclic writing/recording from realtime to a file and subsequent playback from it is added (double click of the left mouse button anywhere in the top spectrogram). The size of the MB file is specified in the settings file (Cyclic file size=100).

jChIP is a GUI-based Java application for ChIP-Seq data analysis. It was created to build binding profiles between proteins and loci in the genome. In addition it computes statistics of the number of loci/positions containing specified amount of tags. jChIP is able to load data in several common formats (SAM, BAM, WIG, BED, Bowtie) and download loci definitions directly from the Ensembl database.

Here we developed a novel analysis framework named MACE (model-based analysis of ChIP-exo) dedicated to ChIP-exo data analysis. MACExo has the following four steps: 1) sequencing data normalization and bias correction; 2) signal consolidation and noise reduction; 3) single nucleotide resolution border detection using Chebyshev Inequality; and 4) border matching using Gale-Shapley’s stable matching algorithm. When applied to yeast Reb1 and human CTCF ChIP-exo data, MACE is able to define TFBSs with higher sensitivity, specificity and spatial resolution, as evidenced by multiple criteria, such as motif enrichment, sequence conservation, nucleosome positioning, and open chromatin states.

...(ii) COV2HTML, a visualization interface allowing a real-time analysis of data with selected criteria. Thus this interface offers a visualization of NGS mapping coverage data (DNA-seq, RNA-seq, ChIP-seq and TSS) performed in different prokaryotic organisms (bacteria, viruses...) or different experimental conditions (mutant versus wild type strains or different growth states…) facilitating studies.

This is the beta-version of a shape-based peak calling tool for ChIP-Seq data analysis. It is built using Generalized Linear Model with Negative binomial distribution. This tool accounts for the common peak shape, local variability of peak shifting distance and negative binomial background noise.