Unlock your biological data

?

Try: RNA sequencing CRISPR Genomic databases DESeq

1 - 13 of 13 results
filter_list Filters
language Programming Language
build Technology
healing Disease
settings_input_component Operating System
tv Interface
computer Computer Skill
copyright License
1 - 13 of 13 results
HOMER / Hypergeometric Optimization of Motif EnRichment
star_border star_border star_border star_border star_border
star star star star star
(5)
Performs peak finding and downstream data analysis for next-generation sequencing analysis. HOMER affords several tools and methods to make use of ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and other types of functional genomics sequencing data sets. This software offers support to UCSC visualization, peaks annotation, quantification of transcripts and repeats or differential features, enrichment and expression.
MEME Suite
star_border star_border star_border star_border star_border
star star star star star
(1)
Provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms--MAST, FIMO and GLAM2SCAN--allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm TOMTOM. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and TOMTOM), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters.
SignalSpider
star_border star_border star_border star_border star_border
star star star star star
(1)
Serves as a probabilistic model for deciphering the combinatorial genome-wide binding patterns of DNA-binding proteins from normalized ChIP-Seq profiles. SignalSpider extracts higher-order binding patterns among the DNA-binding proteins of interests from their ChIP-Seq data. This model can cluster genome into several region types and considers the ChIP-Seq signal contributions from weak bindings of transcription factors (TFs).
Jmosaics
A probabilistic method for jointly analyzing multiple ChIP-seq datasets. jMOSAiCS (joint model-based one- and two-sample analysis and inference for ChIP-seq) is a probabilistic model for integrating multiple ChIP-seq datasets to identify combinatorial patterns of enrichment. The key components of jMOSAiCS are base models for the sequencing reads of each individual ChIP-seq experiment and a model that governs the relationship of enrichment among different samples. It facilitates joint analysis of multiple ChIP-seq datasets for both identifying enrichment patterns of a single TF across multiple conditions and characterizing enrichment patterns of multiple epigenomic marks in one or more conditions.
MM-ChIP / Model-based Meta-analysis of ChIP data
Allows the integrative analysis of ChIP-chip/seq data across platforms and between laboratories. MM-ChIP proceeds by modeling the characteristic fragment size of the sequenced ChIP-DNA library for each individual data source. It uses then the 3’ direction to represent the protein-DNA interaction sites. Finally, a sliding window is used to score the significance of signal enrichment in the ChIP samples by measuring and comparing tags within the same windows between ChIP.
MuSERA / Multiple Sample Enriched Region Assessment
A broadly useful standalone tool for both interactive and batch analysis of combined evidence from enriched regions (ERs) in multiple ChIP-seq or DNase-seq replicates. Besides rigorously combining sample replicates to increase statistical significance of detected ERs, it also provides quantitative evaluations and graphical features to assess the biological relevance of each determined ER set within its genomic context; they include genomic annotation of determined ERs, nearest ER distance distribution, global correlation assessment of ERs and an integrated genome browser.
DChIPRep
Compares profiles of enrichment in histone modifications around classes of genomic elements, e.g. transcription start sites (TSS). DChIPRep tests for differential enrichment at each nucleotide position of a metagene/metafeature profile and determines positions with significant differences in enrichment between experimental groups. DChIPRep provides two plotting functions to represent and inspect the final results of the analysis. The yeast case study demonstrates DChIPRep’s favourable performance when compared to a pipeline inspired by the csaw-package for differential binding analysis.
FullSignalRanker
To fully understand a gene's function, it is essential to develop probabilistic models on multiple ChIP-Seq profiles for deciphering the combinatorial gene transcription. To this end, we propose FullSignalRanker for regression tasks on ChIP-Seq data. The proposed method is compared with other existing methods on ENCODE ChIP-seq datasets, demonstrating its regression and classification ability. The results suggest that FullSignalRanker is the best-performing method for recovering the signal ranks on the promoter and enhancer regions. In addition, FullSignalRanker is also the best-performing method for peak sequence classification.
0 - 0 of 0 results
1 - 6 of 6 results
filter_list Filters
computer Job seeker
Disable 2
person Position
thumb_up Fields of Interest
public Country
language Programming Language
1 - 6 of 6 results

By using OMICtools you acknowledge that you have read and accepted the terms of the end user license agreement.