Showing 15 of total 15 results (show query)
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TFBSTools:Software Package for Transcription Factor Binding Site (TFBS) Analysis
TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software.
Maintained by Ge Tan. Last updated 3 days ago.
motifannotationgeneregulationmotifdiscoverytranscriptionalignment
10.0 match 28 stars 12.36 score 1.1k scripts 18 dependentsbioc
universalmotif:Import, Modify, and Export Motifs with R
Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others.
Maintained by Benjamin Jean-Marie Tremblay. Last updated 4 months ago.
motifannotationmotifdiscoverydataimportgeneregulationmotif-analysismotif-enrichment-analysissequence-logocpp
10.0 match 28 stars 11.04 score 342 scripts 12 dependentsbioc
memes:motif matching, comparison, and de novo discovery using the MEME Suite
A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows.
Maintained by Spencer Nystrom. Last updated 5 months ago.
dataimportfunctionalgenomicsgeneregulationmotifannotationmotifdiscoverysequencematchingsoftware
10.0 match 49 stars 8.68 score 117 scripts 1 dependentsbioc
RCAS:RNA Centric Annotation System
RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery.
Maintained by Bora Uyar. Last updated 5 months ago.
softwaregenetargetmotifannotationmotifdiscoverygotranscriptomicsgenomeannotationgenesetenrichmentcoverage
10.0 match 6.32 score 29 scripts 1 dependentsbioc
omicsViewer:Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer
omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript.
Maintained by Chen Meng. Last updated 2 months ago.
softwarevisualizationgenesetenrichmentdifferentialexpressionmotifdiscoverynetworknetworkenrichment
10.0 match 4 stars 6.02 score 22 scriptsbioc
MotifPeeker:Benchmarking Epigenomic Profiling Methods Using Motif Enrichment
MotifPeeker is used to compare and analyse datasets from epigenomic profiling methods with motif enrichment as the key benchmark. The package outputs an HTML report consisting of three sections: (1. General Metrics) Overview of peaks-related general metrics for the datasets (FRiP scores, peak widths and motif-summit distances). (2. Known Motif Enrichment Analysis) Statistics for the frequency of user-provided motifs enriched in the datasets. (3. De-Novo Motif Enrichment Analysis) Statistics for the frequency of de-novo discovered motifs enriched in the datasets and compared with known motifs.
Maintained by Hiranyamaya Dash. Last updated 2 months ago.
epigeneticsgeneticsqualitycontrolchipseqmultiplecomparisonfunctionalgenomicsmotifdiscoverysequencematchingsoftwarealignmentbioconductorbioconductor-packagechip-seqepigenomicsinteractive-reportmotif-enrichment-analysis
10.0 match 2 stars 5.48 score 6 scriptsbioc
periodicDNA:Set of tools to identify periodic occurrences of k-mers in DNA sequences
This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website.
Maintained by Jacques Serizay. Last updated 5 months ago.
sequencematchingmotifdiscoverymotifannotationsequencingcoveragealignmentdataimport
10.0 match 6 stars 5.26 score 5 scriptsbioc
rGADEM:de novo motif discovery
rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software.
Maintained by Arnaud Droit. Last updated 5 months ago.
microarraychipchipsequencingchipseqmotifdiscoveryopenmp
10.0 match 4.95 score 56 scriptsbioc
sarks:Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains
Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing.
Maintained by Dennis Wylie. Last updated 5 months ago.
motifdiscoverygeneregulationgeneexpressiontranscriptomicsrnaseqdifferentialexpressionfeatureextractionopenjdk
10.0 match 3 stars 4.78 score 3 scriptsbioc
BCRANK:Predicting binding site consensus from ranked DNA sequences
Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search
Maintained by Adam Ameur. Last updated 5 months ago.
10.0 match 4.49 score 26 scriptsbioc
seqArchR:Identify Different Architectures of Sequence Elements
seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos.
Maintained by Sarvesh Nikumbh. Last updated 5 months ago.
motifdiscoverygeneregulationmathematicalbiologysystemsbiologytranscriptomicsgeneticsclusteringdimensionreductionfeatureextractiondnaseqnmfnonnegative-matrix-factorizationpromoter-sequence-architecturesscikit-learnsequence-analysissequence-architecturesunsupervised-machine-learning
10.0 match 1 stars 4.48 score 9 scripts 1 dependentsbioc
pqsfinder:Identification of potential quadruplex forming sequences
Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4.
Maintained by Jiri Hon. Last updated 5 months ago.
motifdiscoverysequencematchinggeneregulationcpp
10.0 match 4.41 score 16 scriptsbioc
SELEX:Functions for analyzing SELEX-seq data
Tools for quantifying DNA binding specificities based on SELEX-seq data.
Maintained by Harmen J. Bussemaker. Last updated 5 months ago.
softwaremotifdiscoverymotifannotationgeneregulationtranscriptionopenjdk
10.0 match 4.30 score 8 scriptsbioc
nearBynding:Discern RNA structure proximal to protein binding
Provides a pipeline to discern RNA structure at and proximal to the site of protein binding within regions of the transcriptome defined by the user. CLIP protein-binding data can be input as either aligned BAM or peak-called bedGraph files. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats.
Maintained by Veronica Busa. Last updated 5 months ago.
visualizationmotifdiscoverydatarepresentationstructuralpredictionclusteringmultiplecomparison
10.0 match 4.08 score 12 scriptsbioc
magrene:Motif Analysis In Gene Regulatory Networks
magrene allows the identification and analysis of graph motifs in (duplicated) gene regulatory networks (GRNs), including lambda, V, PPI V, delta, and bifan motifs. GRNs can be tested for motif enrichment by comparing motif frequencies to a null distribution generated from degree-preserving simulated GRNs. Motif frequencies can be analyzed in the context of gene duplications to explore the impact of small-scale and whole-genome duplications on gene regulatory networks. Finally, users can calculate interaction similarity for gene pairs based on the Sorensen-Dice similarity index.
Maintained by Fabrício Almeida-Silva. Last updated 5 months ago.
softwaremotifdiscoverynetworkenrichmentsystemsbiologygraphandnetworkgene-regulatory-networkmotif-analysisnetwork-motifsnetwork-science
10.0 match 1 stars 4.00 score 2 scripts