R-universe search: cage

Showing 11 of total 11 results (show query)

bioc

CAGEfightR:Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor

CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization.

Maintained by Malte Thodberg. Last updated 5 months ago.

software transcription coverage geneexpression generegulation peakdetection dataimport datarepresentation transcriptomics sequencing annotation genomebrowsers normalization preprocessing visualization

32.0 match 8 stars 7.46 score 67 scripts 1 dependents

bioc

CAGEr:Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining

The _CAGEr_ package identifies transcription start sites (TSS) and their usage frequency from CAGE (Cap Analysis Gene Expression) sequencing data. It normalises raw CAGE tag count, clusters TSSs into tag clusters (TC) and aggregates them across multiple CAGE experiments to construct consensus clusters (CC) representing the promoterome. CAGEr provides functions to profile expression levels of these clusters by cumulative expression and rarefaction analysis, and outputs the plots in ggplot2 format for further facetting and customisation. After clustering, CAGEr performs analyses of promoter width and detects differential usage of TSSs (promoter shifting) between samples. CAGEr also exports its data as genome browser tracks, and as R objects for downsteam expression analysis by other Bioconductor packages such as DESeq2, CAGEfightR, or seqArchR.

Maintained by Charles Plessy. Last updated 5 months ago.

preprocessing sequencing normalization functionalgenomics transcription geneexpression clustering visualization

34.9 match 6.12 score 73 scripts

bioc

icetea:Integrating Cap Enrichment with Transcript Expression Analysis

icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis.

Maintained by Vivek Bhardwaj. Last updated 5 months ago.

immunooncology transcription geneexpression sequencing rnaseq transcriptomics differentialexpression cage expression rna-seq

11.5 match 2 stars 5.08 score 7 scripts

bioc

ORFik:Open Reading Frames in Genomics

R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more.

Maintained by Haakon Tjeldnes. Last updated 27 days ago.

immunooncology software sequencing riboseq rnaseq functionalgenomics coverage alignment dataimport cpp

4.9 match 33 stars 10.63 score 115 scripts 2 dependents

bioc

genomation:Summary, annotation and visualization of genomic data

A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input.

Maintained by Altuna Akalin. Last updated 5 months ago.

annotation sequencing visualization cpgisland cpp

3.8 match 75 stars 11.09 score 738 scripts 5 dependents

bedapub

designit:Blocking and Randomization for Experimental Design

Intelligently assign samples to batches in order to reduce batch effects. Batch effects can have a significant impact on data analysis, especially when the assignment of samples to batches coincides with the contrast groups being studied. By defining a batch container and a scoring function that reflects the contrasts, this package allows users to assign samples in a way that minimizes the potential impact of batch effects on the comparison of interest. Among other functionality, we provide an implementation for OSAT score by Yan et al. (2012, <doi:10.1186/1471-2164-13-689>).

Maintained by Iakov I. Davydov. Last updated 4 months ago.

design-of-experiments randomization

3.9 match 8 stars 7.28 score 24 scripts

bioc

crisprViz:Visualization Functions for CRISPR gRNAs

Provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework.

Maintained by Jean-Philippe Fortin. Last updated 5 months ago.

crispr functionalgenomics genetarget bioconductor bioconductor-package crispr-analysis crispr-design grna grna-sequence grna-sequences sgrna sgrna-design visualization

3.8 match 7 stars 6.23 score 6 scripts 2 dependents

ngreifer

fwb:Fractional Weighted Bootstrap

An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the 'boot' package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves drawing weights randomly that are applied to the data rather than resampling units from the data. See Xu et al. (2020) <doi:10.1080/00031305.2020.1731599> for details.

Maintained by Noah Greifer. Last updated 11 days ago.

3.6 match 6 stars 5.39 score 1 scripts

bioc

seqArchRplus:Downstream analyses of promoter sequence architectures and HTML report generation

seqArchRplus facilitates downstream analyses of promoter sequence architectures/clusters identified by seqArchR (or any other tool/method). With additional available information such as the TPM values and interquantile widths (IQWs) of the CAGE tag clusters, seqArchRplus can order the input promoter clusters by their shape (IQWs), and write the cluster information as browser/IGV track files. Provided visualizations are of two kind: per sample/stage and per cluster visualizations. Those of the first kind include: plot panels for each sample showing per cluster shape, TPM and other score distributions, sequence logos, and peak annotations. The second include per cluster chromosome-wise and strand distributions, motif occurrence heatmaps and GO term enrichments. Additionally, seqArchRplus can also generate HTML reports for easy viewing and comparison of promoter architectures between samples/stages.

Maintained by Sarvesh Nikumbh. Last updated 5 months ago.

annotation visualization reportwriting go motifannotation clustering

2.2 match 1 stars 4.00 score 2 scripts

bioc

edgeR:Empirical Analysis of Digital Gene Expression Data in R

Differential expression analysis of sequence count data. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models, quasi-likelihood, and gene set enrichment. Can perform differential analyses of any type of omics data that produces read counts, including RNA-seq, ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral counts. RNA-seq analyses can be conducted at the gene or isoform level, and tests can be conducted for differential exon or transcript usage.

Maintained by Yunshun Chen. Last updated 5 days ago.

alternativesplicing batcheffect bayesian biomedicalinformatics cellbiology chipseq clustering coverage differentialexpression differentialmethylation differentialsplicing dnamethylation epigenetics functionalgenomics geneexpression genesetenrichment genetics immunooncology multiplecomparison normalization pathways proteomics qualitycontrol regression rnaseq sage sequencing singlecell systemsbiology timecourse transcription transcriptomics openblas

0.5 match 13.40 score 17k scripts 255 dependents

bioc

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.

software genetarget motifannotation motifdiscovery go transcriptomics genomeannotation genesetenrichment coverage

0.5 match 6.32 score 29 scripts 1 dependents