Package: TRESS 1.13.0

Zhenxing Guo

TRESS: Toolbox for mRNA epigenetics sequencing analysis

This package is devoted to analyzing MeRIP-seq data. Current functionalities include 1. detect transcriptome wide m6A methylation regions 2. detect transcriptome wide differential m6A methylation regions.

Authors:Zhenxing Guo [aut, cre], Hao Wu [ctb]

TRESS_1.13.0.tar.gz
TRESS_1.13.0.zip(r-4.5)TRESS_1.13.0.zip(r-4.4)TRESS_1.13.0.zip(r-4.3)
TRESS_1.13.0.tgz(r-4.4-any)TRESS_1.13.0.tgz(r-4.3-any)
TRESS_1.13.0.tar.gz(r-4.5-noble)TRESS_1.13.0.tar.gz(r-4.4-noble)
TRESS_1.13.0.tgz(r-4.4-emscripten)TRESS_1.13.0.tgz(r-4.3-emscripten)
TRESS.pdf |TRESS.html✨
TRESS/json (API)
NEWS

# Install 'TRESS' in R:

install.packages('TRESS', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

Basal - Bin-level and region-level data from basal mouse brain samples
DMR_M3vsWT - Transcriptome location and read counts of 200 candidate DMRs, and size factors.
DMR_SixWeekvsTwoWeek - Transcriptome location and read counts of 200 candidate DMRs.

On BioConductor:TRESS-1.13.0(bioc 3.21)TRESS-1.12.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

epigenetics rnaseq peakdetection differentialmethylation

3.48 score 1 packages 5 scripts 173 downloads 15 exports 69 dependencies

Last updated 22 days agofrom:a40c256bb1. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 19 2024
R-4.5-win	NOTE	Nov 19 2024
R-4.5-linux	NOTE	Nov 19 2024
R-4.4-win	NOTE	Nov 19 2024
R-4.4-mac	NOTE	Nov 19 2024
R-4.3-win	NOTE	Nov 19 2024
R-4.3-mac	NOTE	Nov 19 2024

Exports:CallCandidates CallDMRs.paramEsti CallPeaks.multiRep CallPeaks.oneRep CallPeaks.paramEsti CoefName DivideBins DMRInfer filterRegions findBumps meRatio ShowOnePeak TRESS_DMRfit TRESS_DMRtest TRESS_peak

Dependencies:abind AnnotationDbi askpass BH Biobase BiocGenerics BiocIO BiocParallel Biostrings bit bit64 bitops blob cachem cli codetools cpp11 crayon curl DBI DelayedArray fastmap formatR futile.logger futile.options generics GenomeInfoDb GenomeInfoDbData GenomicAlignments GenomicFeatures GenomicRanges glue httr IRanges jsonlite KEGGREST lambda.r lattice lifecycle Matrix MatrixGenerics matrixStats memoise mime openssl pkgconfig plogr png R6 RCurl restfulr Rhtslib rjson rlang Rsamtools RSQLite rtracklayer S4Arrays S4Vectors snow SparseArray SummarizedExperiment sys UCSC.utils vctrs XML XVector yaml zlibbioc

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Bin-level and region-level data from basal mouse brain samples	Basal
Call candidate m6A regions or candidate differential m6A regions.	CallCandidates
Model fitting and parameter estimation by TRESS for each candidate DMR.	CallDMRs.paramEsti
m6A peak calling with multiple replicates.	CallPeaks.multiRep
m6A peak calling with only one replicate.	CallPeaks.oneRep
Parameter estimation in m6A peak calling with multiple replicates.	CallPeaks.paramEsti
Obtain the name of each coefficient in design matrix.	CoefName
Obtain genomic bins and bin-level read counts from BAM files.	DivideBins
Transcriptome location and read counts of 200 candidate DMRs, and size factors.	DMR_M3vsWT
Transcriptome location and read counts of 200 candidate DMRs.	DMR_SixWeekvsTwoWeek
P-value calculation given Wald statistics.	DMRInfer
Pre-filtering of candidate DMRs.	filterRegions
Bump-finding from transcriptome bins.	findBumps
Observed m6A methylation ratio.	meRatio
Visulization of a single peak along the genome.	ShowOnePeak
Differential m6A methylation analysis for MeRIP-seq data under general experimental design	TRESS_DMRfit
Hypothesis testing on candidate DMRs.	TRESS_DMRtest
Detecting m6A methylation regions from Methylated RNA Immunoprecipitation Sequencing.	TRESS_peak