Package: UPDhmm 1.9.0

Marta Sevilla

UPDhmm: Detecting Uniparental Disomy through NGS trio data

Uniparental disomy (UPD) is a genetic condition where an individual inherits both copies of a chromosome or part of it from one parent, rather than one copy from each parent. This package contains a HMM for detecting UPDs through HTS (High Throughput Sequencing) data from trio assays. By analyzing the genotypes in the trio, the model infers a hidden state (normal, father isodisomy, mother isodisomy, father heterodisomy and mother heterodisomy).

Authors:Marta Sevilla [aut, cre], Carlos Ruiz-Arenas [aut]

UPDhmm_1.9.0.tar.gz
UPDhmm_1.9.0.zip(r-4.7)UPDhmm_1.9.0.zip(r-4.6)UPDhmm_1.9.0.zip(r-4.5)
UPDhmm_1.9.0.tgz(r-4.6-any)UPDhmm_1.9.0.tgz(r-4.5-any)
UPDhmm_1.9.0.tar.gz(r-4.7-any)UPDhmm_1.9.0.tar.gz(r-4.6-any)
UPDhmm_1.9.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
UPDhmm/json (API)

# Install 'UPDhmm' in R:
install.packages('UPDhmm', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/martasevilla/updhmm/issues

Datasets:
  • hmm - HMM data for predicting UPD events in trio genomic data

On BioConductor:UPDhmm-1.9.0(bioc 3.24)UPDhmm-1.8.0(bioc 3.23)

softwarehiddenmarkovmodelgenetics

5.92 score 4 stars 14 scripts 5 exports 72 dependencies

Last updated from:deaeef8804. Checks:8 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE190
linux-devel-x86_64NOTE237
source / vignettesOK259
linux-release-x86_64NOTE286
macos-release-arm64NOTE162
macos-oldrel-arm64NOTE146
windows-develNOTE150
windows-releaseNOTE135
windows-oldrelNOTE139
wasm-releaseOK190

Exports:calculateEventscollapseEventsidentifyRecurrentRegionsmarkRecurrentRegionsvcfCheck

Dependencies:abindAnnotationDbiaskpassBHBiobaseBiocBaseUtilsBiocGenericsBiocIOBiocParallelBiostringsbitbit64bitopsblobBSgenomecachemcigarilloclicodetoolscpp11crayoncurlDBIDelayedArrayfastmapformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomicAlignmentsGenomicFeaturesGenomicRangesglueHMMhttrIRangesjsonliteKEGGRESTlambda.rlatticelifecycleMatrixMatrixGenericsmatrixStatsmemoisemimeopensslpkgconfigpngR6RCurlrestfulrRhtslibrjsonrlangRsamtoolsRSQLitertracklayerS4ArraysS4VectorsSeqinfosnowSparseArraySummarizedExperimentsysUCSC.utilsVariantAnnotationvctrsXMLXVectoryaml

Long reads VCF Preprocessing User Guide
Data source | 1. Download phased VCFs for each individual | 2. Merge individual VCFs into a trio VCF | 3. Filter variants for UPDhmm input | 4. UPDhmm analysis in R | Session Info
Rendered fromlong-reads-processing.Rmdusingknitr::rmarkdown

Last update: 2026-02-11
Started: 2026-02-11

UPDhmm User Guide: From Detection to Postprocessing
Background | Method overview | Set-up the packages | Loading libraries | Quick start | Detecting and postprocessing UPD events | 1. Load and preprocess the VCFs | 2. Detect UPD events using the HMM-based approach | Results description | 3. Collapse and summarize results per individual | 4. Perform postprocessing analyses | 5. Visualizing UPD events across the genome | Summary | Session Info
Rendered fromUPDhmm.Rmdusingknitr::rmarkdown

Last update: 2026-02-11
Started: 2025-10-09

VCF Preprocessing User Guide
Inputs | 0. Normalize and Left-Align Variants (optional) | 1. Remove Extra Annotations | 2. Merge Trio into a Single VCF | 3. Mask Structural Variant Regions | Session Info
Rendered frompreprocessing.Rmdusingknitr::rmarkdown

Last update: 2025-10-09
Started: 2025-10-09