R-universe search: hiddenmarkovmodel

Showing 19 of total 19 results (show query)

bioc

infercnv:Infer Copy Number Variation from Single-Cell RNA-Seq Data

Using single-cell RNA-Seq expression to visualize CNV in cells.

Maintained by Christophe Georgescu. Last updated 5 months ago.

software copynumbervariation variantdetection structuralvariation genomicvariation genetics transcriptomics statisticalmethod bayesian hiddenmarkovmodel singlecell jags cpp

11.0 match 595 stars 10.91 score 674 scripts

bioc

TitanCNA:Subclonal copy number and LOH prediction from whole genome sequencing of tumours

Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data.

Maintained by Gavin Ha. Last updated 5 months ago.

sequencing wholegenome dnaseq exomeseq statisticalmethod copynumbervariation hiddenmarkovmodel genetics genomicvariation immunooncology 10x-genomics copy-number-variation genome-sequencing hmm tumor-heterogeneity

11.0 match 96 stars 8.47 score 68 scripts

bioc

nullranges:Generation of null ranges via bootstrapping or covariate matching

Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package.

Maintained by Michael Love. Last updated 5 months ago.

visualization genesetenrichment functionalgenomics epigenetics generegulation genetarget genomeannotation annotation genomewideassociation histonemodification chipseq atacseq dnaseseq rnaseq hiddenmarkovmodel bioconductor bootstrap genomics matching statistics

11.0 match 27 stars 8.16 score 50 scripts 1 dependents

bioc

AneuFinder:Analysis of Copy Number Variation in Single-Cell-Sequencing Data

AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data.

Maintained by Aaron Taudt. Last updated 5 months ago.

immunooncology software sequencing singlecell copynumbervariation genomicvariation hiddenmarkovmodel wholegenome cpp

11.0 match 17 stars 7.70 score 37 scripts

bioc

GBScleanR:Error correction tool for noisy genotyping by sequencing (GBS) data

GBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population.

Maintained by Tomoyuki Furuta. Last updated 2 days ago.

geneticvariability snp genetics hiddenmarkovmodel sequencing qualitycontrol cpp

11.0 match 4 stars 5.90 score 6 scripts

bioc

NuPoP:An R package for nucleosome positioning prediction

NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at https://github.com/jipingw. The Fortran codes have complete functonality as the R package. Note: NuPoP has two separate functions for prediction of nucleosome positioning, one for MNase-map trained models and the other for chemical map-trained models. The latter was implemented for four species including yeast, S.pombe, mouse and human, trained based on our recent publications. We noticed there is another package nuCpos by another group for prediction of nucleosome positioning trained with chemicals. A report to compare recent versions of NuPoP with nuCpos can be found at https://github.com/jiping/NuPoP_doc. Some more information can be found and will be posted at https://github.com/jipingw/NuPoP.

Maintained by Ji-Ping Wang. Last updated 5 months ago.

genetics visualization classification nucleosomepositioning hiddenmarkovmodel fortran

11.0 match 5.04 score 11 scripts

bioc

epigraHMM:Epigenomic R-based analysis with hidden Markov models

epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions.

Maintained by Pedro Baldoni. Last updated 5 months ago.

chipseq atacseq dnaseseq hiddenmarkovmodel epigenetics zlib openblas cpp openmp

11.0 match 4.94 score 88 scripts

bioc

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).

Maintained by Marta Sevilla. Last updated 5 months ago.

software hiddenmarkovmodel genetics

11.0 match 1 stars 4.54 score 3 scripts

rfael0cm

RTIGER:HMM-Based Model for Genotyping and Cross-Over Identification

Our method integrates information from all sequenced samples, thus avoiding loss of alleles due to low coverage. Moreover, it increases the statistical power to uncover sequencing or alignment errors <doi:10.1093/plphys/kiad191>.

Maintained by Rafael Campos-Martin. Last updated 1 years ago.

genomeannotation hiddenmarkovmodel sequencing

11.0 match 4 stars 4.30 score 5 scripts

bioc

iCNV:Integrated Copy Number Variation detection

Integrative copy number variation (CNV) detection from multiple platform and experimental design.

Maintained by Zilu Zhou. Last updated 5 months ago.

immunooncology exomeseq wholegenome snp copynumbervariation hiddenmarkovmodel

11.0 match 4.30 score 5 scripts

bioc

betaHMM:A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data

A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.

Maintained by Koyel Majumdar. Last updated 3 months ago.

dnamethylation differentialmethylation immunooncology biomedicalinformatics methylationarray software multiplecomparison sequencing spatial coverage genetarget hiddenmarkovmodel microarray

11.0 match 4.18 score

bioc

partCNV:Infer locally aneuploid cells using single cell RNA-seq data

This package uses a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information (e.g., regional deletion or amplification) to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH).

Maintained by Ziyi Li. Last updated 5 months ago.

software copynumbervariation hiddenmarkovmodel singlecell classification

11.0 match 4.18 score 4 scripts

bioc

BUMHMM:Computational pipeline for computing probability of modification from structure probing experiment data

This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment.

Maintained by Alina Selega. Last updated 5 months ago.

immunooncology geneticvariability transcription geneexpression generegulation coverage genetics structuralprediction transcriptomics bayesian classification featureextraction hiddenmarkovmodel regression rnaseq sequencing

11.0 match 4.15 score 14 scripts

bioc

methimpute:Imputation-guided re-construction of complete methylomes from WGBS data

This package implements functions for calling methylation for all cytosines in the genome.

Maintained by Aaron Taudt. Last updated 5 months ago.

immunooncology software dnamethylation epigenetics hiddenmarkovmodel sequencing coverage cpp openmp

11.0 match 4.11 score 13 scripts

bioc

planttfhunter:Identification and classification of plant transcription factors

planttfhunter is used to identify plant transcription factors (TFs) from protein sequence data and classify them into families and subfamilies using the classification scheme implemented in PlantTFDB. TFs are identified using pre-built hidden Markov model profiles for DNA-binding domains. Then, auxiliary and forbidden domains are used with DNA-binding domains to classify TFs into families and subfamilies (when applicable). Currently, TFs can be classified in 58 different TF families/subfamilies.

Maintained by Fabrício Almeida-Silva. Last updated 5 months ago.

software transcription functionalprediction genomeannotation functionalgenomics hiddenmarkovmodel sequencing classification functional-genomics gene-families hidden-markov-models plant-genomics plants protein-domains transcription-factors

11.0 match 4.00 score 5 scripts

bioc

hummingbird:Bayesian Hidden Markov Model for the detection of differentially methylated regions

A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups.

Maintained by Eleni Adam. Last updated 5 months ago.

hiddenmarkovmodel bayesian dnamethylation biomedicalinformatics sequencing geneexpression differentialexpression differentialmethylation cpp

11.0 match 4.00 score 1 scripts

bioc

frenchFISH:Poisson Models for Quantifying DNA Copy-number from FISH Images of Tissue Sections

FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting.

Maintained by Adam Berman. Last updated 5 months ago.

software biomedicalinformatics cellbiology genetics hiddenmarkovmodel preprocessing

11.0 match 4.00 score 3 scripts

rli012

Hapi:Inference of Chromosome-Length Haplotypes Using Genomic Data of Single Gamete Cells

Inference of chromosome-length haplotypes using a few haploid gametes of an individual. The gamete genotype data may be generated from various platforms including genotyping arrays and sequencing even with low-coverage. Hapi simply takes genotype data of known hetSNPs in single gamete cells as input and report the high-resolution haplotypes as well as confidence of each phased hetSNPs. The package also includes a module allowing downstream analyses and visualization of identified crossovers in the gametes.

Maintained by Ruidong Li. Last updated 7 years ago.

snp genomicvariation genetics hiddenmarkovmodel singlecell sequencing microarray

11.0 match 3.79 score 41 scripts

bioc

DMCHMM:Differentially Methylated CpG using Hidden Markov Model

A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks.

Maintained by Farhad Shokoohi. Last updated 5 months ago.

differentialmethylation sequencing hiddenmarkovmodel coverage

11.0 match 3.78 score 3 scripts