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bioMate AI

@bioMate-AI

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Tools to analyze & visualize Illumina Infinium methylation arrays.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to (Tree)SummarizedExperiment objects.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

HiContacts provides a collection of tools to analyse and visualize Hi-C datasets imported in R by HiCExperiment.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

R generic interface to Hi-C contact matrices in `.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other Hi-C processed file formats. Contact matrices can be partially parsed using a random access method, allowing a memory-efficient representation of Hi-C data in R. The `HiCExperiment` class stores the Hi-C contacts parsed from local contact matrix files. `HiCExperiment` instances can be further investigated in R using the `HiContacts` analysis package.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw con

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

This package builds on existing tools and adds some simple but extremely useful capabilities for working wth ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also implemented.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

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.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Reactome is a free, open-source, curated and peer-reviewed pathway database. Their goal is to provide intuitive bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

ChIPseeker is a Bioconductor package for annotating ChIP-seq data analysis. Peak Annotation is performed by the annotatePeak function. The position and strand information of nearest genes are reported, in addition to the distance from the p

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. bio

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Extract the genomic locations of genes, transcripts, exons, introns, and CDS, for the gene models stored in a TxDb object. A TxDb object is a small database that contains the gene models of a given organism/assembly. Bioconductor provides a

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working wit

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls and reports cytosine methylation as well as frequencies of hypermethylated epialleles at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Among other things, this package can also extract and visualise methylation patterns and assess allele specificity of methylation.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

A toolset for deciphering and managing biological sequences.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

algorithm for determining cluster count and membership by stability evidence in unsupervised analysis

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly and annotation quality with BUSCOs and comparisons of statistics with publicly available genomes on the NCBI; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualizat

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and '

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses.

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte

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bioMate-AI/biomate-bioconductor-kb797Updated Jun 21, 2026

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