# How to Choose the Right R Package for Visualizing Genomic Data The difference between the best r package for genomic visualization options comes down to one question: does the package understand genomic coordinates, or does it only understand data frames? General-purpose tools like ggplot2 treat your data as rows and columns and will plot anything you hand them, while domain-specific packages like ChIPseeker, Plotgardener, and PopGenHelpR know what a chromosome position, a peak, or a genotype matrix means and produce figures that would take days to build by hand [1][2][3]. The rule of thumb is simple. Start with ggplot2 and the tidyverse for PCA plots, boxplots, and heatmaps of ordinary tables. Switch to a specialized package when your figure needs genomic context, such as read coverage tracks aligned to a locus, peak annotation relative to transcription start sites, ancestry bar charts, or [linkage disequilibrium](/knowledge/bioinformatics/linkage-disequilibrium-and-haplotype-mapping) heatmaps [4][1][2][3]. This article walks through the main options in r for bioinformatics, compares them side by side, and gives you worked scenarios so you can match a package to your data, your skill level, and the figure you actually need to publish. ## What Genomic Visualization Actually Requires in R ![Three-step flow: install BiocManager from CRAN, install the package such as ChIPseeker, then read the package vignette before coding.](/article-images/illustrations/best-r-package-for-genomic-visualization-bioconductor-install.webp) Genomic data arrives in formats that carry positional information. A VCF file stores genetic variants with chromosome, position, reference and alternate alleles, genotype, read depth, and quality metrics [5]. A BED file stores genomic intervals. A bigWig file stores per-base coverage. A count matrix from RNA-seq stores gene by sample values with no positional information at all. The first filter when choosing a package is whether your visualization depends on those coordinates. If it does not, meaning you want to compare sample groups, display principal components, or color cells in a heatmap by expression value, then any general R plotting layer works. A recent review of thyroid cancer genomics, for example, used ggplot2 together with dplyr, ggrepel, and tidyr from the tidyverse to build its multi-omics figures, which shows how far general-purpose tools carry a genomics project [4]. If it does depend on coordinates, you need a package that can map data onto a genome. That is where the specialized Bioconductor ecosystem earns its keep. Bioconductor is the curated repository for [computational biology](/knowledge/bioinformatics/vaccinomics-and-the-future-of-personalized-vaccines) R packages, and installing from it follows a standard two-step pattern that appears in every bioconductor tutorial: ```r install.packages("BiocManager") BiocManager::install("ChIPseeker") ``` Most Bioconductor packages ship their own vignettes, which are worked, executable examples written by the package authors. Reading the vignette before writing any code is the single highest-return habit in this field. A third layer has emerged in the last few years: interactive applications built with R Shiny. These give you a graphical interface over R code, which lowers the barrier for colleagues who do not script. XVCF, for instance, is a Shiny app that wraps vcfR and maftools so users can load, summarize, and visualize VCF files through a GUI instead of the command line [5]. The yEvo Mutation Browser, also built with R Shiny, gives students an interactive chromosome map of mutations with a gene viewer and a protein viewer [6]. ## Core Comparison Table | Package | Data type | Interface | Main output | Best for | Key limitation | |--|--|--|--|--|--| | ggplot2 + tidyverse [4] | Any data frame | Code | Fully custom figures | PCA, boxplots, heatmaps, volcano plots | No native genomic coordinate support | | PopGenHelpR [3] | SNP datasets | Code | Near-publication quality population figures | Heterozygosity, FST, ancestry bar charts, piechart maps, differentiation heatmaps | Focused on population genomics only | | adegenet [7] | Genotype matrices | Code | DAPC scatterplots, structure plots | Population structure and clustering | Analysis-first, visualization second | | ChIPseeker [1] | ChIP-seq peaks (BED) | Code | Peak coverage, TSS profiles, annotation | Peak annotation and dataset comparison | ChIP-seq specific | | Plotgardener [2] | Genomic tracks, ranges | Code or desktop app | Multi-panel publication-ready locus figures | Complex multi-track figure layouts | App currently macOS only | | RepViz [8] | Sequencing counts per region | Code | Replicate-grouped region plots | Inspecting ChIP-seq and ATAC-seq replicate variation | Narrow scope, region-level viewing | | XVCF [5] | VCF files | Shiny GUI | QC summaries, variant visualizations | Fast variant QC without scripting | Interactive, so less reproducible by default | | LDblockR [9] | VCF, HapMap, PLINK files | Code | LD heatmaps, regional plots with annotation | Linkage disequilibrium analysis and visualization | LD-focused | | moiraine [10] | Multi-omics datasets | Code | Integration result visualizations | Comparing outputs of integration methods | Assumes an integration analysis step | | Spectra [11] | Mass spectrometry data | Code | Spectra plots, infrastructure | MS data handling and visualization | Mass spec domain | | ArchetypeShift [12] | scRNA-seq | Code | Dot plots, UMAPs, heatmaps, trajectories | Annotating single-cell archetypes | Tied to archetype analysis | | CPSM [13] | [Transcriptomics](/knowledge/bioinformatics/modern-transcriptomics-bulk-single-cell-spatial) plus clinical data | Code | Individualized survival curves, nomograms | Patient-level survival visualization | Cancer survival modeling | | longreadvqs (QoALa) [14] | Long-read viral sequencing | Code | Quasispecies diversity comparisons | Viral haplotype diversity across samples | Viral quasispecies workflow | | GenomeCAT [15] | Copy number variants | Standalone Java app | Cohort CNV visualizations | CNV comparison across patient cohorts | Not an R package, separate application | ## The General-Purpose Layer: ggplot2 and the Tidyverse ggplot2 implements the grammar of graphics, which means you describe a plot as data plus mappings plus geometric marks, and the package handles the rendering. In genomics practice it sits at the bottom of nearly everything. The thyroid cancer review cited above built its mutation mapping and biomarker-discovery figures with ggplot2, ggrepel for non-overlapping gene labels, and tidyr for reshaping data [4]. Those same functions draw PCA plots of expression data, FST distributions, and volcano plots regardless of the assay that produced the numbers. ### Pros and cons Pros: - Universal. Every dataset that can become a data frame can be plotted, so you learn one grammar that covers RNA-seq, metabolomics, clinical tables, and population summaries. - Enormous ecosystem of extensions for specialized chart types. - Publication customization through themes gives complete control over fonts, sizes, and panel layout. - Pairs naturally with the data-wrangling verbs in dplyr and tidyr, which the tidyomics initiative has carried into modern Bioconductor infrastructure as well [11]. Cons: - Zero genomic awareness. ggplot2 does not know that chromosome 1 position 1,000,000 follows position 999,000 on the same chromosome, or that a track from a bigWig file needs a genome assembly to render. Anything coordinate-aware requires you to build it yourself or move up to a specialized package. - Multi-panel genomic layouts, such as a gene model over a coverage track over a variant track, take substantial manual effort. The honest role of ggplot2 in r for bioinformatics is the default starting point and the finishing layer. Specialized packages often return ggplot objects, so you can keep customizing their output with the same grammar. ## Population Genetics Visualization Population genomic datasets impose a real barrier for researchers because the analyses require genetics, statistics, and computing skills at once [3]. Two R packages dominate the visualization side. ### PopGenHelpR PopGenHelpR estimates diversity within populations and differentiation among populations and individuals from SNP data. It covers standard measures such as observed heterozygosity and FST, and adds five heterozygosity measures that were previously unavailable in this format, including the proportion of heterozygous loci and homozygosity by locus [3]. The visualization side is what makes it a strong answer for its niche. It integrates ancestry bar charts, piechart maps, and genetic differentiation heatmaps, tools that previously required assembling several separate software packages [3]. Two design decisions matter for long-term reliability. PopGenHelpR is deliberately minimally dependent on other R packages, which reduces the chance that an upstream dependency change breaks it, and the project maintains a website with tutorials and educational resources [3]. Pros: - One package covers estimate-plus-figure workflows for diversity and differentiation. - Near-publication quality output without hand-tuning. - Low dependency burden improves long-term stability. Cons: - Limited to population genomic summaries. It will not draw coverage tracks or annotate peaks. - You still need a general layer for anything beyond its built-in figure types. ### adegenet adegenet handles multivariate population structure analysis. In a four-country study of Holstein cattle populations, researchers used adegenet to run discriminant analysis of principal components (DAPC) and admixture analysis on 10,822 SNPs from 399 animals after quality control and pruning in PLINK [7]. DAPC scatterplots and ancestry bar charts from that workflow are standard population genetics figures. adegenet is analysis-first, so its plots are serviceable rather than polished, and many groups compute with adegenet and then re-plot in ggplot2. ## ChIP-Seq and Peak Annotation: ChIPseeker Once peak calling produces a BED file of ChIP-seq peaks, the questions become genomic. Which genes are nearest? Are peaks enriched at promoters relative to transcription start sites (TSS)? Do two transcription factors overlap more than expected? ChIPseeker answers all three and visualizes the results. It annotates peaks, shows peak coverage over chromosomes, profiles peak binding around TSS regions, compares peak profiles and annotations across samples, and evaluates significant overlap among ChIP-seq datasets [1]. A distinctive feature is its built-in database of 15,000 BED files from the GEO repository. You can download public datasets and compare them against your own data to find significant overlaps that suggest co-regulation or shared [transcription factor](/knowledge/molecular-biology/transcription-factor) complexes [1]. ChIPseeker is released under the Artistic-2.0 license and is distributed through Bioconductor [1]. Pros and cons: - Pros: purpose-built annotation statistics, TSS profile plots that would be painful to construct manually, public dataset comparison, active Bioconductor distribution. - Cons: ChIP-seq specific. If your peak-like data comes from ATAC-seq you may still use its annotation machinery, but coverage and replicate inspection are better handled elsewhere. ## Locus-Level Genomic Tracks: Plotgardener and RepViz ### Plotgardener A genomic locus figure in a paper typically stacks several elements: a gene model, coverage tracks from multiple samples, a signal heatmap, and perhaps a variant track, all sharing one coordinate axis. Plotgardener is an R package built precisely for generating these high-quality genomic visualizations with programmatic control over every panel [2]. Its code-driven nature is a strength for reproducibility and a barrier for lab members who do not program. To address that, the authors released the Plotgardener App, a macOS desktop application built with an Electron.js backend, a React frontend, and a Python parser that dynamically reads the Plotgardener package so interface fields stay synchronized with package updates [2]. The app enables users with no programming experience to create publication-ready genomic plots, and both the download and the source code are available through the project's website and GitHub [2]. Pros: - The most flexible option for complex, multi-panel locus layouts in R. - Programmatic version produces fully reproducible figures. - Desktop app opens the tool to non-coders [2]. Cons: - The app is currently macOS only [2]. - The programmatic version has a learning curve because you place panels explicitly rather than accepting automatic layout. ### RepViz RepViz solves a narrower but persistent problem: viewing replicate structure. Standard genome browsers do not offer convenient ways to view different groups of replicates side by side. RepViz, a Bioconductor package, lets you inspect intra-group and inter-group variation of sequencing counts on a genomic region and compare that variation to analysis outputs such as peak calls [8]. The developers demonstrate it with ChIP-seq and ATAC-seq data, using it to evaluate normalization, spot outlier samples, and sanity-check differential peak calls before trusting them [8]. This is the package you reach for during quality assessment, before the final figure exists. It catches the case where one replicate drives a peak call, which would otherwise surface only during peer review. ## Variant Data: VCF Exploration and Linkage Disequilibrium ### XVCF Most tools for visualizing variants in VCF files require command-line knowledge, which is exactly what many collaborators lack. XVCF is a Shiny application built on vcfR and maftools that provides a graphical interface for loading, summarizing, and visualizing genomic variation data [5]. It extracts read depth, mapping quality, genotype, quality control summaries, and allele frequency from unannotated data, and a second module handles cancer genomic contexts [5]. Pros: - No scripting required, which makes it a good bridge for clinical collaborators and students [5]. - Draws on established, well-tested backends (vcfR, maftools) rather than reinventing parsing [5]. Cons: - Interactive sessions are hard to reproduce exactly. For a methods section you want the underlying packages or a scripted pipeline, not a click history. ### LDblockR Linkage disequilibrium (LD) analysis sits at the center of GWAS interpretation, haplotype characterization, and marker selection, yet the common tools fragment the work across format converters, platform-specific binaries, and separate visualization programs. LDblockR is a pure-R, cross-platform toolkit that reads VCF or compressed VCF, HapMap, PLINK BED/BIM/FAM or PED/MAP files, and numeric 0/1/2 genotype matrices directly. It computes dosage-based or phased-haplotype-based r² and Lewontin's D', estimates haplotype frequencies with an expectation-maximization procedure, supports multiple block definitions, tag-SNP selection, LD decay plots, and population-group comparisons [9]. The authors are explicit that the contribution is workflow integration rather than a new LD estimator. A single R-native object model connects genotype inputs, LD statistics, regional annotation, and publication-ready visualization, and in a phased benchmark LDblockR agreed with LDBlockShow 1.41 to three-decimal precision on r² values [9]. Pros: - One package replaces a chain of format conversions and external binaries [9]. - Cross-platform because it is pure R. Cons: - If you need LD statistics beyond its supported definitions you still need the standalone estimators. A note on CNV data: GenomeCAT handles [copy number variant analysis](/blog/guides/copy-number-variant-analysis) and integrative cohort visualization well, but it is a standalone Java application with a MySQL backend, not an R package [15]. Include it in your toolkit only if you accept leaving R for that step. ## Multi-Omics Pipelines: moiraine When your project combines transcriptomics, metabolomics, or proteomics, the bottleneck shifts from plotting to orchestration. Integration methods differ in input formats and result structures, which makes applying and comparing several of them on the same dataset tedious. moiraine is an R package for constructing reproducible multi-omics integration pipelines. It preprocesses and formats datasets for the integration step, builds visualizations of the results with sample and feature metadata attached, and critically allows side-by-side comparison of results from different integration tools so you can judge robustness [10]. This is the option for the figure that says "we ran three integration methods and the same biology emerged from each." A detailed tutorial is maintained by the developers, and both a GitHub repository and an archival Zenodo snapshot are available [10]. ## Specialized Assays and Contexts Several strong packages serve narrow domains and should be chosen only when the domain matches. **Mass spectrometry.** Spectra is an R/Bioconductor infrastructure package for storing, handling, and visualizing mass spectrometry data. It adapts to different data types and sizes while keeping a consistent interface, integrates with third-party Python tools, and follows modern tidyomics principles so it works with general tidy data science tools [11]. Choose it as infrastructure when MS data is in the project, not as a standalone plotting choice. **Single-cell.** ArchetypeShift integrates KEGG-informed and IPA-derived functional annotation for single-cell RNA-seq archetypes and generates the corresponding figures: dot plots, UMAPs of archetype weights, archetype maps, heatmaps of top genes per archetype program, and trajectory graphics [12]. Reach for it when archetype analysis is part of the analysis plan. **Survival genomics.** CPSM builds individualized survival risk models from transcriptomics and clinical data. Unlike aggregate Kaplan-Meier curves, it produces personalized survival curves and nomograms through a four-step pipeline covering [preprocessing](/blog/data-analysis/what-is-data-preprocessing), feature selection, risk group prediction, and visualization. The developers demonstrated it on TCGA data for glioblastoma, acute myeloid leukemia, pancreatic adenocarcinoma, and breast cancer [13]. The individualized curves are specifically designed to translate complex statistics into intuitive visuals for patient-clinician communication [13]. **Viral quasispecies from long reads.** The QoALa workflow, implemented in the longreadvqs R package, minimizes position-wise noise in read alignments, standardizes sample sizes, and compares viral quasispecies across related samples with integrated visualization. Benchmarking on simulated SARS-CoV-2 and HIV-1 datasets showed it outperformed existing error-correction methods at preserving nucleotide diversity and population structure, and evaluation on real long-read data from five viruses including HCV, HBV, and influenza A recovered rare haplotypes the original studies had not reported [14]. **Interactive education and exploration.** The yEvo Mutation Browser shows what R Shiny does well in genomics education: an interactive chromosome map of mutated genes, graphs categorizing mutation types, a gene viewer, and a protein viewer, all designed so high school students can interpret whole-genome sequencing of evolved yeast [6]. The same architecture scales to lab-internal data browsers. ## Choosing by Criteria, Not by Hype When you weigh the best r package for genomic visualization options against each other, six criteria decide real outcomes. 1. **Input match.** Does the package read your file format natively? LDblockR reading PLINK and VCF directly eliminates a conversion chain [9]. ChIPseeker consuming BED files matches the peak-caller output [1]. Fighting formats manually is where most pipelines acquire silent bugs. 2. **Coordinate awareness.** General tools cannot render a genome-aligned track. If the x-axis of your figure is a genomic position, choose a coordinate-aware package [2][8]. 3. **Interactivity versus reproducibility.** GUIs like XVCF [5] and the Plotgardener App [2] accelerate exploration and lower barriers, but a script reproduces a figure in one command a year later. A common compromise is GUI-driven exploration followed by scripted final figures. 4. **Learning curve versus output quality.** Plotgardener demands explicit panel placement and rewards you with complete layout control. PopGenHelpR trades generality for near-publication quality defaults [3]. 5. **Maintenance and dependencies.** Packages with minimal dependency trees survive CRAN and Bioconductor churn better, a consideration PopGenHelpR states as a design goal [3]. Bioconductor distribution itself signals active curation. 6. **Scope honesty.** A package tailored to RIP-seq statistics [16], to survival modeling [13], or to viral quasispecies [14] will resist your attempts to bend it toward another assay. Fighting scope costs more than learning the right tool. ## Worked Scenarios **Scenario 1: Population structure in a livestock or wildlife SNP dataset.** You have PLINK-filtered SNPs for several populations and need a DAPC plot, an ancestry bar chart, and an FST heatmap for a manuscript. Compute structure with adegenet [7], then generate the diversity, differentiation, ancestry, and piechart-map figures with PopGenHelpR, whose tutorials shorten the ramp-up [3]. Add ggplot2 only for custom touches. This is the highest-leverage combination for the domain. **Scenario 2: A ChIP-seq paper figure.** Peaks are called. You need annotation barplots, a TSS enrichment profile, and a comparison against a published transcription factor dataset. ChIPseeker handles annotation, profiles, overlap significance, and can pull comparable GEO datasets directly [1]. Before finalizing peaks, run RepViz to confirm no single replicate drives the call [8]. **Scenario 3: A multi-panel locus figure with recalcitrant layout requirements.** A reviewer wants coverage tracks for six samples, a gene model, and a methylation track aligned over a 50 kb region, with consistent fonts and panel widths. Plotgardener gives explicit placement control, and colleagues without R access can adjust the figure in the macOS desktop app [2]. **Scenario 4: Clinical collaborators and a VCF file.** A physician wants QC summaries and variant visualizations from a tumor-normal VCF before a meeting, and nobody has time to script. XVCF provides the GUI over vcfR and maftools, extracting depth, mapping quality, genotype, and allele frequency metrics interactively [5]. For the manuscript, replicate the key summaries with a scripted call to the same underlying packages so the numbers are reproducible. **Scenario 5: Multi-omics integration with a robustness claim.** You have metabolite and [gene expression](/blog/guides/gene-expression) matrices from a crop science project and reviewers will ask whether findings depend on the integration method. moiraine formats the datasets, runs the methods, attaches metadata to the result visualizations, and supports direct comparison across tools [10]. ## Common Mistakes and Limitations ![Comparison of XVCF, the Plotgardener App and a scripted pipeline across interface, best use and reproducibility.](/article-images/illustrations/best-r-package-for-genomic-visualization-gui-vs-script.webp) - **Building genomic coordinate plots in ggplot2 from scratch.** Hand-rolled coverage tracks break on chromosome boundaries, gaps, and assembly inconsistencies. If the axis is genomic, use Plotgardener or RepViz [2][8]. - **Treating a Shiny app session as an analysis record.** XVCF and similar GUIs are exploration tools [5]. Screenshots are not methods. Keep the scripted path for anything that enters a publication. - **Skipping the vignette.** Every Bioconductor package cited here ships executable documentation [1][8][11]. Starting from Stack Overflow snippets instead of the vignette produces figures whose parameters nobody can explain. - **Assuming one package covers the whole project.** Even a tightly scoped tool like PopGenHelpR covers population summaries only [3]. Real pipelines stack a general layer, a domain layer, and sometimes a GUI layer. Budget time for the seams. - **Ignoring dependency risk.** A package whose analysis depends on a long chain of upstream packages can break when one link changes. Minimal-dependency design exists for this reason and is worth checking before you commit a pipeline to it. - **Mixing platforms unnecessarily.** GenomeCAT is capable but is a Java application with its own database [15]. If your pipeline is R-based, prefer R-native alternatives unless the Java tool offers something nothing in R does. - **Overstating precision when comparing tools.** Cross-tool agreement claims should be backed by the benchmark the developers actually ran, as LDblockR did against LDBlockShow to three-decimal precision [9], rather than by eyeballing two figures. - **Remembering that visualization never validates the measurement.** Platform and protocol differences affect the underlying genomic measurements themselves, as shown in systematic cross-platform comparisons of methylation, RNA-seq, and array platforms [17]. A beautiful plot of a biased measurement is still biased. ## Frequently Asked Questions ### What is the best R package for genomic visualization overall? There is no single winner, and any article claiming one is selling something. For general tables, ggplot2 is the default. For coordinate-aware locus figures, Plotgardener is the most capable layout engine. For population genomics, PopGenHelpR is the most complete single package [3]. For ChIP-seq annotation, ChIPseeker [1]. Match the package to the data type and the figure, and your answer usually names two or three tools working together. ### Do I need Bioconductor packages or are CRAN packages enough? Both repositories matter and most projects use both. Bioconductor hosts the coordinate-aware genomics infrastructure such as ChIPseeker, RepViz, and Spectra [1][8][11], installed through BiocManager. CRAN hosts ggplot2, the tidyverse, and domain tools like PopGenHelpR's companion utilities [3]. A typical session loads packages from both sources. ### Can I visualize genomic data without writing R code? Yes, through R Shiny applications and desktop apps built on R packages. XVCF offers a GUI for VCF summarization and visualization [5], the Plotgardener App brings publication-ready locus plotting to non-programmers on macOS [2], and the yEvo Mutation Browser shows how interactive chromosome and gene viewers work in practice [6]. The tradeoff is reproducibility, since interactive sessions are harder to document than scripts. ### Which R package should I use for ChIP-seq peak plots? ChIPseeker is the standard choice for peak annotation, chromosome-level coverage, TSS binding profiles, and comparisons among peak datasets, including overlap tests against 15,000 public GEO BED files [1]. For replicate-level inspection of the underlying counts before you commit to peaks, add RepViz [8]. ### How do I visualize variant data from a VCF file in R? Two good paths exist. Scripted: parse with vcfR and plot with ggplot2. Interactive: XVCF wraps vcfR and maftools in a Shiny GUI that extracts read depth, mapping quality, genotype, QC summaries, and allele frequency without any coding [5]. For regional linkage disequilibrium figures from genotype files, LDblockR produces LD heatmaps and regional annotation plots directly from VCF or PLINK inputs [9]. ### Can one R package handle RNA-seq, ChIP-seq, and variant data together? No single package does all three well, and that is by design rather than deficiency. The realistic pattern is a common general layer, ggplot2 plus tidyverse, underneath domain-specific packages for each assay, for example ChIPseeker for peaks [1] and moiraine where multi-omics integration and cross-method comparison are needed [10]. Planning for a layered toolkit from the start prevents the dead end of forcing one package into every role. ## Sources 1. [ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization.](https://pubmed.ncbi.nlm.nih.gov/25765347/) 2. [Plotgardener App: a graphical interface for publication-ready genomic visualization.](https://pubmed.ncbi.nlm.nih.gov/42664080/) 3. [PopGenHelpR: An R Package to Streamline and Facilitate Informed Population Genomic Analyses and Visualization of Genetic Ancestry, Diversity and Differentiation.](https://pubmed.ncbi.nlm.nih.gov/42183976/) 4. [Synergistic role of molecular imaging and genomics in thyroid cancer management: from diagnosis to personalized treatment.](https://pubmed.ncbi.nlm.nih.gov/42806342/) 5. [XVCF: Exquisite visualization of VCF data from genomic experiments.](https://pubmed.ncbi.nlm.nih.gov/42323052/) 6. [The yEvo Mutation Browser: Enhancing student understanding of experimental evolution and genomics through interactive data visualization.](https://pubmed.ncbi.nlm.nih.gov/42933842/) 7. [Genomic diversity and population structure analysis of four geographically distinct Holstein cattle populations using genome-wide DNA profiling.](https://pubmed.ncbi.nlm.nih.gov/42626155/) 8. [RepViz: a replicate-driven R tool for visualizing genomic regions.](https://pubmed.ncbi.nlm.nih.gov/31324268/) 9. [LDblockR: a cross-platform R toolkit for multi-format linkage disequilibrium analysis and integrated regional genomics visualization.](https://pubmed.ncbi.nlm.nih.gov/43005392/) 10. [moiraine: an R package to construct reproducible pipelines for the application and comparison of multi-omics integration methods.](https://pubmed.ncbi.nlm.nih.gov/41692950/) 11. [An Open Infrastructure for Mass Spectrometry Data in R.](https://pubmed.ncbi.nlm.nih.gov/42878754/) 12. [ArchetypeShift: An R Package Integrating KEGG-Informed Pathway Analysis and IPA-Derived Functional Predictions for Validation of Single-Cell Archetypes.](https://pubmed.ncbi.nlm.nih.gov/42591774/) 13. [CPSM: an R package for cancer patient survival risk model using transcriptomics and clinical data.](https://pubmed.ncbi.nlm.nih.gov/42233233/) 14. [QoALa: A comprehensive workflow for viral quasispecies diversity comparison using long-read sequencing data.](https://pubmed.ncbi.nlm.nih.gov/42048366/) 15. [GenomeCAT: a versatile tool for the analysis and integrative visualization of DNA copy number variants.](https://pubmed.ncbi.nlm.nih.gov/28061750/) 16. [RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments.](https://pubmed.ncbi.nlm.nih.gov/23455476/) 17. [Evaluation of cross-platform and interlaboratory concordance via consensus modelling of genomic measurements.](https://pubmed.ncbi.nlm.nih.gov/30084929/)