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A bioinformatics tool for viewing and calculating base modification frequencies from BAM files

Simple test framework for Nextflow pipelines

Reactr is an modularized, Snakemake workflow for automated, species-agnostic characterization of gene families from sequence to experimental design. Given a query protein sequence and NCBI taxonomy IDs (or RefSeq assembly accessions), reactr retrieves genomic data and runs comprehensive analysis across 4 integrated tiers: (1) evolutionary analysis, including homolog detection, domain-based clustering, multiple sequence alignment, and phylogenetic inference; (2) synteny and selection analysis, detecting collinear blocks and calculating Ka/Ks ratios; (3) structural and regulatory characterization, including motif discovery, chromosomal mapping, biochemical property prediction, subcellular localization prediction, and promoter analysis; and (4) experimental design tools, generating PCR primers and scored CRISPR gRNAs for lab validation. Reactr bridges computational prediction and experimental validation, thus enabling rapid transition from genomic discovery to functional studies.

Harmonize numerical values extracted from medical images (e.g. acquired with different models of image-acquisition system)

Browser-based viewer for GenBank and GenPept records — .gb, .gbk, .gbff, .gp and plain GenBank text. Renders an interactive linear and circular feature map, including circular plasmid maps, alongside the annotated source text and the nucleotide/protein sequence. Translates CDS features using the record's own genetic code and translation qualifiers, flags where the stored /translation disagrees with a plain translation, and adds optional computed layers: ORF prediction and restriction-site mapping. Drag a range in the sequence band to select it, then copy that stretch — either strand, as DNA or as protein, plain or FASTA. Filters features by type, handles multi-record files, and keeps open records as local sessions, so a closed tab can be picked up where it was left. Runs entirely in the browser — files are never uploaded.

ArrayAnalysis is a web-based application for transcriptomic data analysis. It supports the analysis of both microarray and RNA-seq data. The tool may also be installed locally as a desktop app, Docker image, or R package.

Supernova is a software package for de novo assembly from Chromium Linked-Reads that are made from a single whole-genome library from an individual DNA source. A key feature of Supernova is that it creates diploid assemblies, thus separately representing maternal and paternal chromosomes over very long distances. Almost all other methods instead merge homologous chromosomes into single incorrect 'consensus' sequences. Supernova is the only practical method for creating diploid assemblies of large genomes.

edf2csv is a local command-line tool for converting EDF, EDF+, BDF, and BDF+ physiological recordings into CSV and JSON files. It exports signal values, channel information, annotations, and recording metadata while preserving original sampling rates, physical units, and discontinuities.

Plotwright is a browser-based statistics and scientific graphing workbench for life-science researchers. It provides a Data to Analysis to Graph workflow for statistical tests, regression and curve fitting, survival and ROC analysis, and editable scientific figures. It can import supported content from modern GraphPad Prism .prism files and reports preserved, approximated, and unsupported objects for review. Projects can be saved as local files. Plotwright is commercial software with a 14-day no-card trial.

Predicting the effect of mutations on protein-RNA binding with Deep Learning | This repository contains all DeepCLIP Python code | A context-aware neural network for modeling and predicting protein binding to nucleic acids using only sequence input | DeepCLIP is a neural network with shallow convolutional layers connected to a bidirectional LSTM layer

Plant Compound Extractor is a desktop application that builds a ready-to-use, deduplicated library of 3D ligand structures for a given plant. It queries multiple natural-product and chemical databases (COCONUT, LOTUS, Wikidata, PubChem, PlantaeDB, USDA Dr. Duke's, KNApSAcK and IMPPAT) in parallel, resolves each compound against PubChem for a canonical structure, and falls back to direct source retrieval when needed. Retrieved structures are then converted to 3D using RDKit, with configurable conformer generation and physicochemical filters (molecular weight, rotatable bonds, ring size, etc.). It can also process a manually supplied compound list, or convert an existing folder of 2D structures to 3D.

Desktop viewer for microscopy and whole slide pathology images on Windows and macOS. Opens whole slide scanner formats (Aperio SVS, Hamamatsu NDPI, MIRAX MRXS, Leica SCN, Ventana BIF) alongside acquisition formats (Zeiss CZI, Nikon ND2, DICOM, TIFF/OME-TIFF) in a single application, providing pyramid navigation of gigapixel images, metadata inspection, calibrated measurements, annotations, and on-device segmentation and object counting.

Pan.bio is a cloud genomics platform for pipeline execution, exploratory analysis, and clinical variant interpretation. Workflows runs validated Nextflow and nf-core pipelines including Sarek, rnaseq, scrnaseq, mag, ampliseq, chipseq and atacseq without local installation. Notebooks provides Python and R sessions with a preinstalled bioinformatics stack, importing public data from GEO, SRA and IPG by accession and reading Workflows outputs directly. VAIC applies ACMG/AMP variant classification with gene-specific rule sets from CanVIG-UK and ClinGen ENIGMA, with automated evidence criteria implemented for BRCA1 and BRCA2. Cohorts provides federated analysis of patient data within a Trusted Research Environment.

Novel machine learning-based method for prediction of ligand binding sites from protein structure.

PhonaLab is a browser-based platform for acoustic analysis of voice recordings, aimed at speech-language pathologists, voice clinicians, and researchers. It computes validated multiparametric acoustic indices — including the Acoustic Voice Quality Index (AVQI), Acoustic Breathiness Index (ABI), smoothed cepstral peak prominence (CPPS), and glottal-to-noise excitation ratio (GNE) — from sustained-vowel and connected-speech recordings, using Praat algorithms via the Parselmouth interface. Audio is processed in memory and not stored. Interface available in English, Brazilian Portuguese, and Spanish.

Gelyze is a web-based image analysis tool for agarose gel electrophoresis of nucleic acids. A user uploads a gel image, confirms the proposed lane positions and the ladder baseline, and a deterministic algorithm then estimates fragment size from ladder calibration and quantifies relative band intensity by densitometry. Measurement and interpretation are kept separate: the numbers come from the algorithm, and an assistive AI layer explains the resulting pattern in plain language without producing any of the values. For research use only.

MailsDaddy MBOX to PST Converter is designed to move single or multiple MBOX file to Outlook PST, EML, MSG, HTML etc.

FT-ITC Analysis is free, open-source desktop software for processing, fitting, and analysing isothermal titration calorimetry (ITC) data. It imports raw data from MicroCal, TA Instruments/NanoAnalyze, and PEAQ ITC project files, as well as integrated heat data and FT-ITC project files. The software supports baseline correction, injection integration, interaction-model fitting, global analysis, uncertainty estimation, advanced thermodynamic analyses, and publication-figure export on macOS, Windows, and Linux.

Browser-based viewer that maps sequencing reads onto one short reference — an amplicon, gene or plasmid. Reads open as Sanger AB1/ABIF, SCF, FASTA, FASTQ or a SAM somebody else already mapped (gzipped files are unpacked in place); the reference as FASTA, GenBank or a read. Both read orientations are tried automatically. The pileup reports per-position depth, where reads disagree with the target, and the consensus — phred-weighted for capillary reads, which keep their chromatogram under the letters. An optional protein lane translates target and consensus side by side. Reads are placed by minimap2 compiled to WebAssembly, or by the built-in aligner. One read or the whole alignment saves as FASTA. Runs entirely in the browser — files are never uploaded.

The snoBoard database compiles ribosomal RNA (rRNA) modifications, box C/D and box H/ACA snoRNA guides sequences for a number of species including Saccharomyces cerevisiae (Sc), Human (Hs), the plant Arabidopsis thaliana (At) and tomato Solanum lycopersicum (Sl). Mapped modifications are 2’-O methylation, pseudouridylation and other known modifications when available. The user interface allows the access to rRNA targets, mapped modifications and snoRNA guides under different views. Modifications can be accessed through a table (one line per modification), or mapped on their target RNA sequences and secondary structures. A comparative view allows to access to mapped sites conserved between a selected subset of species. Using the comparative view, it is possible to interrogate and visualize the conservation of target nucleotides sequences and modified sites. Information on modifications targets and snoRNA guides can be accessed by organism, snoRNA guide family and modification type.

Pan1c is a Snakemake workflow that simplifies the creation of pangenomes by focusing on the comparison of similar chromosomes, using either PGGB or Minigraph-Cactus. A pangenome graph is first generated individually for each chromosome, and then these chromosome-scale pangenomes are concatenated into a final graph. This approach reduces construction time through parallelization and facilitates interpretation as well as downstream analyses. Throughout the process, various graphs and statistics are produced to help users assess the quality of the pangenome graph and interpret the results. All these visualizations and statistics are accessible via a web page generated by Pan1c-view.

Browser-based bioinformatics platform for running protein structure prediction, molecular docking, protein design, molecular dynamics, sequence analysis, and related life-science workflows without local installation.

zitools allows for zero inflated count data analysis by either using down-weighting of excess zeros or by replacing an appropriate proportion of excess zeros with NA. Through overloading frequently used statistical functions (such as mean, median, standard deviation), plotting functions (such as boxplots or heatmap) or differential abundance tests, it allows a wide range of downstream analyses for zero-inflated data in a less biased manner. This becomes applicable in the context of microbiome analyses, where the data is often overdispersed and zero-inflated, therefore making data analysis extremly challenging.

Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk.

Define a relatively light class for managing Xenium data using Bioconductor. Address use of parquet for coordinates, SpatialExperiment for assay and sample data. Address serialization and use of cloud storage.

The package allows users to readily import spatial data obtained from the 10X Xenium Analyzer pipeline. Supported formats include 'parquet', 'h5', and 'mtx' files. The package mainly represents data as SpatialExperiment objects.

xCell2 provides methods for cell type enrichment analysis using cell type signatures. It includes three main functions - 1. xCell2Train for training custom references objects from bulk or single-cell RNA-seq datasets. 2. xCell2Analysis for conducting the cell type enrichment analysis using the custom reference. 3. xCell2GetLineage for identifying dependencies between different cell types using ontology.

XAItest is an R Package that identifies features using eXplainable AI (XAI) methods such as SHAP or LIME. This package allows users to compare these methods with traditional statistical tests like t-tests, empirical Bayes, and Fisher's test. Additionally, it includes simThresh, a system that enables the comparison of feature importance with p-values by incorporating calibrated simulated data.

Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys.

Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm.

The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest.

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.

High-throughput single-cell measurements of DNA methylation allows studying inter-cellular epigenetic heterogeneity, but this task faces the challenges of sparsity and noise. We present vmrseq, a statistical method that overcomes these challenges and identifies variably methylated regions accurately and robustly.

This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexagonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects.

The package allows users to readily import spatial data obtained from either the 10X website or from the Space Ranger pipeline. Supported formats include tar.gz, h5, and mtx files. Multiple files can be imported at once with *List type of functions. The package represents data mainly as SpatialExperiment objects.

This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class.

This package provides functions for handling and analyzing immune receptor repertoire data, such as produced by the CellRanger V(D)J pipeline. This includes reading the data into R, merging it with paired single-cell data, quantifying clonotype abundances, calculating diversity metrics, and producing common plots. It implements the E-M Algorithm for clonotype assignment, along with other methods, which makes use of ambiguous cells for improved quantification.

Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.)

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).

a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name.

The arrangement of hypotheses in a hierarchical structure appears in many research fields and often indicates different resolutions at which data can be viewed. This raises the question of which resolution level the signal should best be interpreted on. treeclimbR provides a flexible method to select optimal resolution levels (potentially different levels in different parts of the tree), rather than cutting the tree at an arbitrary level. treeclimbR uses a tuning parameter to generate candidate resolutions and from these selects the optimal one.

The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.

This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots.

The TMSig package contains tools to prepare, analyze, and visualize named lists of sets, with an emphasis on molecular signatures (such as gene or kinase sets). It includes fast, memory efficient functions to construct sparse incidence and similarity matrices and filter, cluster, invert, and decompose sets. Additionally, bubble heatmaps can be created to visualize the results of any differential or molecular signatures analysis.

Implements a DelayedArray backend for reading and writing dense or sparse arrays in the TileDB format. The resulting TileDBArrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances.

'tidySingleCellExperiment' is an adapter that abstracts the 'SingleCellExperiment' container in the form of a 'tibble'. This allows *tidy* data manipulation, nesting, and plotting. For example, a 'tidySingleCellExperiment' is directly compatible with functions from 'tidyverse' packages `dplyr` and `tidyr`, as well as plotting with `ggplot2` and `plotly`. In addition, the package provides various utility functions specific to single-cell omics data analysis (e.g., aggregation of cell-level data to pseudobulks).

`tidyCoverage` framework enables tidy manipulation of collections of genomic tracks and features using `tidySummarizedExperiment` methods. It facilitates the extraction, aggregation and visualization of genomic coverage over individual or thousands of genomic loci, relying on `CoverageExperiment` and `AggregatedCoverage` classes. This accelerates the integration of genomic track data in genomic analysis workflows.

Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra.

Provides a structured S4 approach to importing data files from the 10X pipelines. It mainly supports Single Cell Multiome ATAC + Gene Expression data among other data types. The main Bioconductor data representations used are SingleCellExperiment and RaggedExperiment.

Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn.