Find open-source science resources

Easily visualize and inspect microarrays for spatial artifacts.

The Semantic Resource Types Vocabulary was created for NSF's EarthCube Program's Resource Repository. Includes entries for things like 'thesaurus', 'ontology', 'controlled vocabulary', 'taxonomy'.

This ontology defines a taxonomy of software functions based on the work of the NSF-funded EarthCube Resource Registry working group. The functions are generally organized by their role in the research process.

The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette.

The Data Model Language Controlled Vocabulary was created for NSF's EarthCube Program's Resource Registry. At this point it merely lists a few of the languages used by data model resources in the registry.

The Audience Types Controlled Vocabulary was created for NSF's EarthCube program's Resource Registry. The vocabulary defines the types of audience each resource in the program is targeted to. At this point the vocabulary is very bare - no term definitions even; however, the intention is to extend the vocabulary over time. If you would like to assist with this or in extending any of the other controlled vocabularies/ontologies developed as part of the Resource Registry project, please see https://github.com/earthcubearchitecture-ecresourcereg.

This mini-ontology contains classes and instances for each version of the licenses that are commonly used in software projects, particularly open source software projects. The URI's for each are the canonical URI's for that license (where they exist).

Customizable pipeline for differential expression analysis with an intuitive GUI.

CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree.

The Extensible Observation Ontology (OBOE) is a formal ontology for capturing the semantics of scientific observation and measurement. The ontology supports researchers to add detailed semantic annotations to scientific data, thereby clarifying the inherent meaning of scientific observations.

loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB.

An OWL 2 DL ontology developed for creating sets, bags and lists of resources, and for inferring collection properties even in the presence of incomplete information.

An ontology for describing the administrative information of research projects, e.g., grant applications, funding bodies, project partners, etc.

An ontology for the characterisation of the roles of agents – people, corporate bodies and computational agents in the publication process. These agents can be, e.g. authors, editors, reviewers, publishers or librarians.

Embeddable genome viewer. Integration data from a wide variety of sources, and can load data directly from popular genomics file formats including bigWig, BAM, and VCF.

Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues.

Prediction of mRNA subcellular localization using deep recurrent neural networks | RNATracker is a deep learning approach to learn mRNA subcellular localization patterns and to infer its outcome. It operates on the cDNA of the longest isoformic protein-coding transcript of a gene with or without its corresponding secondary structure annnotations. The learning targets are fractions/percentage of the transcripts being localized to a fixed set of subcellular compartments of interest

The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as sva and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.

Flexible circular visualization of genome-associated data with BioPerl and SVG.

geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR.

Subsampling of high throughput sequencing count data for use in experiment design and analysis.

JavaScript library for drawing canvas-based gene diagrams.

TogoID is an ID conversion service implementing unique features with an intuitive web interface and an API for programmatic access. TogoID supports datasets from various biological categories such as gene, protein, chemical compound, pathway, disease, etc. TogoID users can perform exploratory multistep conversions to find a path among IDs. To guide the interpretation of biological meanings in the conversions, we crafted an ontology that defines the semantics of the dataset relations. (from https://togoid.dbcls.jp/)

Contains functions and classes that are needed by arrayCGH packages.

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

This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses.

The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method.

Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results.

This package is used for the analysis of long-range chromatin interactions from 3C-seq assay.

VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions.

The Shape Expressions (ShEx) language describes RDF nodes and graph structures. A node constraint describes an RDF node (IRI, blank node or literal) and a shape describes the triples involving nodes in an RDF graph. These descriptions identify predicates and their associated cardinalities and datatypes. ShEx shapes can be used to communicate data structures associated with some process or interface, generate or validate data, or drive user interfaces.

thromboSeq is a bioinformatics tool designed for the analysis of thrombosis-related sequencing data, providing functionalities for variant calling, annotation, and functional interpretation. It streamlines the processing of high-throughput sequencing data to identify genetic variants associated with thrombotic disorders.

Expertly curated genomics papers to get up to speed on genomics, RNA-seq, statistics (used in genomics), software development, and more.

Perl package for circular plots, which are well suited for genomic rearrangements.

Telseq is a tool for estimating telomere length from whole genome sequence data.

AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output.

All alleles from the IPD IMGT/HLA <https://www.ebi.ac.uk/ipd/imgt/hla/> and IPD KIR <https://www.ebi.ac.uk/ipd/kir/> database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation.

An ontology that enables the description of reviews of scientific articles and other scholarly resources.

This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data.

Virtual machine with all software and sample data to run 3D-e-Chem Knime workflows

Variant Discovery in High-Throughput Sequencing Data.

This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS.

Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments.

Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation.

A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz.

A wee tool for random access into BGZF files.

Upper-Level ontology for Biology and Medicine. Compatible with BFO, DOLCE, and the UMLS Semantic Network

An ontology of histopathological morphologies used by pathologists to classify/categorise animal lesions observed histologically during regulatory toxicology studies. The ontology was developed using real data from over 6000 regulatory toxicology studies donated by 13 companies spanning nine species. The original structure of the histopathology ontology was designed ab initio when the [INHAND](http://www.goreni.org/) manuscripts were not available. However, the ontology has been repetitively reviewed and updated to align with the subsequently published INHAND manuscripts. During this process cross references to INHAND lesion identifiers were added to the ontology. [from GitHub]