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An ontology transcription of definitions in the Functional Mock-up Interface (FMI) standard document from https://fmi-standard.org/ that enables representing Functional Mock-up Units (FMUs) in RDF

RepEnrich is a method to estimate repetitive element enrichment using high-throughput sequencing data.

[@crazyhottommy](https://github.com/crazyhottommy)'s notes on various steps and considerations when doing RNA-seq analysis.

Automated strain separation of low-complexity metagenomes

Crystal property prediction

Computation Pipeline library for python widely used in science and bioinformatics.

Spike detection and clustering-based spike sorting.

qam is a Python library and command-line tool to compute 3D surface-distances for evaluating liver ablation/tumor completeness based on segmentation images.

Pythonic access to the UCSC Genome database.

NanoSV is a software package that can be used to identify structural genomic variations in long-read sequencing data, such as data produced by Oxford Nanopore Technologies’ MinION, GridION or PromethION instruments, or Pacific Biosciences RSII or Sequel sequencers.

Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data.

Molecule validation and standardization based on [RDKit](http://www.rdkit.org/).

The Reagent Ontology (ReO) adheres to OBO Foundry principles (obofoundry.org) to model the domain of biomedical research reagents, considered broadly to include materials applied “chemically” in scientific techniques to facilitate generation of data and research materials. ReO is a modular ontology that re-uses existing ontologies to facilitate cross-domain interoperability. It consists of reagents and their properties, linking diverse biological and experimental entities to which they are related. ReO supports community use cases by providing a flexible, extensible, and deeply integrated framework that can be adapted and extended with more specific modeling to meet application needs.

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

A port of [pyVCF](https://github.com/jamescasbon/PyVCF) using Cython for speed.

Tool to generate a count matrix for expression data in Galaxy. generate_count_matrix reads in one or more input text files with expression counts and produces a single combined file. Each input will have a column in the matrix containing expression values. The column containing gene (or feature) names should be identical for all input count files.

Automate common SAM & BAM conversions.

Membrane Protein-Lipid Interaction Database. A large-scale experimentally validated dataset of 80685 residue-level lipid contact annotations across 4712 membrane proteins derived from PDB crystal and cryo-EM structures. Provides pre-computed binary contact labels, continuous distance values, sequence-identity-based cluster assignments, and ready-made train-validation-test splits for machine learning.

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.