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NuclearPhaser is a method for phasing of dikaryotic genomes into the two haplotypes using Hi-C contact graphs. This is an overview of the phasing pipeline for dikaryons.

a robust molecular representation learning framework against distribution shifts.

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e.

The Science Data Discovery Ontology (sddo) is being developed to provide a semantic foundation for the discovery of information managed by NASA's Science Mission Directorate. This information spans many scientific disciplines, fields and subfields, including heliophysics, earth science, planetary science, astrophysics, biology, astrobiology, and physical science. [from repository]

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Filtering and trimming of long read sequencing data.

Open Drug Discovery Toolkit, a modular and comprehensive toolkit for use in cheminformatics, molecular modeling etc.

Toolkit for processing molecules, reactions and condensed graphs of reactions. Can be used for chemical standardization, MCS search, tautomers generation with backward compatibility to RDKit and NetworkX.

Go Get Data; A command line interface for obtaining genomic data.

A cookiecutter template for bioinformatics projects, with a focus on building bioinformatics workflows that can run on the MPI-IE cluster according to FAIR principles.

Hierarchical Generation of Molecular Graphs using Structural Motifs.

Learning nonlinear operators

# ChemGPT 1.2B ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.

# ChemGPT 19M ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.

# ChemGPT 4.7M ChemGPT is based on the GPT-Neo model and was introduced in the paper Neural Scaling of Deep Chemical Models.

Algorithm Metadata Vocabulary is a vocabulary for capturing and storing the metadata about the algorithms (a procedure or a set of rules that is followed step-by-step to solve a problem, especially by a computer). There are uncountable algorithms present in every area (e.g., Computer Science, Mathematics), which makes it hard for specialists, academicians, application engineers, and so forth to discover, distinguish, select, and reuse them. [from repository]

AI for chemical reaction prediction and synthesis planning

Spherical CNNs for astronomy

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.

Deep learning for chemistry and materials science remains a novel field with lots of potiential. However, the popularity of transfer learning based methods in areas such as NLP and computer vision have not yet been effectively developed in computational chemistry + machine learning.

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.

This desktop application enables users to upload DICOM data along with associated clinical information to QP-Insights—the data management platform of the UPV Reference Node within EUCAIM.

This module provides a command line tool to validate DICOM SEG files against predefined requirements specified in an Excel file. It contains components for finding relevant DICOM files, loading and parsing validation requests and applying validation rules. The main validation process checks each DICOM file for compliance with the Type 1, 1C, 2, 2C and 3 attributes specified in the requirements file. A detailed report is generated highlighting issues such as missing, invalid or conditionally required attributes, including file paths and affected DICOM tags. The tool is designed to ensure data integrity and compliance with DICOM standards.

A tool that checks the clinical metadata quality (validity, completeness), the integrity between images and clinical metadata provided as well as their accuracy, the de-identification protocol applied, and existence of annotation together with the consistency between the images and the annotation files and informs the user on corrective actions prior to data upload.

Automatically detects duplicate and near-duplicate DICOM image series in large medical imaging datasets. Uses a tiered pipeline combining DICOM metadata analysis, SHA-based pixel hashing, and image similarity metrics (SSIM, cosine, MAD) to identify exact copies, re-exported series, and near-identical acquisitions. All findings are reported for human expert review — no files are modified or deleted automatically. For scenarios requiring strict, image-level deduplication based on pixel content, fully agnostic to metadata changes, consider using [https://bio.tools/image_duplicate_check_tool]