Active users of the gget website since its creation:

🧑‍🤝‍🧑 Dependents

The following applications build on gget:

• Therapeutics Data Commons (TDC)
  Artificial intelligence foundation for therapeutic science (source code, Nat Chem Bio paper) by Harvard's Artificial Intelligence for Medicine and Science lab.
• BioDiscoveryAgent
  BioDiscoveryAgent is an LLM-based AI agent for closed-loop design of genetic perturbation experiments (preprint) by the Stanford Network Analysis Project.
• DeepChopper
  Language models to identify chimeric artificial reads in NanoPore direct-RNA sequencing data by the Yang lab at Northwestern.
• BRAD
  A LLM powered chatbot for bioinformatics (documentation, project main page).
• scPRINT
  scPRINT is a large transformer model built for the inference of gene networks (connections between genes explaining the cell's expression profile) from scRNAseq data (preprint).
• AnoPrimer
  AnoPrimer is a Python package for primer design in An. gambiae and An. funestus, whilst considering genetic variation in wild whole-genome sequenced specimens in malariagen_data.
• AvaTaR
  Optimizing LLM Agents for Tool-Assisted Knowledge Retrieval (NeurIPS 2024) by James Zou Lab at Stanford University.
• GRLDrugProp
  Graph representation learning for modelling drug properties.
• MicrobioLink2
  A computational tool that analyzes the impact of host-microbe interaction on downstream signaling in human cells and tissues.
• Rust implementation of gget: https://github.com/noamteyssier/ggetrs
• https://github.com/Superbio-ai/getbio
• https://github.com/yonniejon/AchillesPrediction
• https://github.com/ELELAB/cancermuts
• https://github.com/Benoitdw/SNPrimer
• https://github.com/louisjoecodes/a16z-hackathon-project
• https://github.com/EvX57/BACE1-Drug-Discovery
• https://github.com/vecerkovakaterina/hidden-genes-msc
• https://github.com/vecerkovakaterina/llm_bioinfo_agent
• https://github.com/greedjar74/upstage_AI_Lab
• https://github.com/alphavector/all

Also see: https://github.com/pachterlab/gget/network/dependents

📃 Featured publications

• David Bradley et al., The fitness cost of spurious phosphorylation. The EMBO Journal (2024). DOI: 10.1038/s44318-024-00200-7
• Mikael Nilsson et al., Resolving thyroid lineage cell trajectories merging into a dual endocrine gland in mammals. Nature Portfolio (under review) (2024). DOI: 10.21203/rs.3.rs-5278325/v1
• Avasthi P et al., Repeat expansions associated with human disease are present in diverse organisms. Arcadia (2024). DOI: 10.57844/arcadia-e367-8b55
• Ibrahim Al Rayyes et al., Single-Cell Transcriptomics Reveals the Molecular Logic Underlying Ca2+ Signaling Diversity in Human and Mouse Brain. bioRxiv (2024). DOI: 10.1101/2024.04.26.591400
• David R. Blair & Neil Risch. Dissecting the Reduced Penetrance of Putative Loss-of-Function Variants in Population-Scale Biobanks. medRxiv (2024). DOI: 10.1101/2024.09.23.24314008
• Shanmugampillai Jeyarajaguru Kabilan et al., Molecular modelling approaches for the identification of potent Sodium-Glucose Cotransporter 2 inhibitors from Boerhavia diffusa for the potential treatment of chronic kidney disease. Journal of Computer-Aided Molecular Design (under review) (2024). DOI: 10.21203/rs.3.rs-4520611/v1
• Joseph M Rich et al., The impact of package selection and versioning on single-cell RNA-seq analysis. bioRxiv (2024). DOI: 10.1101/2024.04.04.588111
• Sanjay C. Nagi et al., AnoPrimer: Primer Design in malaria vectors informed by range-wide genomic variation. Wellcome Open Research (2024).
• Yasmin Makki Mohialden et al., A survey of the most recent Python packages for use in biology. NeuroQuantology (2023). DOI: 10.48047/NQ.2023.21.2.NQ23029
• Kimberly Siletti et al., Transcriptomic diversity of cell types across the adult human brain. Science (2023). DOI: 10.1126/science.add7046
• Beatriz Beamud et al., Genetic determinants of host tropism in Klebsiella phages. Cell Reports (2023). DOI: 10.1016/j.celrep.2023.112048
• Nicola A. Kearns et al., Generation and molecular characterization of human pluripotent stem cell-derived pharyngeal foregut endoderm. Cell Reports (2023). DOI: 10.1016/j.devcel.2023.08.024
• Jonathan Rosenski et al., Predicting gene knockout effects from expression data. BMC Medical Genomics (2023). DOI: 10.1186/s12920-023-01446-6
• Peter Overby et al., Pharmacological or genetic inhibition of Scn9a protects beta-cells while reducing insulin secretion in type 1 diabetes. bioRxiv (2023). DOI: 10.1101/2023.06.11.544521
• Mingze Dong et al., Deep identifiable modeling of single-cell atlases enables zero-shot query of cellular states. bioRxiv (2023). DOI: 10.1101/2023.11.11.566161

📰 News

• gget opentargets release blog post by the Open Targets platform
• Documentary short film about gget: https://youtu.be/cVR0k6Mt97o
• Podcast episode for the Prototype Fund Public Interest Podcast about the importance of open-source software and its role in academic research (in German): https://public-interest-podcast.podigee.io/33-pips4e4
• Prototype Fund announcement: https://prototypefund.de/project/gget-genomische-datenbanken

🚂 gget repository traffic