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Achievement: Pair Extraordinairex2Achievement: StarstruckAchievement: Pull Sharkx3Achievement: YOLOAchievement: Quickdraw

Achievements

Achievement: Pair Extraordinairex2Achievement: StarstruckAchievement: Pull Sharkx3Achievement: YOLOAchievement: Quickdraw

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nictru/README.md

💫 About Me:

🔭 I’m currently working on single-cell data processing, circRNA analysis, and identification of differentially active transcription factors
👯 I’m looking to collaborate on an Angular package for investigating candidate genes/regions
🌱 I’m currently deepening my understanding of biological data and how to process them
💬 Ask me about nf-core, dependency management, and frontend development

💻 Tech Stack:

HTML5 Java JavaScript LaTeX Markdown Python R TypeScript YAML Angular Anaconda NodeJS Nginx Gunicorn Matplotlib NumPy Pandas Plotly scikit-learn Scipy TensorFlow Keras PyTorch GitHub Actions GitHub Docker Poetry

📊 GitHub Stats:

💰 You can help me by Donating

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  1. nf-core/scdownstream nf-core/scdownstream Public

    A single cell transcriptomics pipeline for QC, integration and making the data presentable

    Nextflow 40 12

  2. nf-core/circrna nf-core/circrna Public

    circRNA quantification, differential expression analysis and miRNA target prediction of RNA-Seq data

    Nextflow 44 24

  3. nf-core/tfactivity nf-core/tfactivity Public

    Bioinformatics pipeline that makes use of expression and open chromatin data to identify differentially active transcription factors across conditions.

    Nextflow 9 3

  4. Mye-InfoBank/scRAFIKI Mye-InfoBank/scRAFIKI Public

    A nextflow pipeline for processing sc-RNASeq data, performing QC and integration and preparing the results for further investigation using cellxgene.

    Nextflow 9

  5. daisybio/TF-Prioritizer daisybio/TF-Prioritizer Public

    Bioinformatics pipeline to identify differentially active transcription factors between conditions using expression and epigenetic data

    Java 13

  6. algobio algobio Public

    This project aims on making the algorithms covered in the corresponding lecture at LMU Munich easier to understand by providing step-by-step logging and result visualization.

    Python 8 1