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daniil-11-ger/README.md

Hi, I'm Daniil πŸ‘‹

Biotechnology Student | 3rd Year | Bioinformatics Enthusiast

I am a researcher and data analyst focused on the intersection of Plant Genomics and Digital Phenotyping. My work bridges the gap between raw biological data and actionable agricultural insights.

Technical Expertise:

  • Genomics: De novo genome assembly (Hifiasm, Jellyfish), Annotation (Prokka), and Homology (BLAST+).
  • Transcriptomics: Differential Gene Expression (DGE) analysis, Volcano plots, and statistical filtering in R.
  • Phenomics: Statistical analysis of field trials, automated plot summarization, and trait correlation.
  • Data Science: Functional programming in R (Tidyverse), hypothesis testing (ANOVA, t-test), and Linear Regression.
  • Engineering: Laboratory automation (Python GUIs) and Cloud-based sample tracking (MIT App Inventor).

Experience:

  • KAUST (Jesse Poland Lab): Research internship focusing on digital phenotyping and high-throughput field data analysis.

Featured Projects

Plant Genome Assembly (Rice)

  • Focus: Eukaryotic genome reconstruction using PacBio HiFi reads.
  • Tools: Hifiasm, Jellyfish, RagTag, Augustus.
  • Key Skill: Scaffolding, k-mer profiling, and organelle sorting.
  • Live Report: View Project Website

Bacterial Assembly Pipeline

  • Focus: Automated workflow for E. coli genome assembly.
  • Tools: fastp, SPAdes, Prokka, QUAST.
  • Key Skill: Automation & Genomic Annotation.

πŸ“Š Differential Gene Expression (DGE)

  • Focus: Identifying significant genes in stress-response transcriptomes (LS vs HS).
  • Tools: R, Tidyverse, ggplot2.
  • Key Skill: Statistical hypothesis testing and biological visualization (Volcano Plots).

🌾 Agronomic Data Automation (Project Connor)

  • Focus: Automated summarization of multi-trait field breeding trials.
  • Tools: R, Functional Programming, dplyr.
  • Key Skill: Data engineering and automated plot-level variability analysis.

πŸ” Sequence Homology Search (BLAST)

  • Focus: Large-scale sequence identification and hit validation.
  • Tools: NCBI BLAST+, Bioconductor, R.
  • Key Skill: Processing command-line output into statistical reports.

πŸ“– Foundations of Statistics & R

  • Focus: Intensive curriculum for biological data analysis and inference.
  • Topics: CLT simulations, ANOVA, Tukey HSD, and Linear Regression.
  • Key Skill: Mathematical foundations and reproducible modeling.

Biotech Lab Assistant (GUI)

  • Focus: Python desktop tools for routine lab calculations.
  • Features: Tm calculation (Wallace Rule), DNA-to-RNA transcription.
  • Key Skill: Python Software Development & UI Design.

BioSample Cloud Tracker

  • Focus: Cloud-based Android application for laboratory inventory.
  • Features: Real-time data sync via CloudDB, item management for reagents/samples.
  • Key Skill: Mobile Development (MIT App Inventor) & Cloud Integration.

Tech Stack

Bash R Python Git

Connect with me: LinkedIn

Pinned Loading

  1. agronomic-data-automation-r agronomic-data-automation-r Public

    Automated data processing pipeline for multi-trait plant breeding trials. Uses functional programming in R to summarize plot-level statistics (Mean, SD, N) and analyze inter-plot variability

    R

  2. differential-gene-expression-R differential-gene-expression-R Public

    Gene expression profiling (LS vs HS samples) using Tidyverse. Includes paired t-tests, fold change calculation, and visualization via Volcano plots and Boxplots.

    R

  3. oryza-sativa-genome-assembly oryza-sativa-genome-assembly Public

    Advanced plant genome assembly pipeline for Oryza sativa using HiFi reads, k-mer profiling (Jellyfish), and Hifiasm

    HTML

  4. plant-phenomics-kaust plant-phenomics-kaust Public

    Field phenotyping data analysis from KAUST internship. Includes trait distribution, species comparison (t-test), and automated plot summarization in R.

    R

  5. r-statistics-foundations r-statistics-foundations Public

    A comprehensive 10-day intensive curriculum on Statistics and R for Biological Sciences. Covers data wrangling, probability distributions, hypothesis testing (t-test, ANOVA), and linear regression

    R

  6. sequence-homology-search-blast sequence-homology-search-blast Public

    Bioinformatics pipeline for sequence identification using NCBI BLAST+. Includes command-line data extraction and R-based statistical filtering of genomic hits

    R