-Biotechnology student.
-Learning Python for Bioinformatics.
-Exploring genomics, data analysis & computational biology.
-Working with NCBI, BLAST & sequence analysis tools.
-Strong foundation in molecular biology & genetics.
-Building projects and growing every day.

• Sequence Analysis: Reading FASTA/FASTQ files, FASTA/FASTQ processing, GC content calculation, sequence length, motif finding, DNA/RNA/protein sequence manipulation
• Next-Generation Sequencing (NGS): FastQC, Trimmomatic, Cutadapt, read alignment (BWA, Bowtie2, HISAT2), variant calling (SAMtools, GATK), RNA-seq analysis (DESeq2, edgeR), differential expression
• Genome & Transcriptome: Genome assembly (SPAdes, Canu), genome annotation (Prokka, MAKER), isoform analysis
• Structural Bioinformatics: Protein structure visualization (PyMOL, Chimera), molecular docking
• Functional Genomics & Pathways: Gene Ontology (GO), KEGG, Reactome, enrichment analysis
• Metagenomics / Microbiome: QIIME2, Kraken, MetaPhlAn, alpha/beta diversity analysis
• Single-cell / Advanced Transcriptomics: Seurat, Scanpy, cell clustering, pseudotime trajectory analysis
• Databases & Resources: NCBI, GenBank, Ensembl, UCSC Genome Browser, UniProt, PDB, ClinVar, gnomAD
• Workflow & Pipelines: Snakemake, Nextflow, Docker, Conda environments, HPC/Cluster computing (SLURM, PBS)
• Programming & Scripting for Bioinformatics: Python (Biopython), R (Bioconductor), Bash/Shell scripting, Jupyter notebooks
• Data Analysis & Visualization: pandas, numpy, matplotlib, seaborn, ggplot2, data summaries and plotting
• Version Control & Collaboration: Git, GitHub, Pull Requests, open-source contributions, documentation, code reviews