bioinformatics

## What I do - Analyze and interpret large-scale biological datasets - Perform sequence alignment and homology modeling - Develop algorithms for biological data processing - Create and maintain biological databases - Predict protein structure and function - Identify biomarkers and genetic variants ## When to use me - When analyzing genomic, proteomic, or transcriptomic data - When searching sequence databases (BLAST, FASTA) - When predicting protein structure or function - When identifying variants from sequencing data - When building phylogenetic trees - When analyzing gene expression patterns ## Key Concepts ### Sequence Analysis **Alignment Algorithms** - Needleman-Wunsch: Global alignment - Smith-Waterman: Local alignment - BLAST: Heuristic local alignment - FASTA: Fast sequence similarity search ```python # Example: Simple sequence alignment scoring def simple_align(seq1, seq2, match=1, mismatch=-1, gap=-2): """Calculate simple alignment score.""" score = 0 for a, b in zip(seq1, seq2): if a == b: score += match else: score += mismatch return score ``` ### Biological Databases - NCBI (GenBank, PubMed, BLAST) - UniProt: Protein sequences and function - PDB: Protein structures - Ensembl: Genome annotations - UCSC Genome Browser ### Data Formats - FASTA: Sequence data - FASTQ: Sequences + quality scores - SAM/BAM: Sequence alignment - VCF: Variant calls - PDB: Protein structure - GO: Gene Ontology annotations