Bioinformatics Expert

Before Starting

1. Sequence, structure, or expression data?
2. Genomics, transcriptomics, proteomics, or metagenomics?
3. Tool usage or algorithm development?

Core Expertise Areas

Sequence Analysis

Pairwise alignment: Needleman-Wunsch (global), Smith-Waterman (local). Multiple sequence alignment: ClustalW, MUSCLE, MAFFT — progressive and iterative. Database search: BLAST — scoring matrix (BLOSUM62), E-value, bit score. Sequence motifs: MEME, JASPAR — position weight matrices for transcription factors.

NGS Data Processing

Quality control: FastQC, trimming (Trimmomatic, fastp). Alignment: short reads (BWA, Bowtie2), RNA-seq (STAR, HISAT2), long reads (minimap2). Variant calling: GATK, FreeBayes, DeepVariant. RNA-seq: featureCounts/HTSeq for quantification, DESeq2/edgeR for DE analysis.

Structural Bioinformatics

Protein structure: PDB format, secondary structure prediction (PSIPRED). Structure prediction: AlphaFold2 — revolutionary accuracy for single domains. Molecular docking: AutoDock, Glide — predicting protein-ligand binding. Homology modeling: MODELLER, Swiss-Model — template-based structure prediction.

Single-Cell Analysis

scRNA-seq pipeline: Cell Ranger (10x), Seurat/Scanpy for analysis. Quality control: UMI counts, gene counts, mitochondrial fraction. Clustering: graph-based (Leiden, Louvain), UMAP/tSNE visualization. Trajectory analysis: RNA velocity, Monocle, PAGA.

Python for Bioinformatics

BioPython: sequence parsing, BLAST interface, structure analysis. Pandas: tabular data — VCF files, expression matrices. Matplotlib/Seaborn: volcano plots, heatmaps, PCA plots. Scikit-learn: classification of sequences, feature extraction.

Key Patterns

Best Practices

• Always check data quality before analysis
• Use containerized tools (Docker/Singularity) for reproducibility
• Version control analysis scripts and document software versions
• Validate bioinformatic findings with experimental orthogonal methods
• Consider batch effects in multi-sample analyses

Common Pitfalls

Pitfall	Fix
Not checking alignment quality	Always review alignment stats and coverage
Ignoring multiple testing	Use FDR correction (Benjamini-Hochberg)
Wrong reference genome build	Confirm genome version matches annotation
Skipping normalization	Always normalize before comparing samples

Related Skills

• genomics-expert
• molecular-biology-expert
• machine-learning-expert
• python-expert