Techniques and Skills
Laboratory Skills
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ELISA, PCR, qPCR, spectrophotometry, enzyme activity assays
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Collection and processing of blood, tissue, and urine samples
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IR, NMR, UV-Vis, Mass Spectrometry, fluorescence spectroscopy
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Protein detection and quantification
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Protein and nucleic acid separation
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Compound separation and analysis
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Structural determination of compounds
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Studying material stability
Technical Skills
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Python (6+ years), R, JavaScript, SQL
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MOE, AutoDock, Chimera, PyMOL, EPI2ME, BLAST, GATK, VCFtools, BEDtools
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GROMACS, AMBER, AutoDock Vina, molecular dynamics simulations, protein-ligand docking, ligand-based virtual screening
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TensorFlow, Keras, Scikit-learn, Random Forest, SVM, XGBoost, deep learning for bioinformatics, neural networks for drug prediction
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R (dplyr, ggplot2), Python (Pandas, NumPy, Matplotlib, Seaborn), survival analysis, Cox regression
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Data preprocessing, variant calling, RNA-seq, DNA-seq, WES, transcriptome analysis
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Principal component analysis (PCA), clustering algorithms (k-means, hierarchical), heatmaps, Volcano plots, bioinformatics data pipelines
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AWS, Google Cloud, Docker, Kubernetes, cloud bioinformatics workflows, distributed computing
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SQL, NoSQL, PostgreSQL, MongoDB, cloud databases, data pipelines
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Jupyter Notebooks, GitHub (version control), development of bioinformatics tools, scripting for automation
Soft Skills
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Scientific authoring (research papers, grants, presentations), succinct and lucid documentation, ability to articulate technical concepts to non-technical individuals
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Inter-functional team working in research and clinical settings, familiarity working with cross-disciplinary teams
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Strong analytical acumen for gap identification in research, troubleshooting bugs in computational biology
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Managing simultaneous projects with effective time management, delivering within timelines, agile development
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Leading research initiatives, mentoring junior researchers, presenting findings at meetings and conferences
