Skills for computational biologists¶
Computational biology is a rapidly growing field that requires a diverse set of skills. Technical skills such as programming and data analysis are essential for working with large datasets and developing computational models. However, soft skills such as communication at various levels of detail and collaboration are essential for working effectively in interdisciplinary teams.
Content¶
Open up a Top Hat discussion question and prompt the students to submit what skills should a successful computational biologist have. Make the responses pop-up on the screen with anonymous mode turned on. As answers are submitted, the instructor should discuss these aspects and add context. Encourage for everyone to submit something as this will contribute to their participation grade.
After there is a lull in responses, move forward with slides that discuss what the instructor believes are important skills to have.
Hard skills¶
Hard skills are technical abilities or knowledge specific to a particular field. They can be learned through education, training, or on-the-job experience.
- Command line environment scripting like bash.
- Programming languages such as
- Machine learning frameworks such as
- Sequencing tools.
- Knowledge of
- molecular biology,
- cell biology,
- physiology,
- genetics,
- microbiology,
- biochemistry, etc.
- Understanding of linear algebra, probability and statistics, and differential equations.
- Familiarity with visualization tools to effectively communicate data and results.
For example,
- matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python;
- seaborn: A Python data visualization library based on matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics;
- plotly: An open-source data analytics and visualization tool that creates interactive charts for web browsers and supports multiple languages, such as Python, Julia, R, and MATLAB;
- D3.js: A JavaScript library for manipulating documents based on data. It helps you bring data to life using HTML, SVG, and CSS;
- p5.js: A JavaScript library for creative coding that makes coding accessible for artists, designers, educators, and beginners;
- Adobe Illustrator: A vector graphics editor developed by Adobe Systems;
- Inkscape: An open-source vector graphics editor similar to Adobe Illustrator;
- Jmol: A free and open source viewer of molecular structures with features for chemicals, crystals, materials and biomolecules;
- BioRender: An online tool for creating scientific figures and illustrations;
- GIMP: An open-source image editing program;
- MGLTools: A collection of methods for visualization and analysis of biomolecular systems;
- ImageMagick: A software suite to create, edit, compose, or convert bitmap images from the command line;
- VMD: A molecular visualization program for displaying, animating, and analyzing large biomolecular systems using 3-D graphics and built-in scripting;
- CIRCOS: Designed for visualizing genomic data but can create figures from data in any field;
- ChimeraX: A program for the interactive visualization and analysis of molecular structures and related data;
- Blender: An open-source 3D creation suite.
Soft skills¶
Soft skills are often called “people” or “interpersonal” skills and relate to how you interact with and relate to others.
Collaboration
Collaboration is a pivotal soft skill for computational biologists, enabling them to collaborate with diverse peers to attain shared objectives proficiently. Given the multidisciplinary nature of computational biology, which merges insights from various domains like biology, computer science, mathematics, and statistics, professionals often work within teams encompassing a range of expertise.
Effective collaboration empowers computational biologists to harness their colleagues' specialized knowledge, culminating in comprehensive and sensible solutions to intricate research problems. Team members pool their insights through synergy, build upon each other's concepts, and offer constructive feedback and assistance, collectively surmounting challenges. Fostering collaboration cultivates innovation and creativity, as it urges team members to venture beyond conventional strategies and explore novel avenues.
Problem-solving
Problem-solving is a quintessential aptitude for computational biologists, equipping them to address intricate research quandaries and devise pragmatic remedies. Computational biology hinges on utilizing computational methodologies to dissect and decipher intricate biological datasets.
The significance of problem-solving skills for computational biologists resides in their capacity to delineate the core challenges and constraints tied to a research problem. This encompasses deconstructing intricate predicaments into more manageable segments, employing logical deduction and critical evaluation to scrutinize the scenario from multiple angles, and deploying inventiveness and originality to foster pioneering solutions.
Critical thinking
Critical thinking is a pivotal competency for computational biologists, enabling them to dissect information judiciously and form well-considered judgments. In computational biology, computational approaches are harnessed to dissect and interpret expansive and intricate biological data.
The essence of critical thinking for computational biologists is its capacity to evaluate evidence, pinpoint assumptions and biases, and entertain many viewpoints. This necessitates meticulous scrutiny and data analysis, contemplation of alternate explanations, and making sound decisions informed by the available evidence.
Creativity
Creativity is pivotal for computational biologists, furnishing them with the capability to spawn novel concepts and methodologies to confront challenges. Profound creativity skills are critical for computational biologists as they facilitate thinking beyond conventional paradigms, paving the way for inventive strategies in data analysis. This can encompass forging innovative algorithms or models or applying existing techniques in unprecedented ways. By harnessing creativity, computational biologists guide fresh insights and breakthroughs that might evade conventional approaches.
Emotional intelligence
Emotional intelligence takes center stage as a crucial skill for computational biologists, endowing them with the ability to comprehend and navigate their own emotions alongside those of their peers. Given the collaborative milieu of computational biology, which often entails cross-disciplinary teamwork, strong emotional intelligence bolsters effective communication and collaboration.
For instance, a computational biologist adept in emotional intelligence can swiftly discern a colleague's frustration or overwhelm, extending the appropriate support or aid. By showcasing robust emotional intelligence, computational biologists position themselves as adept leaders and communicators, unearthing new avenues for professional advancement.
Adaptability
Adaptability emerges as a pivotal skill for computational biologists, mirroring the perpetual evolution of the field. With continuous developments in technologies, methodologies, and techniques, computational biologists must seamlessly assimilate these shifts to remain current in their domain.
Adaptability empowers computational biologists to swiftly amass and apply fresh skills and insights, all while remaining flexible in their problem-solving methodologies. This could involve mastering novel programming languages, staying abreast of the latest research advancements, or exploring innovative angles in data analysis.
Negotiation and conflict resolution
Negotiation and conflict resolution skills assume pivotal roles in the toolkit of computational biologists, capacitating them to navigate disagreements and disputes constructively. Computational biology's collaborative framework often entails interaction with colleagues from diverse disciplines, and adept negotiation and conflict resolution skills facilitate effective communication and harmonious collaboration.
Negotiation involves arriving at mutually advantageous agreements through dialogue and compromise. This entails pinpointing common ground, identifying shared objectives, and collaborating on solutions that cater to all parties involved. Conversely, conflict resolution entails the ability to address conflicts and discord constructively, which consists in discerning the underlying roots of the disagreement, fostering candid communication, and collaborating to generate solutions accepted by all.
Time management
Time management is important for computational biologists, empowering them to prioritize and manage their workload methodically. Juggling multiple projects is a norm in computational biology, necessitating adept allocation of time and resources to meet deadlines and accomplish objectives.
Effective time management encompasses:
- Clear goal-setting and prioritization
- Breaking intricate tasks into manageable segments
- Employing tools like calendars and to-do lists to stay organized
Furthermore, it entails adaptability to shifting circumstances, whether unexpected delays or novel priorities, and the flexibility to revise plans as necessary.