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Chances are, you’re here because you’d like to get some answers about Python and biology.

I’ve done some research on the most common burning questions about Python & biology and found some answers!

In this article, I’ll cover 15 key things about Python and Biology that you should know!

Read on for more information on the answers!

1. Is Python Used in Biology?

Python is used in biology. Python is commonly used in biology for applications in bioinformatics and genomics involving large biological datasets. Data analysis and cleaning are done on biological datasets in Python using biological computational libraries such as Biopython to determine biological insight for research.

Data Analyst VS BI Analyst: 7 Key D...
Data Analyst VS BI Analyst: 7 Key Differences

You’d be surprised that Python is actually used quite often in biology!

Python is great for handling biological data since it tends to get really large. The human genome which is 1.5 GB large, can even be analyzed in 1 day!

Here’s why Python is such a great choice in biology:

Python is great for biology for its collection of libraries that can perform data transformation, cleaning, data analysis, and machine learning.

I’ll share more in the next question!

2. How Is Python Used in Biology?

Source: Biopython

Python is used in biology for data analysis of biological datasets and for programming bioinformatics tools. Data analysis libraries in Python like Biopython are used for their power data handling capabilities. Python is also used for programming bioinformatics tools for custom data analysis in biological research.

In my programming experience with Python, I’ve had to use Python regularly for data analysis and it’s the libraries in Python that have proven to really useful!

Here are some of the common libraries in Python that biologists and bioinformaticians use:

Not all of them are used by all biologists but these are some of the commonly used ones!

However, Python isn’t the only language that can help with biological data analysis!

There’s also another statistical programming language loved by biologists – the R programming language.

This article about Python and R might interest you too!

3. Is Coding Useful in Biology?

Coding is useful in biology. Coding languages such as Python and R handle data transformation, analysis, and visualization of biological data well. Having coding knowledge would bring deeper biological insight to experimental data. However, not all coding languages are useful in biology.

Coding is such a crucial skill when publishing any data for biology research!

All those graphs and tables you’ve seen in biology research articles are mostly made from data visualization libraries in Python and R!

That’s why it’s so useful to have coding knowledge in biology!

Here are some examples of useful libraries within common coding languages:

  • Plotly for most data visualizations in Python
  • ETE toolkit for genetic visualizations in Python
  • ggplot2 for most data visualizations in R

To give you some idea of how this would look, here’s a gene tree diagram visualization made from the ETE toolkit!

Personally coming from a pure biology background, I’d just like to add that knowing coding is one of the best ways to better appreciate biology!

In fact, coding would even help you in your job prospects!

Here are some jobs where coding is used in biology:

  • Biostatisticians
  • Bioinformaticians
  • Biomedical data scientists

If you’re interested to pick up programming for yourself, do check out these recommendations:

Recommended Resources to Learn Programming in Biology

Course TitleType of ContentWho Should Take This?
Biology Meets Programming: Bioinformatics for BeginnersShort Online CourseBiologists who want an introduction to Python, beginning with basics
Bioinformatics SpecializationOnline SpecializationBiologists who are committed to entering the bioinformatics space

4. Is Python Used in Bioinformatics?

Python is used in bioinformatics. Python is used for data analysis, visualization, and developing bioinformatics tools. It has libraries for accessing online biological datasets easily, processing them, and outputting visuals. Python is also used to build custom bioinformatics tools according to research needs.

Bioinformatics is a specialized field within the larger biological research field.

You’ll likely hear of bioinformaticians focussing on computer “dry” labs rather than the usual biologists conducting live experiments!

Bioinformatics is actually a combination of both biology and computer science. It involves the analysis, storage, and processing of biological data.

Naturally, you’d believe that Python is going to play a huge role in bioinformatics!

Python plays two main roles within bioinformatics:

  1. Data analysis
  2. Custom bioinformatics tool development

Check out this video I’ve found that will give you a good introduction to bioinformatics in Python!

Want to know more about bioinformatics and how different it is from data science?

Check out this article I wrote here.

5. What is Python Used For in Bioinformatics?

Python is used for biological data analysis and developing custom tools in bioinformatics. Python is used for Next-Generation Sequencing (NGS), molecule visualization, population genetics, and microarray data analysis. Moreover, wider custom uses of Python in bioinformatics are constantly being developed in Python.

Being a general-purpose, object-oriented programming language, Python is really versatile.

And that’s what makes it a great programming language for bioinformatics!

Bioinformatics is a growing field in biology, especially when large biological datasets are just waiting to be analyzed!

Each biological experiment gives so much data that Python can be applied in a ton of different scenarios!

Here’s a list of the most common applications I’ve found:

  1. Next-Generation Sequencing (NGS)
  2. Accessing protein databases
  3. Analyzing protein expressions
  4. Macromolecular visualization
  5. Drug discovery
  6. Population genetics
  7. Genomic analysis
  8. Gene annotation
  9. Single-nucleotide polymorphism (SNP) analysis
  10. Single-cell sequencing
  11. DNA/RNA Sequence alignment
  12. Microarray data analysis

Now you might not recognize some of these applications, but they’re all REALLY crucial to bioinformatics!

To help you better understand one of the applications, here’s a video I found from the freecodecamp featuring the Data Professor on an application of Python in bioinformatics!

6. Is Python Good for Bioinformatics?

Python is good for bioinformatics. Python has a collection of powerful libraries made for biological data analysis and for bioinformatics tool development. However, Python should not be the only language one should learn for bioinformatics. R programming, Bash, and Perl are also good languages for bioinformatics.

7. Is Python Necessary for Bioinformatics?

Python is not necessary for bioinformatics. Bioinformatics requires biological data analysis, which several other programming languages can perform. Such languages include R programming, Perl, and Julia. However, Python is a good choice for its versatility, easy-to-understand syntax, and tool development capabilities.

8. Should I Learn R or Python for Bioinformatics?

R should be learned for bioinformatics. However, this may vary depending on biology and computing background. R is more biologically-focused, which suits biologists, for its gentler learning curve. However, Python should be learned for users with computing knowledge but without biology background for its familiarity.

Bioinformatics can be a confusing field, with not much content and help out there on which language is best to start with.

Here’s an easy way for you to choose your starting language:

  • If you’re coming from a biology background, go ahead with R. You’ll have an easier time learning!
  • If you’re coming from a computing background, go ahead with Python. Python should have lots of similarites with other languages so you’ll get started in no time!

Let’s have a look at where each of the languages shine when working in bioinformatics!

Here’s a summary table of factors to consider to help you decide better:

FactorsPythonR
1Learning CurveSteeperGentler
2Development capabilitiesEasierTougher
3Amount of bioinformatics librariesSmallerLarger
4Bioinformatics community supportSmallerLarger
5Amount of machine learning libariesLargerSmaller

However, other than R or Python, there’s another language that all bioinformaticians MUST know!

And that’s Bash!

Bash is a UNIX shell and command language commonly used by bioinformaticians for its data analysis tools.

A commonly overlooked language, Bash is a crucial skill in handling big data in bioinformatics that many beginners tend to ignore!

Here are some tools that Bash has:

Without going too much into the details, Bash basically unlocks a larger, more powerful set of toolkits that can be used to process big biological data.

Here’s a great video from OMGenomics that I’ve found that should give a better explanation:


9. What Programming Language Is Used in Bioinformatics?

R, Python, Bash, Perl and Julia programming languages are used in bioinformatics. These languages provide the necessary tools required for a standard bioinformatics data analysis pipeline. However, these languages are used in combination to achieve biological insight.

10. Is Bioinformatics a Good Career?

Bioinformatics is a good career. It involves using computing technnology to analyze biological data to discover biological insights. Bioinformaticians have stable careers within academia and private research institute. A bioinformatics career is for those who enjoy computing and want to contribute to biology research.

Looking to explore a bioinformatics career?

Here’s a few questions to consider:

  • Do you enjoy programming?
  • Are you comfortable working with computers?
  • Will you be comfortable solving complex math problems?
  • Are you inspired to improve biology?

If all the answers to those questions are YES, then bioinformatics is the career for you.

If you want to hear from an actual bioinformatician yourself, check out this blog by a bioinformatician I found.

11. What is Python Used For in Genetics?

Python is used for data processing and analysis in genetics. Genetics research produces large amounts of sequencing data. Libraries in Python like Biopython process DNA, RNA & protein sequence leading to applications of Python in genetics such as sequence alignment and protein motif search.

Genetics is an exciting field and I’ve actually done some research on RNA sequencing myself!

When it comes to Python and its applications to genetics, you’ll most likely encounter sequencing.

This is where Biopython comes in!

Within genetics, Biopython allows DNA, RNA, and protein sequences to be aligned in preparation for analysis!


12. How Python Can Be Used in Medicine?

Python can be used in medicine through data analytics and bioinformatics. Python is a versatile programming language, which can be applied within the medical field through machine learning for clinical applications. It can also apply to data analysis of biomedical data from patient samples like blood and urine.

Curious to learn more about how Python can be used for medicine?

You’d like this article I wrote about 5 applications over here!


13. Can a Biology Student Learn Python?

A biology student can learn Python. Python is a high-level programming language, making it easily adopted by biologists without programming knowledge. Biology students can use Python to process biological datasets from laboratory experiments to facilitate learning. However, first-time learners may take longer to learn.

Python is such a widely-known programming language and as a biology student I can totally relate to this concern of fear of learning Python!

Here’s my personal story:

I was a biology undergraduate myself when I first encountered Python. That’s when I picked up some personal projects of my own where I analyzed data using Python!

Because of the large community around Python, I was able to learn much faster despite have NO programming experience at all.

After practicing Python for a couple of years, I transitioned out of biology to healthcare data analytics!

You can read more about my story here.

I hope this inspires you to learn Python as a biology student!

Here’s a video of a story by the Data Professor that inspired me to learn coding as a biologist:

You’ll also like this article on: can biologists become data scientists?


14. Should Biologists Learn Python?

Biologists should learn Python. Python has many applications within the biology field and its applications within healthcare. Biologists who understand Python are able to perform data analysis on biological experimental data. By learning Python, a biologist can also explore careers in biostatistics and bioinformatics.

If you’re interested to learn Python as a biologist, I’d highly recommend the following resources:

Here’s a good video sharing more about learning Python as a biologist:


15. Is Python Useful for Biotechnology?

Python is not useful for biotechnology. Biotechnology involves creating raw products made through living organisms. Any knowledge in Python will not be useful in the highly experimental and manual process of biotechnology. However, Python is useful in processing downstream data from manufacturing.

Many get confused on the definition of biotechnology and think that Python can be used..

Here’s the definition of biotechnology:

Biotechnology is the production of raw materials and products made through the technological advances in harvesting from living organisms.

This means that biotechnology is very much a manual process, like manufacturing!

This makes it tough for any applications in Python to be useful in biotechnology.

However, Python is great for processing downstream data made from biotech!

Here’s why:

Biotechnology produces lots data from sensors measuring the manufacturing processes. This data can be analyzed to optimize any processes!

Final Thoughts

That’s all the 15 things you should know about Python in biology! Python is such a powerful language and its use in biology is really amazing.

I hope you’ve learned something from this blog post! Thanks for reading!

Sources

  1. https://portlandpress.com/biochemist/article/43/5/54/229826/Understanding-large-and-complex-biological-data
  2. http://etetoolkit.org/gallery/
  3. https://www.datascienceblog.net/post/commentary/studying-bioinformatics-is-it-worth-it/
  4. https://en.wikipedia.org/wiki/Biotechnology
  5. Programming for Biologists
  6. How To Learn Coding As A Biologist

My Favorite Learning Resources:

Here are some of the learning resources I’ve personally found to be useful as a data analyst and I hope you find them useful too!

These may contain affiliate links and I earn a commission from them if you use them.

However, I’d honestly recommend them to my juniors, friends, or even my family!

My Recommended Learning Platforms!

Learning PlatformWhat’s Good About the Platform?
1CourseraCertificates are offered by popular learning institutes and companies like Google & IBM (OFFER: Get $100 off Coursera Plus Annual Subscriptions – promo ends Sep 29
2DataCampComes with an integrated coding platform, great for beginners! (PROMO: Get 60% OFF Datacamp Annual Subscription – ends Sep 30)
3PluralsightStrong focus on data skills, taught by industry experts (PROMO: Get 50% OFF Monthly subscriptions!)
4StratascratchLearn faster by doing real interview coding practices for data science
5UdacityHigh-quality, comprehensive courses (Back-to-learning: Get 70% OFF all Udacity Nanodegree programs! — Ends Sep 27)

My Recommended Online Courses + Books!

TopicOnline CoursesBooks
1Data AnalyticsGoogle Data Analytics Professional Certificate
2Data ScienceIBM Data Science Professional Certificate
3ExcelExcel Skills for Business Specialization
4PythonPython for Everybody SpecializationPython for Data Analysis
5SQLIntroduction to SQLSQL: The Ultimate Beginners Guide: Learn SQL Today
6TableauData Visualization with TableauPractical Tableau
7Power BIGetting Started with Power BI DesktopBeginning Microsoft Power BI
8R ProgrammingData Science: Foundations using R SpecializationLearning R
9Data VisualizationBig Book of Dashboards

To see all of my most up-to-date recommendations, check out this resource I’ve put together for you here.

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