Introduction to Python for Bioinformatics – 5

Hello, visitor! As biology is saturated with complex datasets that have to be sorted, explored, and “looked into”, anyone handling data generation, analysis or decision-making based on data has to gain some level of “data science” skills. The increasing necessity to process big data and develop algorithms in all fields of science means that programming is becoming an essential skill for scientists, with Python the language of choice for the majority of bioinformaticians. In most biological and biomedical settings, you will be expected to run or implement programs written in Python, R, and others. Getting started can be hard - programming is like learning a new language! That is why we offer easy-to-follow, structured, and simple coding tutorials designed around bioinformatics challenges. To view the full path on getting started, explore the links below or go to the learn portal link and get started!

Click here to Register for Biomedical Data Science in Python mentor-guided program

Expert-Developed Asynchronous Courses to Start with Bioinformatics in Python

OL BioInfo in Python

 

 

Getting Started with Bioinformatics in Python: As the need for management and analysis of data grows, coding is becoming a must-have skill for a bioinformatician. This does not have to be an overwhelming career-change for a biologist, so our team put together elementary tutorials to understand programming basics using examples of loading, analysis and visualization of structured data. In this course, you will get started with bioinformatics by analyzing genomic sequences and finding patterns that can help us interpret the language of DNA, RNA and protein.

Biomedical Data Science in Python: This course is designed to introduce elements of data science in Python, such as data wrangling, visualization, statistical analysis, and machine learning. The methods will be reviewed in the context of biomedical and other scientific problems using -omics data. The methods will be reviewed in the context of biomedical and other scientific problems using -omics data. The exercises focus on importing and understanding various data types, transforming them into categorical variables, continuous data and extracting meaningful patterns for visualization. Then, the training continues to include statistical analysis, complex data visualization, machine learning and an introduction to deep learning. After completing these modules, you will be able to apply your skills to various types of omics data, as described below.

OL BioML – Python

How is Python used in Bioinformatics?

Bioinformatics can be defined as “the application of computational tools to organize, analyze, understand, visualize and store information associated with biological macromolecules”. Dealing with data efficiently to process, analyze, visualize and annotate will ultimately require some coding - even if the code launches other scripts developed by a more experienced programmer. Therefore, everyone dealing with data (and especially omics data) needs to develop an understanding of how to read, write, change or optimize code.

Coding is very important to bioinformatics. Having a bioinformatician tell you they never had a programming class is like having a MD tell you they never took anatomy. Soon you will realize that in order to “do” bioinformatics, you have to have a minimum knowledge on programming, even for running someone else's software. You can be a basic bioinformatician and perform analysis using available software but as soon as you want to modify some of the outputs, inputs or settings you will often be limited if you can’t go deeper into the code and at least read it to change it according to what you want to do. 

Thus, you can look into practical examples of scripting languages for visualization and “making sense” of biological data. 

What's included?

Choose a tenure that supports your needs and join a community of our program experts!

45 Days

 Coding Sessions
 Hands-on Assignments
 Group Sessions
 Session Recordings
 Omicslogic Program Access
 Example Cloud Pipelines
 Online Mentor Support
 Online Technical Support

OUTCOME:

⭐️ Certification of Completion

⭐️ Job Opportunities

60 Days

 Coding Sessions
 Hands-on Assignments
 Group Sessions
 Online Mentor Support
 Omicslogic Program Access
 Education Cloud Pipelines
 One-on-One Mentor Sessions

OUTCOME:

⭐️ Certificate of Completion

⭐️ Job Opportunity

⭐️ Research Poster

90 Days

 Coding Sessions
 Hands-on Assignments
 Group Sessions
 Online Mentor Support
 Omicslogic Program Access
 Education Cloud Pipelines
 Research Project

OUTCOME:

⭐️ Certificate of Excellence

⭐️ Job Opportunities

⭐️ Research Publication

Payment Checkout Links for 45/60/90 Days Tenure

Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: marketing@omicslogic.com and we will find the best possible way for you to participate and get the best outcomes from the training program.

We also have the option for financial assistance with weekly/monthly installment options and payment options according to your country (e.g., INR, etc.)

WhatsApp.svg+91-9876134120

Transcriptomics Analysis in Python

T- R1-1

RNA Seq Data Analysis

We'll start by learning how to load your data into Python, check what type of data it contains & learn about various packages & libraries. Next, we will learn how to process, normalize and visualize data.

T- R2-1

Dimensionality reduction

Analysis of high-dimensionality datasets is challenging making it hard to spot trends that define your data. PCA and t-SNE are the dimensionality reduction methods to explore & visualize data.

T-R5-1

Machine Learning Methods

Learn about various machine learning methodologies, syntax in python utilizing specific packages & libraries for supervised and unsupervised machine learning and practice the python code block.

Biomedical Data Science in Python (Beginners/Intermediate): 

Session Title

Topics Covered

Online Course work session

Session 1: Introduction to the Course

Session 1: Biomedical Data Science in Python

 

Topics To Be Covered: 

  • Big biomedical data: omics
  • Course objectives
  • Skills and projects

Associated online course/resources

Session 2: Data Processing and Exploratory Analysis

Session 2 Biomedical Data Science in Python

 

Topics to be Covered: 

  • Python Function: Introduction & Execution
  • Data Wrangling: Loading, Transforming & Exploring Data
  • Data Visualization: Bar Plots, Box Plots, Histogram, Heatmap
  • Statistical Summaries
  • Basic comparative analysis (t-test)

Online Code Lessons

Colab Notebooks

  • Getting Started with Data Visualization: Bar Plots and Box Plots
  • Bar plots, Box plots, introduction to T-Test
  • T-Test and Heatmap Results in Python

Session 3: Machine Learning Methods: Unsupervised and Supervised Types of the Analysis

Session 3: Biomedical Data Science in Python

 

Topics to be Covered: 

  • Machine Learning: Introduction & Various Techniques
  • Unsupervised ML Overview: Dimensionality Reduction & Clustering
  • Supervised ML Overview: Classification & Feature Selection 

Online Code Lessons

Session 4: Dimensionality Reduction: Ordination and Embedding
Session 4: Biomedical Data Science in Python

 

Topics to be Covered: 

  • Decomposition: PCA
  • Ordination: MDS and NMDS
  • Manifold Learning: tSNE and UMAP

Online Code Lessons

 

Colab Notebooks

  • Basic plotting in matplotlib
  • PCA Noise Variance Explained
  • MDS NMDS
  • tSNE and UMAP for Single Cell

Session 5:  Unsupervised Analysis: Clustering

Session 5: Biomedical Data Science in Python

 

Topics to be Covered: 

  • Clustering: K-Means, Hierarchical Clustering, Spectral
  • Use Python Clustering Packages: SciKit & SciPy  
  • Cluster Evaluation Using Scoring: Inertia, Silhouette Score
  • PCA Scatter Plot To Evaluate Clustering Methods (Visually)

Online Code Lessons

Lesson 5: Introduction to Data Science BioML 

Colab Notebooks

  • Clustering of Samples
  • Clustering Genes

Session 6: Supervised Analysis: Discriminant Analysis and Classification

 Session 6: Biomedical Data Science in Python

 

 Topics Covered:

  • Decision Trees & Random Forest Classification (RF)
  • Support Vector Machine (SVM): Support Vector Classifier (SVC) and linearSVC
  • Linear Discriminant Analysis (LDA) & stepwise LDA 
  • Uniform Manifold Approximation and Projection (UMAP)

Coursework

Lesson 6: Python Course 2 https://learn.omicslogic.com/Python/python-course-2-introduction-to-data-science-bioml/lesson/06-machine-learning-classification

Colab Notebooks

  • Classification: Random Forest
  • Classification: SVM
  • Classification on UMAP
  • Classification Assignments:
    • SVM Notebook
    • Random Forest Notebook
    • LazyPredict Notebook
    • Classification Exercise

Session 7: Feature Selection

Session 7: Biomedical Data Science in Python

 

Topics Covered

  • Introduction & Types: Feature Selection
  • Filter-based Methods
  • Wrapper-based Methods
  • Hybrid Methods
  • Embedded Methods

Coursework

Colab Notebooks

  • PCA, Random Forest and Feature Selection
  • Classification, Model Optimization and Feature Selection
  • Explainable AI & Microbiome Data

Session 8: Regression

Session 8: Biomedical Data Science in Python

 

Topics Covered

  • Simple linear regression
  • Multiple Regression
  • Logistic Regression
  • Data Preparation & Generalization

 

Coursework

Lesson 10: Transcriptomics Course https://learn.omicslogic.com/Learn/course-5-transcriptomics/lesson/10-regression-and-factor-regression-analysis

 

Lecture 9: Generalized Linear Models

 Session 9: Biomedical Data Science in Python

 

Topics Covered

  • Introduction: Generalized Linear Models (GLM)
  • Simple Linear Regression
  • Binary Logistic Regression
  • Poisson Regression
  • GLM vs Traditional Regression

 

 

Session 10: Network Analysis

Session 10: Biomedical Data Science in Python

 

Topics Covered

  • Introduction: Network Analysis
  • Examples of Networks
  • Types of Graphs
  • Network Visualization

 

Session 11: Deep Learning

Session 11: Biomedical Data Science in Python

 

Topics Covered

  • Introduction to Deep Learning: Multi-layer Perceptron (MLP)
  • Types of DL networks
  • Learn & Practice Code for DL

 

Coursework

Lesson 8: Introduction to Data Science BioML https://learn.omicslogic.com/Python/python-course-2-introduction-to-data-science-bioml/lesson/08-dimensionality-reduction-with-deep-learning

Colab Notebooks

  • Dimensionality Reduction with Autoencoders
  • Full assignment
  • Feature selection exercise session

Session 12: Model Accuracy and Validation

Session 12: Biomedical Data Science in Python

 

Topics Covered

  • Introduction to Deep Learning: Multi-layer Perceptron (MLP)
  • Types of DL networks
  • Learn & Practice Code for DL

 

Session 13: Project Examples and Case Studies

Session 13: Biomedical Data Science in Python

 

 

Example Projects:

Student Projects:

Session 14: How to design your project?

 

 

Coursework

Box plot for log scale transformed data.

Getting started in Python can be a challenge

There are many things to consider as you are getting started - installing packages, debugging, learning the environment and the list goes on. But to get started, you need to make sure you can get through these challenges quickly and learn how to do it before your motivation runs out!

That is why on our portal, you can practice and run the analysis right in the browser by using our  console on the Omicslogic learn portal and gain immediate feedback for your code. 

In each tutorial, you will be able to learn the syntax, run the provided code and complete challenges where sections of the code need to be completed independently - all right within the browser!

Research Projects and Case Studies

There are opportunities to leverage what you know, discover a lot more and make a difference.

, you might be thinking how your skills in bioinformatics can make a difference and what can you do to make an impact. That is why it is important to learn about bioinformatics in the context of a research area you can relate to. This includes precision medicine, population health, agrobiology and astrobiology - all areas with active utilization of omics data. Learn more by exploring some of the projects and programs we offer that cover these areas in detail.

 

Register for the upcoming webinar

Payment Checkout Links for 45/60/90 Days Tenure

Note: For Low-Income Countries scholarship opportunities are available, please reach out to us and our team at: marketing@omicslogic.com and we will find the best possible way for you to participate and get the best outcomes from the training program.

We also have the option for financial assistance with weekly/monthly installment options and payment options according to your country (e.g., INR, etc.)

WhatsApp.svg+91-9876134120

Here is what our users say about these courses:

Aheria
"I am impressed with the clarity of the course material, the focused approach of its contents and the great graphics for illustrating the key concepts. The practice code blocks are really helpful to learn and imply the code. I will recommend this to all students who are biological science students".
- Aheria Dey , Postgraduate Student
Ayomide
"The course was structured in a way that it was easy to understand for anyone without prior knowledge of bioinformatics. It clearly explains the concept that appears very technical in an interesting way. The glossary links for technical terms also made the course interesting and easy to comprehend".
- Ayomide Samson Fasemire , Graduate Student
Abdul Azeez lanihun
"I am very glad that I took the courses. The courses are precise but at the same time very informative. I liked the balance between demonstrating while also allowing me to figure out the issues within the inputs. The courses provide good information Python coding & data visualization".
- Lanihun , University Representative
dhruv
"Each step was very clear and easy to understand for a beginner like me. I also found the Webpage design to be user-friendly. I will surely continue to learn through this platform. Code was easy to understand. The variety of command modules made it easy to comprehend piece by piece".
- Dhruv Mehra , Postgraduate Student

What is Omics Logic?

A Growing Community of Students, Experts and Mentors

Our community leverages publicly available data, online tools for big data analysis and a network of mentors to help students learn bioinformatics, apply their skills to meaningful research projects and work with mentors on turning their projects into publications or research posters. The programs we offer provide training, access to high quality tutorials and tools anyone can learn to use independently. The program is offered at university, high school or community college levels as well as directly to citizen scientists around the world. To join, simply create an account for free on learn.omicslogic.com!