Big Data Science with the BD2K-LINCS Data Coordination and Integration Center

Go to Course: https://www.coursera.org/learn/bd2k-lincs

Introduction

### Course Review: Big Data Science with the BD2K-LINCS Data Coordination and Integration Center The “Big Data Science with the BD2K-LINCS Data Coordination and Integration Center” course on Coursera offers an intriguing delve into the world of bioinformatics, specifically tailored to the insights gained from the Library of Integrative Network-based Cellular Signatures (LINCS) initiative. Spanning 10 years (2012-2021) under the NIH Common Fund program, LINCS serves as a cornerstone for researchers interested in cellular responses to various perturbations. This course not only teaches foundational concepts in bioinformatics but also how to utilize large datasets effectively. #### Course Overview and Objectives In an era where data drives scientific discovery, the ability to analyze and interpret large datasets is crucial. The course is designed for individuals looking to gain a solid grounding in bioinformatics as it pertains to cellular biology. The course aims to empower participants with skills that are directly applicable to real-world research, enabling them to manipulate and analyze data from LINCS and other genomics resources. #### Syllabus Breakdown 1. **LINCS Program Overview**: The course starts with a comprehensive introduction to LINCS, laying the foundation for understanding the datasets involved. 2. **Metadata and Ontologies**: This module is critical for grasping how data is organized and contextualized in bioinformatics. Understanding these concepts aids in more knowledgeable data analysis. 3. **Serving Data with APIs**: You'll learn how to access and utilize data programmatically, which is key in modern data manipulation. 4. **Bioinformatics Pipelines**: This important concept is vital for processing biological data efficiently, encompassing all steps from data acquisition to analysis. 5. **The Harmonizome**: The project integrates multi-source data about genes and proteins, showcasing the diversity of available biological data. 6. **Data Normalization** and **Data Clustering**: These modules dive into mathematical techniques that help improve data quality and discover underlying patterns, respectively. 7. **Midterm Exam**: A robust assessment of your grasp on the first seven modules, testing both conceptual understanding and practical application. 8. **Enrichment Analysis**: This important module dives into powerful analytical methods that link genomics with biological knowledge, thereby enhancing interpretation. 9. **Machine Learning**: A fantastic introduction to supervised learning techniques that are increasingly prominent in biological research. 10. **Benchmarking**: Understanding how to compare and evaluate different bioinformatics methodologies ensures you can choose the best approach for your data needs. 11. **Interactive Data Visualization**: This is an essential skill for any data scientist, allowing you to effectively communicate findings through dynamic visual tools. 12. **Crowdsourcing Projects**: This final module opens the door to real-world applications, providing avenues to engage with ongoing LINCS projects beyond the classroom. 13. **Final Exam**: A comprehensive exam covering all modules, ensuring that you can apply the knowledge gained throughout the course. #### Course Experience The teaching format combines video lectures with hands-on assignments, ensuring that theoretical knowledge is reinforced through practical experience. The quizzes and exams are well-structured to test comprehension while also challenging participants to think critically about data analysis techniques. The interactive programming elements, especially concerning data visualization, allow participants to turn theory into practice effectively. Students will find plenty of opportunities to engage with peers, facilitating networking and collaboration within the field. #### Recommendations I highly recommend this course to: - **Graduate Students**: Especially those in bioinformatics, biology, or related fields looking to bolster their analytical toolkit. - **Researchers**: Professionals who wish to employ computational techniques in their work will find the skills acquired here invaluable. - **Data Scientists**: Those interested in applying machine learning and data analysis in genomics and proteomics can expand their capabilities greatly through this course. - **Industry Practitioners**: Anyone working in biotechnology or pharmaceutical sectors looking to deepen their understanding of biological data. Overall, "Big Data Science with the BD2K-LINCS Data Coordination and Integration Center" is an enriching course that reinforces the intersection of computational techniques and biological research. With practical applications, a rich curriculum, and insightful evaluations, it stands as a robust resource for anyone eager to navigate the vast field of bioinformatics. Don't miss the chance to enhance your data science skills in this vital area of research!

Syllabus

The Library of Integrated Network-based Cellular Signatures (LINCS) Program Overview

This module provides an overview of the concept behind the LINCS program; and tutorials on how to get started with using the LINCS L1000 dataset.

This module includes a broad high level description of the concepts behind metadata and ontologies and how these are applied to LINCS datasets.

In this module we explain the concept of accessing data through an application programming interface (API).

This module describes the important concept of a Bioinformatics pipeline.

This module describes a project that integrates many resources that contain knowledge about genes and proteins. The project is called the Harmonizome, and it is implemented as a web-server application available at: http://amp.pharm.mssm.edu/Harmonizome/

This module describes the mathematical concepts behind data normalization.

This module describes the mathematical concepts behind data clustering, or in other words unsupervised learning - the identification of patterns within data without considering the labels associated with the data.

The Midterm Exam consists of 45 multiple choice questions which covers modules 1-7. Some of the questions may require you to perform some analysis with the methods you learned throughout the course on new datasets.

This module introduces the important concept of performing gene set enrichment analyses. Enrichment analysis is the process of querying gene sets from genomics and proteomics studies against annotated gene sets collected from prior biological knowledge.

This module describes the mathematical concepts of supervised machine learning, the process of making predictions from examples that associate observations/features/attribute with one or more properties that we wish to learn/predict.

This module discusses how Bioinformatics pipelines can be compared and evaluated.

This module provides programming examples on how to get started with creating interactive web-based data visualization elements/figures.

This final module describes opportunities to work on LINCS related projects that go beyond the course.

The Final Exam consists of 60 multiple choice questions which covers all of the modules of the course. Some of the questions may require you to perform some analysis with the methods you learned throughout the course on new datasets.