Bioinformatic Methods I

Go to Course: https://www.coursera.org/learn/bioinformatics-methods-1

Introduction

**Course Review: Bioinformatic Methods I on Coursera** In the ever-evolving landscape of biological research, the intricacies of bioinformatics have become essential for modern-day scientists. A prime course for anyone looking to delve into this field is Coursera’s “Bioinformatic Methods I.” This course provides an invaluable foundation for understanding and analyzing complex biological data generated from large-scale projects like the sequencing of the human genome, RNA-seq, and microarrays. **Overview** “Bioinformatic Methods I” is designed to equip participants with the skills required to access, analyze, and interpret vast quantities of biological data. The course primarily leverages web-based bioinformatic resources and databases to empower biologists and researchers in extracting meaningful insights from their data. Through its structured educational approach, the course addresses the challenges scientists face today regarding data accessibility and analysis. **Syllabus Breakdown** 1. **NCBI/Blast I** - This initial module introduces participants to the National Center for Biotechnology Information (NCBI) and the powerful BLAST (Basic Local Alignment Search Tool) tool used for sequence searching. Participants learn how to identify similar sequences within the non-redundant sequence database, thereby inferring homology and, consequently, predicting gene or protein function. 2. **Blast II/Comparative Genomics** - Building on the first module, this section delves deeper into various types of BLAST searches (BlastP, PSI-Blast, and Translated Blast). Students engage in comparative genomics, understanding the genetic similarities across species, which is crucial for evolutionary studies. 3. **Multiple Sequence Alignments** - This module employs various alignment tools like Clustal, MUSCLE, and MAFFT, allowing students to recognize conserved and variable regions within sequences. The knowledge gained here is not just theoretical; it has real-world applications in designing PCR primers across different species. 4. **Phylogenetics** - Using the aligned sequences, learners perform phylogenetic analyses through neighbor-joining and maximum likelihood methods. These methods help depict evolutionary relationships, tracing back speciation or gene duplication events. 5. **Selection Analysis** - In this analytical section, students investigate orthologous sequences from bacteria to discern selective pressures acting on certain residues. This is fundamental for understanding biological function and the evolutionary adaptations of sequences in response to external factors. 6. **'Next Gen' Sequence Analysis (RNA-Seq) / Metagenomics** - With the dramatic reduction in sequencing costs, this module explores RNA-Seq data to ascertain gene expression patterns and alternative splicing events. Participants also study metagenomic data, gaining insights into microbial diversity within distinct environmental niches. 7. **Final Review and Assignment** - Culminating the course, this segment synthesizes all learned modules, providing students with a comprehensive understanding of the core bioinformatic methods, and ends with a practical assignment that tests students' grasp of the course material. **Conclusion and Recommendation** “Bioinformatic Methods I” stands out as a cornerstone course for individuals looking to enrich their knowledge of bioinformatics. The structured modules progressively build expertise, starting from foundational tools to more complex analyses, making it accessible for beginners while still relevant for more experienced learners. The course content is thoughtfully arranged, with practical applications integrated throughout to enhance learning retention. Given the rapid advancement of technology in biological research, the skillset acquired from this course is not only relevant but in high demand in academic and industry settings. I highly recommend "Bioinformatic Methods I" for anyone interested in harnessing the power of bioinformatics to analyze biological data. Whether you are a student, researcher, or a professional looking to pivot into the field, this course will equip you with essential tools and knowledge to succeed in the world of biological data analysis. Enroll today to unlock the vast potential of bioinformatics and become proficient in addressing key questions in biology!

Syllabus

NCBI/Blast I

In this module we'll be exploring the amazing resources available at NCBI, the National Centre for Biotechnology Information, run by the National Library of Medicine in the USA. We'll also be doing a Blast search to find similar sequences in the enormous NR sequence database. We can use similar sequences to infer homology, which is the primary predictor of gene or protein function.

In this module we'll continue exploring the incredible resources available at NCBI, the National Centre for Biotechnology Information. We will be performing several different kinds of Blast searches: BlastP, PSI-Blast, and Translated Blast. We can use similar sequences identified by such methods to infer homology, which is the primary predictor of gene or protein function. We'll also be comparing parts of the genomes of a couple of different species, to see how similar they are.

In this module we'll be doing multiple sequence alignments with Clustal and MUSCLE (as implemented in MEGA), and MAFFT. Multiple sequences alignments can tell you where in a sequence the conserved and variable regions are, which is important for understanding the biology of the sequences under investigation. It also has practical applications, such as being able to design PCR primers that will amplify sequences from a number of different species, for example.

In this module we'll be using the multiple sequence alignments we generated last lab to do some phylogenetic analyses with both neighbour-joining and maximum likelihood methods. The tree-like structure generated by such analyses tells us how closely sequences are related one to another, and suggests when in evolutionary time a speciation or gene duplication event occurred.

In this module we'll take a set of orthologous sequences from bacteria and use DataMonkey to analyze them for the presence of certain sites under positive, negative or neutral selection. Such an analysis can help understand the biology of a set of protein coding sequences by identifying residues that might be important for biological function (those residues under negative selection) or those that might be involved in response to external influences, such as drugs, pathogens or other factors (residues under positive selection).

In this module we'll explore some of the data that have been generated as a result of the rapid decrease in the cost of sequencing DNA. We'll be exploring a couple of RNA-Seq data sets that can tell us where any given gene is expressed, and also how that gene might be alternatively spliced. We'll also be looking at a couple of metagenome data sets that can tell us about the kinds of species (especially microbial species that might otherwise be hard to culture) that are in a given environmental niche.