Udemy: 5 Bioinformatics Course One Must Know!

Udemy: 5 Bioinformatics Course One Must Know!

Students and researchers are always enthusiastic about something that will nourish their skills and help them learn new things every day and make them up-to-date with the latest innovations and methods.

Bioinformatics is an interdisciplinary field in which biological data techniques and software tools are developed, particularly where data sets are large and complex. As an interdisciplinary field for the study and interpretations of biological facts, bioinformatics combines biology, informatics, information technology, mathematics and statistics. Biological information technology has been used for biological science using quantitative and statistical methods.

Udemy is one of the best online educational programmes, which allows users to learn about a variety of subjects that are typically unexpected or hard to find. Here we’ll provide you with the best Udemy Bioinformatics courses for students and researchers.


1. DNA Research using Biopython

An Introduction To Bioinformatics

DNA Research using Biopython

What you’ll learn:

  • DNA
  • RNA
  • Genomes
  • Chromosomes
  • Bioinformatics
  • Biopython
  • Anaconda IDE
  • SPYDER Framework

Requirements:

  • Students should have basic knowledge of some Object Oriented Programming Language.

Description:

DNA Research using Biopython, An Introduction To Bioinformatics, is a crash hacker course that will teach you Hybrid Developer skills. You will use your existing OOPL development skills to fly through python code effortlessly. You will first learn what is deoxyribonucleic acid (DNA) and how to work with it to simultaneously implement medical research and python code to work toward and infer a solution. You will learn how to use biopython and its libraries to help you research

  • Statistics
  • Datasets
  • Genomes
  • Nucleotides
  • Chromosomes
  • mRNA
  • DNA sequences

DNA Research using Biopython, An Introduction To Bioinformatics course asks you but one important question. Will you find the next big cure?

Who this course is for:

  • Students that aspire a career in Bioinformatics with an emphasis on DNA Research

2. Differential Gene Expression Analysis – Your Complete A to Z

Become a bioinformatic analysis master: qPCR, RNAseq, Functional Genomics, Transcriptomics, R, RStudio, TUXEDO pipeline

What you’ll learn:

  • You’ll be able to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinformatics generally
  • You’ll be able to undertake an end-to-end RNAseq analysis pipeline in R
  • You’ll be able to do a qPCR analysis in R
  • You’ll be able to do a pathway analysis
  • You’ll be able to design bioinformatics experiments and do data interpretation
  • You’ll get a solid foundation on techniques used in bioinformatics
  • You’ll learn statistical models and methods used in differential gene expression

Requirements:

  • Understanding of basic molecular biology terms such as DNA, RNA, gene and protein will be beneficial
  • Familiarity with R programming is advantageous but not necessary as it’ll be covered
  • Being open-minded and ready to learn!

Description:

Do you want to be a bioinformatician but don’t know what it entails? Or perhaps you’re struggling with biological data analysis problems? Are you confused amongst the biological, medicals, statistical and analytical terms? Do you want to be an expert in this field and be able to design biological experiments, appropriately apply the concepts and do a complete end-to-end analysis?

This is a comprehensive and all-in-one-place course that will teach you differential gene expression analysis with focus on next-generation sequencing, RNAseq and quantitative PCR (qPCR)

In this course we’ll learn together one of the most popular sub-specialities in bioinformatics: differential gene expression analysis. By the end of this course you’ll be able to undertake both RNAseq and qPCR based differential gene expression analysis, independently and by yourself, in R programming language. The RNAseq section of the course is the most comprehensive and includes everything you need to have the skills required to take the FASTQ library of next-generation sequencing reads and end up with complete differential expression analysis.

Although the course focuses on R as a biological analysis environment of choice, you’ll also have the opportunity not only to learn about UNIX terminal based TUXEDO pipeline, but also online tools. Moreover you’ll become well grounded in the statistical and modelling methods so you can explain and use them effectively to address bioinformatic differential gene expression analysis problems. The course has been made such that you can get a blend of hands-on analysis and experimental design experience – the practical side will allow you to do your analysis, while the theoretical side will help you face unexpected problems.

Here is the summary of what will be taught and what you’ll be able to do by taking this course:

  • You’ll learn and be able to do a complete end-to-end RNAseq analysis in R and TUXEDO pipelines: starting with FASTQ library through doing alignment, transcriptome assembly, genome annotation, read counting and differential assessment
  • You’ll learn and be able to do a qPCR analysis in R: delta-Ct method, delta-delta-Ct method, experimental design and data interpretation
  • You’ll learn how to apply the knowledge of molecular biology to solve problems in differential gene expression analysis specifically, and bioinformatics generally
  • You’ll learn the technical foundations of qPCR, microarray, sequencing and RNAseq so that you can confidently deal with differential gene expression data by understanding what the numbers mean
  • You’ll learn and be able to use two main modelling methods in R used for differential gene expression: the general linear model as well as non-parametric rank product frameworks
  • You’ll learn about pathway analysis methods and how they can be used for hypothesis generation
  • You’ll learn and be able to visualise gene expression data from your experiments

Who this course is for:

  • STEM graduates who don’t have a sufficient grasp of molecular biology and want to start a career in bioinformatics
  • Anybody who needs a refresher in biological foundations of bioinformatics and differential gene expression analysis
  • Students who want to start a higher degree (Bachelor, Masters or PhD) project related to bioinformatics
  • People working at pharmaceutical companies or at university and who want to learn about differential gene expression analysis
  • Curious learners that want to gauge bioinformatics and differential gene expression analysis

3. Databases in Bioinformatics, Become NCBI Professional

Learning the usage along with different techniques and databases of NCBI

What you’ll learn:

  • After this course Students will have Basic Knowledge of NCBI in Bioinformatics.
  • They will be able to use different tools for Sequence Analysis and will know different categories of sub-databases.

Requirements:

  • You only need to have a thirst of knowledge for this Course.

Description:

This course is mainly about the primary database browser called NCBI. The National Center for Biotechnology Information (NCBI) is part of the United States National Library of Medicine, a branch of the National Institutes of Health. The NCBI houses a series of Databases relevant to Biotechnology and Bio-medicine and is an important resource for Bioinformatics.

In this course, we will be mainly covering the utilization gene or protein sequence from NCBI. The file formats that are used in NCBI. Utilizing the different databases of NCBI mainly including RefSeq sequence database, Homologene database, Taxonomy database and PubMed database.

Who this course is for:

  • Bioinformaticians are encouraged to take this course.
  • Other Students can also take this course to know more about NCBI in Bioinformatics.

4. Molecular Dynamic Simulations for Drug Discovery

Molecular Dynamic Simulations for Drug Discovery

What you’ll learn:

  • Introduction to Molecular dynamic simulations
  • What is OPLS force field
  • How to setup the simulation for execution
  • How to execute the simulation step-by-step
  • How to analyze the simulation output
  • How to interpret the simulation output graphs

Requirements:

  • Basic understanding of In silico drug discovery
  • Basics of Biology and drug discovery

Description:

A perfect course for Bachelors’ / Masters’ / PhD students who are getting started into computational drug discovery and aware of the In silico drug discovery basics. By the time you complete this course, you will be equipped with the knowledge required to execute molecular dynamic simulations on your own starting from setting up the software to analyzing results.

Who this course is for:

  • Beginner level Molecular Dynamic Simulations learning enthusiasts

5. Computational Biology Research – Gene Expression

Understand how gene expression research can be used to bring precision medicine.

What you’ll learn:

  • Gene expression
  • microarray
  • Disease research in RNA level (example: Parkinson’s and Alzheimer’s diseases will be used as examples)
  • t-test
  • enrichment testing
  • NCBI tools
  • Gene Ontology
  • Signalling Pathway tools
  • computational biology
  • genetics
  • research skills

Requirements:

  • High school biology class or similar knowledge

Description:

This course is designed to facilitate transcriptome research (analyze gene expression data) for people with high school biology-level knowledge. It covers overall transcriptome research that scientists use with application to a disease of focus.

The course covers 1) precision medicine and current biotechnology advances, 2) their implications for our day-to-day health, 3) roles of genes in health and diseases, and 4) the complex influence of daily behaviors on gene functions.

This course emphasizes the impact of real-life environmental factors on and ways in which research can solve certain kinds of problems.

It also covers biology basics and statistical concepts helpful in doing transcriptome research.

Please note that the current version consists of combined lectures from the summer camps we run yearly. This is so that prospective students who want to join our miRcore volunteer programs this school year can take it and join in a timely manner. While the audiences in the lectures are summer campers, the curriculum was prepared as an online course. Please understand some discrepancies in dates and groups in the lectures.

Who this course is for:

  • Students (of any age!) with a love of learning & research
  • miRcore volunteer program candidates
  • Anyone with a broad interest in genetics
Sakshi Sharmahttps://bioinformaticsindia.com
I am a Managing Partner at Bioinformatics India where I write blogs, look after all the partners, and manages the affiliates associated with the website.

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