Best Bioinformatics Courses

Find the best online Bioinformatics Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Bioinformatics Courses.

Basic Python/Machine Learning in Bioinformatics

Please download the kaggle for the code

Created by William Kang - Bioinformatics Researcher

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Students: 6961, Price: Free

Students: 6961, Price:  Free

This is a course intended for beginners interested in applying Python in Bioinformatics. We will go over basic Python concepts, useful Python libraries for bioinformatics/ML, and going through several mini-projects that will use these Python/ML concepts. These mini-projects include a sequence analysis (with no libraries) Python example, a Python sequence analysis example using libraries, and a basic Sklearn Machine Learning example.

Complete Victory on Bioinformatics Databases

Detail Concepts with Practical Exposure

Created by Shradheya R R Gupta - Bioinformatician | Researcher | Coder

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Students: 4667, Price: $19.99

Students: 4667, Price:  Paid

  • Are you lagging behind in the fast-moving data-driven life science?

  • You know bioinformatics databases but have confusion about using them?

  • You want the correct biological information but don't know how to and where to find it?

Then this course is only for you!

In this course, you will get a detailed explanation of each terminology, practical exposure to each database, merits, and demerits of databases, lecture notes and many other useful materials which will not disappoint you.

The course is divided into 6 sections:-

Introduction:-

The purpose of the section is to create the initial concrete background for the bioinformatics and database for you. The concepts are deep and it will test you. Do not worry you will pass through it, it is necessary for every budding scientist like you.

Primary Sequence Database:-

It is the primary place where raw data is stored. Is it the right place for us to retrieve the data for our valuable research? Or are we making a grave mistake of selecting the data from it? Do not worry; the course will help you answer all these questions.

Secondary Sequence Database:-

A secondary or derived database is a major source of curated data with different types of accession code. But which accession code will be most useful for our research? You will find the answer in this course.

Primary Structure Database:-

The database provides us with the structural information of nucleic acids and proteins, but how to select the best quality data?

Secondary Structure Database:-

A range of information like structures, binding sites, metabolic interactions, molecular action, functional relationships, protein families, motifs and homologous are part of the secondary structure database. Searching for the required information from the haystack is not an easy task.

Composite Database:-

Composite databases are formed by the merging of the primary and secondary databases for special needs. Are these database valuable to our research community, or were they created solely to win trophies?

Enjoy Learning!

Artificial Intelligence In Bioinformatics

Take the first step into machine learning for bioinformatics with a step-by-step approach

Created by Yash Gupta - Chief Technical Officer at The Helyx Initiative

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Students: 3899, Price: Free

Students: 3899, Price:  Free

This free course is heavily based on the ideology of "learning by doing" and, as a result, will be very hands-on. Students will learn a lot more about the emerging field of the future, artificial intelligence, and machine learning, through a series of mini-projects relating to biology in a field known as bioinformatics that can be expanded on after the course. As a result of the projects being done throughout the course, students will also gain further research and publication ideas, such as science fairs and journals. These projects include:

  • Using breast cancer dimension data to classify breast cancer

  • Classifying bioassay data

  • Detecting pneumonia by analyzing lung images with convolutional neural networks

  • Detecting malaria by analyzing skin images

Biopython

Performing the Daily Tasks of Bioinformatics

Created by Ahmed Karam - Junior Bioinformatics Author

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Students: 3133, Price: $19.99

Students: 3133, Price:  Paid

The course content consists of two main parts. The first part deals with an introduction to python, the goal of which is to lay down the basics of algorithms and programming languages in general. The first part contains the following:

  1. Install python, pycharm, and biopython.

  2. Knowledge of basic syntax, which includes variables and line write methods in python.

  3. Knowing the five variable types, which include number, string, list, tuple, and dictionary.

  4. Knowing the operator types, including arithmetic, comparison, assignment, logical, membership, and identity.

  5. Understanding decision-making strategies, including the use of "if .. else", "if .. elif .. else" and "nested if.".

  6. Understanding loops, writing, and controlling while loop and for loop.

The second part is an introduction to biopython, which is a package based on python, so we will apply what was understood in the first part. The second part contains the following:

  1. Dealing with the NCBI database through Entrez, requires an internet connection, and we will use einfo, espell, esearch, esummary, egquery, and efetch.

  2. Working with files, writing, and converting files using seqio. dealing with the two most popular types of sequence files in terms of reading and writing in detail.

  3. Working with sequences through python, they understand some functions such as slice, find, count, len, lower, upper, replace, split, join and strip.

  4. Transcription of molecules as cell, transcription and reverse transcription of DNA and RNA respectively, DNA translation. manufacture of complement and reverse complement of DNA.

  5. Simple basic analysis of sequences, including GC content, molecular weight, and six reading frames. search inside sequences using nt_search.

  6. Pairwise alignment, understanding, and implementing both local and global alignment. work with results and understand matches and gaps.

  7. Multiple sequence alignment, execute and read multiple sequence alignment and extract data for the phylogenetic tree.

  8. Blast, sequence search in the NCBI database. build a local database and implement blast offline. dealing with results in detail.

Biotechnology/Biotech Business, Policy, Law, and Science

Your Complete Guide to Starting and Understanding Biotechnology Companies: Business, Law, Regulations, Policy & Science

Created by Yali Friedman, Ph.D. - Publisher, DrugPatentWatch

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Students: 3125, Price: $49.99

Students: 3125, Price:  Paid

         Learn the business of biotechnology, how to start and manage biotechnology companies, and how to better service the needs of biotechnology companies. 

  Going beyond simple 'science for non-scientists' or 'mini MBA' offerings, this course describes the convergence of scientific, political, regulatory, and commercial factors that drive the biotechnology industry and define its scope. 

Topics covered include:

  • The development of biotechnology

  • An accessible introduction to the science of biotechnology

  • Drug development

  • Laws, Regulations, and Policy

  • Intellectual Property

  • Patents

  • The Business of Biotechnology

  • Finance

  • Marketing

  • Management

         This course is based on the leading business-of-biotechnology text, "Building Biotechnology," which has been adopted by more than 50 schools, and is taught by Yali Friedman, Ph.D., publisher of the Journal of Commercial Biotechnology and a former instructor of biotechnology management at the National Institutes of Health.

I hope you enjoy this course as much as I did putting it together.

For those interested in drug development specifically, you can also check out my other Udemy Course: Generic Drug Entry & Branded Drug Lifecycle Management

About the Author:

Yali Friedman, Ph.D. has more than 20 years experience providing business intelligence to life science companies. He was recently named one of the 100 most influential people in biotechnology by Scientific American. He is also author of the MBA-level textbook Building Biotechnology and publisher of the Journal of Commercial Biotechnology.

Genetics and Next Generation Sequencing for Bioinformatics

For software professionals entering Bioinformatics: DNA sequencing data analysis, NGS, & Biology prerequisites

Created by Shreeya Kumaresan - DNA Analysis Expert

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Students: 2576, Price: $39.99

Students: 2576, Price:  Paid

Do you want to enter the field of Bioinformatics, but don't know enough about DNA, RNA, and Genetics?

Are you curious about the recent advances in DNA sequencing technology, and how it can be applied to Personalized Cancer Therapy and Disease Research?

Do you want to use Bioinformatics tools to analyze data generated by Next Generation Sequencing?

By the end of this course:

  • You will have a strong foundation in DNA, RNA, and Genetics

  • You will have a thorough understanding of Next Generation DNA Sequencing Analysis

  • You will use a cloud-based platform called Galaxy for the analysis of large datasets

  • You will assess the quality of raw data

  • You will use FastQC and Trimmomatic to improve data quality

This course is a starting point in NGS. It covers Biology prerequisites and quality control. Future courses will cover data analysis in more detail.

Project:

This course includes a step-by-step guided project.

This project will assess the quality of raw data from an Illumina sequencer.

You will then use FastQC and Trimmomatic to improve the quality of this data.

Prerequisites:

  • Biology and Chemistry at high school 10th grade level

  • Elementary Statistics such as interpreting charts, histograms, and box-and-whisker plots

  • You must enjoy Biology

This is an introductory course ideal for those with no prior experience in Next Generation Sequencing Analysis.

Enroll today, and launch your career in Bioinformatics.

Gene Expression Analysis Science Project (Step by Step)

Learn how to do a novel bioinformatics research project all on your computer! Great for high school students.

Created by Andrew Gao - Bioinformatics Researcher

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Students: 2548, Price: Free

Students: 2548, Price:  Free

This FREE course is perfect for teens curious about biotechnology and eager to conduct a virtual science project with original findings.

It takes LESS THAN TWO HOURS!

You will learn how to use tools like GEO2R, StringDB, PantherDB, and more to analyze publicly available gene expression data!

The course will guide you on choosing a research topic, finding a dataset, processing the data, and analyzing the data graphically. As a bonus, you will get insight into how to write a paper about your project.

Example topics for research include:

  1. Identifying potential biomarkers for cancer (useful in diagnostics)

  2. Analyzing changes in gene expression when a sample is treated with X drug or under Y condition

  3. Differences in gene expression between early and late stage cancer (useful in prognosis and drug development)

The example project being done in this course is for identifying blood biomarkers for early stage Parkinson's disease.

Materials needed:

1. Computer

2. Google Account or Excel

3. Internet Connection

If there is enough interest, another course will be created that features gene expression analysis with machine learning, Python, R, and other computer science techniques.

Bioinformatics with Python

A Complete Guide For Beginners

Created by Jesse E. Agbe - Developer

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Students: 1268, Price: $59.99

Students: 1268, Price:  Paid

Do you know that the human genomic sequence if printed out in a normal text font, would stretch for 5000 km, which is like the distance from London to Montreal, Los Angeles to Panama, Accra to Cape Town,  Tokyo to Calcutta.

This same sequence would fill about 3000 books the size of a normal book.

Understanding and analyzing this sequence is clearly going to be a huge task.

But with the advent of powerful tools and databases we can be able to grabs a simple understanding of some aspect of it.

In this introductory course we will explore the various Python tools and libraries used in analyzing DNA,RNA and genome sequence.

Hence if you are interested in analyzing large sum of biological data or are curious about DNA sequence,protein synthesis,and how vaccines are designed. Then this course is for you.

Whether you are a student or a researcher, data scientist or bioinformatics engineer,computational biologist, this course will serve as a helpful guide when doing bioinformatics in Python.

We will be exploring bioinformatics with BioPython, Biotite, Scikit-Bio, BioJulia and more.

Data is everywhere, biological data is in every living organism.Let us analyse it for useful insights

We will learn

  • how to do sequence analysis with BioPython,Biotite,etc

  • how to perform sequence alignment with code.

  • how to create our own custom functions for analyzing DNA,RNA and Proteins.

  • how to do some bioinformatics with Python.

  • how to analysis the DNA sequence of Covid 19, MERS and more.

NOTE: This is an introductory course structured like a reference material for anyone interested in doing bioinformatics with python.

Join us as we explore the world of biological data with Python

Differential Gene Expression Analysis – Your Complete A to Z

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

Created by Alexander Abdulkader Kheirallah, PhD - Data scientist with background in bioinformatics

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Students: 1209, Price: $49.99

Students: 1209, Price:  Paid

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 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 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

Primer Design For Polymerase Chain Reaction-Tips & Tricks

DNA primer design, what your mentor never taught you

Created by Shahed Sariera - Easy Experiment Academy

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Students: 468, Price: $19.99

Students: 468, Price:  Paid

In this course you will learn:

  • The basic concepts of the standard polymerase chain reaction (PCR) technique.

  • The criteria required to design a DNA primer for PCR.

  • The online programs we usually use to design a DNA primer.

  • Tips for troubleshooting gel electrophoresis results. 

Polymerase chain reaction (PCR)   

You will learn in this section:

  • Steps involved in the PCR (PCR      cycling): denaturation, annealing, extension

  • Components of the PCR reaction:      DNA template, DNA polymerase, dNTPs, forward primer and reverse primer.

  • PCR amplification program.

  • Exponential amplification.

  • The size difference between the      PCR amplification products of the first, second, and third cycle.

Criteria for PCR primer design

You will learn in this section a detailed explanation of the PCR primer design criteria and how they affect the primer sensitivity and stability including; primer length, primer melting temperature, primer annealing temperature, GC% of the primer, GC-clamp, cross homology and primer secondary structure. 

Tools and methods 

In this section you will learn how to:

  • Retrieve a Gene Sequence form NCBI, and Determine the Exact Location for Each Exon on the Chromosome Using Graphics.

  • Compare Different mRNA Transcripts and Select One to Evaluate a Gene Expression in Novel Cells.

  • Understand Primer3 Setting.

  • Calculate Primer Self-Complementarity Score.

  • Check for Primer Cross Homology Using BLAT.

  • Evaluate Primers Depending on Delta G.

Gel Electrophoresis Troubleshooting

In this section you will find tips for successful gel electrophoresis. It includes all the possible problems you may encounter, and suggest you a solution for each problem. 

Learn Bioinformatics From Scratch (Theory & Practical)

Best Bioinformatics course for Students, Academia and Industry Professionals to learn Bioinformatics and biological data

Created by Muhammad Dujana - Dr Muhammad Dujana

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Students: 436, Price: $34.99

Students: 436, Price:  Paid

This course is going to game changer for you. Currently, there is an explosion of biological data. You need to develop your skills to handle the data in the era of this big biological data. It is humanly impossible to tackle the high biological data using conventional techniques. Here comes Bioinformatics. Bioinformatics is at the intersection of biology and computer science. Without this basic skill, you may not stand anywhere in research, academia and industry in the coming five to ten years. Keeping this need of time in view, we brought here a basic bioinformatics course for you.

This course includes five modules

(1) Databases

(2) Alignment

(3) Genomic Bioinformatics

(4) Structural Bioinformatics

(5) Evolutionary Bioinformatics 

This course is a unique blend of theory and practical. You will learn basic theory then perform practical and afterwards attempt the quizzes to check your knowledge. There is a total of 93 Lectures among them, there are 19 practical tutorials. Furthermore, there are 12 Assignments and 5 quizzes in this course along with theory lectures. We assure you that after taking this course, your perspective will be very different for biological data.  Students will learn the basics of bioinformatics, starting from biological databases to protein-ligand docking.

We hope this course will be worth your money and time.

Your Complete A-Z Guide to RNA-seq with No Coding Required!

Learn to process & analyse RNA-seq data without code: Transcriptomics, Differential expression, STAR, Pathway analysis

Created by Adrian Bourke - PhD Student in Synthetic Biology

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Students: 381, Price: $34.99

Students: 381, Price:  Paid

Ever wonder which technologies allow researchers to discover new markers of cancer or to get a greater understanding of genetic diseases? Or even just what genes are important for cellular growth?

This is usually carried out using an application of Next Generation Sequencing Technology called RNA sequencing. Throughout this course, you will be equipped with the tools and knowledge to not only understand but perform RNA sequencing and discover how the transcriptome of a cell changes throughout its growth cycle.  To avoid the need for complex software installations, coding experience and in some cases a Linux operating system we will be using a free bioinformatics tool called Galaxy for the whole analysis! Not only that, but we will also be using the STAR pipeline which is currently supported by the ENCODE project!

Once you've completed this course you will know how to:

  1. Download publically available data from papers straight onto Galaxy.

  2. Obtain the needed raw files for genome alignment.

  3. Perform genome alignment using a tool called STAR.

  4. Create count tables from your alignment using FeatureCounts.

  5. Carry out a differential expression using DESeq2 to find out what changes between a cell on day 4 Vs day 7 of growth.

  6. Carry out gene ontology analysis to understand what pathways are up and down-regulated.

  7. Use Pathview to create annotated KEGG maps that can be used to look at specific pathways in more detail.

  8. Use a web browser-based tool called DEGUST as an alternative to using DESeq2.

Practical Based

The course has one initial lecture explaining some of the basics of sequencing and what RNA sequencing can be used for. Then it's straight into the practical! Throughout the 14 lectures, you are guided step by step through the process from downloading the data to how you could potentially interpret the data at the final stages. Unlike most courses, the process is not simplistic. The project has real-world issues, such as dealing with galaxies limitations and how you can get around them with some initiative!

This course is made for anyone that has an interest in Next-Generation Sequencing and the technologies currently being used to make breakthroughs in genetic and medical research! The course is also meant for beginners in RNA-seq to learn the general process and complete a full walkthrough that is applicable to there own data!

Build Strong Foundations- PCR, Multiplex PCR, DNA Sequencing

Become a Master < Learn PCR/ mPCR tips; familiarize yourself with downloadable sequence view tools;Solve PCR failures>

Created by Biju Joseph Ph.D. - Ph.D; MT(ASCP); MB (ASCP)

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Students: 170, Price: $29.99

Students: 170, Price:  Paid

Do you intent to refresh your PCR knowledge and also learn new, unaware PCR tips?  Small steps you could take- lead to success in PCR

Are you planning to upgrade your PCR skills to higher  level with new possibilities? increase per PCR output

Do you want to take advantage of PCR skills for fluorescent DNA sequencing application?  not handing over your precious samples completely

(Master sample processing upto capillary electrophoresis step, if  electrophoresis is done elsewhere outside your lab)

How to visualize your gene sequence graphs after Sanger sequencing? available free to download

Curious how PCR failures are handled to resolve the issue? Step-by-Step detailed methodology given

Look no further and enrol now and see for yourself.

The course is for

  • Biology, Biotechnology, Molecular biology  (undergraduate and Master's) students

  • Undergraduate interns at PCR laboratories, research labs

  • Genetic/Molecular/Medical laboratory professionals

    What motivated me to  construct the course

PCR is  the foundation of molecular biology which led to development of other molecular methods.

Felt disconnect between  PCR practical knowledge and traditional classroom instruction.

The tips and PCR improvements learned through my trial and error methods during my PCR journey

The learning format include 

  1. Complete one-stop shop- Primers to PCR gel analysis 

  2. Study review questions, assignments and practice tests

  3. Images from real-bench work, examples and web-based demonstrations for easy following.

  4. Example of using well established molecular genetic resource to retrieve gene information

  5. Learn the ten steps for confident PCR

  6. Acquire skills  in researching and gathering genomic data from web-based Databases(NCBI as example),  processing the gene sequence to FASTA format for primer design or sequence analysis( NCBI primer-BLAST tool)

I guarantee my personal attention to your queries during your learning experience and enthusiastic about ensuring a  good learning experience for you now and in future. You could also add the course to your wish list for future enrollment.

Thank you and see you soon after enrollment.

Biju Joseph

Databases in Bioinformatics, Become NCBI Professional.

Learning the usage along with different techniques and databases of NCBI

Created by Usama Riaz - Online Instructor

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Students: 101, Price: $19.99

Students: 101, Price:  Paid

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 form NCBI. The file formats that are used in NCBI. Utilizing the different database of NCBI mainly including Refseq sequence database, Homologene database, Taxonomy database and PubMed database.

Genome Databases Bioinformatics-Detailed Use of UCSC Browser

Learn Use of UCSC Genome Browser For Research Propose and Annotation of Sars-Cov-2 Genome

Created by Abdul Rehman Ikram - Bioinformatician/Data Scientist /Python Developer

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Students: 86, Price: $19.99

Students: 86, Price:  Paid

It's WORTH your money! This is a self-paced course that suits to your own daily busy schedules. Self-paced gives you the freedom to complete each topic based on your own available time before proceeding to the next chapter without hurry. Basically, I will teach you about the bioinformatics Genome browser.

This course is mainly about the primary database browser called UCSC. The University of California Santa Cruz (UCSC) Genome Browser is a popular Web-based tool for quickly displaying a requested portion of a genome at any scale, accompanied by a series of aligned annotation “tracks.” The annotations generated by the UCSC Genome Bioinformatics Group and external collaborators include gene.

It is an interactive website offering access to genome sequence data from a variety of vertebrate and invertebrate species and major model organisms, integrated with a large collection of aligned annotations. The Browser is a graphical viewer optimized to support fast interactive performance and is an open-source, web-based tool suite built on top of a MySQL database for rapid visualization, examination, and querying of the data at many levels. The Genome Browser Database, browsing tools, downloadable data files, and documentation can all be found on the UCSC Genome Bioinformatics website.

In this Course we will be mainly covering the utilization of UCSC Genome Browser and performing the whole scenario on SARS-COV-2(a strain of corona virus).

And mainly learning about the Table Browser. Table Browser is also used to calculate intersections between tracks, and to retrieve DNA sequence covered by a track.

All these videos will be divided into three parts containing the Introduction, Explanation and Summary so All of you can easily understand the Course.

Detailed Beginners Guide to Bioinformatics Databases

Complete Guide of Databases Used in Bioinformatics along with their utilization and annotation

Created by Abdul Rehman Ikram - Bioinformatician/Data Scientist /Python Developer

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Students: 85, Price: $19.99

Students: 85, Price:  Paid

What is bioinformatics ?

In biology, bioinformatics is defined as, “the use of computer to store, retrieve, analyze or predict the composition or structure of bio-molecules” . Bioinformatics is the application of computational techniques and information technology to the organization and management of biological data. Classical bioinformatics deals primarily with sequence analysis.

Aims of bioinformatics

  • Development of database containing all biological information.

  • Development of better tools for data designing, annotation and mining.

  • Design and development of drugs by using simulation software.

  • Design and development of software tools for protein structure prediction function, annotation and docking analysis.

  • Creation and development of software to improve tools for analyzing sequences for their function and similarity with other sequences

Biological databases

Biological data are complex, exception-ridden, vast, and incomplete. Therefore several databases have been created and interpreted to ensure unambiguous results. A collection of biological data arranged in a computer-readable form that enhances the speed of search and retrieval and convenient to use is called a biological database. A good database must have updated information.

Importance of biological database

A range of information like biological sequences, structures, binding sites, metabolic interactions, molecular action, functional relationships, protein families, motifs and homologous can be retrieved by using biological databases. The main purpose of a biological database is to store and manage biological data and information in computer readable forms.

In this course we learned about the different biological databases that are being used in bioinformatics and get to know a little bit about their details. Mainly these databases are divided into four categories and we learned about them base by base. And explained the difference among the primary and secondary database and explained their utilization in bioinformatics.

This course will be extremely helpful to students of data analyst and bioinformaticians because they use the databases a lot in their work.

If you guys have any questions or suggestions please let me know in instructor inbox I’ll try to answer all of your questions within 12 hours.

Learn the Command Line … for Science!

Start from zero, harness the power of the command line, and cultivate a healthier relationship with your computer

Created by Brian Hall - Growth / Hacking

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Students: 82, Price: $89.99

Students: 82, Price:  Paid

You should take this course because Science. For real, though, every
branch of science has a growing computational component, but traditional
science education is lagging behind. If you're a scientist, you don't
necessarily need to learn to code, but you do need to level up your computer skills. That's what this course is about.

Think
about it -- you may understand mitosis, or RNA transcription, or galaxy
formation, or covalent bonds, but how well do you understand what's
going on inside your computer? Learning the command line is the first
step toward a healthier relationship with your machine. It's great
preparation for learning to code. It puts a TON of free, open source
scientific computing tools (and infrastructure) at your fingertips. And
it's fun!

So make 2016 the year you finally learn the command
line. This course is custom designed for scientific computing -- that's
why it goes beyond simple navigation and file operations to include
installing software and even building programs from source code. Get up
to speed with your colleagues, fancy up your resume, and become a part
of the scientific computing community. It's easier than you think!

Featuring Joshua Treeson as The Voice of the Command Line!

Plant Databases in Bioinformatics Be Phytozome Professional

Complete Guide of Phytozome Plant Database Used in Bioinformatics along with phytozome utilization, annotation

Created by Salman Ikram - Pythoneer/Bioinformatician/Data Scientist

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Students: 60, Price: $19.99

Students: 60, Price:  Paid

Phytozome is a comparative hub for plant genomes that allow users to access, visualize,

analyze or download sequenced plant genomes as well as selected genomes and datasets that

have been sequenced in other databases. It is the plant genomics portal of the Department of

Energy’s Joint Genome Institute. The point of JGI is that it stores and monitors genomic,

proteomic and various other information related to plants. It is the most utilized database for

various research on plant genomics such as to retrieve the genes of a particular plant genome,

its annotation, enzyme pathways or interpro families. Families of related genes representing the modern descendants of ancestral genes are constructed at key phylogenetic nodes.

These families allow easy access to clade-specific orthology or paralogy relationships as well as insights into clade-specific novelties and expansions.

We will discuss different tools used in phytozome along with its utilization and Visualization of data.

The number of sequenced plant genomes and associated genomic resources is growing rapidly with the advent of both an increased focus on plant genomics from funding agencies, and the application of inexpensive next generation sequencing. To interact with this increasing body of data, jgi has developed Phytozome a comparative hub for plant genome and gene family data and analysis. Phytozome provides a view of the evolutionary history of every plant gene at the level of sequence, gene structure, gene family and genome organization, while at the same time providing access to the sequences and functional annotations of a growing number of complete plant genomes, including all the land plants and selected algae sequenced at the Joint Genome Institute, as well as selected species sequenced elsewhere. Through a comprehensive plant genome database and web portal, these data and analyses are available to the broader plant science research community, providing powerful comparative genomics tools that help to link model systems with other plants of economic and ecological importance.

Phytozome, the Plant Comparative Genomics portal of the Department of Energy's Joint Genome Institute, provides JGI users and the broader plant science community a hub for accessing, visualizing and analyzing JGI-sequenced plant genomes, as well as selected genomes and datasets that have been sequenced elsewhere. As of release v12.1.6, Phytozome hosts 93 assembled and annotated genomes, from 82 Viridiplantae species. More than half of these genomes have been sequenced, assembled and/or annotated with JGI Plant Science program resources. By integrating this large collection of plant genomes into a single resource and performing comprehensive and uniform annotation and analyses, Phytozome facilitates accurate and insightful comparative genomics studies

BLAST Mastery- A Beginner’s Guide to Tool Used in Bioinfo

A complete and Upto Date Blast Video Guide Most Important Tool Used in Bioinformatics

Created by Salman Ikram - Pythoneer/Bioinformatician/Data Scientist

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Students: 59, Price: $19.99

Students: 59, Price:  Paid

Introduction of Course

Searching for similarities between biological sequences is the principal means by which bioinformatics contributes to our understanding of biology. Of the various informatics tools developed to accomplish this task, the most widely used is BLAST, the basic local alignment search tool. This course discusses the principles, workings, applications and potential pitfalls of BLAST, focusing on the implementation developed at the National Center for Biotechnology Information.

Similarity searching, including sequence comparison, is one of the principal techniques used by computational biologists and has found widespread use among biologists in general. The most popular tool for this purpose is BLAST (basic local alignment search tool), which performs comparisons between pairs of sequences, searching for regions of local similarity. In the 11 years since its publication, the original paper describing BLAST has been cited over 12,000 times, and use of BLAST has become a fundamental tool of biology. It is therefore important to know how it works and what it accomplishes, how to use it properly and how to interpret someone else's published results. Today there are several implementations of the BLAST algorithm, with two that share a common ancestry - NCBI BLAST and WU-BLAST - enjoying the broadest use. NCBI BLAST is available from the National Center for Biotechnology Information (NCBI), while WU-BLAST is available from Washington University in St. Louis. This article discusses the principles, workings, applications and potential pitfalls of BLAST, focusing on the NCBI version. Further details can be found in several excellent resources, and additional BLAST-based programs are listed in the upcoming lectures

BLAST is one of the more popular bioinformatics tools. Researchers use command-line applications to perform searches locally, often searching custom databases and performing searches in bulk, possibly distributing the searches on their own computer cluster. The current BLAST command-line applications were available to the public in late 1997. They are part of the NCBI C toolkit and are supported on a number of platforms that currently includes Linux, various flavors of UNIX (including Mac OS X), and Microsoft Windows.

The initial BLAST applications from 1997 lacked many features that are presently taken for granted. Within three years of the initial public release, BLAST was modified to handle databases with more than 2 billion letters, to limit a search by a list of Gen Info Identifiers, and to simultaneously search multiple databases. PHI-BLAST, IMPALA, and composition-based statistics were also introduced within this time period, followed by Mega BLAST and the concept of query-concatenation (whereby the database is scanned once for many queries). Chris Joerg of Compaq Computer Corporation suggested performance enhancements in 1999. A group at Apple, Industries. suggested other enhancements in 2002. These and other features were of great importance to BLAST users, but the continual addition of unforeseen modifications made the BLAST code fragile and difficult to maintain.

Many mammalian genomes contain a large fraction of interspersed repeats, with 38.5% of the mouse genome and 46% of the human genome reported as interspersed repeats. Traditionally, the only supported method available to mask interspersed repeats in stand-alone BLAST has been to execute a separate tool (e.g., RepeatMasker) on a query, produce a FASTA file with the masked region in lower-case letters, and have BLAST treat the lower-case letters as masked query sequence. This requires separate processing on each query before the BLAST search.

NCBI recently redesigned the BLAST web site to improve usability, which helped to identify issues that might also occur in the stand-alone BLAST command-line applications. These changes have, unfortunately, made it more difficult to match parameters used in a stand-alone search with default parameters on the NCBI web site.

The advent of complete genomes resulted in much longer query and subject sequences, leading to new challenges that the current framework cannot handle. At the same time, increases in generally available computer memory made other approaches to similarity searching viable. BLAST uses an index stored in memory. Cameron and collaborators designed a "cache-conscious" implementation of the initial word finding module of BLAST. The concerns listed in this section and the start of a new C++ toolkit at the NCBI motivated to rewrite the BLAST code and release a completely new set of command-line applications. Here we report on the design of the new BLAST code, the resulting improvements, and a new set of BLAST command-line applications.

Bioinformatics tools for covid research

analyzing coronavirus

Created by Matthew Cserhati - Bioinformatics PhD

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Students: 51, Price: $24.99

Students: 51, Price:  Paid

The covid-19 pandemic is of utmost concern. In almost a year and a half since the virus broke out in Wuhan, China, over 100,000 articles related to covid19 have been published in scientific journals because of the pandemic. In this course you can learn about practical bioinformatics tools which you can use in your own covid19 research. These include getting data from covid19 databases, live covid19 tracking software and sequence analysis tools such as whole genome assembly and variant calling. NGS tools are also discussed for covid research. Databases include resources on the National Center for Biotechnology Information Website and the GISAID database. Tools include Nextstrain and Nextclade as well as Exatype. This course is tailored towards students and researchers of all levels who are interested in studying coronavirus to help eradicate it. The tools shown during the course are fairly easy to use and do not presuppose too much prior knowledge of Windows or Linux. We will also cover primer design, because PCR (poly chain reaction) is a tool that is use in covid19 diagnosis. For this, we will learn how to use a tool called primer3 in Windows. This itself is a generally useful tool used in many other areas of research. Thanks for joining, and I do hope that you will greatly enjoy the course and learn new things to help win the battle against covid.

Absolute Beginner’s Guide To Plant Databases bioinformatics

Complete up-to Date Beginner's Guide of top Plant Databases Used in Bioinformatics along with their utilization

Created by Salman Ikram - Pythoneer/Bioinformatician/Data Scientist

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Students: 35, Price: $19.99

Students: 35, Price:  Paid

What is bioinformatics ?

In biology, bioinformatics is defined as, “the use of computer to store, retrieve, analyze or predict the composition or structure of bio-molecules” . Bioinformatics is the application of computational techniques and information technology to the organization and management of biological data. Classical bioinformatics deals primarily with sequence analysis.

Aims of bioinformatics

  • Development of database containing all biological information.

  • Development of better tools for data designing, annotation and mining.

  • Design and development of drugs by using simulation software.

  • Design and development of software tools for protein structure prediction function, annotation and docking analysis.

  • Creation and development of software to improve tools for analyzing sequences for their function and similarity with other sequences

Biological databases

Biological data are complex, exception-ridden, vast and incomplete. Therefore several databases has been created and interpreted to ensure unambiguous results. A collection of biological data arranged in computer readable form that enhances the speed of search and retrieval and convenient to use is called biological database. A good database must have updated information.

Importance of biological database

A range of information like biological sequences, structures, binding sites, metabolic interactions, molecular action, functional relationships, protein families, motifs and homologous can be retrieved by using biological databases. The main purpose of a biological database is to store and manage biological data and information in computer readable forms.

In this course we learned about the different biological databases that are being used in bioinformatics and get to know a little bit about their details. Mainly these databases are divided into four categories and we learned about them base by base. And explained the difference among the primary and secondary database and explained their utilization in bioinformatics.

This course will be extremely helpful to students of data analyst and bioinformatics because they use the databases a lot in their work.

If you guys have any questions or suggestions please let me know in instructor inbox I’ll try to answer all of your questions within 12 hours.

Practical Bioinformatics I

Biological Data Manipulation

Created by Ahmed Karam - Junior Bioinformatics Author

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Students: 25, Price: $19.99

Students: 25, Price:  Paid

Practical applications will be on nucleic acids

The programs used are popular, free, and online (majority) or software installed on all operating systems

The first part discusses the DNA Sanger Sequencing because of its great importance so far despite the old method, and we will talk in this part about:

  1. Explanation of the Sanger Sequencing method, what files it produces, problems, and their causes.

  2. A practical application to open and manipulate the Sanger Sequencing files using three different programs.

The second part discusses High-throughput Sequencing, which is the basis of recent nucleic acid analysis research, and we will talk in this part about:

  1. Common High-throughput Sequencing methods are Illumina, Ion Torrent, Pacific Biosciences, and Oxford Nanopore.

  2. Various applications of High-throughput Sequencing in different fields.

The third part discusses bioinformatics files, which are the raw material for biologists where sequences, alignments, variations, and annotations are stored. The files mainly found are:

  1. FASTQ

  2. FASTA

  3. Genbank

  4. GTF/GFF3

  5. BED

  6. SAM/BAM

  7. BCF/VCF

Parts from the fourth to the last are practical applications on the aforementioned files using various programs from online packages such as:

  1. Sequence Manipulation Suite 2.

  2. EMBOSS.

  3. Packages on the Galaxy platform:

    1. Seqtk.

    2. Bedtools.

    3. Samtools.

    4. Bcftools.

The course contains many programs, some of which are basic in manipulation and have been explained in detail, and other programs were found to help clearly understand the examples, and these were not explained in detail but were used to perform a specific function.

Sequence Alignment Mastery in Bioinformatics beginner to Pro

Master the topic of Sequence Alignment in Bioinformatics and the tools use to interpret sequence Alignment

Created by Abdul Rehman Ikram - Bioinformatician/Data Scientist /Python Developer

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Students: 9, Price: $19.99

Students: 9, Price:  Paid

Biological sequences evolve through a process of mutation and natural selection. By comparing two sequences, we can determine whether two sequences have a common evolutionary origin if their similarity is unlikely to be due to chance. Before we get into how this is done, we must also consider that there are many types of evolutionary relationships among sequences.

Sequence alignment is the process of arranging the characters of a pair of sequences such that the number of matched characters is maximized.

in this Course, we will be learning the topic of sequence Alignment. its history and elevation of sequence alignment throughout the history, explaining the aligned sequences.

Also, we will be covering the types of sequence Alignment

Local and Multiple Alignment

Pairwise sequence alignment methods are used to find the best-matching piecewise (local or global) alignments of two query sequences. Pairwise alignments can only be used between two sequences at a time, but they are efficient to calculate and are often used for methods that do not require extreme precision (such as searching a database for sequences with high similarity to a query). The three primary methods of producing pairwise alignments are dot-matrix methods, dynamic programming, and word methods; however, multiple sequence alignment techniques can also align pairs of sequences. Although each method has its individual strengths and weaknesses, all three pairwise methods have difficulty with highly repetitive sequences of low information content - especially where the number of repetitions differs in the two sequences to be aligned.

Multiple sequence alignment is an extension of pairwise alignment to incorporate more than two sequences at a time. Multiple alignment methods try to align all of the sequences in a given query set. Multiple alignments are often used in identifying conserved sequence regions across a group of sequences hypothesized to be evolutionarily related. Such conserved sequence motifs can be used in conjunction with structural and mechanistic information to locate the catalytic active sites of enzymes. Alignments are also used to aid in establishing evolutionary relationships by constructing phylogenetic trees. Multiple sequence alignments are computationally difficult to produce and most formulations of the problem lead to NP-complete combinatorial optimization problems.

We Will also discuss the tools used for Sequence Alignment in pairwise and multiple alignments.