Introduction to Python for genetics

Introduction to Python for genetics, available at $79.99, has an average rating of 4.25, with 66 lectures, 10 quizzes, based on 274 reviews, and has 1359 subscribers.

You will learn about You will learn how to model genetics problems with Python The basic mendelian genetics DNA replication DNA transcription DNA translation The Hardy-Weinberg Theorem The main variable types User input Arithmetic operations Relational and logical operators Conditional statements For and while loops Tuples, lists and dictionaries Functions Modules File I/O How to calculate the frequency of recombinant genotypes and estimate the gene distance How to model the transcription process How to model the translation process How to solve a population genetics problem This course is ideal for individuals who are Biologists/biotechnologists or Biology/biotechnologists students or Programmers or computer scientists curious to understand the possible application of Python in genetics or Python and/or genetics enthusiasts It is particularly useful for Biologists/biotechnologists or Biology/biotechnologists students or Programmers or computer scientists curious to understand the possible application of Python in genetics or Python and/or genetics enthusiasts.

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Summary

Title: Introduction to Python for genetics

Price: $79.99

Average Rating: 4.25

Number of Lectures: 66

Number of Quizzes: 10

Number of Published Lectures: 66

Number of Published Quizzes: 10

Number of Curriculum Items: 76

Number of Published Curriculum Objects: 76

Original Price: $29.99

Quality Status: approved

Status: Live

What You Will Learn

You will learn how to model genetics problems with Python

The basic mendelian genetics

DNA replication

DNA transcription

DNA translation

The Hardy-Weinberg Theorem

The main variable types

User input

Arithmetic operations

Relational and logical operators

Conditional statements

For and while loops

Tuples, lists and dictionaries

Functions

Modules

File I/O

How to calculate the frequency of recombinant genotypes and estimate the gene distance

How to model the transcription process

How to model the translation process

How to solve a population genetics problem

Who Should Attend

Biologists/biotechnologists

Biology/biotechnologists students

Programmers or computer scientists curious to understand the possible application of Python in genetics

Python and/or genetics enthusiasts

Target Audiences

Biologists/biotechnologists

Biology/biotechnologists students

Programmers or computer scientists curious to understand the possible application of Python in genetics

Python and/or genetics enthusiasts

Many students and professionals of biosciences are not familiar with programming. However, the increasing amount of biological data generated every year, along with advances in biotechnology and the increasing role of informatics in life sciences makes the programming knowledge essential for biologists, biotechnologists, and so on! In this introductory course, you will learn how to model simple genetics problems using the programming language Python. First, you will learn how to implement the commandsor data structures in Python. And Finally, we will model some problems in the field of genetics using the knowledge acquired so far.

Since this is an introductory course, we will not use third-party libraries, such as Biopython, matplotlib, pandas, etc., but only the built-in commands, modules, and data structures.

In the first section of the course, we have a very brief introduction to refresh some concepts of genetics, such as genes, alleles, frequency, etc In the second part of the course, you’ll learn the basic commands, data structures, and functions of Python. In these chapters, you must apply the knowledge acquired so far in the chapter to solve some problems related to genetics in Python! There are more than 20 exercises and challenges to be solved throughout the course!

Therefore, if you want to:

1. Learn to think algorithmically

2. Model problems of genetics in a programming language

3. Learn how to use the most popular programming languages to model problems of biology

this course is for you!

Course Curriculum

Chapter 1: Introduction to the course

Lecture 1: Course overview/introduction

Lecture 2: About the speed of the lectures

Chapter 2: ####PART 1: BASIC GENETICS ######

Lecture 1: Basic concepts

Lecture 2: The gene-protein flow and building blocks

Lecture 3: Inheritance and recombination

Lecture 4: The transcription process

Lecture 5: The translation process

Lecture 6: Population genetics: the Hardy-Weinberg Theorem

Chapter 3: #####PART 2: PROGRAMMING WITH PYTHON #####

Lecture 1: Introduction to Python

Lecture 2: The importance of Python for biosciences

Lecture 3: The COLAB environment

Chapter 4: Variables and types

Lecture 1: Main variables and types

Lecture 2: Arithmetic operators

Lecture 3: User input

Lecture 4: Exercises

Lecture 5: Solution – Estimating recombination frequency

Lecture 6: Erratum

Lecture 7: Solution – Calculating allele frequencies

Chapter 5: Strings and text manipulation

Lecture 1: Strings – concept and methods

Lecture 2: Exercises

Lecture 3: Solution: finding start and termination codons

Lecture 4: Solution: manipulating a DNA string

Chapter 6: Relational/logical operators and conditional statements

Lecture 1: Relational/logical operators

Lecture 2: Conditional statements

Lecture 3: Nested and complex conditions

Lecture 4: Exercises

Lecture 5: Biomolecule classifier

Lecture 6: Polypetide sequence analysis with string manipulation

Chapter 7: For and while loops

Lecture 1: The for loops

Lecture 2: The nested for loops

Lecture 3: The while commands

Lecture 4: Exercises

Lecture 5: Solution – base pairing verification

Lecture 6: Solution – Simulating the transcription process

Lecture 7: Solution – Hamming distance

Chapter 8: Tuples, lists and dictionaries

Lecture 1: Tuples

Lecture 2: Lists

Lecture 3: Dictionaries

Lecture 4: Exercises

Lecture 5: Solution – Transcription process with dictionaries

Lecture 6: Solution – Modelling the translation process

Lecture 7: Solution – challenge 1: RNA splicing

Chapter 9: Functions

Lecture 1: Functions: definition and declaration

Lecture 2: Functions: optional parameters, scope and docstrings

Lecture 3: Exercises

Lecture 4: Solution – A function that extracts the proportion (%) of ATs/CGs

Lecture 5: Solution – function that calculates the recombination frequency

Lecture 6: Solution – Challenge 1: Hardy Weinberg Theorem – interpretation of the results

Chapter 10: Modules

Lecture 1: The math module

Lecture 2: The random module

Lecture 3: The time module

Lecture 4: Exercises

Lecture 5: Solution – generating a random DNA sequence

Lecture 6: Solution – Timing the random sequence generation function

Lecture 7: Solution – Challenge 3: simulating the reproduction of two genotypes

Chapter 11: Handling errors and exceptions in Python

Lecture 1: Types of errors and exceptions in Python

Lecture 2: Try and except

Lecture 3: Exercise

Lecture 4: Solution – treating exceptions in a previous exercise

Chapter 12: Working with text files

Lecture 1: Reading and writing .txt files

Lecture 2: FASTA files

Lecture 3: Exercises

Lecture 4: Reading a Sars-Cov-2 sequence with Python

Lecture 5: Solution: Challenge 3 – part 1

Lecture 6: Solution: Challenge 3 – part 2

Lecture 7: Bonus lecture

Instructors

Guilherme Matos Passarini, phD
Professor

Rating Distribution

1 stars: 4 votes

2 stars: 5 votes

3 stars: 43 votes

4 stars: 100 votes

5 stars: 122 votes

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