Biological Sequence Analysis using R Programming
Unraveling Nature’s Code: Biological Sequence Analysis with R Programming
MODE
Online/ e-LMS
Online/ e-LMS
TYPE
Self Paced
Self Paced
LEVEL
Moderate
Moderate
DURATION
1.5 Hours/Day
1.5 Hours/Day
VIDEO LENGTH
4.5 Hours
4.5 Hours
Program Aim
The aim of this program is to provide participants with hands-on experience in utilizing R Studio for computational biology and bioinformatics tasks. Participants will gain practical skills in analyzing DNA and protein sequences, constructing phylogenetic trees, conducting differential gene expression analysis, and performing functional annotation using R.
About Program
Program Objectives
- To introduce participants to the fundamentals of R Studio and its applications in computational biology.
- To guide participants in installing and managing requisite libraries for bioinformatics analyses.
- To instruct participants on reading and storing DNA sequences, and applying basic statistical analyses.
- To familiarize participants with techniques for transforming data, finding motifs, and performing advanced statistical analyses on DNA sequences.
- To enable participants to analyze protein properties using R.
- To teach participants Multiple Sequence Alignment (MSA) and phylogenetic tree construction in R.
- To guide participants through the creation of Neighbor-Joining (NJ) trees and understanding bootstrapping in phylogenetic analysis.
- To introduce participants to Bioconductor and its applications in bioinformatics.
- To instruct participants on conducting differential gene expression analysis of RNA sequencing data using R.
- To enable participants to generate heat maps for visualizing gene expression patterns.
- To provide participants with the skills needed for functional annotation of genes using Bioconductor tools.
Program Structure
Day 1:Â
- Introduction to R studio
- Installing requisite libraries
- Read and Store DNA sequences
- Transform, Find motif and basic statistics
Day 2:
- Analysing Protein Properties
- MSA with R
- Phylogenetic Tree Construction in R
- NJ tree, Bootstrapping
Day 3:
- Introduction to Bioconductor
- Differential gene expression analysis of RNA seq
- Heat map generation
- Functional annotation
Requirement: The program is meant for participants with moderate level of programming proficiency or
with basic idea of R . Requirement any OS with latest version of R and R studio installed
Program Eligibility
- Educational Background: Typically, applicants should have a bachelor’s degree or higher in a relevant field such as biology, bioinformatics, computational biology, genetics, biotechnology, or a related discipline.
- Prerequisites: Some programs may require applicants to have completed specific coursework in molecular biology, genetics, bioinformatics, and/or programming fundamentals to ensure they have a strong foundation in the subject matter.
- Computer Skills: Proficiency in programming languages, particularly R programming, as well as familiarity with bioinformatics software packages commonly used for sequence analysis (e.g., BLAST, ClustalW, Bioconductor) may be necessary to effectively participate in the program.
- Research Experience: Applicants with prior research experience in molecular biology, bioinformatics, or computational biology may be given preference, as they are likely to have a deeper understanding of the concepts and techniques covered in the program.
- Letters of Recommendation: Some programs may require letters of recommendation from professors, supervisors, or professionals who can attest to the applicant’s academic abilities, research experience, and suitability for the program.
- Statement of Purpose: Applicants may need to submit a statement of purpose or personal statement outlining their academic background, research interests, career goals, and reasons for applying to the program.
- Language Proficiency: Proficiency in the language of instruction or communication used in the program may be required, especially if the program is conducted in a language other than the applicant’s native language.
- Application Materials: Applicants may need to submit materials such as transcripts, a resume or curriculum vitae (CV), standardized test scores (if applicable), and/or writing samples, depending on the requirements of the program.
Important Dates
Registration Ends
2024-01-20
Indian Standard Timing 02:30 PM
Program Dates
2024-01-20 to 2024-01-22
Indian Standard Timing 03:30 PM
Program Outcomes
- Proficient use of R Studio for computational biology and bioinformatics analyses.
- Ability to independently install and manage requisite libraries for bioinformatics tasks.
- Competence in reading, storing, and statistically analyzing DNA sequences.
- Advanced skills in transforming data, identifying motifs, and conducting statistical analyses on DNA sequences.
- Capability to analyze protein properties using R.
- Proficiency in Multiple Sequence Alignment (MSA) and phylogenetic tree construction.
- Understanding of Neighbor-Joining (NJ) tree creation and bootstrapping in phylogenetic analysis.
- Familiarity with Bioconductor and its applications in bioinformatics.
- Skill in conducting differential gene expression analysis of RNA sequencing data.
- Ability to generate heat maps for visualizing gene expression patterns.
- Competence in functional annotation of genes using Bioconductor tools.
Mentor Profile
Name: DR. SUBARNA THAKUR
Designation: Assistant Professor
Affiliation: Assistant Professor, Department of Bioinformatics, University of North Bengal
Designation: Assistant Professor
Affiliation: Assistant Professor, Department of Bioinformatics, University of North Bengal
Dr. Subarna Thakur is an Assistant Professor in the Department of Bioinformatics at the University of North Bengal. She received his/her Ph.D. Degree in Botany from the University of North Bengal in 2014 and then worked as a Post Doc in various institutes like JNU, Bose Institute and IRHS, Angers, France for 4 Years. She is having more than 10 Years of Experience in the field of Computational Biology and Genomics. Her Area of Expertise includes NGS Data analysis, Transcriptomics, Network Biology. She is the author of 10 original research articles in high-impact peer-reviewed journals.
Fee Structure
Student
INR. 1199
USD. 50
Ph.D. Scholar / Researcher
INR. 1499
USD. 55
Academician / Faculty
INR. 1999
USD. 60
Industry Professional
INR. 2499
USD. 85
Certificate
Program Assesment
- Practical Assignments: Participants may be given practical assignments that require them to write R scripts to perform various sequence analysis tasks, such as sequence alignment, motif discovery, phylogenetic tree construction, or gene expression analysis. These assignments assess participants’ ability to apply programming skills to solve real-world biological problems.
- Project Work: Participants may undertake individual or group projects where they analyze biological sequence data using R programming and present their findings in a written report or oral presentation. Projects may involve exploring specific research questions, developing new algorithms or methods, or applying existing tools to analyze biological sequences.
- Coding Exercises: Participants may be given coding exercises or quizzes to assess their understanding of R programming concepts and their ability to write efficient and effective code for biological sequence analysis tasks.
- Exams: Written or online exams may assess participants’ knowledge of biological concepts, R programming syntax, and their ability to interpret and analyze biological sequence data. Exams may include multiple-choice questions, short answer questions, or coding challenges.
- Peer Review: Participants may review and provide feedback on each other’s project work or coding assignments, fostering collaboration, constructive criticism, and peer learning.
- Final Project: A culminating final project may require participants to design and implement a complete analysis pipeline for a specific biological sequence analysis task using R programming. Participants may be evaluated on their ability to integrate multiple techniques, interpret results, and communicate findings effectively.
- Lab Practical: In some cases, participants may be required to complete a lab practical exam where they demonstrate their proficiency in performing common sequence analysis tasks using R programming in a supervised setting.
Future Career Prospects
- Bioinformatics Analyst: Analyzing biological sequence data using R programming to extract meaningful insights, identify genetic variations, and predict functional elements in genomes.
- Computational Biologist: Applying computational techniques to analyze biological data, model biological processes, and uncover patterns in genetic sequences using R programming.
- Genomic Data Scientist: Analyzing genomic data to study genetic variation, gene expression, and regulatory elements in genomes using R programming tools and techniques.
- Biomedical Researcher: Conducting research in academic or industry settings to investigate the genetic basis of diseases, identify potential drug targets, and develop personalized medicine approaches using R programming for sequence analysis.
- Pharmaceutical Scientist: Contributing to drug discovery and development projects by analyzing biological sequences to identify drug targets, predict drug interactions, and optimize drug candidates using R programming.
- Clinical Bioinformatician: Supporting clinical research and healthcare initiatives by analyzing patient genetic data, identifying disease-associated variants, and informing treatment decisions using R programming for sequence analysis.
- Academic Researcher: Pursuing careers in academia to conduct independent research projects in bioinformatics, computational biology, or genetics, leveraging R programming for biological sequence analysis.
- Data Scientist: Applying computational and statistical methods to analyze large-scale biological datasets, including genomic, transcriptomic, and proteomic data, using R programming tools and libraries.
- Bioinformatics Consultant: Providing expertise in bioinformatics and computational biology to research institutions, biotechnology companies, and healthcare organizations, offering services such as data analysis, tool development, and training in R programming for sequence analysis.
- Software Developer: Developing bioinformatics software tools, algorithms, and databases to support biological sequence analysis tasks using R programming and other programming languages.
- Educator or Trainer: Teaching bioinformatics, computational biology, or genetics courses and workshops, and training the next generation of scientists in R programming for biological sequence analysis.
- Regulatory Affairs Specialist: Ensuring compliance with regulatory requirements for bioinformatics software tools and genetic data analysis methods, and contributing to the development of standards and guidelines for sequence analysis using R programming.
