Diploma in Computational Method for Sequence Analysis

Bharathiar University
In Coimbatore

Price on request
You can also call the Study Centre
42224... More

Important information

  • Diploma
  • Coimbatore
Description

Goals: The goal of this course is to introduce the main principles of bioinformatics. The. coverage will include concepts like sequence alignments, phylogenetic trees, and. structure prediction. Objectives: Understand Genomic data acquisition and analysis, comparative and. predictive analysis of DNA and protein sequence, Phylogenetic inference etc

Important information
Venues

Where and when

Starts Location
On request
Coimbatore
Bharathiar University, Coimbatore, 641046., Tamil Nadu, India
See map

Course programme

Subject description :This paper describes how to acquire information from biological
databases, use of computational approaches to analyze this information, and interpret the
results as a guide to experiments in biology.
Goals: The goal of this course is to introduce the main principles of bioinformatics. The
coverage will include concepts like sequence alignments, phylogenetic trees, and
structure prediction.
Objectives: Understand Genomic data acquisition and analysis, comparative and
predictive analysis of DNA and protein sequence, Phylogenetic inference etc
UNIT-I
Introduction to bioinformatics, Classification of biological databases, Biological data
formats, Application of bioinformatics in various fields. Introduction to single letter code
of aminoacids, symbols used in nucleotides, data retrieval- Entrez and SRS.
UNIT-II
Introduction to Sequence alignment. Substitution matrices, Scoring matrices - PAM and
BLOSUM. Local and Global alignment concepts, Dot plot. Dynamic programming
methodology: Needleman and Wunsch algorithm. Smith-Waterman algorithm. Statistics of alignment score. Multiple sequence alignment. Progressive alignment. Database search
for similar sequences using FASTA and BLAST Programs.
UNIT-III
Evolutionary analysis: distances, Cladistic and Phenetic methods. Clustering Methods.
Rooted and unrooted tree representation. Bootstrapping strategies, Use of Clustal and
PHYLIP.
UNIT-IV
Gene finding methods. Gene prediction: Analysis and prediction of regulatory regions.
Fragment assembly. Genome sequence assembly, Restriction Mapping, Repeat Sequence
finder.
UNIT-V
Concepts of secondary structure prediction of RNA and Protein. Probabilistic models:
Markov chain, Hidden Markov Models-other applications.