L02 Finding the Origin of DNA Replication (oriC) Part-2 | Bioinformatics & Computing in Bangla

In this video, we continue the study of bioinformatics algorithms for locating the origin of DNA replication (oriC), moving beyond frequent words and clump finding to more advanced biological and computational insights.

Earlier approaches rely on identifying frequent k-mers and clumps in a genome to locate replication origins. However, many real genomes do not contain clear or unique clumps near oriC. This lecture explains why those methods are sometimes insufficient and introduces new strategies based on replication asymmetry and nucleotide composition bias.

We examine the directionality of DNA strands (5′ → 3′) and explain how unidirectional DNA polymerases create leading and lagging strands, resulting in Okazaki fragments. This biological asymmetry causes different mutation rates on the two strands, particularly affecting cytosine deamination.

As a result, bacterial genomes show a measurable imbalance between guanine (G) and cytosine (C), known as GC skew (#G − #C). This video explains how to compute and interpret skew diagrams, and how the minimum point of the skew diagram can indicate the origin of replication, while the maximum often corresponds to the terminus.

Using Escherichia coli as a case study, we demonstrate how skew diagrams help narrow down the oriC region—even when frequent words are absent. We then introduce the Frequent Words with Mismatches Problem, which allows detection of mutated DnaA boxes that differ slightly from canonical patterns but remain biologically functional.

The video concludes by discussing real-world challenges in oriC detection, including complex skew patterns, weak signals in some genomes, and open problems in computational genomics.

🔬 Topics Covered:
• Limitations of clump-based oriC detection
• DNA strand direction and replication asymmetry
• Leading vs lagging strands and Okazaki fragments
• Mutation bias in single-stranded DNA
• GC skew and its biological meaning
• Skew diagrams and genome-wide analysis
• Locating oriC using skew minimum
• Case study: oriC detection in E. coli
• Frequent Words with Mismatches
• Identification of DnaA boxes
• Open problems in replication origin discovery

🎯 Who This Video Is For:
• Bioinformatics students
• Computer Science students
• Biotechnology and Life Science learners
• Anyone studying genome analysis or computational biology

📚 Based on:
Bioinformatics Algorithms: An Active Learning Approach
by Phillip Compeau & Pavel Pevzner