Chapter 10: Microbial Identification Techniques
Synopsis
Author
Miss Bhavya Sree Aluri,
Assistant Professor, Department of Pharmacology, Sir C.R. Reddy College of Pharmaceutical Sciences, Eluru, Andhra Pradesh, India
Abstract
Microbial identification has transitioned from phenotypic observation to precise genomic characterization, driven by computational alignment and machine learning. This section examines the principles of sequence alignment through the Basic Local Alignment Search Tool (BLAST), detailing how heuristic algorithms rapidly compare unknown sequences against global databases to infer taxonomic identity based on statistical metrics like E-value, percent identity, and query coverage. It extends beyond single-gene analysis to explore open-source AI tools that utilize k-mer frequencies and deep learning to classify microbial sequences without reliance on traditional alignment, enabling the scalable analysis of metagenomic data. AI algorithms deconvolute complex environmental samples by "binning" DNA fragments into individual genomes, revealing the community composition of unculturable microbiomes. Furthermore, genome mining represents a frontier in natural product discovery, where deep learning models like DeepBGC scan microbial genomes to predict Biosynthetic Gene Clusters (BGCs). These tools identify the genetic machinery responsible for producing novel antibiotics and antifungals, unlocking the chemical potential of the microbial world. Predicting the structure and activity of these cryptic metabolites allows researchers to prioritize candidates for synthesis, revitalizing the search for new therapeutic agents needed to combat antimicrobial resistance.
Keywords: BLAST Algorithm, Metagenomics, Binning (MAGs), 16S rRNA Sequencing, Alignment-Free Classification, Microbial Genome Mining
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