FN-Identify: Novel Restriction Enzymes-Based Method for Bacterial Identification in Absence of Genome Sequencing.
Mohamed AwadOsama OudaAli El-RefyFawzy A El-FekyKareem A MosaMohamed HelmyPublished in: Advances in bioinformatics (2015)
Sequencing and restriction analysis of genes like 16S rRNA and HSP60 are intensively used for molecular identification in the microbial communities. With aid of the rapid progress in bioinformatics, genome sequencing became the method of choice for bacterial identification. However, the genome sequencing technology is still out of reach in the developing countries. In this paper, we propose FN-Identify, a sequencing-free method for bacterial identification. FN-Identify exploits the gene sequences data available in GenBank and other databases and the two algorithms that we developed, CreateScheme and GeneIdentify, to create a restriction enzyme-based identification scheme. FN-Identify was tested using three different and diverse bacterial populations (members of Lactobacillus, Pseudomonas, and Mycobacterium groups) in an in silico analysis using restriction enzymes and sequences of 16S rRNA gene. The analysis of the restriction maps of the members of three groups using the fragment numbers information only or along with fragments sizes successfully identified all of the members of the three groups using a minimum of four and maximum of eight restriction enzymes. Our results demonstrate the utility and accuracy of FN-Identify method and its two algorithms as an alternative method that uses the standard microbiology laboratories techniques when the genome sequencing is not available.
Keyphrases
- genome wide
- single cell
- bioinformatics analysis
- machine learning
- copy number
- dna methylation
- big data
- deep learning
- transcription factor
- genome wide identification
- heat shock protein
- oxidative stress
- social media
- electronic health record
- decision making
- health information
- quantum dots
- single molecule
- staphylococcus aureus
- infectious diseases