by: John Patrick Limbo (Inosine) & Josiah Caleb Magollado (Chromoplexy)
Microorganisms play a complex role in our world—they can be both beneficial and harmful. Due to the unique characteristics they possess, various microorganisms are harnessed for a wide range of beneficial purposes, such as the production of food, antibiotics, hormones, amino acids, and other therapeutic compounds, making them invaluable tools in fields ranging from medicine to industry. Yet, it is often these same unique traits that render countless microorganisms a threat to public health and the environment.
The physiology of a microorganism is shaped by the interplay between its genetic information and its environment. Therefore, identifying and understanding the underlying genetic factors is key to mitigating these risks, and this task can be effectively accomplished through bioinformatics.
Bioinformatics is an emerging field that unites biology, information technology, and computer science in the pursuit of analyzing complex biological data. Additionally, it can be used as an application of computational techniques to analyze different information associated with biomolecules. This has enabled bioinformatics to establish itself as a central discipline in molecular biology, particularly in addressing genomics and gene expression studies of microorganisms. Consequently, various software tools can be used to collect, analyze, and integrate biological and genetic information to aid in microbial identification and characterization.
Figure from Xiong, J. (2006). Essential bioinformatics. Cambridge University Press.
As shown in the figure, bioinformatics plays a vital role in a wide range of applications not only in genomics and molecular biology but also in biotechnology and biomedical sciences. This can be accomplished by the utilization of specialized software technologies, with biological databases serving as essential resources for storing, managing, and sharing data globally.
Microbial Bioinformatics Data Analysis and Software
Sample Collection and Extraction
In sequencing microbial DNA, the process begins with proper sample collection using methods such as flash freezing or microbiome preservation media to maintain DNA integrity and prevent degradation. This is followed by DNA extraction, which ensures the efficient lysis of microbial cells and the isolation of high-quality DNA suitable for downstream applications.
Library Preparation and Sequencing
Library preparation is then performed to fragment the DNA, ligate sequencing adapters, and generate sequencing-ready templates for next-generation sequencing (NGS). Microbial sequencing typically employs amplicon sequencing, which typically targets marker genes such as 16S rRNA and ITS for taxonomic identification.
High-Throughput Sequencing
Libraries are pooled and sequenced often using platforms like Illumina, which rely on Sequencing-by-Synthesis (SBS) chemistry. This generates sequence reads in FASTQ format, which contain nucleotide sequences along with quality scores (Phred scores) that indicate the accuracy of each base call.
Quality Control and Pre-processing
Depending on the downstream analysis, raw reads may require preprocessing to remove low-quality regions and sequences inserted during library preparation. Tools such as Trimmomatic are commonly used for this quality control step.
Genome Assembly and Annotation
When sequencing purified microbial strains, de novo assembly may be performed, assembling reads into contiguous sequences (contigs) without relying on a reference genome. This approach is especially useful when studying novel organisms, uncovering unique genetic elements, or characterizing structural variations. Once a draft genome is assembled, genome annotation is conducted to identify functional elements such as protein-coding genes, rRNA and tRNA genes, operons, CRISPR arrays, and genomic islands. Annotation tools such as the NCBI Prokaryotic Genome Annotation Pipeline (PGAP), RAST server, and Prokka are widely used.
Strain Typing and Phylogenetics
Multilocus sequence typing (MLST) can be performed to assign sequence types and infer phylogenetic or epidemiological traits. This facilitates the prediction of phenotypic characteristics such as serotype, strain identity, antimicrobial resistance genes, and virulence determinants. Tools like BLAST (Basic Local Alignment Search Tool) allow for rapid sequence comparison with public databases, while MEGA (Molecular Evolutionary Genetics Analysis) facilitates evolutionary analysis and visualization of phylogenetic relationships.
Reads-to-Type Approaches
Alternatively, a ‘reads-to-type’ approach can be used for rapid characterization of gene presence or allele identity without full genome assembly. Tools such as SRST2 and ReMatch enable accurate strain typing and detection of antimicrobial resistance genes directly from raw sequencing reads. This method is particularly advantageous in clinical or outbreak settings due to its speed, although genotypic predictions should be validated case-by-case to ensure reliability.
Data Visualization
Finally, data visualization integrates genomic and epidemiological information to interpret and communicate findings. Tools such as Microreact, GenGIS 2, and PHYLOViZ are commonly used to generate interactive phylogenetic trees and metadata-driven visualizations, enabling clearer insights into the relationships among microbial isolates and supporting data-driven decision-making.
The software and tools mentioned above are just some of the widely used and tested in the discipline but are far from exhaustive. The layout of a microbial genomics workflow will depend on the research question to be addressed. Whether one aims to discover pathogenicity markers, investigate metabolic potential, carry out outbreak tracking, or undertake evolutionary analysis, the analytical pathway from sequencing strategy to data interpretation can vary significantly.
Bioinformatics is a rapidly changing field with new algorithms and software being developed to meet emerging issues in microbial research. Therefore, effective analysis frequently relies not just on the choice of suitable tools but also on critically assessing their assumptions, constraints, and appropriateness for the data in question. Careful integration of experimental design with adaptive computational approaches continues to be essential to unlocking accurate and meaningful results from microbial genomic data.
Carriço, J., Rossi, M., Moran-Gilad, J., Van Domselaar, G., & Ramirez, M. (2018). A primer on microbial bioinformatics for nonbioinformaticians. Clinical Microbiology and Infection, 24(4), 342–349. https://doi.org/10.1016/j.cmi.2017.12.015
Dadlani, M. (2023, October 23). How do you sequence a microbiome? 5 steps explained | CosmosID. Cosmos ID. https://www.cosmosid.com/blog/how-do-you-sequence-a-microbiome-5-steps-explained/#
Franco-Duarte, R., Černáková, L., Kadam, S., Kaushik, K. S., Salehi, B., Bevilacqua, A., Corbo, M. R., Antolak, H., Dybka-Stępień, K., Leszczewicz, M., Tintino, S. R., De Souza, V. C. A., Sharifi-Rad, J., Coutinho, H. D. M., Martins, N., & Rodrigues, C. F. (2019). Advances in chemical and biological methods to identify Microorganisms—From Past to present. Microorganisms, 7(5), 130. https://doi.org/10.3390/microorganisms7050130
Luscombe, N. M., Greenbaum, D., & Gerstein, M. (2001). What is bioinformatics? An introduction and overview. Yearbook of medical informatics, 10(01), 83-100.
Xiong, J. (2006). Essential bioinformatics. Cambridge University Press.
Posts
0 Comments