Introduction to genome sequencing, principles and its applications to a diagnostic medical microbiology laboratory

Vandana Govindan; S M Vaishali Kumar; Varun Shamanna; N Iyer Ranganathan; Kadahalli Lingegowda Ravi Kumar

doi:10.4103/jacm.jacm_14_22

REVIEW ARTICLE

Year : 2022 | Volume : 24 | Issue : 3 | Page : 1-7

Introduction to genome sequencing, principles and its applications to a diagnostic medical microbiology laboratory

Vandana Govindan¹, S M Vaishali Kumar¹, Varun Shamanna¹, N Iyer Ranganathan², Kadahalli Lingegowda Ravi Kumar¹
¹ Department of Central Research Laboratory, KIMS Medical College, Bengaluru, Karnataka, India
² Department of Microbiology, Global Hospitals, Hyderabad, Telangana, India, India

Date of Submission	13-Sep-2022
Date of Acceptance	17-Oct-2022
Date of Web Publication	11-Nov-2022

Correspondence Address:
Kadahalli Lingegowda Ravi Kumar
PI - National Centre for Pneumococcal Immunogenicity Evaluation, GHRU, India Unit - Genomic AMR Surveillance CRL, Kempegowda Institute of Medical Sciences, Banashankari 2^nd Stage, Bengaluru - 560 070, Karnataka
India

Source of Support: None, Conflict of Interest: None

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DOI: 10.4103/jacm.jacm_14_22

Abstract

Microbiology diagnostic laboratory plays a significant role in public health surveillance, outbreak investigation, infection prevention and control strategies. It is moving towards incorporating molecular biology techniques for the surveillance and identification of pathogens causing infectious diseases. Next-generation sequencing (NGS) holds potential for improving clinical and public health microbiology. In addition to identifying pathogens more rapidly and precisely than traditional methods, sequencing technologies can provide new insights into disease transmission, virulence and antimicrobial resistance. NGS has not only reduced the cost of total sequencing but has also introduced versatile applications under one platform. This review will discuss the methods, principles and applications of genome sequencing in microbiology diagnostic laboratories.

Keywords: Genomics, medical microbiology, next-generation sequencing

How to cite this article:
Govindan V, Kumar S M, Shamanna V, Ranganathan N I, Kumar KL. Introduction to genome sequencing, principles and its applications to a diagnostic medical microbiology laboratory. J Acad Clin Microbiol 2022;24, Suppl S1:1-7

How to cite this URL:
Govindan V, Kumar S M, Shamanna V, Ranganathan N I, Kumar KL. Introduction to genome sequencing, principles and its applications to a diagnostic medical microbiology laboratory. J Acad Clin Microbiol [serial online] 2022 [cited 2023 Jun 3];24, Suppl S1:1-7. Available from: https://www.jacmjournal.org/text.asp?2022/24/3/1/360976

Introduction

Microbiology laboratories detect and identify pathogenic organisms on a periodic basis, which helps to ensure and track the spread of illnesses and antibiotic resistance. For patient management and infection control, the information provided by a microbiology laboratory is extremely valuable.^[1] The assessment of phenotypic properties of microbe cultures grown in optimum growth circumstances has historically been a part of routine work in clinical microbiological laboratories. However, the inability to adequately define all medically relevant bacteria and provide results in a timely manner limits this method. The use of new molecular biology and genomic techniques enables rapid, highly specific and more comprehensive microbiology diagnostics.

Genome sequencing, a method used for analysing the genetic make-up of a specific organism or cell type, is a versatile technology, broadly applicable to viruses, bacteria, fungi, parasites, animal vectors and human hosts.^[2] The information has influenced the identification of pathogenic organisms, mutations that drive drug resistance, tracking disease outbreaks and patient management.^[3] Rapidly dropping sequencing costs and the ability to produce large volumes of data with today's sequencers make genome sequencing a powerful tool for research. Unlike focused approaches such as exome sequencing or targeted resequencing, which analyse a limited portion of the genome, whole-genome sequencing delivers a comprehensive view of the entire genome. It is ideal for applications, such as identifying causative variants and novel genome assembly, detecting single-nucleotide variants (SNV), insertions/deletions, copy number changes and large structural variants.^[4],[5] In recent years, next-generation sequencing (NGS) has become an integrated part of precision microbial diagnostics.

Methods

The publications for this review were found using the following search strings in PubMed and Google Scholar: 'Genome Sequencing and NGS', 'NGS and diagnostic laboratory', 'NGS and India'.

Strategies Used for Genome Sequencing

The different strategies used for genome sequencing include Sanger method, shotgun sequencing, pairwise-end sequencing and NGS [Table 1].

Table 1: Different strategies used for whole-genome sequencing

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Sanger method

Chain termination sequencing was the first nucleic acid sequencing method which revolutionised molecular biology, resulting in the 1980 Nobel Prize. Chain termination, also called Sanger sequencing as it was developed by Fred Sanger in 1977, uses the selective incorporation of dideoxynucleotides during an in vitro DNA replication reaction.^[6] [Figure 1].

Figure 1: The Sanger sequencing method in six steps

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The Sanger sequencing method consists of the following six steps:

The double-stranded DNA is denatured into two single-stranded DNA (ssDNA)
A primer that corresponds to one end of the sequence is attached
Four polymerase solutions with four types of dNTPs but only one type of ddNTP are added
The DNA synthesis reaction initiates and the chain extends until a termination nucleotide is randomly incorporated
The resulting DNA fragments are denatured into ssDNA
The denatured fragments are separated by gel electrophoresis and the sequence is determined.

Shotgun sequencing and pairwise-end sequencing

In the shotgun sequencing method, several copies of a DNA fragment are cut randomly into many smaller pieces. All of the segments are then sequenced using the chain-sequencing method. Then, with the help of a computer, the fragments are analysed to see where their sequences overlap. By matching the overlapping sequences at the end of each fragment, the entire DNA sequence can be reformed. Originally, shotgun sequencing only analysed one end of each fragment for overlaps. This was sufficient for sequencing small genomes. However, the desire to sequence larger genomes, such as that of a human, led to the development of double-barrel shotgun sequencing, more formally known as pairwise-end sequencing. In pairwise-end sequencing, both the ends of each fragment are analysed for overlap. Pairwise-end sequencing is, therefore, more cumbersome than shotgun sequencing, but it is easier to reconstruct the sequence because there is more available information.^[7]

Next-generation sequencing

Since 2005, automated sequencing techniques used by laboratories are under the umbrella of NGS, which is a group of automated techniques used for rapid DNA sequencing. These automated, low-cost sequencers can generate sequences of hundreds of thousands or millions of short fragments (25–500 base pairs) in the span of one day. Sophisticated software is used to manage the cumbersome process of putting all the fragments in order.

The different strategies used for whole-genome sequencing include Sanger method, shotgun sequencing, pairwise-end sequencing and NGS [Table 1].

Role of Next-Generation sequencing in Clinical Microbiology Laboratories

In the recent decade, molecular diagnostic approaches have gained popularity, and they are now playing an increasingly essential role in clinical microbiology laboratories. These techniques can identify the presence or absence of nucleic acids from organisms in a sample without the need for culture growth.^[8] Real-time polymerase chain reaction (PCR), often employed in clinical microbiology laboratories, amplifies pathogen-specific nucleic acids, allowing for high sensitivity and specificity detection and quantification of a pathogen's genetic material in a specimen. Multiplex PCR-based assays have been developed to detect many targets at the same time. However, even multiplex PCRs can only identify pre-defined targets, so one must have suspect organisms or targets in mind in order to detect them.^[9] NGS platforms capable of comprehensive detection of multiple pathogens simultaneously and directly from a patient sample have moved to routine use in the clinical microbiology laboratory.^[10] Genomics and bioinformatics have contributed immensely to our understanding of infectious diseases. Bioinformatics is applied in the understanding of host and pathogen genome biology to genome-wide association studies.

Next-Generation Sequencing Technologies in Clinical Diagnostics

Surveillance and outbreak investigations

The precise and detailed data provided by sequencing can be beneficial to infection prevention efforts in the hospital setting. Genome sequencing can be used to identify the environmental source of an outbreak, trace the transmission of infectious agents between patients and better understand the transmission dynamics of antimicrobial resistance genes. The pathogen genomic epidemiology uses comparative analysis of the genomes of pathogens isolated from patients suspected to be part of an outbreak, in combination with other epidemiological data, to determine whether patients are indeed the part of an outbreak and if so, to establish its source/s and the chain of transmission between patients and any other environmental reservoirs of infection. The method used to achieve this is to sequence the whole genomes of pathogens taken from different patients and different places, potentially at different times, and use the number of differences identified between the genomes to construct 'family trees'. These family trees are constructed on the principle that: 'The extent of sequence variation between the genomes of pathogens isolated from different people or locations in the environment is proportional to how closely related the pathogens are i.e. how recently they share a common ancestor'. Thus, isolates of the pathogen that have identical or near-identical genomes will be placed close together on these trees, and it can be inferred that these infected individuals are likely to have been exposed to the same source of the infection. Where isolates of the pathogen have genomes that differ widely in their sequence are placed further apart on the family tree and epidemiologists can infer that it is unlikely that these infections were directly transmitted between these individuals and that they are also unlikely to share a common source. Genomics has been applied to investigate tuberculosis (TB) outbreaks, genotyping of the outbreak-associated lineages and their evolution during the outbreak.^[11]

Characterisation of an organism

In a new study, from the Wellcome Sanger Institute and European Molecular Biology Laboratory's European Bioinformatics Institute, the researchers standardised all bacterial genome data held in the European Nucleotide Archive before 2019, creating a searchable and accessible database of genomic assemblies. In the research, published on 9 November 2021 in PLOS Biology, the researchers reviewed all of the bacterial data available as of November 2018 and assembled it into over 660,000 genomes. This has been released as a new open-access database designed to help scientists all around the world answer basic questions on bacterial evolution, by considering all data in a standardised and comprehensive manner. This has led to the advent of using whole-genome comparisons between related species to determine the average nucleotide identity between two genomes.^[12]

The four main potential applications of whole-genome sequencing (WGS) for bacterial pathogen characterisation in the diagnostic microbiology laboratory include: identification, resistance detection, typing and virulence gene detection.

Identification and resistance detection

Although current susceptibility methods from organism culture are likely to be more rapid and reliable for routine testing, as with organism identification, WGS methods may be useful for slow-growing organisms, organisms that are unable to be cultured or where phenotypic susceptibility testing is unreliable. WGS can be made as a routine tool for clinical microbiology by applying directly on clinical samples and with the use of fast and reliable bioinformatic tools. This could reduce diagnostic times and thereby improve control and treatment.

WGS studies for mapping genetic heterogeneity and identifying determinants of drug resistance among clinical isolates in India are limited. The first Indian report on genome-wide comparison of multidrug-resistant (MDR) Escherichia coli from blood stream infections provided information on the lineages circulating in India. Data from this study provided public health agencies with baseline information on AMR and virulent genes in pathogenic E. coli in the region. WGS of Mycobacterium tuberculosis in clinical isolates from India revealed genetic heterogeneity and region-specific variations that affect drug susceptibility. The study identified 12,802 novel genetic variations in M. tuberculosis isolates including 343 novel SNVs in 38 genes which are known to be associated with drug resistance and are not currently used in the diagnostic kits for detection of drug-resistant TB. The study highlighted the significance of employing WGS in diagnosis and for monitoring further development of MDR-TB strains.

Typing

Typing of bacterial pathogens for epidemiological surveillance is an obvious and immediate application of NGS. Typing is organism specific and requires constant validation. NGS has the capacity to supersede traditional typing methods, through either in silico typing or superior discriminatory capacity.^[13],[14] For instance, MLST, which is traditionally performed by sequencing of a set of housekeeping genes, can be simulated by mapping WGS reads to the reference sequences of those genes,^[15] or using the Basic Local Alignment Search Tool to identify the alleles of the housekeeping genes.^[16]

Comparative genomics

Several studies have illustrated the capabilities of WGS to describe the evolution and epidemiology of important infections.^{[17],[18],[19],[20],[21],[22]} In an era of increasing antimicrobial resistance, mapping the epidemiology of such multidrug-resistant infections to direct public health responses and antimicrobial prescribing practices is vital. There have been numerous studies reporting the use of WGS to inform hospital infection control responses to suspected pathogen transmission. [Table 2] summarises WGS studies from India.^{[23],[24],[25],[26],[27],[28],[29],[30],[31],[32],[33],[34],[35],[36],[37],[38],[39],[40],[41],[42],[43],[44],[45]}

Table 2: Whole-genome sequencing studies from India

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Comparative genomic studies have also attempted to clarify transmission events and outbreak propagation. These methods relied upon established 'molecular clocks' to estimate the time to the most common recent ancestor and dates of presumed transmission events, using phylogenomic models.^[45] Some defined thresholds for the number of SNPs between independent isolates that are required to infer whether they are epidemiologically linked although mutation and recombination rates vary between species and lineages, and the rates of microevolution of endemic clones, may need to be defined in each context.

Culture-independent identification and metagenomics

WGS has been demonstrated to be a useful tool as a culture-independent method of bacterial identification, predominantly through metagenomic analyses. Although it is yet to be implemented in routine diagnostics, metagenomics involves sequencing all DNA content in a clinical sample, before using bioinformatics analyses to filter out human and non-pathogenic organism DNA to identify the causative agent. High-quality samples with sufficient concentrations of genomic nucleic acid, such as tissue or fluid aspirates, are paramount for this application of WGS. Previous methods including broad-range 16S rRNA PCR and sequencing have been used for diagnosis of culture-negative bacterial infections^[13] (https://www. ncbi.nlm.nih.gov/pmc/articles/PMC4389090/-R61). However, these methods frequently had low sensitivity if insufficient pathogen DNA was present, and were affected by the presence of contaminating DNA from other bacterial species. Metagenomic analysis of NGS data from a clinical sample has the capacity to overcome these limitations by filtering out unwanted DNA in the post-sequencing analysis.

Barriers and challenges to implementing whole-genome sequencing in the clinical microbiology laboratory

WGS technology has advanced quickly, and it is now reasonable for clinical microbiology laboratories to consider implementing WGS in-house without sending isolates to central laboratories or public health departments. However, this option is not without significant barriers that must be considered. As is true of any stewardship or infection-prevention initiative, advanced technologies lose their potential impact when a solid infrastructure that supports the testing is not in place. Some key barriers to WGS surveillance and implementation in the clinical microbiology laboratory include cost, expertise, information technology infrastructure, data sharing and communication and quality.^[46]

Conclusion

As a tool for hospital-based surveillance, NGS shows a great deal of promise. While technology has progressed to the point where microbiology laboratories may be able to use it, creating a supportive infrastructure inside the hospital is required. When considering NGS for surveillance, laboratories should make every effort to ensure that the testing fits into the clinical microbiology workflow and that the results are interpretable and actionable, with a focus on clear communication between the microbiology laboratory and diagnostics.

Author's contribution

Dr. Vandana Govindan wrote the manuscript with support for microbiology aspects from Ms Vaishali SM and bioinformatics aspects from Mr Varun. All authors provided critical feedback and helped shape the research, analysis and manuscript.

Financial support and sponsorship

Nil.

Conflicts of interest

The authors whose names are listed in the article certify that they have NO affiliations with or involvement in any organisation or entity with any financial interest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership or other equity interest and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

References

1.	Simões AS, Couto I, Toscano C, Gonçalves E, Póvoa P, Viveiros M, et al. Prevention and control of antimicrobial resistant healthcare-associated infections: The microbiology laboratory rocks! Front Microbiol 2016;7:855.
2.	Evans JP, Powell BC, Berg JS. Finding the rare pathogenic variants in a human genome. JAMA 2017;317:1904-5.
3.	Armstrong GL, MacCannell DR, Taylor J, Carleton HA, Neuhaus EB, Bradbury RS, et al. Pathogen genomics in public health. N Engl J Med 2019;381:2569-80.
4.	Available from: https://www.illumina.com/techniques/sequencing/dna-sequencing/whole-genome-sequencing.html. [Last accessed on 2022 Aug 12].
5.	Obranic S. Molecular diagnostics in the clinical microbiology laboratory: New developments. Mol Exp Biol Med 2019;2:1-8.
6.	M. Muthukumar, C. Y. Kon. Simulation of polymer translocation through protein channels. PNAS 2006;103:5273-8.
7.	Boundless. General Biology. Biotechnology and Genomics: 2021;17.3A:1-2.
8.	Morshed MG, Lee MK, Jorgensen D, Isaac-Renton JL. Molecular methods used in clinical laboratory: Prospects and pitfalls. FEMS Immunol Med Microbiol 2007;49:184-91.
9.	Wang H, Jean S. Next-generation sequencing for infectious diseases diagnostics – Is it worth the hype? Clin Lab News 2021.
10.	Deurenberg RH, Bathoorn E, Chlebowicz MA, Couto N, Ferdous M, García-Cobos S, et al. Application of next generation sequencing in clinical microbiology and infection prevention. J Biotechnol 2017;243:16-24.
11.	Robinson ER, Walker TM, Pallen MJ. Genomics and outbreak investigation: From sequence to consequence. Genome Med 2013;5:36.
12.	Available from: https://www.sanger.ac.uk/news_item/new-database-of-660000-assembled-bacterial-genomes-shed s-light-on-the-evolution-of-bacteria/. [Last Accessed on 2022 Aug 13].
13.	Kwong JC, McCallum N, Sintchenko V, Howden BP. Whole genome sequencing in clinical and public health microbiology. Pathology 2015;47:199-210.
14.	Quainoo S, Coolen JP, van Hijum SA, Huynen MA, Melchers WJ, van Schaik W, et al. Whole-genome sequencing of bacterial pathogens: The future of nosocomial outbreak analysis. Clin Microbiol Rev 2017;30:1015-63.
15.	Uelze L, Grützke J, Borowiak M, Hammerl JA, Juraschek K, Deneke C, et al. Typing methods based on whole genome sequencing data. One Health Outlook 2020;2:3.
16.	Page AJ, Alikhan NF, Carleton HA, Seemann T, Keane JA, Katz LS. Comparison of classical multi-locus sequence typing software for next-generation sequencing data. Microb Genom 2017;3:e000124.
17.	Moran-Gilad J, Sintchenko V, Pedersen SK, Wolfgang WJ, Pettengill J, Strain E, et al. Proficiency testing for bacterial whole genome sequencing: An end-user survey of current capabilities, requirements and priorities. BMC Infect Dis 2015;15:174.
18.	Schürch AC, van Schaik W. Challenges and opportunities for whole-genome sequencing-based surveillance of antibiotic resistance. Ann N Y Acad Sci 2017;1388:108-20.
19.	van Soolingen D, Jajou R, Mulder A, de Neeling H. Whole genome sequencing as the ultimate tool to diagnose tuberculosis. Int J Mycobacteriol 2016;5 Suppl 1:S60-1.
20.	European Centre for Disease Prevention and Control. Expert Opinion on Whole Genome Sequencing for Public Health Surveillance. Stockholm: European Centre for Disease Prevention and Control; 2016.
21.	Ellington MJ, Ekelund O, Aarestrup FM, Canton R, Doumith M, Giske C, et al. The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: Report from the EUCAST subcommittee. Clin Microbiol Infect 2017;23:2-22.
22.	Struelens MJ, Brisse S. From molecular to genomic epidemiology: Transforming surveillance and control of infectious diseases. Euro Surveill 2013;18:20386.
23.	Mbengue A, Namdev P, Kumar T, Haldar K, Bhattacharjee S. Next generation whole genome sequencing of Plasmodium falciparum using NextSeq500 technology in India. BioRxiv. [doi: https://doi.org/10.1101/068676].
24.	Jones S, Prasad R, Nair AS, Dharmaseelan S, Usha R, Nair RR, et al. Whole-genome sequences of influenza A (H1N1) pdm09 virus isolates from Kerala, India. Genome Announc 2017;5:e00598-17.
25.	Hussain A, Shaik S, Ranjan A, Nandanwar N, Tiwari SK, Majid M, et al. Risk of transmission of antimicrobial resistant Escherichia Coli from commercial broiler and free-range retail chicken in India. Front Microbiol 2017;8:2120.
26.	Chatterjee A, Nilgiriwala K, Saranath D, Rodrigues C, Mistry N. Whole genome sequencing of clinical strains of Mycobacterium Tuberculosis from Mumbai, India: A potential tool for determining drug-resistance and strain lineage. Tuberculosis (Edinb) 2017;107:63-72.
27.	Beg AZ, Khan AU. Genome analyses of bla_NDM-4 carrying ST 315 Escherichia Coli isolate from sewage water of one of the Indian hospitals. Gut Pathog 2018;10:17.
28.	Ragupathi NK, Bakthavatchalam YD, Mathur P, Pragasam AK, Walia K, Ohri VC, et al. Plasmid profiles among some ESKAPE pathogens in a tertiary care centre in south India. Indian J Med Res 2019;149:222-31. [PUBMED] [Full text]
29.	Advani J, Verma R, Chatterjee O, Pachouri PK, Upadhyay P, Singh R, et al. Whole genome sequencing of Mycobacterium Tuberculosis clinical isolates from India reveals genetic heterogeneity and region-specific variations that might affect drug susceptibility. Front Microbiol 2019;10:309.
30.	Rufai SB, Singh S. Whole-genome sequencing of two extensively drug-resistant Mycobacterium Tuberculosis isolates from India. Microbiol Resour Announc 2019;8:e00007-19.
31.	Katiyar A, Sharma P, Dahiya S, Singh H, Kapil A, Kaur P. Genomic profiling of antimicrobial resistance genes in clinical isolates of Salmonella typhi from patients infected with Typhoid fever in India. Sci Rep 2020;10:8299.
32.	Karade S, Sen S, Shergill SP, Jani K, Shouche Y, Gupta RM. Whole genome sequence of colistin-resistant Escherichia Coli from western India. Med J Armed Forces India 2021;77:297-301.
33.	Sethi S, Hao Y, Brown SM, Walker T, Yadav R, Zaman K, et al. Elucidation of drug resistance mutations in Mycobacterium Tuberculosis isolates from North India by whole-genome sequencing. J Glob Antimicrob Resist 2020;20:11-5.
34.	Pragasam AK, Jennifer SL, Solaimalai D, Muthuirulandi Sethuvel DP, Rachel T, Elangovan D, et al. Expected plazomicin susceptibility in India based on the prevailing aminoglycoside resistance mechanisms in gram-negative organisms derived from whole-genome sequencing. Indian J Med Microbiol 2020;38:313-8. [Full text]
35.	Shankar C, Mathur P, Jacob JJ, Rodrigues C, Walia K, Chitnis DS, et al. Genomic insights into multi-drug and extensively drug resistant Klebsiella Pneumoniae from India. Int J Infect Dis 2020;101:12-3.
36.	De R, Mukhopadhyay AK, Dutta S. Metagenomic analysis of gut Microbiome and Resistome of diarrheal Fecal samples from Kolkata, India, reveals the core and variable Microbiota including signatures of microbial dark matter. Gut Pathog 2020;12:32.
37.	Duffy SC, Srinivasan S, Schilling MA, Stuber T, Danchuk SN, Michael JS, et al. Reconsidering Mycobacterium Bovis as a proxy for zoonotic tuberculosis: A molecular epidemiological surveillance study. Lancet Microbe 2020;1:e66-73.
38.	Deval H, Nyayanit DA, Mishra SK, Yadav PD, Zaman K, Shankar P, et al. Genome sequencing reveals a mixed picture of SARS-CoV-2 variant of concern circulation in Eastern Uttar Pradesh, India. Front Med (Lausanne) 2021;8:781287.
39.	Kumar V, Kumar S, Singh D. Metagenomic insights into Himalayan glacial and kettle lake sediments revealed microbial community structure, function, and stress adaptation strategies. Extremophiles 2021;26:3.
40.	Yadav A, Singh A, Wang Y, Haren MH, Singh A, de Groot T, et al. Colonisation and transmission dynamics of Candida Auris among chronic respiratory diseases patients hospitalised in a chest hospital, Delhi, India: A comparative analysis of whole genome sequencing and microsatellite typing. J Fungi (Basel) 2021;7:81.
41.	Khan N, Bhat R, Jain V, Raghavendhar BS, Patel AK, Nayak K, et al. Epidemiology and molecular characterization of chikungunya virus from human cases in North India, 2016. Microbiol Immunol 2021;65:290-301.
42.	Muttineni R, Kammili N, Bingi TC, Rao MR, Putty K, Dholaniya PS, et al. Clinical and whole genome characterization of SARS-CoV-2 in India. PLoS One 2021;16:e0246173.
43.	Sarkar B, Mandal AK, Ghati A, Ghosh P, Mandal S, Kati A. Whole-genome shotgun (WGS) sequence of cis-isoprene polymer-degrading Nocardia sp. strain BSTN01. Microbiol Resour Announc 2022;11:e0117521.
44.	Jacob JJ, Priya TT, Solaimalai D, Yesudoss M, Malaiyappan JR, Rachel T, et al. Draft genome sequences data of rare Salmonella Enterica sub sp. Enterica serovar ceyco and serovar Hillegersberg isolated from diarrheal patients in India. Data Brief 2022;41:107875.
45.	Clarridge JE 3^rd. Impact of 16S rRNA gene sequence analysis for identification of Bacteria on clinical microbiology and infectious diseases. Clin Microbiol Rev 2004;17:840-62.
46.	Rossen JW, Friedrich AW, Moran-Gilad J, ESCMID Study Group for Genomic and Molecular Diagnostics (ESGMD). Practical issues in implementing whole-genome-sequencing in routine diagnostic microbiology. Clin Microbiol Infect 2018;24:355-60.