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Open Access Highly Accessed Short Report

De novo identification of viral pathogens from cell culture hologenomes

Ashok Patowary1, Rajendra Kumar Chauhan1, Meghna Singh1, Shamsudheen KV1, Vinita Periwal1, Kushwaha KP2, Gajanand N Sapkal3, Vijay P Bondre3, Milind M Gore4*, Sridhar Sivasubbu1* and Vinod Scaria1*

Author Affiliations

1 CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi 110007, India

2 BRD Medical College and Nehru Hospital, Gorakhpur, Uttar Pradesh, India

3 National Institute of Virology (ICMR), Pune, India

4 National Institute of Virology (ICMR), Gorakhpur Unit, Gorakhpur, India

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BMC Research Notes 2012, 5:11  doi:10.1186/1756-0500-5-11

Published: 6 January 2012

Abstract

Background

Fast, specific identification and surveillance of pathogens is the cornerstone of any outbreak response system, especially in the case of emerging infectious diseases and viral epidemics. This process is generally tedious and time-consuming thus making it ineffective in traditional settings. The added complexity in these situations is the non-availability of pure isolates of pathogens as they are present as mixed genomes or hologenomes. Next-generation sequencing approaches offer an attractive solution in this scenario as it provides adequate depth of sequencing at fast and affordable costs, apart from making it possible to decipher complex interactions between genomes at a scale that was not possible before. The widespread application of next-generation sequencing in this field has been limited by the non-availability of an efficient computational pipeline to systematically analyze data to delineate pathogen genomes from mixed population of genomes or hologenomes.

Findings

We applied next-generation sequencing on a sample containing mixed population of genomes from an epidemic with appropriate processing and enrichment. The data was analyzed using an extensive computational pipeline involving mapping to reference genome sets and de-novo assembly. In depth analysis of the data generated revealed the presence of sequences corresponding to Japanese encephalitis virus. The genome of the virus was also independently de-novo assembled. The presence of the virus was in addition, verified using standard molecular biology techniques.

Conclusions

Our approach can accurately identify causative pathogens from cell culture hologenome samples containing mixed population of genomes and in principle can be applied to patient hologenome samples without any background information. This methodology could be widely applied to identify and isolate pathogen genomes and understand their genomic variability during outbreaks.

Keywords:
Epidemics; Mixed Population Genomes; Hologenome; De novo assembly; Japanese encephalitis; Next generation sequencing