High-throughput sequencing

During the last four years, the virology team has invested strong efforts in developing dedicated highthroughput sequencing techniques for researches in virus diagnosis, eco-epidemiology and experimental evolution.  The Illumina technology was preferred due  to both a low error rate (<0.5%) and the high number of  sequences produced  (~ 10 million / run).

For diagnosis purposes, we focused on sequencing small interfering RNAs (siRNAs) of 21-24 nt from sample pools. This approach proved to be highly versatile. Once sequence reads had been assembled, the method appeared powerful to identify positive- and negative-sense single-stranded RNA viruses, double-stranded RNA viruses, doublestranded DNA viruses as well as viroids and viral sequences integrated in host genomes.

For landscape epidemiology studies, we developed a method of high-throughput sequencing on RT-PCR amplicons (~500bp) that can be easily multiplexed. This approach allows to assess intra-host diversity and to process a high number of samples when conducting studies at the landscape scale. A similar sequencing technique was developed for research on resistance durability in order to infer the relative roles of selection and genetic drift from sequence data on the intra-host temporal dynamics of viral populations. All these approaches were developed through collaborations with private partners or other laboratories that have dedicated material and participated in the training of students, technicians and researchers of the virology team.

Databases

The Virology team is currently developing dedicated databases to manage data and samples from both laboratory collections and field sampling. These databases are developed under the postgreSQL system, which is a free and highly flexible software allowing to spatialize the records through its PostGIS extension. Spatial information is indeed a prerequisite for spatial/landscape genetics studies.

Landscape genetics

The team and its collaborators recently developed a new method, MAPI (Mapping Averaged Pairwise Information) to visualize the spatial variation of measures computed between pairs of samples (e.g. genetic distances). The method allows identifying significant areas of high genetic (dis)continuity, and produces georeferenced maps that can be overlaid on landscape layers. Results can also be used in further analyses to assess the relationships between genetic structure and landscape variables.

Piry, S., Chapuis, M. P., Gauffre, B., Papaix, J., Cruaud, A., Berthier, K. (2016). Mapping averaged pairwise information (MAPI): a new exploratory tool to uncover spatial structure. Methods in Ecology and Evolution, 7 (12), 1463-1475. DOI : 10.1111/2041-210X.12616