Back

Whole genome sequencing of hepatitis B virus (HBV) using tiled amplicon (HEP-TILE) and probe-based enrichment on Illumina and Nanopore platforms.

Lumley, S. F.; Kent, C.; Jennings, D.; Chai, H.; Airey, G.; Waddilove, E.; Delphin, M.; Trebes, A.; McNaughton, A.; Mohammed, K. S.; Wilkinson, S.; Wu, Y.; MacIntyre-Cockett, G.; Kimono, B.; Kwizera, M.; Ojambo, K.; Maponga, T.; de Lara, C.; Martin, J.; Campbell, J.; Van Schalkwyk, M.; Goedhals, D.; Newton, R.; Barnes, E.; Loman, N. J.; Piazza, P.; Quick, J.; Ansari, M. A.; Matthews, P. C.

2024-09-13 infectious diseases
10.1101/2024.09.11.24313306 medRxiv
Show abstract

Hepatitis B virus (HBV) whole genome sequencing (WGS) is currently limited as the DNA viral loads (VL) of many clinical samples are below the threshold required to generate full genomes using current sequencing methods. We developed two pan-genotypic viral enrichment methods, using probe-based capture and tiled amplicon PCR (HEP-TILE) for HBV WGS. We demonstrate using mock samples that both enrichment methods are pan-genotypic (genotypes A-J). Using clinical samples, we demonstrate that HEP-TILE amplification successfully amplifies full genomes at the lowest HBV VL tested (30 IU/ml), and the PCR products can be sequenced using both Nanopore and Illumina platforms. Probe-based capture with Illumina sequencing required VL >300,000 IU/ml to generate full length HBV genomes. The capture-Illumina and HEP-TILE-Nanopore pipelines had consensus sequencing accuracy of 100% in mock samples with known DNA sequences. Together, these protocols will facilitate the generation of HBV sequence data, enabling a more accurate and representative picture of HBV molecular epidemiology, cast light on persistence and pathogenesis, and enhance understanding of the outcomes of infection and its treatment.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.