Benchmark study of Adaptive Sampling algorithms: Simulation of various viral mixtures - CEA - Commissariat à l’énergie atomique et aux énergies alternatives
Poster De Conférence Année : 2024

Benchmark study of Adaptive Sampling algorithms: Simulation of various viral mixtures

Résumé

Background. To monitor the spread of a disease, wastewaters surveillance offers a global view of both the nature and quantity of circulating pathogens, compared to traditional screening methods [1]. It provides access to the entire population (including asymptomatic patients) at a cheaper cost than individual tests and gives an early indication of the epidemic’s state. It is even possible to quantify the relative abundances of the different circulating lineages of a single pathogen, like a given virus, as was shown during the COVID-19 pandemic [2]. However, most of these techniques uses Polymerase Chain Reaction, which among otherthings, biasesthe relative abundance of the target species due to the amplification step. Wet lab protocol. How can one monitor several viruses from a wastewater sample in a single sequencing experiment, while biasing the relative abundances of the different target species as little as possible? To selectively enrich the sample in target species, we plan to test and optimize a hybridization-capture protocol targeting one or multiple species. The final products will be sequenced using the Oxford Nanopore Technologies (ONT) MinION long-read sequencer. This technology enables real-time analysis of the electric signal induced by the DNA fragments as they pass through the device’s molecular pores. Adaptive sampling. In particular, it is possible to reverse the voltage across the pore to reject the DNA molecule before it is fully sequenced. The decision to reject the read is based on the knowledge of the target genomes and the insight gained from the signal generated by sequencing the first few hundred bases of the read. Taking this decision quickly enough allows us to spend more time sequencing the targets rather than non-relevant genomic material, thus improving the signal-to-noise ratio and effectively enriching in target species. This process is called Adaptive Sampling or Targeted Sequencing. Benchmark. Before we integrate Adaptive Sampling (AS) to our wastewater sample analysis protocol (and possibly develop our own algorithm), we conducted a benchmark of existing AS methods, in order to identify the best design choices to make, and to define the experimental conditions in which AS is the most efficient – in the context of virus search in wastewater samples. Developed by the team of AS pioneer Matthew Loose, ReadFish [3] usesthe basecalled translation of the signal together with traditional mapping against a reference dataset to determine whether the read should be fully sequenced or not. More recently, BOSS-RUNS [4] elaborates on ReadFish to optimize the information gain along the target genome. In contrast, DeepSelectNet [5] uses the raw electric signal to feed a deep convolutional neural network with residual connections. To evaluate those methods, we take advantage of the Icarust simulator [6] that mimics the MinKNOW (ONT’s sequencing software) behavior during a real sequencing run. Several experiments were performed in order to evaluate AS algorithms. The relative enrichment of targetspeciesin comparison with a sequencing run without AS along with the estimation of their relative abundances were used to compare the algorithms.
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Dates et versions

cea-04734006 , version 1 (07-11-2024)

Identifiants

  • HAL Id : cea-04734006 , version 1

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Benjamin Vacus, Edith Le Floch, Zuzana Gerber, Jean-François Deleuze, Arnaud Gloaguen. Benchmark study of Adaptive Sampling algorithms: Simulation of various viral mixtures. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM), Jun 2024, Toulouse, France. ⟨cea-04734006⟩
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