RNA viruses, characterized by high replication rates and the absence of proofreading mechanisms,
are susceptible to errors during replication. This characteristic allows them to form diverse
communities of genome mutants known as "viral quasispecies". Each individual genome
mutant is referred to as a haplotype. Analyzing viral populations involves reconstructing individual
haplotypes, using sequencing reads. This process is challenging due to the unknown
number of haplotypes, their high similarity, and varied abundances. Common sequencing
methods add to this complexity. Next-generation sequencing provides short reads lacking
sufficient information for haplotype reconstruction, while third-generation sequencing (TGS)
offers longer but error-prone reads. Recent advancements in TGS, such as PacBio HiFi reads,
deliver long, accurate reads, providing new opportunities for haplotype assembly. However,
the majority of TGS viral haplotype assembly tools rely on reference sequences and utilize
alignment-based methods. Moreover, during outbreaks suitable reference genomes might be
unavailable. In the last two decades, machine learning has enabled us to uncover patterns
within and between biological sequences, and to discover important biological attributes. In
this study, we introduce GoViral, a pipeline designed to reconstruct haplotypes specific to
genomic regions and estimate their abundances. Our pipeline features a transformer-based
classification model fine-tuned on a self-constructed dataset to classify read pairs, identifying
those originating from the same haplotype. Predictions are made irrespective of coverage and
abundance, removing reliance on alignment-based methods. In addition, our pipeline employs
a community detection algorithm to cluster and reconstruct region-specific haplotypes, estimating
their abundances. GoViral achieves high performance across real data and diverse
RNA viruses, including SARS-CoV-2, HIV-1, and HCV-1b, surpassing existing tools.

Lineage abundance estimation of SARS-CoV-2 in wastewater is a technique that aims to monitor the lineage prevalence in communities and help contain the COVID-19 pandemic. Lineages are collections of closely related mutants of a virus. It is suggested that the genome sequences of lineages differ across the globe due to random mutations or distinct immune responses of populations that mutate the virus. In order to estimate the lineage abundance in a specific community, wastewater data collected from the community are compared to reference SARS-CoV-2 genome sequences of different lineages. However, such region-related variation in the genome sequences of lineages could impact the abundance estimates. The main aim of this study is to identify an optimal way of sourcing reference genome sequences such that the lineage abundance estimates are improved. For the purpose of evaluating the performance of different reference sets, simulated wastewater data are used. We demonstrate that continent-specific reference sets are the most reliable option. The overall country interactions with other parts of the world could be considered for constructing an optimal reference set. Additionally, results show that considering immune-response related mutations for the reference set construction does not influence performance. Finally, it is suggested that a higher number of sequences per lineage and the inclusion of recently sourced sequences in the reference set improve results.