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.

AmpliDiff is an algorithm for studying environmental samples. As the study of those DNA samples is complicated, the runtime of AmpliDiff holds back its usability. One time-consuming part is exactly solving a variant of the Set Cover problem. This paper researches whether this exact solution can be replaced by heuristics to reduce the runtime. The Iterated Local Search framework is applied as heuristic and translated onto our problem. After that, it is tested and compared to the original version of the algorithm based on the found solution size. From the results we can deduce that the heuristic version finds the same solutions as the original. It does so in a faster relative time frame as well. However, due to time constraints, the dataset that was used for testing is rather small and therefore not representative of more complex problems, which are usually solved by AmpliDiff. Thus, for future work we recommend to test the heuristics version on a larger, more representative dataset to check its viability. This paper has laid the groundwork by implementing the heuristic and showing that it effectively works for small datasets, giving reason to also try it out on more complicated problems.

Abundance estimation with the use of environmental samples has been used during the SARS-CoV-2 pandemic to identify the abundances of different lineages. AmpliDiff, an algorithm that tries to find parts of DNA that can differentiate between different input genomes was used on a SARS-CoV-2 dataset to find these amplicons. The AmpliDiff algorithm was able to run on the SARS-CoV-2 set but seemed infeasible for datasets that contain larger or more complex genomes because of the computational requirements and runtime. We introduce a new pre-processing strategy based on selecting the most differentiable coding regions and show the modifications done to AmpliDiff to make AmpliDiff work following this new method. Based on the results we conclude that the approach is promising but still requires more research to be used optimally.

In this study we introduce a different approach to the primer selection problem in the AmpliDiff [12] algorithm. Two different metrics, being the Hamming distance [2], and the Levenshtein distance [4], are used to compute sets of similar primers. This is done such that locations where mismatches between the primers and the target sequence can occur are determined by the locations of similar primers. The impact that said mismatches have on the solution set of amplicons and their respective primers is outlined. With this we show potential benefits of allowing mismatches to occur, as well as their drawbacks.

AmpliDiff provides a method which takes a list of genomes and their lineages, and finds a set of amplicons and their primers in such a way that these amplicons can be used to differentiate between the lineages of a specific virus. While it has been shown that AmpliDiff find results comparable to whole genome sequencing for SARS-CoV-2 when looking at abundance estimations, it is not know how well it performs for other viruses, or what factors of a virus impacts the performance of the amplicons found by AmpliDiff.\\
In this paper we will be showing the effectiveness of AmpliDiff on Human monkeypox, HIV-1 and Influenza-A.
By running AmpliDiff for the three viruses mentioned above, we obtain sets of amplicons, which are used to do a lineage abundance estimation. By then comparing the estimation to the know abundance we calculate the Mean Average Error (MAE). This MAE will then be used to compare against the MAE obtained from doing a abundance estimation based on whole genome sequencing.
By comparing the amplicons against whole genome sequencing (wgs), we show that using viruses with longer genomes positively impacts the performance of the amplicons. We also show that the amount of misalignment characters added by the Multiple Sequence Alignemnt (MSA), impacts the required settings for AmpliDiff to find amplicons, and can negatively impact the MAE.
Finally, we show that AmpliDiff can be run, with some minor changes to the code base, on segmented genomes, with performance similar to that of single segment genomes.

The human microbiome, the ensemble of microorganisms found in and on the human body, plays a key role in human health and disease. However, the current state of microbiome analysis represents a significant challenge for machine learning algorithms. Datasets of microbiome sequences are often characterized by a regime of large dimensionality and relatively few labels, making it difficult for a model to discriminate features from random noise and avoid overfitting. It is, therefore, paramount to reduce the dimensionality of the input data while preserving their structure and information for a model to properly learn from them. K-mer frequency vectors and learnable representations through encoders are some of the embedding methods that have been proposed in literature to reduce the dimensions of the input space for machine learning algorithms operating on biological sequences. This work aims to compare how various embedding techniques influence the performance of a downstream disease detection task from microbiome sequencing data. In particular, the research shows that k-mer frequency vectors lead to better classification metrics (AUC = 0.88) compared to NeuroSEED embeddings (AUC = 0.76) on euclidean space. The work also presents how the classification problem formulation is critical to improving the overall disease detection performance.

Viral quasispecies refers to viral populations that comprises of numerous viral strains closely related to each other due to within-host evolution or co-infection. The reconstruction of viral strain-specific genomes using sequencing reads is referred to as viral quasispecies assembly, and it is also crucial to determine the relative abundances of the viral strains in the mixture for various treatments. There are currently many software tools available to transform NGS sequencing reads into haplotypes but earlier benchmarks of viral quasispecies reconstruction tools were only tested using simulated datasets but do not reflect closely on the real-world scenarios and on virus evolution. In this research, using realistic evolutionary viral populations, we assessed six viral quasispecies assembly tools. The existing real dataset mix that is still being used for experiments is a decade old, so it has become important to create broader and complex high quality real datasets as a new standard for future haplotype caller experiments. We introduce a new high quality benchmarking dataset for viral quasispecies assembly from real samples. The aim of this research is to evaluate extensive performance of six tools approaches that allow for reconstruction of unique viral haplotypes which are necessary to research complex and heterogeneous virus communities thoroughly. A comparative study of the performance of these tools has been done. Based on the results achieved, to improve the haplotypes generated, an existing de novo method is used for reconstructing full-length haplotypes from pre-assembled contigs of challenging mixed samples. In general, this improved the overall accuracy of the assembly and abundance estimations.

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.

Since the start of the SARS-CoV-2 pandemic, the monitoring of SARS-CoV-2 by way of viral RNA sequencing of wastewater has proven to be an efficient and effective way of estimating COVID-19 cases in population groups. A recently developed pipeline also enables us to estimate SARS-CoV-2 variant abundance using viral samples from wastewater. This is done by repurposing an RNA-seq quantification algorithm to quantify reads, belonging to variants, from DNA-sequencing data. However, the impact of sequencing errors and contaminating viruses on this process is unknown. Here I show that, in simulated data, the credibility of the prediction results is dependent on the error rate of the sequencing machines used. I also show that contaminating the simulated dataset with certain human coronaviruses has a significant effect on prediction accuracy. However, most viruses currently found in wastewater have no effect. Furthermore, adding a reference genome for these human corona-viruses to the reference set removes any impact. The results demonstrate that it is important to assess the credibility of the pipeline on a case by case basis and to tailor the testing setup and reference set to this assessment.

During a viral infection, we expel remnants of the virus. This makes it possible to conduct wastewater analysis which aid in the efforts to track the evolution of the current Covid-19 pandemic. It has been shown that by repurposing the kallisto algorithm, the abundance of SARS-CoV-2 variants in wastewater samples can be estimated. Since this is a novel method for this scope, its precision could probably be improved by adjusting certain aspects. In this work, I look at one of those aspects: sequencing particular genomic regions of the virus rather than the entire genome. I have indeed found that the regions that code for the spike (S) and nucleocapsid (N) regions and a section around the region coding for the non-structural protein 3 (nsp3) give particularly accurate results when sequenced on their own. In addition, in at least one case, combining two well-performing regions further improves accuracy at lower simulated abundances of variants. This suggests that sequencing depth is preferred over sequencing breath as long as the region being sequenced contains enough information to distinguish between variants. These findings are important as they can aid in the improvement of this method of variant quantification. Moreover, they can also help in improving other algorithms applied to the SARS-CoV-2 genome by highlighting the genomic sections containing the most differentiating information between variants.

Monitoring of SARS-CoV-2 variants is crucial to efforts in combating the COVID-19 pandemic. Lineage level abundance estimates for SARS-CoV-2 can be obtained from viral material present in domestic wastewater. The abundance predictions can be made at different levels of granularity-individual lineage level(high granularity) or variant level(low granularity). The question this paper answers is to what extent abundance predictions are more accurate at lower granularity. Here we show that when wastewater samples contain only one lineage low granularity predictions are in general more accurate than high granularity for all lineages across Alpha, Delta and Mu variants. No variant level overestimation was observed for this experiment, which was thought to be something that could have made low granularity predictions less accurate than those at high granularity. When lineages of a variant were combined into a wastewater sample, the prediction error rose because of the smaller relative abundances of the genome sequences. Overestimation due to predictions of all lineages being pooled into one lineage was observed here with the overestimated high granularity lineage being more accurate than the low granularity predictions. If samples are expected to contain a very small amount of lineages then it is better to make predictions at low granularity. On the other hand, as the relative abundances of lineages decrease in a sample due to a large number of lineages, the chances of lineage level predictions having a smaller relative prediction error rate increases- making high granularity the better choice for more accurate predictions.