TNet: Phylogeny-Based Inference of Disease Transmission Networks Using Within-Host Strain Diversity

Dhar, Saurav; Zhang, Chengchen; Măndoiu, Ion; Bansal, Mukul S.

doi:10.1007/978-3-030-57821-3_18

Cited by 11 publications

(7 citation statements)

References 22 publications

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“…Fitch or Sankoff algorithms [63, 28] and their modifications. However, straightforward maximum parsimony approach alone often leads to epidemiologically unrealistic results [76]; furthermore, there are usually many most parsimonious solutions [20, 65]. Within maximum likelihood framework, ancestral labels can be inferred using so-called “migration model” [62].…”

Section: Resultsmentioning

confidence: 99%

SOPHIE: viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Skums

Mohebbi

Tsyvina

et al. 2022

Preprint

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Genomic epidemiology is now widely used for viral outbreak investigations. Still, this methodology faces many challenges. First, few methods account for intra-host viral diversity. Second, maximum parsimony principle continues to be employed, even though maximum likelihood or Bayesian models are usually more consistent. Third, many methods utilize case-specific data, such as sampling times or infection exposure intervals. This impedes study of persistent infections in vulnerable groups, where such information has a limited use. Finally, most methods implicitly assume that transmission events are independent, while common source outbreaks violate this assumption. We propose a maximum likelihood framework SOPHIE (SOcial and PHilogenetic Investigation of Epidemics) based on integration of phylogenetic and random graph models. It infers transmission networks from viral phylogenies and expected properties of inter-host social networks modelled as random graphs with given expected degree distributions. SOPHIE is scalable, accounts for intra-host diversity and accurately infers transmissions without case-specific epidemiological data. SOPHIE code is freely available at https://github.com/compbel/SOPHIE/ .

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Section: Resultsmentioning

confidence: 99%

SOPHIE: viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Skums

Mohebbi

Tsyvina

et al. 2022

Preprint

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“…In bioinformatics, sequence homology (shared ancestry) detection between a pair of proteins and prediction of disease transmission using the Phylogeny-based method [11] are essential tasks. The use of k-mers counts for phylogenetic applications was first explored in [12], which proposed constructing accurate phylogenetic trees from several coding and non-coding nDNA sequences.…”

Section: Related Workmentioning

confidence: 99%

Efficient Approximate Kernel Based Spike Sequence Classification

Ali

Sahoo

Khan

et al. 2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Several methods have been proposed to compute the similarity between sequences, such as the exact approach that counts the number of matches between k-mers (sub-sequences of length k) and an approximate approach that estimates pairwise similarity scores. Although exact methods yield better classification performance, they pose high computational costs, limiting their applicability to a small number of sequences. The approximate algorithms are proven to be more scalable and perform comparably to (sometimes better than) the exact methods -they are designed in a "general" way to deal with different types of sequences (e.g., music, protein, etc.). Although general applicability is a desired property of an algorithm, it is not the case in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic, there is a need for an approach that can deal specifically with the coronavirus. To this end, we propose a series of ways to improve the performance of the approximate kernel (using minimizers and information gain) in order to enhance its predictive performance pm coronavirus sequences. More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e.g., Alpha, Beta, Gamma). We report results using different classification and clustering algorithms and evaluate their performance using multiple evaluation metrics. Using two datasets, we show that our proposed method helps improve the kernel's performance compared to the baseline and state-of-the-art approaches in the healthcare domain.

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“…Phylogeny based inference of disease transmission [14] and sequence homology (shared ancestry) detection between a pair of proteins are important tasks in bioinformatics and biotechnology. Sequence classification is a widely studied problem in both of these domains [22].…”

Section: Related Workmentioning

confidence: 99%

A k-mer Based Approach for SARS-CoV-2 Variant Identification

Ali¹,

Sahoo²,

Ullah³

et al. 2021

Preprint

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With the rapid spread of the novel coronavirus (COVID-19) across the globe and its continuous mutation, it is of pivotal importance to design a system to identify different known (and unknown) variants of SARS-CoV-2. Identifying particular variants helps to understand and model their spread patterns, design effective mitigation strategies, and prevent future outbreaks. It also plays a crucial role in studying the efficacy of known vaccines against each variant and modeling the likelihood of breakthrough infections. It is well known that the spike protein contains most of the information/variation pertaining to coronavirus variants. In this paper, we use spike sequences to classify different variants of the coronavirus in humans. We show that preserving the order of the amino acids helps the underlying classifiers to achieve better performance. We also show that we can train our model to outperform the baseline algorithms using only a small number of training samples (1% of the data). Finally, we show the importance of the different amino acids which play a key role in identifying variants and how they coincide with those reported by the USA's Centers for Disease Control and Prevention (CDC).

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TNet: Phylogeny-Based Inference of Disease Transmission Networks Using Within-Host Strain Diversity

Cited by 11 publications

References 22 publications

SOPHIE: viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

SOPHIE: viral outbreak investigation and transmission history reconstruction in a joint phylogenetic and network theory framework

Efficient Approximate Kernel Based Spike Sequence Classification

A k-mer Based Approach for SARS-CoV-2 Variant Identification

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