Epidemic simulations require the ability to sample contact networks from various random graph models. Existing methods can simulate city-scale or even country-scale contact networks, but they are unable to feasibly simulate global-scale contact networks due to high memory consumption. NiemaGraphGen (NGG) is a memory-efficient graph generation tool that enables the simulation of global-scale contact networks. NGG avoids storing the entire graph in memory and is instead intended to be used in a data streaming pipeline, resulting in memory consumption that is orders of magnitude smaller than existing tools. NGG provides a massively-scalable solution for simulating social contact networks, enabling global-scale epidemic simulation studies.

The study of viral and bacterial species requires the ability to load and traverse ultra-large phylogenies with tens of millions of tips, but existing tree libraries struggle to scale to these sizes. We introduce CompactTree, a lightweight header-only C++ library with a user-friendly Python wrapper for traversing ultra-large trees that can be easily incorporated into other tools. We show that CompactTree is orders of magnitude faster and requires orders of magnitude less memory than existing tree packages. CompactTree is freely accessible as an open source project: https://github.com/niemasd/CompactTree.

Motivation

The ability to simulate coalescent viral phylogenies constrained by a given transmission network can enable the benchmarking of computational tools used in molecular epidemiology as well as the ability to gain insights into unobservable aspects of the virology of a novel pathogen. However, such simulation experiments require generating a large number of technical simulation replicates, and existing tools for coalescent simulations along a transmission network are too slow to conduct such experiments at the scale of the global population.

Results

CoaTran is a massively scalable tool that simulates a coalescent viral phylogeny constrained by a user-provided transmission network. CoaTran is written in highly-optimized C++ code and can generate global population scale phylogenetic coalescent simulations in seconds to minutes.

Availability

CoaTran is freely available at https://github.com/niemasd/CoaTran as an open-source software project.

Contact

niema@ucsd.edu

Supplementary information

Supplementary data are available online.

Motivation

In molecular epidemiology, the identification of clusters of transmissions typically requires the alignment of viral genomic sequence data. However, existing methods of multiple sequence alignment scale poorly with respect to the number of sequences.

Results

ViralMSA is a user-friendly reference-guided multiple sequence alignment tool that leverages the algorithmic techniques of read mappers to enable the multiple sequence alignment of ultra-large viral genome datasets. It scales linearly with the number of sequences, and it is able to align tens of thousands of full viral genomes in seconds.

Availability

ViralMSA is freely available at https://github.com/niemasd/ViralMSA as an open-source software project.

Contact

a1moshir@ucsd.edu

Summary

Highly-used methods for identifying transmission clusters of rapidly-evolving pathogens from molecular data require a user-determined distance threshold. The choice of threshold is often motivated by epidemiological information known a priori, which may be unfeasible for epidemics without rich epidemiological information. TreeN93 is a fully non-parametric distance-based method for transmission cluster identification that scales polynomially.

Availability and implementation

TreeN93 is implemented in Python 3 and is freely available at https://github.com/niemasd/TreeN93/ .

Contact

niemamoshiri@gmail.com

The ability to sample random networks that can accurately represent real social contact networks is essential to the study of viral epidemics. The Barab asi-Albert model and its extensions attempt to capture reality by generating networks with power-law degree distributions, but properties of the resulting distributions (e.g. minimum, average, and maximum degree) are often unrealistic of the social contacts the models attempt to capture. I propose a novel extension of the Barab asi-Albert model, which I call the "dual-Barab asi-Albert" (DBA) model, that attempts to better capture these properties of real networks of social contact.

Background

The ability to infer transmission clusters from molecular data is critical to designing and evaluating viral control strategies. Viral sequencing datasets are growing rapidly, but standard methods of transmission cluster inference do not scale well beyond thousands of sequences.

Results

I present TreeCluster, a cross-platform tool that performs transmission cluster inference on a given phylogenetic tree orders of magnitude faster than existing inference methods and supports multiple clustering optimization functions.

Conclusions

TreeCluster is a freely-available cross-platform open source Python 3 tool for inferring transmission clusters from phylogenetic trees. Code, usage information, and in-depth descriptions of the implemented clustering modes are available publicly at the following repository: https://github.com/niemasd/TreeCluster

The ability to sample models of tree evolution is essential in the analysis and interpretation of phylogenetic trees. The dual-birth model is an extension of the traditional birth-only model and allows for sampling trees of varying degrees of balance. However, for a tree with n leaves, the tree sampling algorithm proposed in the original paper is 𝒪( n log n ). I propose an algorithm to sample trees under the dual-birth model in 𝒪( n ), and I provide a fast C++ implementation of the proposed algorithm.

The ongoing outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in millions of cases and hundreds of thousands of deaths. Given the current lack of treatments or vaccines available, it may be useful to trace the evolu-tion and spread of the virus to better develop methods of preventative intervention. In this study, we analyzed over 4,000 full genome sequences of human SARS-CoV-2 using novel tool ViReport [13], an automated workflow for performing phylogenetic analyses on viral sequences and generating comprehensive molecular epidemiologi-cal reports. The complete ViReport output can be found at https://github.com/mirandajsong/ViReport-SARS-CoV-2 .

Motivation

In viral molecular epidemiology, reconstruction of consensus genomes from sequence data is critical for tracking mutations and variants of concern. However, as the number of samples that are sequenced grows rapidly, compute resources needed to reconstruct consensus genomes can become prohibitively large.

Results

ViralConsensus is a fast and memory-efficient tool for calling viral consensus genome sequences directly from read alignment data. ViralConsensus is orders of magnitude faster and more memory-efficient than existing methods. Further, unlike existing methods, ViralConsensus can pipe data directly from a read mapper via standard input and performs viral consensus calling on-the-fly, making it an ideal tool for viral sequencing pipelines.

Availability and implementation

ViralConsensus is freely available at https://github.com/niemasd/ViralConsensus as an open-source software project.