Biology subjects

Moshiri, N.

Publications and source records attributed to Moshiri, N..

At least 19 recordsLinked to original sources

bioRxiv2025

Epigenetic motifs distinguishing endogenous from exogenous retroviral integrants

Retroviruses are subject to epigenetic regulation by the host genome after integrating, similar to vertebrate genes. However, their patterns of integration, and therefore their likely epigenetic regulation, differ between genera. Beta- and gammaretroviruses are two types of simple retroviruses that have a strong tendency to infect germ cells and endogenize. While ancient endogenous retroviruses are often easy to spot due to mutations rendering them non-functional, more recent integrants can maintain the capacity for full viral production, making it sometimes difficult to discern which integrants are exogenous and likely more clinically relevant. Because endogenous retroviruses generally spend a longer time integrated and subject to host epigenetic regulation as proviral DNA, we hypothesized we could show these integrants exhibit sequence differences from their exogenous counterparts, likely resulting from DNA methylation and histone modifications, and that endogenous retroviruses would generally show habituation to host promoters. Therefore, we have used statistical analyses of publicly available sequence data to demonstrate that endogenous retroviral variants exhibit decreased CpG dinucleotide and trinucleotide frequencies over time, and that they will show evidence for loss of motifs associated with active histone modifications. Close examination of these patterns provides further clues for distinguishing endogenous and exogenous retroviral variants, potentially aiding in the study of retroviruses in less well-characterized wildlife species. ImportanceExpression of vertebrate genes is regulated by chemical modifications made directly to the DNA or to the proteins associated with it, termed epigenetics. Because retroviruses integrate into DNA, they are subject to the same epigenetic modifications as regular genes. Retroviruses will tend to endogenize, meaning they will become a permanent part of a species genome when their hidden DNA is passed down to progeny during reproduction. However, sometimes it is difficult to discern whether a retroviral sequence is endogenous (permanently fixed) or exogenous (an infectious entity). We hypothesized that changes to the retroviral sequences over time after endogenization would result from epigenetic modifications, and that these changes could help distinguish an endogenous retrovirus from an exogenous one. In this paper, we show that changes to the viral sequences associated with epigenetics indeed take place after endogenization.

microbiology↗

bioRxiv2024

CompactTree: A lightweight header-only C++ library for ultra-large phylogenetics

MotivationThe 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. ResultsWe introduce CompactTree, a lightweight header-only C++ library for traversing ultra-large trees that can be easily incorporated into other tools, and we show that it is orders of magnitude faster and requires orders of magnitude less memory than existing tree packages. AvailabilityCompactTree can be accessed at: https://github.com/niemasd/CompactTree Contactniema@ucsd.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

HyperGen: Compact and Efficient Genome Sketching using Hyperdimensional Vectors

MotivationGenomic distance estimation is a critical workload since exact computation for whole-genome similarity metrics such as Average Nucleotide Identity (ANI) incurs prohibitive runtime overhead. Genome sketching is a fast and memory-efficient solution to estimate ANI similarity by distilling representative k-mers from the original sequences. In this work, we present HyperGen that improves accuracy, runtime performance, and memory efficiency for large-scale ANI estimation. Unlike existing genome sketching algorithms that convert large genome files into discrete k-mer hashes, HyperGen leverages the emerging hyperdimensional computing (HDC) to encode genomes into quasi-orthogonal vectors (Hypervector, HV) in high-dimensional space. HV is compact and can preserve more information, allowing for accurate ANI estimation while reducing required sketch sizes. In particular, the HV sketch representation in HyperGen allows efficient ANI estimation using vector multiplication, which naturally benefits from highly optimized general matrix multiply (GEMM) routines. As a result, HyperGen enables the efficient sketching and ANI estimation for massive genome collections. ResultsWe evaluate HyperGens sketching and database search performance using several genome datasets at various scales. HyperGen is able to achieve comparable or superior ANI estimation error and linearity compared to other sketch-based counterparts. The measurement results show that HyperGen is one of the fastest tools for both genome sketching and database search. Meanwhile, HyperGen produces memory-efficient sketch files while ensuring high ANI estimation accuracy. AvailabilityA Rust implementation of HyperGen is freely available under the MIT license as an open-source software project at https://github.com/wh-xu/Hyper-Gen. The scripts to reproduce the experimental results can be accessed at https://github.com/wh-xu/experiment-hyper-gen. Contactwexu@ucsd.edu

genomics↗

bioRxiv2023

Hidden evolutionary constraints dictate the retention of coronavirus accessory genes

Coronaviruses exhibit many mechanisms of genetic innovation, including the acquisition of accessory genes that originate by capture of cellular genes or through duplication of existing viral genes. Accessory genes influence viral host range and cellular tropism, but little is known about how selection acts on these variable regions of virus genomes. We used experimental evolution of mouse hepatitis virus (MHV) encoding a cellular AKAP7 phosphodiesterase and an inactive native phosphodiesterase, NS2 to model the evolutionary fate of accessory genes. After courses of serial infection, the gene encoding inactive NS2, ORF2, unexpectedly remained intact, suggesting it is under cryptic constraint uncoupled from the function of NS2. In contrast, AKAP7 was retained under strong selection but rapidly lost under relaxed selection. Experimental evolution also led to altered viral replication in a cell type-specific manner and changed the relative proportions of subgenomic viral RNA in plaque-purified viral isolates, revealing additional mechanisms of adaptation. Guided by the retention of ORF2 and similar patterns in related betacoronaviruses, we analyzed ORF8 of SARS-CoV-2, which arose via gene duplication and contains premature stop codons in several globally successful lineages. As with MHV ORF2, the coding-defective SARS-CoV-2 ORF8 gene remains largely intact, mirroring patterns observed during MHV experimental evolution, challenging assumptions on the dynamics of gene loss in virus genomes and extending these findings to viruses currently adapting to humans.

evolutionary biology↗

bioRxiv2023

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Wholesale Seafood Market, the site with the most reported wildlife vendors in the city of Wuhan, China. Here, we analyze publicly available qPCR and sequencing data from environmental samples collected in the Huanan market in early 2020. We demonstrate that the SARS-CoV-2 genetic diversity linked to this market is consistent with market emergence, and find increased SARS-CoV-2 positivity near and within a particular wildlife stall. We identify wildlife DNA in all SARS-CoV-2 positive samples from this stall. This includes species such as civets, bamboo rats, porcupines, hedgehogs, and one species, raccoon dogs, known to be capable of SARS-CoV-2 transmission. We also detect other animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them to those from other markets. This analysis provides the genetic basis for a short list of potential intermediate hosts of SARS-CoV-2 to prioritize for retrospective serological testing and viral sampling.

genomics↗

bioRxiv2023

Single-linkage molecular clustering of viral pathogens

IntroductionPublic health faces the ongoing mission of safeguarding the populations health against various infectious diseases caused by a great number of pathogens. Epidemiology is an essential discipline in this field. With the rise of more advanced technologies, new tools are emerging to enhance the capability to intervene and control an epidemic. Among these approaches, molecular clustering comes forth as a promising option. However, appropriate genetic distance thresholds for defining clusters are poorly explored in contexts outside of Human Immunodeficiency Virus-1 (HIV-1). MethodsIn this work, using the well-used pairwise Tamura-Nei 93 (TN93) distance threshold of 0.015 for HIV-1 as a point of reference for molecular cluster properties of interest, we perform molecular clustering on whole genome sequence datasets from HIV-1, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Zaire ebolavirus, and Mpox virus, to explore potential pairwise distances thresholds for these other viruses. ResultsWe found the following pairwise TN93 distance thresholds as potential candidates for use in molecular clustering: 0.00016 (3 mutations) for Ebola, 0.00014 (4 mutations) for SARS-CoV-2, and 0.0000051 (1 mutation) for Mpox. ConclusionThis study provides valuable information for epidemic control strategies, and public health efforts in managing infectious diseases caused by these viruses. The identified pairwise distance thresholds for molecular clustering can serve as a foundation for future research and intervention to combat epidemics effectively. Availability and implementationAll relevant data and results can be found in the following repository: https://github.com/Niema-Lab/ENLACE-2023

bioinformatics↗

bioRxiv2023

ViralConsensus: A fast and memory-efficient tool for calling viral consensus genome sequences directly from read alignment data

MotivationIn 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. ResultsViralConsensus 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. AvailabilityViralConsensus is freely available at https://github.com/niemasd/ViralConsensus as an open-source software project. Contactniema@ucsd.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2022

ViReaDB: A user-friendly database for compactly storing viral sequence data and rapidly computing consensus genome sequences

MotivationIn viral molecular epidemiology, reconstruction of consensus genomes from sequence data is critical for tracking mutations and variants of concern. However, storage of the raw sequence data can become prohibitively large, and computing consensus genome from sequence data can be slow and requires bioinformatics expertise. ResultsViReaDB is a user-friendly database system for compactly storing viral sequence data and rapidly computing consensus genome sequences. From a dataset of 1 million trimmed mapped SARS-CoV-2 reads, it is able to compute the base counts and the consensus genome in 16 minutes, store the reads alongside the base counts and consensus in 50 MB, and optionally store just the base counts and consensus (without the reads) in 300 KB. AvailabilityViReaDB is freely available on PyPI (https://pypi.org/project/vireadb) and on GitHub (https://github.com/niemasd/ViReaDB) as an open-source Python software project. Contactniema@ucsd.edu

bioinformatics↗

bioRxiv2020

An Evaluation of Phylogenetic Workflows in Viral Molecular Epidemiology

The use of computational techniques to analyze viral sequence data and ultimately inform public health intervention has become increasingly common in the realm of epidemiology. These methods typically attempt to make epidemiological inferences based on multiple sequence alignments and phylogenies estimated from the raw sequence data. Like all estimation techniques, multiple sequence alignment and phylogenetic inference tools are error-prone, and the impacts of such imperfections on downstream epidemiological inferences are poorly understood. To address this, we executed multiple commonly-used workflows for conducting viral phylogenetic analyses on simulated viral sequence data modeling HIV, HCV, and Ebola, and we computed multiple methods of accuracy motivated by transmission clustering techniques. For multiple sequence alignment, MAFFT consistently outperformed MUSCLE and Clustal Omega in both accuracy and runtime. For phylogenetic inference, FastTree 2, IQ-TREE, RAxML-NG, and PhyML had similar topological accuracies, but branch lengths and pairwise distances were consistently most accurate in phylogenies inferred by RAxML-NG. However, FastTree 2 was orders of magnitude faster than the other tools, and when the other tools were used to optimize branch lengths along a fixed topology provided by FastTree 2 (i.e., no tree search), the resulting phylogenies had accuracies that were indistinguishable from their original counterparts, but with a fraction of the runtime. Our results indicate that an ideal workflow for viral phylogenetic inference is to (1) use MAFFT to perform MSA, (2) use FastTree 2 under the GTR model with discrete gamma-distributed site-rate heterogeneity to quickly obtain a reasonable tree topology, and (3) use RAxML-NG to optimize branch lengths along the fixed FastTree 2 topology.

bioinformatics↗

bioRxiv2020

Timing the SARS-CoV-2 Index Case in Hubei Province

Understanding when SARS-CoV-2 emerged is critical to evaluating our current approach to monitoring novel zoonotic pathogens and understanding the failure of early containment and mitigation efforts for COVID-19. We employed a coalescent framework to combine retrospective molecular clock inference with forward epidemiological simulations to determine how long SARS-CoV-2 could have circulated prior to the time of the most recent common ancestor. Our results define the period between mid-October and mid-November 2019 as the plausible interval when the first case of SARS-CoV-2 emerged in Hubei province. By characterizing the likely dynamics of the virus before it was discovered, we show that over two-thirds of SARS-CoV-2-like zoonotic events would be self-limited, dying out without igniting a pandemic. Our findings highlight the shortcomings of zoonosis surveillance approaches for detecting highly contagious pathogens with moderate mortality rates.

evolutionary biology↗

bioRxiv2020

SEPIA: Simulation-based Evaluation of Prioritization Algorithms

BackgroundThe ability to prioritize people living with HIV by risk of future transmissions could aid public health officials in optimizing epidemiological intervention. While methods exist to perform such prioritization based on molecular data, their effectiveness and accuracy are poorly understood, and it is unclear how one can directly compare the accuracy of different methods. We introduce SEPIA (Simulation-based Evaluation of PrIoritization Algorithms), a novel simulation-based framework for determining the effectiveness of prioritization algorithms. Under several metrics of effectiveness that we propose, we utilize various properties of the simulated contact networks and transmission histories to compare existing prioritization approaches: one phylogenetic (ProACT) and one distance-based (growth of HIV-TRACE transmission clusters). ResultsUsing all metrics of effectiveness that we propose, ProACT consistently slightly outperformed the transmission cluster growth approach. However, both methods consistently performed just marginally better than random, suggesting that there is significant room for improvement in prioritization tools. ConclusionWe hope that, by providing ways to quantify the effectiveness of prioritization methods in simulation, SEPIA will aid researchers in developing novel tools for prioritizing people living with HIV by risk of future transmissions.

bioinformatics↗

bioRxiv2020

CoaTran: Coalescent tree simulation along a transmission network

MotivationThe 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. ResultsCoaTran 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. AvailabilityCoaTran is freely available at https://github.com/niemasd/CoaTran as an open-source software project. Contactniema@ucsd.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

An Analysis of SARS-CoV-2 Using ViReport

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.

bioinformatics↗

bioRxiv2020

ViralMSA: Massively scalable reference-guided multiple sequence alignment of viral genomes

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{at}ucsd.eduCompeting Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2019

HIV Care Prioritization using Phylogenetic Branch Length

AO_SCPLOWBSTRACTC_SCPLOWIn HIV epidemics, the structure of the transmission network can be dictated by just a few individuals. Public health intervention, such as ensuring people living with HIV adhere to antiretroviral therapy (ART) and are continually virally-suppressed, can help control the spread of the virus. However, such intervention requires utilizing the limited public health resource allocations. As a result, the ability to determine which individuals are most at-risk of transmitting HIV could allow public health officials to focus their limited resources on these individuals. Molecular epidemiology suggests an approach: prioritizing people living with HIV based on patterns of transmission inferred from their sampled viral sequences. In this paper, we introduce ProACT (Prioritization using AnCesTral edge lengths), a phylogenetic approach for prioritizing individuals living with HIV. ProACT uses a simple idea: ordering individuals by their terminal branch length in the phylogeny of their virus. In simulations and also on a dataset of HIV-1 subtype B pol sequences obtained in San Diego, we show that this simple strategy improves the effectiveness of prioritization compared to state-of-the-art methods that rely on monitoring the growth of transmission clusters defined based on genetic distance.

bioinformatics↗

bioRxiv2019

TreeCluster: clustering biological sequences using phylogenetic trees

Clustering homologous sequences based on their similarity is a problem that appears in many bioinformatics applications. The fact that sequences cluster is ultimately the result of their phylogenetic relationships. Despite this observation and the natural ways in which a tree can define clusters, most applications of sequence clustering do not use a phylogenetic tree and instead operate on pairwise sequence distances. Due to advances in large-scale phylogenetic inference, we argue that tree-based clustering is under-utilized. We define a family of optimization problems that, given a (not necessarily ultrametric) tree, return the minimum number of clusters such that all clusters adhere to constraints on their heterogeneity. We study three specific constraints that limit the diameter of each cluster, the sum of its branch lengths, or chains of pairwise distances. These three versions of the problem can be solved in time that increases linearly with the size of the tree, a fact that has been known by computer scientists for two of these three criteria for decades. We implement these algorithms in a tool called TreeCluster, which we test on three applications: OTU picking for microbiome data, HIV transmission clustering, and divide-and-conquer multiple sequence alignment. We show that, by using tree-based distances, TreeCluster generates more internally consistent clusters than alternatives and improves the effectiveness of downstream applications. TreeCluster is available at https://github.com/niemasd/TreeCluster.

bioinformatics↗

bioRxiv2018

TreeN93: a non-parametric distance-based method for inferring viral transmission clusters

SummaryHighly-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.\n\nAvailability and implementationTreeN93 is implemented in Python 3 and is freely available at https://github.com/niemasd/TreeN93/.\n\nContactniemamoshiri@gmail.com

bioinformatics↗

bioRxiv2018

TreeSwift: a massively scalable Python package for trees

Phylogenetic trees are essential to evolutionary biology, and numerous methods exist that attempt to extract phylogenetic information applicable to a wide range of disciplines, such as epidemiology and metagenomics. Currently, the three main Python packages for trees are Bio.Phylo, DendroPy, and the ETE Toolkit, but as dataset sizes grow, parsing and manipulating ultra-large trees becomes impractical for these tools. To address this issue, we present TreeSwift, a user-friendly and massively scalable Python package for traversing and manipulating trees that is ideal for algorithms performed on ultra-large trees.

bioinformatics↗