Biology subjects

Sashittal, P.

Publications and source records attributed to Sashittal, P..

18 recordsLinked to original sources

bioRxiv2026

Inferring Multi-Stage Pathway Progression Models from Tumor Phylogenies

Cancer progression is an evolutionary process driven by the accumulation and selection of somatic mutations, giving rise to genetically diverse subclonal populations within tumors. Understanding the dependencies among mutations and identifying recurrent evolutionary trajectories is critical for understanding cancer progression and informing therapeutic strategies. Recent advances in genomic sequencing and phylogenetic reconstruction now enable large-scale inference of tumor phylogenies, providing detailed representations of intratumor evolutionary histories across patient cohorts. However, modeling cancer progression from these data remains challenging due to extensive inter- and intratumor heterogeneity, often arising from mutations in different genes within the same pathway that confer similar fitness advantages. Existing methods to infer pathway-level progression models summarize each tumor by a single consensus genotype, ignoring intra-tumor heterogeneity, while phylogeny-based methods typically focus on individual mutations and do not model pathways. We introduce PhyloStage, an algorithm for inferring multi-stage pathway-level cancer progression models from large cohorts of tumor phylogenies. PhyloStage represents progression as a partial order over pathways, permitting independent mutations in incomparable pathways while constraining the order of mutations within the same or dependent pathways. The framework also incorporates uncertainty in tumor phylogenies, resolves mutation clusters with unknown ordering, and stratifies patients by progression stage. Applied to a cohort of 120 acute myeloid leukemia (AML) tumor phylogenies, PhyloStage infers progression models that are aligned with known AML progression. On 99 non-small cell lung cancer (NSCLC) patients, PhyloStage stratifies patients into progression stages such that later stages have larger tumor sizes, corroborating phenotypic tumor progression.

bioinformatics↗

bioRxiv2026

A Cophylogenetic Approach for Virus-Host Interaction Prediction

Advances in metagenomics have rapidly expanded viral discovery, revealing vast diversity across Earths virosphere. Yet most virus-host interactions--i.e., which viruses infect which hosts--remain unrecorded. Identifying these interactions is essential for anticipating zoonotic spillover events and advancing biomedical applications such as bacteriophage therapy. However, the sheer diversity of viruses and hosts makes comprehensive experimental mapping infeasible, motivating the need for computational approaches. Most existing prediction methods rely on supervised learning strategies that use sequence-derived features, such as codon usage bias or k-mer frequencies, and do not model the coevolutionary processes that shape virus-host interactions. This limits their ability to generalize and the evolutionary interpretability of their predictions. We introduce CoEvoLink, a framework for predicting virus-host interactions that integrates sequence-based evidence with phylogenetic signal by explicitly modeling the coevolutionary histories of viruses and hosts. CoEvoLink infers likely but unobserved interactions by minimizing the number of evolutionary events required to explain them, yielding the most parsimonious interaction under a coevolutionary model. This formulation generalizes classical maximum parsimony, typically defined on a single phylogeny, by jointly optimizing parsimony across both virus and host phylogenies. Sequence-based information is incorporated by assigning a cost to each potential interaction that reflects its likelihood based on genomic features. By drawing a connection between computing parsimony on interaction matrices and maximum parsimony on phylogenetic networks, we derive a polynomial-time algorithm that balances parsimony with sequence-derived prediction cost. We demonstrate the effectiveness of CoEvoLink on simulated data under diverse coevolutionary models. Applying CoEvoLink, we identified putative bat hosts of betacoronaviruses that have not yet been cataloged in the VIRION database. On a benchmark derived from metagenomic sequencing data, we demonstrate that CoEvoLink improves the performance of existing phage-host prediction tools using cophylogenetic information. Code availabilityhttps://github.com/sashittal-group/CoEvoLink Note: This paper is accepted at RECOMB 2026 (30th Annual International Conference on Research in Computational Molecular Biology).

evolutionary biology↗

bioRxiv2026

POTTR: Identifying Recurrent Trajectories in Evolutionary and Developmental Processes using Posets

Multiple biological processes, including cancer evolution and organismal development, are described as a sequence of events with a temporal ordering. While cancer evolves independently in each patient, DNA sequencing studies have shown that in some cancers different patients share specific orders of mutations and these correlate with distinct morphology, drug response, and treatment outcomes. Several methods have been developed to identify such recurrent trajectories of genetic events from phylogenetic trees, but this is complicated by high intra- and inter-tumor heterogeneity as well as uncertainty in the inferred tumor phylogenies including the ambiguous orders between some mutations. We formalize the problem of finding recurrent mutation trajectories using a novel framework of incomplete partially ordered sets (posets), which generalize representations used in previous works and explicitly account for the uncertainty in tumor phylogenies. We define the problem of identifying the largest recurrent trajectories shared in at least k input phylogenies as the maximum k-common induced incomplete subposet (MkCIIS) problem, which we show is NP-hard. We present a combinatorial algorithm, POsets for Temporal Trajectory Resolution (POTTR), to solve the MkCIIS problem using a conflict graph that models recurrent trajectories as independent sets. Thereby we identify maximum recurrent trajectories while resolving multiple sources of uncertainty, like mutation clusters, in the phylogenetic data. We apply POTTR to TRACERx non-small cell lung cancer bulk sequencing and acute myeloid leukemia single-cell sequencing data and through resolution of mutation clusters discover previously unreported trajectories of high statistical significance. On lineage tracing data of an in vitro embryoid model, POTTR identifies conserved differentiation routes across biological replicates and how these routes change in response to chemical perturbations.

bioinformatics↗

bioRxiv2026

Exploring the Space of Tumor Phylogenies Consistent with Single-Cell Whole-Genome Sequencing Data

Tumors comprise subpopulations of cells that harbor distinct collections of somatic mutations, ranging from single-nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). Single-cell whole-genome sequencing (scWGS) enables direct measurement of these mutations; however, inferring tumor phylogenies from scWGS data remains challenging due to ultra-low coverage ([~]0.05 x). There may be multiple ways of imputing missing information in the data leading to distinct tumor phylogenies that are equally well supported by the data. Existing methods produce a single phylogeny and overlook this uncertainty in reconstructing evolutionary histories from sparse scWGS data. We present SCOPE, a novel algorithmic framework that characterizes the space of tumor phylogenies consistent with scWGS data under a copy-number constrained version of the perfect phylogeny model. Our approach relies on estimating the cell fraction of each mutation, i.e. the proportion of cells within each copy-number cluster that carry the mutation. We derive the necessary and sufficient conditions these fractions must satisfy to admit a copy-number constrained perfect phylogeny. This yields a complete combinatorial description of all tumor phylogenies that are supported by the data under our model. We prove that identifying the largest subset of mutations with cell fractions satisfy model constraints using noisy measurements of cell fractions is NP-hard. On simulated data, SCOPE outperforms existing methods in accuracy with faster runtime in particular on the larger simulations. On scWGS data from a patient-derived ovarian cancer cell line, SCOPE infers a more resolved phylogeny with stronger statistical support compared to existing methods. Using SCOPE to analyze a larger dataset of 4 triple negative breast cancer (TNBC) and 8 high-grade serous ovarian cancer (HGSOC) samples, we show that several samples admit multiple phylogenies. We further find that number of admissible phylogenies increases with lower sequencing coverage and is negatively correlated with the number of copy-number clusters and number of distinct loss of heterozygosity (LOH) events in the clusters, highlighting how data quality and evolutionary constraints jointly shape uncertainty in tumor phylogeny reconstruction. By providing a principled framework for exploring and quantifying phylogenetic uncertainty, SCOPE establishes a new foundation for robust inference of tumor evolution from scWGS data. Code availabilitySoftware is available at https://github.com/sashittal-group/SCOPE

bioinformatics↗

bioRxiv2025

Universal genomic constraints in the evolvability of thermal physiology

Thermal physiological traits such as body temperature often show surprisingly slow evolutionary rates over macroevolutionary time, despite apparent lability at microevolutionary time scales. While long-term stabilizing selection may slow rates of thermal evolution, we propose an alternative hypothesis from a bottom-up, population genomic perspective: the nature of body temperature (Tb) as an organism-level trait that must accommodate diverse protein thermal performances leads to evolutionary constraints at the organismal level. We support this hypothesis using a simulation framework in which we modeled and compared the rates of evolution for Tb alongside one or more proteins. Protein performances and organismal Tb were modeled as evolving, QTL-encoded traits, and organismal fitness was determined based on Tb given the performance curves of each protein. As predicted, a greater number of proteins led to drastic decrease in the rate of Tb evolution. When a shift in environmental temperature was simulated, Tb evolved with an initial rapid pulse toward the new optimum, followed by a phase of gradual evolution as the cumulative fitness costs of mismatching Tb and protein optima constrained thermal adaptation. That is, lability and stasis are predictable features of body temperature evolution: rapid, yet bounded microevolutionary bursts followed by long phases of sluggish evolution are both expected outcomes of directional selection operating on hierarchically structured traits like Tb. We suggest that protein thermal coordination might contribute to intrinsic, universal macroevolutionary patterns of stasis in organismal physiology across endotherms and ectotherms.

evolutionary biology↗

bioRxiv2025

Deriving longitudinal tumor phylogenies from single-cell sequencing data

Tumors evolve over time and in response to treatment, leading to changes in the proportions of clones within the tumor. Single-cell sequencing of tumor samples from multiple timepoints enables the reconstruction of clonal evolution and tracking of temporal changes in tumor composition. However, the high rates of missing data in single-cell sequencing complicate evolutionary analyses, and can lead to implausible conclusions, such as the reappearance of extinct clones. We introduce PO_SCPLOWHYLLOCHRONC_SCPLOW, an algorithm that builds a longitudinal phylogeny from cancer cells sequenced from multiple timepoints and evaluates whether the constraints on clonal proportions imposed by longitudinal sampling are well supported by the data. PO_SCPLOWHYLLOCHRONC_SCPLOW relies on a novel mathematical formulation of a longitudinal perfect phylogeny, an extension of the perfect phylogeny model that is widely used in cancer evolution. We show that PO_SCPLOWHYLLOCHRONC_SCPLOW outperforms existing phylogeny inference methods on simulated single-cell sequencing data. Applied to longitudinal single-cell DNA sequencing data from an acute myeloid leukemia (AML) patient, PO_SCPLOWHYLLOCHRONC_SCPLOW constructs a longitudinal phylogeny containing rare cancer clones that persist through multiple cycles of targeted drug treatment, a crucial finding missed by existing phylogeny inference methods. The PO_SCPLOWHYLLOCHRONC_SCPLOW statistical test supports the presence of these rare clones.

genomics↗

bioRxiv2024

Inferring cell differentiation maps from lineage tracing data

During development, mulitpotent cells differentiate through a hierarchy of increasingly restricted progenitor cell types until they realize specialized cell types. A cell differentiation map describes this hierarchy, and inferring these maps is an active area of research spanning traditional single marker lineage studies to data-driven trajectory inference methods on single-cell RNA-seq data. Recent high-throughput lineage tracing technologies profile lineages and cell types at scale, but current methods to infer cell differentiation maps from these data rely on simple models with restrictive assumptions about the developmental process. We introduce a mathematical framework for cell differentiation maps based on the concept of potency, and develop an algorithm, Carta, that infers an optimal cell differentiation map from single-cell lineage tracing data. The key insight in Carta is to balance the trade-off between the complexity of the cell differentiation map and the number of unobserved cell type transitions on the lineage tree. We show that Carta more accurately infers cell differentiation maps on both simulated and real data compared to existing methods. In models of mammalian trunk development and mouse hematopoiesis, Carta identifies important features of development that are not revealed by other methods including convergent differentiation of specialized cell types, progenitor differentiation dynamics, and the refinement of routes of differentiation via new intermediate progenitors. Code availabilityCarta software is available at https://github.com/raphael-group/CARTA

bioinformatics↗

bioRxiv2024

Genomic evolution of pancreatic cancer at single-cell resolution

We adapted a previously developed targeted single-nucleus DNA sequencing (snDNA-seq) method and constructed a new suite of computational analysis tools to study 137,491 single-nucleus DNA libraries from 24 pancreatic cancers collected under a variety of clinical scenarios including early and late diagnoses, different metastatic sites and before- and after-treatment. We refined the mutational landscape of pancreatic cancer by capturing events missed by bulk sequencing, and validated the evolution pattern of early fixation of driver single-nucleotide variants (SNVs) followed by generation of intratumoral heterogeneity for copy number variations (CNVs). Intertumoral convergent evolution was common, including subclonal inactivation of TGF-{beta} pathway by mutating various components of it; intratumoral convergence was rarely observed, likely due to strong selective force in pancreatic cancer development. Continuous evolution was frequently seen manifesting as CNVs. In the context of non-targeted treatments, no particular pattern was found across metastases or through treatment. In six pancreatic cancers with germline BRCA2 mutation, we discovered varied timing of biallelic inactivation of BRCA2, which sculpted different evolutionary trajectories and could presumably contribute to differential response to treatment. As the first large-scale application of targeted snDNA-seq on pancreatic cancer, this work provides a sample processing and computational analysis pipeline that warrants further clinical utility.

cancer biology↗

bioRxiv2024

Mechanism of neurodegeneration mediated by clonal inflammatory microglia

Langerhans cell Histiocytosis (LCH) and Erdheim-Chester disease (ECD) are clonal myeloid disorders, associated with MAP-Kinase activating mutations and an increased risk of neurodegeneration. Surprisingly, we found pervasive PU.1+ microglia mutant clones across the brain of LCH and ECD patients with and without neurological symptoms, associated with microgliosis, reactive astrocytosis, and neuronal loss. The disease predominated in the grey nuclei of the rhombencephalon, a topography attributable to a local proliferative advantage of mutant microglia. Presence of clinical symptoms was associated with a longer evolution of the disease and a larger size of PU.1+ clones (p= 0.0003). Genetic lineage tracing of PU.1+ clones suggest a resident macrophage lineage or a bone marrow precursor origin depending on patients. Finally, a CSF1R-inhibitor depleted mutant microglia and limited neuronal loss in mice suggesting an alternative to MAPK inhibitors. These studies characterize a progressive neurodegenerative disease, caused by clonal proliferation of inflammatory microglia (CPIM), with a decade(s)-long preclinical stage of incipient disease that represent a therapeutic window for prevention of neuronal death.

immunology↗

bioRxiv2023

A zero-agnostic model for copy number evolution in cancer

MotivationNew low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. A widely used model to infer such copy number phylogenies is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. ResultsWe introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data. AvailabilityLazac is implemented in C++17 and is freely available at: github.com/raphaelgroup/lazac-copy-number.

bioinformatics↗

bioRxiv2023

ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model

Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent targeted scDNA-seq technologies, such as Mission Bio Tapestri, measure SNVs with high fidelity in individual cells, but yield much less reliable measurements of CNAs. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers and partial information about CNAs in the form of clustering of cells with similar copy-number profiles. This copy-number clustering constrains where loss of SNVs can occur in the phylogeny. We develop ConDoR (Constrained Dollo Reconstruction), an algorithm to infer tumor phylogenies from targeted scDNA-seq data using the constrained k-Dollo model. We show that ConDoR outperforms existing methods on simulated data. We use ConDoR to analyze a new multi-region targeted scDNA-seq dataset of 2153 cells from a pancreatic ductal adenocarcinoma (PDAC) tumor and produce a more plausible phylogeny compared to existing methods that conforms to histological results for the tumor from a previous study. We also analyze a metastatic colorectal cancer dataset, deriving a more parsimonious phylogeny than previously published analyses and with a simpler monoclonal origin of metastasis compared to the original study. Code availabilitySoftware is available at https://github.com/raphael-group/constrained-Dollo

bioinformatics↗

bioRxiv2022

Startle: a star homoplasy approach for CRISPR-Cas9 lineage tracing

CRISPR-Cas9 based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. While standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9 induced mutations. Here, we introduce the star homoplasy model, a novel evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the non-modifiability property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies by identifying a relationship between the star homoplasy model and the binary perfect phylogeny model. We use this characterization to develop an algorithm, Startle (Star tree lineage estimator), that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated CRISPR-based lineage tracing data compared to existing methods; particularly on data with high amounts of dropout and homoplasy. Startle also infers more parsimonious phylogenies with fewer metastatic migrations on a lineage tracing dataset from mouse metastatic lung adenocarcinoma. Code availabilitySoftware is available at https://github.com/raphael-group/startle

bioinformatics↗

bioRxiv2022

Application of high-throughput, high-depth, targeted single-nucleus DNA sequencing in pancreatic cancer

Despite insights gained by bulk DNA sequencing of cancer it remains challenging to resolve the admixture of normal and tumor cells, and/or of distinct tumor subclones; high throughput single-cell DNA sequencing circumvents these and brings cancer genomic studies to higher resolution. However, its application has been limited to liquid tumors or a small batch of solid tumors, mainly because of the lack of a scalable workflow to process solid tumor samples. Here we optimized a highly automated nuclei extraction workflow that achieved fast and reliable targeted single-nucleus DNA library preparation of 38 samples from 16 pancreatic adenocarcinoma (PDAC) patients, with an average library yield per sample of 2867 single nuclei. We demonstrate that this workflow not only performs well using low cellularity or low tumor purity samples but reveals novel genomic evolution patterns of PDAC as well.

cancer biology↗

bioRxiv2021

CORSID enables de novo identification of transcription regulatory sequences and genes in coronaviruses

Genes in coronaviruses are preceded by transcription regulatory sequences (TRSs), which play a critical role in gene expression mediated by the viral RNA-dependent RNA-polymerase via the process of discontinuous transcription. In addition to being crucial for our understanding of the regulation and expression of coronavirus genes, we demonstrate for the first time how TRSs can be leveraged to identify gene locations in the coronavirus genome. To that end, we formulate the TRS AND GO_SCPLOWENEC_SCPLOW IO_SCPLOWDENTIFICATIONC_SCPLOW (TRS-GO_SCPLOWENEC_SCPLOW-ID) problem of simultaneously identifying TRS sites and gene locations in unannotated coronavirus genomes. We introduce CORSID (CORe Sequence IDentifier), a computational tool to solve this problem. We also present CORSID-A, which solves a constrained version of the TRS-GO_SCPLOWENEC_SCPLOW-ID problem, the TRS IO_SCPLOWDENTIFICATIONC_SCPLOW (TRS-ID) problem, identifying TRS sites in a coronavirus genome with specified gene annotations. We show that CORSID-A outperforms existing motif-based methods in identifying TRS sites in coronaviruses and that CORSID outperforms state-of-the-art gene finding methods in finding genes in coronavirus genomes. We demonstrate that CORSID enables de novo identification of TRS sites and genes in previously unannotated coronaviruses. CORSID is the first method to perform accurate and simultaneous identification of TRS sites and genes in coronavirus genomes without the use of any prior information.

bioinformatics↗

bioRxiv2021

Jumper Enables Discontinuous Transcript Assembly in Coronaviruses

Genes in SARS-CoV-2 and, more generally, in viruses in the order of Nidovirales are expressed by a process of discontinuous transcription mediated by the viral RNA-dependent RNA polymerase. This process is distinct from alternative splicing in eukaryotes, rendering current transcript assembly methods unsuitable to Nidovirales sequencing samples. Here, we introduce the DO_SCPLOWISCONTINUOUSC_SCPLOW TO_SCPLOWRANSCRIPTC_SCPLOW AO_SCPLOWSSEMBLYC_SCPLOW problem of finding transcripts [Formula] and their abundances c given an alignment [Formula] under a maximum likelihood model that accounts for varying transcript lengths. Underpinning our approach is the concept of a segment graph, a directed acyclic graph that, distinct from the splice graph used to characterize alternative splicing, has a unique Hamiltonian path. We provide a compact characterization of solutions as subsets of non-overlapping edges in this graph, enabling the formulation of an efficient mixed integer linear program. We show using simulations that our method, JO_SCPLOWUMPERC_SCPLOW, drastically outperforms existing methods for classical transcript assembly. On short-read data of SARS-CoV-1 and SARS-CoV-2 samples, we find that JO_SCPLOWUMPERC_SCPLOW not only identifies canonical transcripts that are part of the reference transcriptome, but also predicts expression of non-canonical transcripts that are well supported by direct evidence from long-read data, presence in multiple, independent samples or a conserved core sequence. JO_SCPLOWUMPERC_SCPLOW enables detailed analyses of Nidovirales transcriptomes. Code availabilitySoftware is available at https://github.com/elkebir-group/Jumper

bioinformatics↗

bioRxiv2020

Characterization of SARS-CoV-2 viral diversity within and across hosts

In light of the current COVID-19 pandemic, there is an urgent need to accurately infer the evolutionary and transmission history of the virus to inform real-time outbreak management, public health policies and mitigation strategies. Current phylogenetic and phylodynamic approaches typically use consensus sequences, essentially assuming the presence of a single viral strain per host. Here, we analyze 621 bulk RNA sequencing samples and 7,540 consensus sequences from COVID-19 patients, and identify multiple strains of the virus, SARS-CoV-2, in four major clades that are prevalent within and across hosts. In particular, we find evidence for (i) within-host diversity across phylogenetic clades, (ii) putative cases of recombination, multi-strain and/or superinfections as well as (iii) distinct strain profiles across geographical locations and time. Our findings and algorithms will facilitate more detailed evolutionary analyses and contact tracing that specifically account for within-host viral diversity in the ongoing COVID-19 pandemic as well as future pandemics.

bioinformatics↗

bioRxiv2020

TiTUS: Sampling and Summarizing Transmission Trees with Muti-strain Infections

MotivationThe combination of genomic and epidemiological data hold the potential to enable accurate pathogen transmission history inference. However, the inference of outbreak transmission histories remains challenging due to various factors such as within-host pathogen diversity and multi-strain infections. Current computational methods ignore within-host diversity and/or multi-strain infections, often failing to accurately infer the transmission history. Thus, there is a need for efficient computational methods for transmission tree inference that accommodate the complexities of real data. ResultsWe formulate the Direct Transmission Inference (DTI) problem for inferring transmission trees that support multi-strain infections given a timed phylogeny and additional epidemiological data. We establish hardness for the decision and counting version of the DTI problem. We introduce TiTUS, a method that uses SATISFIABILITY to almost uniformly sample from the space of transmission trees. We introduce criteria that prioritizes parsimonious transmission trees that we subsequently summarize using a novel consensus tree approach. We demonstrate TiTUSs ability to accurately reconstruct transmission trees on simulated data as well as a documented HIV transmission chain. Availabilityhttps://github.com/elkebir-group/TiTUS Contactmelkebir@illinois.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

SharpTNI: Counting and Sampling Parsimonious Transmission Networks under a Weak Bottleneck

BackgroundTechnological advances in genomic sequencing are facilitating the reconstruction of transmission histories during outbreaks in the fight against infectious diseases. However, accurate disease transmission inference using this data is hindered by a number of challenges due to within-host pathogen diversity and weak transmission bottlenecks, where multiple genetically-distinct pathogenic strains co-transmit. ResultsWe formulate a combinatorial optimization problem for transmission network inference under a weak bottleneck from a given timed phylogeny and establish hardness results. We present SharpTNI, a method to approximately count and almost uniformly sample from the solution space. Using simulated data, we show that SharpTNI accurately quantifies and uniformly samples from the solution space of parsimonious transmission networks, scaling to large datasets. We demonstrate that SharpTNI identifies co-transmissions during the 2014 Ebola outbreak that are corroborated by epidemiological information collected by previous studies. ConclusionsAccounting for weak transmission bottlenecks is crucial for accurate inference of transmission histories during outbreaks. SharpTNI is a parsimony-based method to reconstruct transmission networks for diseases with long incubation times and large inocula given timed phylogenies. The model and theoretical work of this paper pave the way for novel maximum likelihood methods to co-estimate timed phylogenies and transmission networks under a weak bottleneck.

bioinformatics↗