Biology subjects

El-Kebir, M.

Publications and source records attributed to El-Kebir, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Summarizing RNA Structural Ensembles via Maximum Agreement Secondary Structures

Summarizing a collection P of related RNA secondary structures is a key challenge in applications like evolutionary analysis, alternative fold studies and mRNA vaccine design. This requires both clustering the input structures into similar groups and identifying the core structural motifs on which they agree or differ. Existing methods fail by focusing on only one of these goals: clustering methods do not output shared motifs, while consensus methods overlook the structural diversity present in the collection. Here, we introduce the MO_SCPLOWAXIMUMC_SCPLOW AO_SCPLOWGREEMENTC_SCPLOW SO_SCPLOWECONDARYC_SCPLOW SO_SCPLOWTRUCTURESC_SCPLOW (MASS) problem, which seeks the largest set F of structural features present in P that partition the input structures into a user-specified number{tau} of distinct clusters. We prove that MASS is NP-hard and also establish its equivalence to a constrained binary matrix projection problem. We present an exact integer linear program, an exact combinatorial algorithm, and a scalable beam-search heuristic. Using simulations we demonstrate the performance of these exact algorithms and heuristics relative to baseline methods that focus on either clustering or identifying a single consensus tree. On real data, we demonstrate that MASS identifies conserved scaffolds in conformational datasets, reveals conserved structural motifs in different species within RNA families, and recovers shared structural features among synonymous transcripts encoding the same protein. MASS provides a general and interpretable framework for summarizing RNA structural organization.

molecular biology↗

bioRxiv2026

Deconvolving Phylogenetic Distance Mixtures

Mixtures of multiple constituent organisms are sequenced in several widely used applications, including metagenomics and metabarcoding. Characterizing the elements of the sequence mixture and their abundance with respect to a reference set of known organisms has been the subject of intense research across several domains, including microbiome analyses, and methods must overcome two key challenges. First, the mixture constituents are related to each other through an evolutionary history, and hence, should not be considered independent entities. Second, sequence data is noisy, with each short read providing a limited signal. While existing approaches attempt to address these challenges, addressing both challenges simultaneously has proved challenging. For evolutionary dependencies, methods either define hierarchical clusters (e.g., taxonomies or operational taxonomic/genomic units) or use phylogenetic trees. For the second challenge, they either assemble reads into contigs, use statistical priors to summarize read placements, or attempt to analyze all reads jointly using k-mers. Despite this rich literature, a natural approach to simultaneously address both challenges has been underexplored: compute a distance from the mixture to all references, deconvolve those distances, and place the sample on multiple branches of a reference phylogeny with associated abundances. This multi-placement approach is a natural extension of the single-read phylogenetic placement used in practice. We argue that by placing the entire sample on multiple branches instead of placing reads individually, we can obtain a less noisy profile of the mixture. We formalize this approach as the phylogenetic distance deconvolution (PDD) problem, show some limits on the identifiability of PDDs, propose a slow exact algorithm, and an efficient heuristic greedy algorithm with local refinements. Benchmarking shows that these heuristics are effective and that our implementation of the PDD approach (called DecoDiPhy) can accurately deconvolve phylogenetic mixture distances while scaling quadratically. Applied to metagenomics, DecoDiPhy consolidates reads mapped to a large number of branches on a reference tree to a much smaller number of placements. The consolidated placements improve the accuracy of downstream tasks, such as sample differentiation and detection of differentially abundant taxa.

evolutionary biology↗

bioRxiv2025

Improving Multi-Trait Genomic Prediction Efficiency Through The Incorporation Of Synthetic Traits Selected Based on Co-heritability

Genomic prediction (GP) is an essential tool in the field of plant breeding to accelerate the cultivar development pipeline by predicting the performance of unphenotyped lines. The precision of prediction is constrained by the heritability of the target trait when applying a single-trait genomic prediction model. To overcome this limitation, a multi-trait genomic prediction model leveraging high-heritability secondary traits co-heritable with the target trait can boost predictive ability for the target trait. However, this is practically challenging because it requires additional phenotyping effort and prior knowledge of trait co-heritability. This study aimed to assess the efficiency of multi-trait genomic prediction models powered by secondary traits derived from high-throughput phenotyping data when predicting important leaf functional target traits, i.e., nitrogen (N) content and specific leaf area (SLA) in diverse sorghum accessions. Since these traits can be predicted from hyperspectral reflectance data, there is significant potential for other wavelengths within the existing dataset to meet the criteria needed to improve prediction accuracy using multi-trait approaches. Therefore, experiments were performed on traditional direct measures of leaf N content and SLA, plus partial least squares regression predictions of them (Leaf N-PLSR, SLA-PLSR), i.e., four target traits in total. Three secondary, "synthetic traits" (S1, S2, S3), each a ratio of two wavelengths within the hyperspectral data, were identified based on high co-heritability with a given target trait. Single-trait GBLUP (Genomic Best Linear Unbiased Predictor) was fitted as a baseline model, followed by three multi-trait GBLUP models using synthetic traits and target traits together. Model performance was assessed using k-fold (k=5) cross-validation (CV), which consisted of single-trait, CV1, and CV2 schemes. The synthetic traits high genetic correlation and heritability met the requirements for their use as secondary traits. There was a significant increase in accuracy when synthetic traits were used in the multi-trait genomic prediction model compared to a single trait alone for all four target traits. It improved prediction accuracy while using secondary traits derived from hyperspectral high-throughput phenotyping data in the multi-trait genomic prediction model, suggesting that this approach could be broadly applied in a post-hoc fashion to many datasets without any additional phenotyping effort. Our analysis highlights a practical approach to improve multi-trait genomic prediction model performance using synthetic traits with no intrinsic biological meaning selected through co-heritability estimation.

genetics↗

bioRxiv2025

Fast tumor phylogeny regression via tree-structured dual dynamic programming

MotivationReconstructing the evolutionary history of tumors from bulk DNA sequencing of multiple tissue samples remains a challenging computational problem, requiring simultaneous deconvolution of the tumor tissue and inference of its evolutionary history. Recently, phylogenetic reconstruction methods have made significant progress by breaking the reconstruction problem into two parts: a regression problem over a fixed topology and a search over tree space. While effective techniques have been developed for the latter search problem, the regression problem remains a bottleneck in both method design and implementation due to the lack of fast, specialized algorithms. ResultsHere, we introduce fastppm, a fast tool to solve the regression problem via tree-structured dual dynamic programming. fastppm supports arbitrary, separable convex loss functions including the{ell} 2, piecewise linear, binomial and beta-binomial loss and provides asymptotic improvements for the{ell} 2 and piecewise linear loss over existing algorithms. We find that fastppm empirically outperforms both specialized and general purpose regression algorithms, obtaining 50-450x speedups while providing as accurate solutions as existing approaches. Incorporating fastppm into several phylogeny inference algorithms immediately yields up to 400x speedups, requiring only a small change to the program code of existing software. Finally, fastppm enables analysis of low-coverage bulk DNA sequencing data on both simulated data and in a patient-derived mouse model of colorectal cancer, outperforming state-of-the-art phylogeny inference algorithms in terms of both accuracy and runtime. Availabilityfastppm is implemented in C++ and available as both a command-line interface and Python library at github.com/elkebir-group/fastppm.git.

bioinformatics↗

bioRxiv2024

Characterizing the Solution Space of Migration Histories of Metastatic Cancers with MACH2

Understanding the migration history of cancer cells is essential for advancing metastasis research and developing therapies. Existing migration history inference methods often rely on parsimony criteria such as minimizing migrations, comigrations, and seeding locations. Importantly, existing methods either yield a single optimal solution or are probabilistic algorithms without guarantees on optimality nor comprehensiveness of the returned solution space. As such, current methods are unable to capture the full extent of uncertainty inherent to the data. To address these limitations, we introduce MACH2, a method that systematically enumerates all plausible migration histories by exactly solving the PO_SCPLOWARSIMONIOUSC_SCPLOW MO_SCPLOWIGRATIONC_SCPLOW HO_SCPLOWISTORYC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWWITHC_SCPLOW TO_SCPLOWREEC_SCPLOW RO_SCPLOWEFINEMENTC_SCPLOW (PMH-TR). In addition to the migration and comigration criteria, MACH2 employs a novel parsimony criterion that minimizes the number of clones unobserved in their inferred location of origin. MACH2 allows one to specify both the order and the set of criteria to include during optimization, allowing users to adapt the model to specific analysis needs. MACH2 also includes a summary graph to identify high-confidence migrations. Finally, we introduce MACH2-viz, an interactive webtool for visualizing and exploring MACH2 solution spaces. Using simulated tumors with known ground truth, we show that MACH2, especially the version that prioritizes the new unobserved clone criterion, outperforms existing methods. On real data, MACH2 detects extensive uncertainty in non-small cell lung, ovarian, and prostate cancers, and infers migration histories consistent with orthogonal experimental data.

cancer biology↗

bioRxiv2024

Pharming: Joint Clonal Tree Reconstruction of SNV and CNAEvolution from Single-cell DNA Sequencing of Tumors

Cancer arises through an evolutionary process in which somatic mutations, including single nucleotide variants (SNVs) and copy number aberrations (CNAs), drive the development of a malignant, heterogeneous tumor. Reconstructing this evolutionary history from sequencing data is critical for understanding the order in which mutations are acquired and the dynamic interplay between different types of alterations. Advances in modern whole genome single-cell sequencing now enable the accurate inference of copy number profiles in individual cells. However, the low sequencing coverage of these low pass sequencing technologies poses a challenge for reliably inferring the presence or absence of SNVs within tumor cells, limiting the ability to simultaneously study the evolutionary relationships between SNVs and CNAs. In this work, we introduce a novel tumor phylogeny inference method, PO_SCPLOWHARMINGC_SCPLOW, that jointly infers the evolutionary histories of SNVs and CNAs. Our key insight is to leverage the high accuracy of copy number inference methods and the fact that SNVs co-occur in regions with CNAs in order to enable more precise tumor phylogeny reconstruction for both alteration types. We demonstrate via simulations that PO_SCPLOWHARMINGC_SCPLOW outperforms state-of-the-art single-modality tumor phylogeny inference methods. Additionally, we apply PO_SCPLOWHARMINGC_SCPLOW to a triple-negative breast cancer case, achieving high-resolution, joint reconstruction of CNA and SNV evolution, including the de novo detection of a clonal whole-genome duplication event. Thus, PO_SCPLOWHARMINGC_SCPLOW offers the potential for more comprehensive and detailed tumor phylogeny inference for high-throughput, low-coverage single-cell DNA sequencing technologies compared to existing approaches. Availabilityhttps://github.com/elkebir-group/Pharming

bioinformatics↗

bioRxiv2024

Sapling: Inferring and Summarizing Tumor Phylogenies from Bulk Data using Backbone Trees

Cancer phylogenies are key to understanding tumor evolution. There exists many important downstream analyses that takes as input a single or small number of trees. However, due to uncertainty, one typically infers many, equally-plausible phylogenies from bulk DNA sequencing data of tumors. We introduce Sapling, a heuristic method to solve the Backbone Tree Inference from Reads problem, which seeks a small set of backbone trees on a smaller subset of mutations that collectively summarize the entire solution space. Sapling also includes a greedy algorithm to solve the Backbone Tree Expansion from Reads problem, which aims to expand an inferred backbone tree into a full tree. We prove that both problems are NP-hard. On simulated and real data, we demonstrate that Sapling is capable of inferring high-quality backbone trees that adequately summarize the solution space and that can expanded into full trees.

cancer biology↗

bioRxiv2024

Evolution-Aware Deep Reinforcement Learning for Single-Cell DNA Copy Number Calling

Recent high-throughput single cell DNA sequencing technologies enable one to detect copy number aberrations (CNAs) on thousands of individual cells within a tumor. Several new methods enable haplotype-specific copy number calling on such data. However, these algorithms do not utilize evolutionary constraints, leading to the inference of spurious CNAs due to measurement noise that are inconsistent with a realistic evolutionary model. To address this gap, we introduce DeepCopy, an evolution-aware deep reinforcement learning algorithm for haplotype-specific copy number calling on single-cell DNA sequencing data. On simulated data, DeepCopys predictions better fit the ground truth than existing methods. As shown on 10x Genomics Single Cell CNV sequencing of several breast cancers and DLP+ sequencing of an ovarian cancer, DeepCopys joint estimation of copy number profiles and evolutionary model parameters results in larger, more realistic clones of cells with identical copy number profiles that lead to more parsimonious CNA phylogenies than existing methods, while retaining agreement with truncal single-nucleotide variants. Finally, on a breast cancer patient sequenced with both ACT and 10x technologies, we demonstrate the consistency of DeepCopys predictions across sequencing technologies.

cancer biology↗

bioRxiv2023

TRIBAL: Tree Inference of B cell Clonal Lineages

B cells are a critical component of the adaptive immune system, responsible for producing antibodies that help protect the body from infections and foreign substances. Single cell RNA-sequencing (scRNA-seq) has allowed for both profiling of B cell receptor (BCR) sequences and gene expression. However, understanding the adaptive and evolutionary mechanisms of B cells in response to specific stimuli remains a significant challenge in the field of immunology. We introduce a new method, TRIBAL, which aims to infer the evolutionary history of clonally related B cells from scRNA-seq data. The key insight of TRIBAL is that inclusion of isotype data into the B cell lineage inference problem is valuable for reducing phylogenetic uncertainty that arises when only considering the receptor sequences. Consequently, the TRIBAL inferred B cell lineage trees jointly capture the somatic mutations introduced to the B cell receptor during affinity maturation and isotype transitions during class switch recombination. In addition, TRIBAL infers isotype transition probabilities that are valuable for gaining insight into the dynamics of class switching. Via in silico experiments, we demonstrate that TRIBAL infers isotype transition probabilities with the ability to distinguish between direct versus sequential switching in a B cell population. This results in more accurate B cell lineage trees and corresponding ancestral sequence and class switch reconstruction compared to competing methods. Using real-world scRNA-seq datasets, we show that TRIBAL recapitulates expected biological trends in a model affinity maturation system. Furthermore, the B cell lineage trees inferred by TRIBAL were equally plausible for the BCR sequences as those inferred by competing methods but yielded lower entropic partitions for the isotypes of the sequenced B cell. Thus, our method holds the potential to further advance our understanding of vaccine responses, disease progression, and the identification of therapeutic antibodies. AvailabilityTRIBAL is available at https://github.com/elkebir-group/tribal

immunology↗

bioRxiv2023

Consensus Tree under the Ancestor-Descendant Distance is NP-hard

Due to uncertainty in tumor phylogeny inference from sequencing data, many methods infer multiple, equally-plausible phylogenies for the same cancer. To summarize the solution space [T] of tumor phylogenies, consensus tree methods seek a single best representative tree S under a specified pairwise tree distance function. One such distance function is the ancestor-descendant (AD) distance d (T, T'), which equals the symmetric difference of the transitive closures of the edge sets E (T) and E (T'). Here, we show that finding a consensus tree S for tumor phylogenies [T] that minimizes the total AD distance {sum}T [isin][T] d (S, T) is NP-hard.

cancer biology↗

bioRxiv2022

Modeling and Predicting Cancer Clonal Evolution with Reinforcement Learning

Cancer results from an evolutionary process that typically yields multiple clones with varying sets of mutations within the same tumor. Accurately modeling this process is key to understanding and predicting cancer evolution. Here, we introduce CloMu (Clone To Mutation), a flexible and low-parameter tree-generative model of cancer evolution. CloMu uses a two-layer neural network trained via reinforcement learning to determine the probability of new mutations based on the existing mutations on a clone. CloMu supports several prediction tasks, including the determination of evolutionary trajectories, tree selection, causality and interchangeability between mutations, and mutation fitness. Importantly, previous methods support only some of these tasks, and many suffer from overfitting on datasets with a large number of mutations. Using simulations, we demonstrate that CloMu either matches or outperforms current methods on a wide variety of prediction tasks. In particular, for simulated data with interchangeable mutations, current methods are unable to uncover causal relationships as effectively as CloMu. On breast cancer and leukemia cohorts, we show that CloMu determines similarities and causal relationships between mutations as well as the fitness of mutations. We validate CloMus inferred mutation fitness values for the leukemia cohort by comparing them to clonal proportion data not used during training, showing high concordance. In summary, CloMus low-parameter model facilitates a wide range of prediction tasks regarding cancer evolution on increasingly available cohort-level datasets.

cancer biology↗

bioRxiv2022

Phertilizer: growing a clonal tree from ultra-low coverage single-cell DNA sequencing data of tumors

Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We present PO_SCPLOWHERTILIZERC_SCPLOW, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance of PO_SCPLOWHERTILIZERC_SCPLOW on simulated data as well as on two real datasets, finding that PO_SCPLOWHERTILIZERC_SCPLOW effectively utilizes the copynumber signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods. Availabilityhttps://github.com/elkebir-group/phertilizer

bioinformatics↗

bioRxiv2022

User-guided local and global copy-number segmentation for tumor sequencing data

Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of important CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation and their control, we introduce CNAVO_SCPLOWIZC_SCPLOW, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAVO_SCPLOWIZC_SCPLOW allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel singlecell DNA sequencing data from the same samples, we show that by using CNAVO_SCPLOWIZC_SCPLOW, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling. CNAVO_SCPLOWIZC_SCPLOW is available at https://github.com/elkebir-group/cnaviz.

genomics↗

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

DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution

Most tumors are heterogeneous mixtures of normal cells and cancer cells, with individual cancer cells distinguished by somatic mutations that accumulated during the evolution of the tumor. The fundamental quantity used to measure tumor heterogeneity from somatic single-nucleotide variants (SNVs) is the Cancer Cell Fraction (CCF), or proportion of cancer cells that contain the SNV. However, in tumors containing copy-number aberrations (CNAs) - e.g. most solid tumors - the estimation of CCFs from DNA sequencing data is challenging because a CNA may alter the mutation multiplicity, or number of copies of an SNV. Existing methods to estimate CCFs rely on the restrictive Constant Mutation Multiplicity (CMM) assumption that the mutation multiplicity is constant across all tumor cells containing the mutation. However, the CMM assumption is commonly violated in tumors containing CNAs, and thus CCFs computed under the CMM assumption may yield unrealistic conclusions about tumor heterogeneity and evolution. The CCF also has a second limitation for phylogenetic analysis: the CCF measures the presence of a mutation at the present time, but SNVs may be lost during the evolution of a tumor due to deletions of chromosomal segments. Thus, SNVs that co-occur on the same phylogenetic branch may have different CCFs. In this work, we address these limitations of the CCF in two ways. First, we show how to compute the CCF of an SNV under a less restrictive and more realistic assumption called the Single Split Copy Number (SSCN) assumption. Second, we introduce a novel statistic, the descendant cell fraction (DCF), that quantifies both the prevalence of an SNV and the past evolutionary history of SNVs under an evolutionary model that allows for mutation losses. That is, SNVs that co-occur on the same phylogenetic branch will have the same DCF. We implement these ideas in an algorithm named DeCiFer. DeCiFer computes the DCFs of SNVs from read counts and copy-number proportions and also infers clusters of mutations that are suitable for phylogenetic analysis. We show that DeCiFer clusters SNVs more accurately than existing methods on simulated data containing mutation losses. We apply DeCiFer to sequencing data from 49 metastatic prostate cancer samples and show that DeCiFer produces more parsimonious and reasonable reconstructions of tumor evolution compared to previous approaches. Thus, DeCiFer enables more accurate quantification of intra-tumor heterogeneity and improves downstream inference of tumor evolution. Code availabilitySoftware is available at https://github.com/raphael-group/decifer

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

ClonArch: Visualizing the Spatial Clonal Architecture of Tumors

MotivationCancer is caused by the accumulation of somatic mutations that lead to the formation of distinct populations of cells, called clones. The resulting clonal architecture is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets with many spatial sequencing samples are becoming increasingly available, enabling the inference of high-resolution tumor clones and prevalences across different spatial coordinates. While temporal and phylogenetic aspects of tumor evolution, such as clonal evolution over time and clonal response to treatment, are commonly visualized in various clonal evolution diagrams, visual analytics methods that reveal the spatial clonal architecture are missing. ResultsThis paper introduces ClonArch, a web-based tool to interactively visualize the phylogenetic tree and spatial distribution of clones in a single tumor mass. ClonArch uses the marching squares algorithm to draw closed boundaries representing the presence of clones in a real or simulated tumor. ClonArch enables researchers to examine the spatial clonal architecture of a subset of relevant mutations at different prevalence thresholds and across multiple phylogenetic trees. In addition to simulated tumors with varying number of biopsies, we demonstrate the use of ClonArch on a hepatocellular carcinoma tumor with ~280 sequencing biopsies. ClonArch provides an automated way to interactively examine the spatial clonal architecture of a tumor, facilitating clinical and biological interpretations of the spatial aspects of intratumor heterogeneity. Availabilityhttps://github.com/elkebir-group/ClonArch

cancer biology↗