Reconstruction Of Evolutionary Trees Research Articles

Assessing the performance of methods for cell clustering from single-cell DNA sequencing data.

Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings. In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate. From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.

ViCloD, an interactive web tool for visualizing B cell repertoires and analyzing intraclonal diversities: application to human B-cell tumors.

High throughput sequencing of adaptive immune receptor repertoire (AIRR-seq) has provided numerous human immunoglobulin (IG) sequences allowing specific B cell receptor (BCR) studies such as the antigen-driven evolution of antibodies (soluble forms of the membrane-bound IG part of the BCR). AIRR-seq data allows researchers to examine intraclonal differences caused primarily by somatic hypermutations in IG genes and affinity maturation. Exploring this essential adaptive immunity process could help elucidate the generation of antibodies with high affinity or broadly neutralizing activities. Retracing their evolutionary history could also clarify how vaccines or pathogen exposition drive the humoral immune response, and unravel the clonal architecture of B cell tumors. Computational methods are necessary for large-scale analysis of AIRR-seq properties. However, there is no efficient and interactive tool for analyzing intraclonal diversity, permitting users to explore adaptive immune receptor repertoires in biological and clinical applications. Here we present ViCloD, a web server for large-scale visual analysis of repertoire clonality and intraclonal diversity. ViCloD uses preprocessed data in the format defined by the Adaptive Immune Receptor Repertoire (AIRR) Community. Then, itperforms clonal grouping and evolutionary analyses, producing a collection of useful plots for clonal lineage inspection. The web server presents diverse functionalities, including repertoire navigation, clonal abundance analysis, and intraclonal evolutionary tree reconstruction. Users can download the analyzed data in different table formats and save the generated plots as images. ViCloD is a simple, versatile, and user-friendly tool that can help researchers and clinicians to analyze B cell intraclonal diversity. Moreover, its pipeline is optimized to process hundreds of thousands of sequences within a few minutes, allowing an efficient investigation of large and complex repertoires.

Lack of evolutionary convergence in multiple primary lung cancer suggests insufficient specificity of personalized therapy

Multiple primary lung cancer (MPLC) is an increasingly prevalent subtype of lung cancer. According to recent genomic studies, the different lesions of a single MPLC patient exhibit functional similarities that may reflect evolutionary convergence. We perform whole-exome sequencing for a unique cohort of MPLC patients with multiple samples from each lesion found. Using our own and other relevant public data, evolutionary tree reconstruction reveals that cancer driver gene mutations occurred at the early trunk, indicating evolutionary contingency rather than adaptive convergence. Additionally, tumors from the same MPLC patient are as genetically diverse as those from different patients, while within-tumor genetic heterogeneity is significantly lower. Furthermore, the aberrant molecular functions enriched in mutated genes for a sample show a strong overlap with other samples from the same tumor, but not with samples from other tumors or other patients. Overall, there is no evidence of adaptive convergence during the evolution of MPLC. Most importantly, the similar between-tumor diversity and between-patient diversity suggest that personalized therapies may not adequately account for the genetic diversity among different tumors in an MPLC patient. To fully exploit the strategic value of precision medicine, targeted therapies should be designed and delivered on a per-lesion basis.

Deep distributed computing to reconstruct extremely large lineage trees.

Phylogeny estimation (the reconstruction of evolutionary trees) has recently been applied to CRISPR-based cell lineage tracing, allowing the developmental history of an individual tissue or organism to be inferred from a large number of mutated sequences in somatic cells. However, current computational methods are not able to construct phylogenetic trees from extremely large numbers of input sequences. Here, we present a deep distributed computing framework to comprehensively trace accurate large lineages (FRACTAL) that substantially enhances the scalability of current lineage estimation software tools. FRACTAL first reconstructs only an upstream lineage of the input sequences and recursively iterates the same produce for its downstream lineages using independent computing nodes. We demonstrate the utility of FRACTAL by reconstructing lineages from >235 million simulated sequences and from >16 million cells from a simulated experiment with a CRISPR system that accumulates mutations during cell proliferation. We also successfully applied FRACTAL to evolutionary tree reconstructions and to an experiment using error-prone PCR (EP-PCR) for large-scale sequence diversification.

RDAClone: Deciphering Tumor Heterozygosity through Single-Cell Genomics Data Analysis with Robust Deep Autoencoder.

Rapid advances in single-cell genomics sequencing (SCGS) have allowed researchers to characterize tumor heterozygosity with unprecedented resolution and reveal the phylogenetic relationships between tumor cells or clones. However, high sequencing error rates of current SCGS data, i.e., false positives, false negatives, and missing bases, severely limit its application. Here, we present a deep learning framework, RDAClone, to recover genotype matrices from noisy data with an extended robust deep autoencoder, cluster cells into subclones by the Louvain-Jaccard method, and further infer evolutionary relationships between subclones by the minimum spanning tree. Studies on both simulated and real datasets demonstrate its robustness and superiority in data denoising, cell clustering, and evolutionary tree reconstruction, particularly for large datasets.

Genogrouping of Infectious Bursal Disease Viruses Circulating in Ethiopian Chickens: Proposal for Assigning Very Virulent Strains in the Country into New Sub Genogroup 3d.

IntroductionIn 2017 infectious bursal disease viruses (IBDVs) were reclassified into genogroups based on nature of clustering on a phylogenetic tree constructed using VP2 gene sequence data rather than according to their pathotype and/or antigenic types. Ethiopian IBD viruses were not reclassified according to the proposed genogrouping.MethodsIn order to genogroup the Ethiopian IBDVs, available VP2 gene sequences data together with reference strain sequences were retrieved from GenBank and genogrouped as recently recommended based on evolutionary tree reconstruction and determination of their clustering on the phylogenetic tree.ResultsThe Ethiopian IBDVs were grouped into genogroups 1 and 3 that antigenically represent classically virulent and very virulent IBDVs, respectively. The genogroup 1 IBDVs were clustered with the vaccine strain while the genogroup 3 viruses were clustered with four known viruses belonging to sub-genogroup 3a and sub-genogroup 3b. Almost half of the Ethiopian IBDVs reported did not cluster with the specific sub-groups of genogroup 3; rather, the isolates were clustered differently suggesting they deserve a different sub-genogroup tentatively proposed as 3d. The two genogroups observed based on clustering on a phylogenetic tree were supported by corresponding deduced amino acid changes in similar positions in VP2 sequences. In addition, virulence marker amino acid genes coupled with second major hydrophilic region (amino acid positions 314–325) were predicted in these sequences that could be responsible for the occurrence of IBD outbreaks.ConclusionA new sub-genogroup of IBDVs, 3d, were observed in the sequences that could be one of the reasons for the frequent occurrence of IBD outbreaks and questions the protective potential of the existing vaccine. To institute disease control in the country, the effectiveness of the vaccine in use needs to be assessed in vivo against both genogroups 1 and 3 viruses and all three sub-genogroup 3 viruses circulating in the country.

Does Relaxing the Infinite Sites Assumption Give Better Tumor Phylogenies? An ILP-Based Comparative Approach.

Most of the evolutionary history reconstruction approaches are based on the infinite sites assumption, which states that mutations appear once in the evolutionary history. The Perfect Phylogeny model is the result of the infinite sites assumption and has been widely used to infer cancer evolution. Nonetheless, recent results show that recurrent and back mutations are present in the evolutionary history of tumors, hence the Perfect Phylogeny model might be too restrictive. We propose an approach that allows losing previously acquired mutations and multiple acquisitions of a character. Moreover, we provide an ILP formulation for the evolutionary tree reconstruction problem. Our formulation allows us to tackle both the Incomplete Directed Phylogeny problem and the Clonal Reconstruction problem when general evolutionary models are considered. The latter problem is fundamental in cancer genomics, the goal is to study the evolutionary history of a tumor considering as input data the fraction of cells having a certain mutation in a set of cancer samples. For the Clonal Reconstruction problem, an experimental analysis shows the advantage of allowing mutation losses. Namely, by analyzing real and simulated datasets, our ILP approach provides a better interpretation of the evolutionary history than a Perfect Phylogeny. The software is at https://github.com/AlgoLab/gppf.

Reconstructing evolutionary trees in parallel for massive sequences

BackgroundBuilding the evolutionary trees for massive unaligned DNA sequences is challenging and crucial. However, reconstructing evolutionary tree for ultra-large sequences is hard. Massive multiple sequence alignment is also challenging and time/space consuming. Hadoop and Spark are developed recently, which bring spring light for the classical computational biology problems. In this paper, we tried to solve the multiple sequence alignment and evolutionary reconstruction in parallel.ResultsHPTree, which is developed in this paper, can deal with big DNA sequence files quickly. It works well on the >1GB files, and gets better performance than other evolutionary reconstruction tools. Users could use HPTree for reonstructing evolutioanry trees on the computer clusters or cloud platform (eg. Amazon Cloud). HPTree could help on population evolution research and metagenomics analysis.ConclusionsIn this paper, we employ the Hadoop and Spark platform and design an evolutionary tree reconstruction software tool for unaligned massive DNA sequences. Clustering and multiple sequence alignment are done in parallel. Neighbour-joining model was employed for the evolutionary tree building. We opened our software together with source codes via http://lab.malab.cn/soft/HPtree/.

A new distributed alignment-free approach to compare whole proteomes

Phylogeny inference has moved in recent years from the analysis of a single or few proteins to that of whole proteomes. However, the reconstruction of evolutionary trees for big number of species poses a significant computational challenge when using complete proteomes, even when relatively fast pairwise sequence comparison algorithms are used. We present a distributed approach that relies on the computation of distance measures based on maximal shared substrings within a bounded Hamming distance. The distributed system we built to implement this approach is flexible in that it supports a variety of design choices. It is based on the Spark framework and covers all the steps required by our approach, starting from the initial indexing of a set of FASTA sequences up to producing a report detailing the distances among these sequences, ranked according to a user-defined measure. Here we apply it to compare all proteins of selected organisms, divide them into groups and perform the comparisons within each group separately. The groups include: the functionally characterized proteins, the ribosomal proteins, and the unannotated proteins. We compute the average distances within the groups and evaluate their relationship and ability to capture the evolutionary closeness of organisms. We run experiments on selected species using a Hadoop computing cluster running Spark. The results show that the system implementing our approach is scalable and accurate.

On the Reconstruction of Text Phylogeny Trees: Evaluation and Analysis of Textual Relationships.

Over the history of mankind, textual records change. Sometimes due to mistakes during transcription, sometimes on purpose, as a way to rewrite facts and reinterpret history. There are several classical cases, such as the logarithmic tables, and the transmission of antique and medieval scholarship. Today, text documents are largely edited and redistributed on the Web. Articles on news portals and collaborative platforms (such as Wikipedia), source code, posts on social networks, and even scientific publications or literary works are some examples in which textual content can be subject to changes in an evolutionary process. In this scenario, given a set of near-duplicate documents, it is worthwhile to find which one is the original and the history of changes that created the whole set. Such functionality would have immediate applications on news tracking services, detection of plagiarism, textual criticism, and copyright enforcement, for instance. However, this is not an easy task, as textual features pointing to the documents’ evolutionary direction may not be evident and are often dataset dependent. Moreover, side information, such as time stamps, are neither always available nor reliable. In this paper, we propose a framework for reliably reconstructing text phylogeny trees, and seamlessly exploring new approaches on a wide range of scenarios of text reusage. We employ and evaluate distinct combinations of dissimilarity measures and reconstruction strategies within the proposed framework, and evaluate each approach with extensive experiments, including a set of artificial near-duplicate documents with known phylogeny, and from documents collected from Wikipedia, whose modifications were made by Internet users. We also present results from qualitative experiments in two different applications: text plagiarism and reconstruction of evolutionary trees for manuscripts (stemmatology).

A navigation system for tree space

The reconstruction of evolutionary trees from data sets on overlapping sets of species is a central problem in phylogenetics. Provided that the tree reconstructed for each subset of species is rooted and that these trees t together consistently, the space of all parent trees that ‘display’ these trees was recently shown to satisfy the following strong property: there exists a path from any one parent tree to any other parent tree by a sequence of local rearrangements (nearest neighbour interchanges) so that each intermediate tree also lies in this same tree space. However, the proof of this result uses a non-constructive argument. In this paper we describe a specic, polynomial-time procedure for navigating from any given parent tree to another while remaining in this tree space. The results are of particular relevance to the recent study of ‘phylogenetic terraces’.

Neighborhoods of Phylogenetic Trees: Exact and Asymptotic Counts

A central theme in phylogenetics is the reconstruction and analysis of evolutionary trees from a given set of data. To determine the optimal search methods for reconstructing trees, it is crucial to understand the size and structure of the neighborhoods of trees under tree rearrangement operations. The diameter and size of the immediate neighborhood of a tree have been well-studied; however, little is known about the number of trees at distance two, three, or (more generally) $k$ from a given tree. In this paper we provide a number of exact and asymptotic results concerning these quantities and identify some key aspects of tree shape that play a role in determining these quantities. We obtain several new results for two of the main tree rearrangement operations---nearest neighbor interchange and subtree prune and regraft---as well as for the Robinson--Foulds metric on trees.

SubClonal Hierarchy Inference from Somatic Mutations: Automatic Reconstruction of Cancer Evolutionary Trees from Multi-region Next Generation Sequencing.

Recent improvements in next-generation sequencing of tumor samples and the ability to identify somatic mutations at low allelic fractions have opened the way for new approaches to model the evolution of individual cancers. The power and utility of these models is increased when tumor samples from multiple sites are sequenced. Temporal ordering of the samples may provide insight into the etiology of both primary and metastatic lesions and rationalizations for tumor recurrence and therapeutic failures. Additional insights may be provided by temporal ordering of evolving subclones—cellular subpopulations with unique mutational profiles. Current methods for subclone hierarchy inference tightly couple the problem of temporal ordering with that of estimating the fraction of cancer cells harboring each mutation. We present a new framework that includes a rigorous statistical hypothesis test and a collection of tools that make it possible to decouple these problems, which we believe will enable substantial progress in the field of subclone hierarchy inference. The methods presented here can be flexibly combined with methods developed by others addressing either of these problems. We provide tools to interpret hypothesis test results, which inform phylogenetic tree construction, and we introduce the first genetic algorithm designed for this purpose. The utility of our framework is systematically demonstrated in simulations. For most tested combinations of tumor purity, sequencing coverage, and tree complexity, good power (≥ 0.8) can be achieved and Type 1 error is well controlled when at least three tumor samples are available from a patient. Using data from three published multi-region tumor sequencing studies of (murine) small cell lung cancer, acute myeloid leukemia, and chronic lymphocytic leukemia, in which the authors reconstructed subclonal phylogenetic trees by manual expert curation, we show how different configurations of our tools can identify either a single tree in agreement with the authors, or a small set of trees, which include the authors’ preferred tree. Our results have implications for improved modeling of tumor evolution and the importance of multi-region tumor sequencing.

A fast neighbor joining method.

With the rapid development of sequencing technologies, an increasing number of sequences are available for evolutionary tree reconstruction. Although neighbor joining is regarded as the most popular and fastest evolutionary tree reconstruction method [its time complexity is O(n(3)), where n is the number of sequences], it is not sufficiently fast to infer evolutionary trees containing more than a few hundred sequences. To increase the speed of neighbor joining, we herein propose FastNJ, a fast implementation of neighbor joining, which was motivated by RNJ and FastJoin, two improved versions of conventional neighbor joining. The main difference between FastNJ and conventional neighbor joining is that, in the former, many pairs of nodes selected by the rule used in RNJ are joined in each iteration. In theory, the time complexity of FastNJ can reach O(n(2)) in the best cases. Experimental results show that FastNJ yields a significant increase in speed compared to RNJ and conventional neighbor joining with a minimal loss of accuracy.

Dynamic Ising model: reconstruction of evolutionary trees

An evolutionary tree is a cascade of bifurcations starting from a single common root, generating a growing set of daughter species as time goes by. ‘Species’ here is a general denomination for biological species, spoken languages or any other entity which evolves through heredity. From the N currently alive species within a clade, distances are measured through pairwise comparisons made by geneticists, linguists, etc. The larger is such a distance that, for a pair of species, the older is their last common ancestor. The aim is to reconstruct the previously unknown bifurcations, i.e. the whole clade, from knowledge of the N(N − 1)/2 quoted distances, which are taken for granted. A mechanical method is presented and its applicability is discussed.

Identifiability and inference of non-parametric rates-across-sites models on large-scale phylogenies

Mutation rate variation across loci is well known to cause difficulties, notably identifiability issues, in the reconstruction of evolutionary trees from molecular sequences. Here we introduce a new approach for estimating general rates-across-sites models. Our results imply, in particular, that large phylogenies are typically identifiable under rate variation. We also derive sequence-length requirements for high-probability reconstruction. Our main contribution is a novel algorithm that clusters sites according to their mutation rate. Following this site clustering step, standard reconstruction techniques can be used to recover the phylogeny. Our results rely on a basic insight: that, for large trees, certain site statistics experience concentration-of-measure phenomena.

Matching Split Distance for Unrooted Binary Phylogenetic Trees

The reconstruction of evolutionary trees is one of the primary objectives in phylogenetics. Such a tree represents the historical evolutionary relationship between different species or organisms. Tree comparisons are used for multiple purposes, from unveiling the history of species to deciphering evolutionary associations among organisms and geographical areas. In this paper, we propose a new method of defining distances between unrooted binary phylogenetic trees that is especially useful for relatively large phylogenetic trees. Next, we investigate in detail the properties of one example of these metrics, called the Matching Split distance, and describe how the general method can be extended to nonbinary trees.

Robust unmixing of tumor states in array comparative genomic hybridization data.

Motivation: Tumorigenesis is an evolutionary process by which tumor cells acquire sequences of mutations leading to increased growth, invasiveness and eventually metastasis. It is hoped that by identifying the common patterns of mutations underlying major cancer sub-types, we can better understand the molecular basis of tumor development and identify new diagnostics and therapeutic targets. This goal has motivated several attempts to apply evolutionary tree reconstruction methods to assays of tumor state. Inference of tumor evolution is in principle aided by the fact that tumors are heterogeneous, retaining remnant populations of different stages along their development along with contaminating healthy cell populations. In practice, though, this heterogeneity complicates interpretation of tumor data because distinct cell types are conflated by common methods for assaying the tumor state. We previously proposed a method to computationally infer cell populations from measures of tumor-wide gene expression through a geometric interpretation of mixture type separation, but this approach deals poorly with noisy and outlier data.Results: In the present work, we propose a new method to perform tumor mixture separation efficiently and robustly to an experimental error. The method builds on the prior geometric approach but uses a novel objective function allowing for robust fits that greatly reduces the sensitivity to noise and outliers. We further develop an efficient gradient optimization method to optimize this ‘soft geometric unmixing’ objective for measurements of tumor DNA copy numbers assessed by array comparative genomic hybridization (aCGH) data. We show, on a combination of semi-synthetic and real data, that the method yields fast and accurate separation of tumor states.Conclusions: We have shown a novel objective function and optimization method for the robust separation of tumor sub-types from aCGH data and have shown that the method provides fast, accurate reconstruction of tumor states from mixed samples. Better solutions to this problem can be expected to improve our ability to accurately identify genetic abnormalities in primary tumor samples and to infer patterns of tumor evolution.Contact: tolliver@cs.cmu.eduSupplementary information:Supplementary data are available at Bioinformatics online.

Parallelized phylogenetic post-analysis on multi-core architectures

Bioinformatics is experiencing a rapid and overwhelming accumulation of molecular sequence data, predominantly driven by novel wet-lab sequencing techniques. This trend poses scalability challenges for tool developers. In the field of phylogenetic inference (reconstruction of evolutionary trees from molecular sequence data), scalability is becoming an increasingly important issue for operations other than the tree reconstruction itself. In this paper we focus on post-analysis tasks in reconstructing very large trees, specifically the step of building (extended) majority rule consensus trees from a collection of equally plausible trees or a collection of bootstrap replicate trees. To this end we present non-parallel optimizations which establish our implementation as the fastest exact implementation in phylogenetics, and our novel parallelized routines are the first of their kind. Our non-parallel optimizations achieve a performance improvement of factor 50 compared to the previous version of our code and we achieve a maximum speedup of 5.5 on a 8-core Nehalem node for building consensus trees comprising up to 55,000 organisms. We also present a parallel approach for drawing bootstrap support values on a candidate tree, and experimentally assess our approach in order to better understand read-only versus read–write parallel hash table accesses on multi-core systems.

Parallel computation of phylogenetic consensus trees

Abstract The field of bioinformatics is witnessing a rapid and overwhelming accumulation of molecular sequence data, predominantly driven by novel wet-lab sequencing techniques. This trend poses scalability challenges for tool developers. In the field of phylogenetic inference (reconstruction of evolutionary trees from molecular sequence data), scalability is becoming an increasingly important issue for operations other than the tree reconstruction itself. In this paper we focus on a post-analysis task in reconstructing very large trees, specifically the step of building (extended) majority rules consensus trees from a collection of equally plausible trees or a collection of bootstrap replicate trees. To this end, we present sequential optimizations that establish our implementation as the current fastest exact implementation in phylogenetics, and our novel parallelized routines are the first of their kind. Our sequential optimizations achieve a performance improvement of factor 50 compared to the previous version of our code and we achieve a maximum speedup of 5.5 on a 8-core Nehalem node for building consensi on trees comprising up to 55,000 organisms. The methods developed here are integrated into the widely used open-source tool RAxML for phylogenetic tree reconstruction.