Nikita Alexeev
George Washington University
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Featured researches published by Nikita Alexeev.
international conference on bioinformatics and biomedical engineering | 2015
Nikita Alexeev; R. R. Aidagulov; Max A. Alekseyev
Genome rearrangements are evolutionary events that shuffle genomic architectures. Most frequent genome rearrangements are reversals, translocations, fusions, and fissions. While there are some more complex genome rearrangements such as transpositions, they are rarely observed and believed to constitute only a small fraction of genome rearrangements happening in the course of evolution. The analysis of transpositions is further obfuscated by intractability of the underlying computational problems. We propose a computational method for estimating the rate of transpositions in evolutionary scenarios between genomes. We applied our method to a set of mammalian genomes and estimated the transpositions rate in mammalian evolution to be around 0.26.
BMC Genomics | 2017
Nikita Alexeev; Max A. Alekseyev
BackgroundThe ability to estimate the evolutionary distance between extant genomes plays a crucial role in many phylogenomic studies. Often such estimation is based on the parsimony assumption, implying that the distance between two genomes can be estimated as the rearrangement distance equal the minimal number of genome rearrangements required to transform one genome into the other. However, in reality the parsimony assumption may not always hold, emphasizing the need for estimation that does not rely on the rearrangement distance. The distance that accounts for the actual (rather than minimal) number of rearrangements between two genomes is often referred to as the true evolutionary distance. While there exists a method for the true evolutionary distance estimation, it however assumes that genomes can be broken by rearrangements equally likely at any position in the course of evolution. This assumption, known as the random breakage model, has recently been refuted in favor of the more rigorous fragile breakage model postulating that only certain “fragile” genomic regions are prone to rearrangements.ResultsWe propose a new method for estimating the true evolutionary distance between two genomes under the fragile breakage model. We evaluate the proposed method on simulated genomes, which show its high accuracy. We further apply the proposed method for estimation of evolutionary distances within a set of five yeast genomes and a set of two fish genomes.ConclusionsThe true evolutionary distances between the five yeast genomes estimated with the proposed method reveals that some pairs of yeast genomes violate the parsimony assumption. The proposed method further demonstrates that the rearrangement distance between the two fish genomes underestimates their evolutionary distance by about 20%. These results demonstrate how drastically the two distances can differ and justify the use of true evolutionary distance in phylogenomic studies.
BMC Bioinformatics | 2016
Nikita Alexeev; Pavel Avdeyev; Max A. Alekseyev
BackgroundGenome median and genome halving are combinatorial optimization problems that aim at reconstruction of ancestral genomes by minimizing the number of evolutionary events between them and genomes of the extant species. While these problems have been widely studied in past decades, their solutions are often either not efficient or not biologically adequate. These shortcomings have been recently addressed by restricting the problems solution space.ResultsWe show that the restricted variants of genome median and halving problems are, in fact, closely related. We demonstrate that these problems have a neat topological interpretation in terms of embedded graphs and polygon gluings. We illustrate how such interpretation can lead to solutions to these problems in particular cases.ConclusionsThis study provides an unexpected link between comparative genomics and topology, and demonstrates advantages of solving genome median and halving problems within the topological framework.
Journal of Computational Biology | 2014
Nikita Alexeev; Peter Zograf
The cycle graph introduced by Bafna and Pevzner is an important tool for evaluating the distance between two genomes, that is, the minimal number of rearrangements needed to transform one genome into another. We interpret this distance in topological terms and relate it to the random matrix theory. Namely, the number of genomes at a given 2-break distance from a fixed one (the Hultman number) is represented by a coefficient in the genus expansion of a matrix integral over the space of complex matrices with the Gaussian measure. We study generating functions for the Hultman numbers and prove that the two-break distance distribution is asymptotically normal.
Journal of Computational Biology | 2017
Nikita Alexeev; Anna Pologova; Max A. Alekseyev
Genome rearrangements can be modeled as k-breaks, which break a genome at k positions and glue the resulting fragments in a new order. In particular, reversals, translocations, fusions, and fissions are modeled as 2-breaks, and transpositions are modeled as 3-breaks. Although k-break rearrangements for [Formula: see text] have not been observed in evolution, they are used in cancer genomics to model chromothripsis, a catastrophic event of multiple breakages happening simultaneously in a genome. It is known that the k-break distance between two genomes (i.e., the minimum number of k-breaks required to transform one genome into the other) can be computed in terms of cycle lengths in the breakpoint graph of these genomes. In this work, we address the combinatorial problem of enumerating genomes at a given k-break distance from a fixed unichromosomal genome. More generally, we enumerate genome pairs, whose breakpoint graph has a given distribution of cycle lengths. We further show how our enumeration can be used for uniform sampling of random genomes at a given k-break distance, and describe its connection to various combinatorial objects such as Bell polynomials.
international conference on computational advances in bio and medical sciences | 2015
Nikita Alexeev; Max A. Alekseyev
Motivation: The ability to estimate evolutionary distance between extant genomes plays a crucial role in many phylogenomic studies. Such distance is often measured as the number of genome rearrangements (such as reversals, translocations, fusions, and fissions) between genomes. This measure is traditionally based on the parsimony assumption, implying that the evolutionary distance between two genomes can be approximated as the rearrangement distance equal the minimal number of genome rearrangements required to transform one genome into the other. However, in reality the parsimony assumption may not always hold, emphasizing the need for estimation that does not rely on the (minimal) rearrangement distance. The evolutionary distance that accounts for the actual (rather than the minimal) number of genome rearrangements between two genomes is often referred to as the true evolutionary distance.
PLOS ONE | 2015
Dmitri Rozanov; Anton Cheltsov; Eduard Sergienko; Stefan Vasile; Vladislav S. Golubkov; Alexander E. Aleshin; Trevor Levin; Elie Traer; Byron Hann; Julia Freimuth; Nikita Alexeev; Max A. Alekseyev; Sergey P Budko; Hans Peter Bächinger; Paul T. Spellman
A high throughput screen for compounds that induce TRAIL-mediated apoptosis identified ML100 as an active chemical probe, which potentiated TRAIL activity in prostate carcinoma PPC-1 and melanoma MDA-MB-435 cells. Follow-up in silico modeling and profiling in cell-based assays allowed us to identify NSC130362, pharmacophore analog of ML100 that induced 65-95% cytotoxicity in cancer cells and did not affect the viability of human primary hepatocytes. In agreement with the activation of the apoptotic pathway, both ML100 and NSC130362 synergistically with TRAIL induced caspase-3/7 activity in MDA-MB-435 cells. Subsequent affinity chromatography and inhibition studies convincingly demonstrated that glutathione reductase (GSR), a key component of the oxidative stress response, is a target of NSC130362. In accordance with the role of GSR in the TRAIL pathway, GSR gene silencing potentiated TRAIL activity in MDA-MB-435 cells but not in human hepatocytes. Inhibition of GSR activity resulted in the induction of oxidative stress, as was evidenced by an increase in intracellular reactive oxygen species (ROS) and peroxidation of mitochondrial membrane after NSC130362 treatment in MDA-MB-435 cells but not in human hepatocytes. The antioxidant reduced glutathione (GSH) fully protected MDA-MB-435 cells from cell lysis induced by NSC130362 and TRAIL, thereby further confirming the interplay between GSR and TRAIL. As a consequence of activation of oxidative stress, combined treatment of different oxidative stress inducers and NSC130362 promoted cell death in a variety of cancer cells but not in hepatocytes in cell-based assays and in in vivo, in a mouse tumor xenograft model.
research in computational molecular biology | 2017
Pavel Avdeyev; Nikita Alexeev; Yongwu Rong; Max A. Alekseyev
One of the key computational problems in comparative genomics is the reconstruction of genomes of ancestral species based on genomes of extant species. Since most dramatic changes in genomic architectures are caused by genome rearrangements, this problem is often posed as minimization of the number of genome rearrangements between extant and ancestral genomes. The basic case of three given genomes is known as the genome median problem. Whole genome duplications (WGDs) represent yet another type of dramatic evolutionary events and inspire the reconstruction of pre-duplicated ancestral genomes, referred to as the genome halving problem. Generalization of WGDs to whole genome multiplication events leads to the genome aliquoting problem.
computing and combinatorics conference | 2016
Nikita Alexeev; Max A. Alekseyev
Construction of phylogenetic trees and networks for extant species from their characters represents one of the key problems in phylogenomics. While solution to this problem is not always uniquely defined and there exist multiple methods for tree/network construction, it becomes important to measure how well the constructed networks capture the given character relationship across the species. In the current study, we propose a novel method for measuring the specificity of a given phylogenetic network in terms of the total number of distributions of character states at the leaves that the network may impose. While for binary phylogenetic trees, this number has an exact formula and depends only on the number of leaves and character states but not on the tree topology, the situation is much more complicated for non-binary trees or networks. Nevertheless, we develop an algorithm for combinatorial enumeration of such distributions, which is applicable for arbitrary trees and networks under some reasonable assumptions.
bioRxiv | 2018
Nikita Alexeev; Javlon Isomurodov; Gennady Korotkevich; Alexey Sergushichev
Motivation Integrative network methods are commonly used for interpretation of high-throughput experimental biological data: transcriptomics, proteomics, metabolomics and others. One of the common approaches consists in finding a connected subnetwork of a global interaction network that best encompasses significant individual changes in the data and represents a so-called active module. Usually methods implementing this approach find a single subnetwork and thus solve a hard classification problem for vertices. This subnetwork inherently contains erroneous vertices, while no instrument is provided to estimate the confidence level of any particular vertex inclusion. To address this issue, in the current study we consider the active module problem as a soft classification problem. We propose a method to estimate probabilities of each vertex to belong to the active module based on Markov chain Monte Carlo subnetwork sampling. Results The proposed method allows to estimate the probability that an individual vertex belongs to the active module as well as the false discovery rate (FDR) for a given set of vertices. Given the estimated probabilities, it becomes possible to provide a connected subgraph in a consistent manner for any given FDR level: no vertex can disappear when the FDR level is relaxed. We show on simulated dataset that the proposed method has good computational performance and high classification accuracy. As an example of the performance of our method on real data, we run it on a protein-protein interaction network together with a gene expression DLBCL dataset. The results are consistent with the previous studies while, at the same time, the proposed approach is more flexible. Source code is available at https://github.com/ctlab/mcmcRanking under MIT licence.