Algorithms and methods for large-scale genome rearrangements identification

Esta tesis por compendio aborda la definición formal de SB, empezando por Pares de Segmentos de alta puntuación (HSP), los cuales son bien conocidos y aceptados. El primer objetivo se centró en la detección de SB como una combinación de HSPs incluyendo repeticiones lo cual incrementó la complejidad del modelo. Como resultado, se obtuvo un método más preciso y que mejora la calidad de los resultados del estado del arte. Este método aplica reglas basadas en la adyacencia de SBs, permitiendo además detectar LSGR e identificarlos como inversiones, translocaciones o duplicaciones, constituyendo un framework capaz de trabajar con LSGR para organismos de un solo cromosoma. Más tarde en un segundo artículo, se utilizó este framework para refinar los bordes de los SBs. En nuestra novedosa propuesta, las repeticiones que flanquean los SB se utilizaron para refinar los bordes explotando la redundancia introducida por dichas repeticiones. Mediante un alineamiento múltiple de estas repeticiones se calculan los vectores de identidad del SB y de la secuencia consenso de las repeticiones alineadas. Posteriormente, una máquina de estados finitos diseñada para detectar los puntos de transición en la diferencia de ambos vectores determina los puntos de inicio y fin de los SB refinados. Este método también se mostró útil a la hora de detectar "puntos de ruptura" (conocidos como break points (BP)). Estos puntos aparecen como la región entre dos SBs adyacentes. El método no fuerza a que el BP sea una región o un punto, sino que depende de los alineamientos de las repeticiones y del SB en cuestión. El método es aplicado en un tercer trabajo, donde se afronta un caso de uso de análisis de metagenomas. Es bien sabido que la información almacenada en las bases de datos no corresponde necesariamente a las muestras no cultivadas contenidas en un metagenoma, y es posible imaginar que la asignación de una muestra de un metagenoma se vea dificultada por un evento reorganizativo. En el articulo se muestra que las muestras de un metagenoma que mapean sobre las regiones exclusivas de un genoma (aquellas que no comparte con otros genomas) respaldan la presencia de ese genoma en el metagenoma. Estas regiones exclusivas son fácilmente derivadas a partir de una comparación múltiple de genomas, como aquellas regiones que no forman parte de ningún SB. Una definición bajo un espacio de comparación múltiple de genomas es más precisa que las definiciones construidas a partir de una comparación de pares, ya que entre otras cosas, permite un refinamiento siguiendo un procedimiento similar al descrito en el segundo artículo (usando SBs, en vez de repeticiones). Esta definición también resuelve la contradicción existente en la definición de puntos de BPs (mencionado en la segunda publicación), por la cual una misma región de un genoma puede ser detectada como BP o formar parte de un SB dependiendo del genoma con el que se compare. Esta definición de SB en comparación múltiple proporciona además información precisa para la reconstrucción de LSGR, con vistas a obtener una aproximación del verdadero ancestro común entre especies. Además, proporciona una solución para el problema de la granularidad en la detección de SBs: comenzamos por SBs pequeños y bien conservados y a través de la reconstrucción de LSGR se va aumentando gradualmente el tamaño de dichos bloques. Los resultados que se esperan de esta línea de trabajo apuntan a una definición de una métrica destinada a obtener distancias inter genómicas más precisas, combinando similaridad entre secuencias y frecuencias de LSGR.Esta tesis es un compendio de tres artículos recientemente publicados en revistas de alto impacto, en los cuales mostramos el proceso que nos ha llevado a proponer la definición de Unidades Elementales de Conservación (regiones conservadas entre genomas que son detectadas después de una comparación múltiple), así como algunas operaciones básicas como inversiones, transposiciones y duplicaciones. Los tres artículos están transversalmente conectados por la detección de Bloques de Sintenia (SB) y reorganizaciones genómicas de gran escala (LSGR) (consultar sección 2), y respaldan la necesidad de elaborar el framework que se describe en la sección "Systems And Methods". De hecho, el trabajo intelectual llevado a cabo en esta tesis y las conclusiones aportadas por las publicaciones han sido esenciales para entender que una definición de SB apropiada es la clave para muchos de los métodos de comparativa genómica. Los eventos de reorganización del ADN son una de las principales causas de evolución y sus efectos pueden ser observados en nuevas especies, nuevas funciones biológicas etc. Las reorganizaciones a pequeña escala como inserciones, deleciones o substituciones han sido ampliamente estudiadas y existen modelos aceptados para detectarlas. Sin embargo, los métodos para identificar reorganizaciones a gran escala aún sufren de limitaciones y falta de precisión, debido principalmente a que no existe todavía una definición de SB aceptada. El concepto de SB hace referencia a regiones conservadas entre dos genomas que guardan el mismo orden y {strand. A pesar de que existen métodos para detectarlos, éstos evitan tratar con repeticiones o restringen la búsqueda centrándose solamente en las regiones codificantes en aras de un modelo más simple. El refinamiento de los bordes de estos bloques es a día de hoy un problema aún por solucionar

Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants

Rubert D, Martinez FHV, Stoye J, Dörr D. Analysis of local genome rearrangement improves resolution of ancestral genomic maps in plants. BMC Genomics. 2020;21(Suppl. 2): 273.Background Computationally inferred ancestral genomes play an important role in many areas of genome research. We present an improved workflow for the reconstruction from highly diverged genomes such as those of plants. Results Our work relies on an established workflow in the reconstruction of ancestral plants, but improves several steps of this process. Instead of using gene annotations for inferring the genome content of the ancestral sequence, we identify genomic markers through a process called genome segmentation. This enables us to reconstruct the ancestral genome from hundreds of thousands of markers rather than the tens of thousands of annotated genes. We also introduce the concept of local genome rearrangement, through which we refine syntenic blocks before they are used in the reconstruction of contiguous ancestral regions. With the enhanced workflow at hand, we reconstruct the ancestral genome of eudicots, a major sub-clade of flowering plants, using whole genome sequences of five modern plants. Conclusions Our reconstructed genome is highly detailed, yet its layout agrees well with that reported in Badouin et al. (2017). Using local genome rearrangement, not only the marker-based, but also the gene-based reconstruction of the eudicot ancestor exhibited increased genome content, evidencing the power of this novel concept

Sorting by reversals, block interchanges, tandem duplications, and deletions

<p>Abstract</p> <p>Background</p> <p>Finding sequences of evolutionary operations that transform one genome into another is a classic problem in comparative genomics. While most of the genome rearrangement algorithms assume that there is exactly one copy of each gene in both genomes, this does not reflect the biological reality very well – most of the studied genomes contain duplicated gene content, which has to be removed before applying those algorithms. However, dealing with unequal gene content is a very challenging task, and only few algorithms allow operations like duplications and deletions. Almost all of these algorithms restrict these operations to have a fixed size.</p> <p>Results</p> <p>In this paper, we present a heuristic algorithm to sort an ancestral genome (with unique gene content) into a genome of a descendant (with arbitrary gene content) by reversals, block interchanges, tandem duplications, and deletions, where tandem duplications and deletions are of arbitrary size.</p> <p>Conclusion</p> <p>Experimental results show that our algorithm finds sorting sequences that are close to an optimal sorting sequence when the ancestor and the descendant are closely related. The quality of the results decreases when the genomes get more diverged or the genome size increases. Nevertheless, the calculated distances give a good approximation of the true evolutionary distances.</p

Genome reconstruction and combinatoric analyses of rearrangement evolution

Cancer is often associated with a high number of large-scale, structural rearrangements. In a highly selective environment, some `driver' mutations conferring clonal growth advantage will be positively selected, accounting for further cancer development. Clarifying their nature, as well as their contribution to the pathology is a major current focus of biomedical research. Next generation sequencing technologies can be used nowadays to generate high-resolution data-sets of these alterations in cancer genomes. This project has been developed along two main lines: 1) the reconstruction of cancer aberrant karyotypes, together with their underlying evolutionary history; 2) the elucidation of some combinatorial properties associated with gene duplications. We applied graph theory to the problem of reconstructing the final cancer genome sequence; additionally, we developed an algorithmic approach for the reconstruction of a multi-step evolution consistent with read coverage and paired end data, giving insights on the possible molecular mechanisms underlying rearrangements. Looking at the combinatorics of both tandem and inverted duplication, we developed an algebraic formalism for the representation of these processes. This allowed us to both explore the geometric properties of sequences arising by Tandem Duplication (TD), and obtain a recursion for the number of tandem duplications evolutions after n events. Such results are missing for inverted duplications, whose combinatorial properties have been nevertheless deeply elucidated. Our results have allowed: 1) the identification, through an original approach, of potential rearrangement mechanisms associated with cancer development, and 2) the definition and mathematical description of the complete evolutionary space of specific rearrangement classes

Next-Generation Sequencing: Acquisition, Analysis, and Assembly

The process of sequencing a genome involves many steps, and accordingly, this project contains work from each of those steps. Genome sequencing begins with acquisition of sequence data, therefore, a novel biochemistry was utilized and optimized for the Sequencing By Ligation (SBL) process. A cyclic SBL protocol was created that could be utilized to extend sequencing reads in both the 5\u27 and 3\u27 directions, for an increase in read length and thru-put. After sequence acquisition, there is the process of data analysis, and the focus shifted to creating software that could take sequence information and match up the individual reads to a reference genome with greater speed and efficiency than other commonly-used software. The Sequence Analysis Workbench Tool, SAWTooth, was written and shown to outperform contemporaries NOVOAlign and BOWTIE. Finally, the last aspect of genome sequencing is de novo assembly, prompting a comparative analysis of three assemblers: CLC Genomics Workbench, Velvet Assembler, and MIRA. Results were generated using Mauve to assess the general effects of different sequencing platforms on the final assembly

Phylogeny, Ancestral Genome, And Disease Diagnoses Models Constructions Using Biological Data

Studies of bioinformatics develop methods and software tools to analyze the biological data and provide insight of the mechanisms of biological process. Machine learning techniques have been widely used by researchers for disease prediction, disease diagnosis, and bio-marker identification. Using machine-learning algorithms to diagnose diseases has a couple of advantages. Besides solely relying on the doctors’ experiences and stereotyped formulas, researchers could use learning algorithms to analyze sophisticated, high-dimensional and multimodal biomedical data, and construct prediction/classification models to make decisions even when some information was incomplete, unknown, or contradictory. In this study, first of all, we built an automated computational pipeline to reconstruct phylogenies and ancestral genomes for two high-resolution real yeast whole genome datasets. Furthermore, we compared the results with recent studies and publications to show that we reconstruct very accurate and robust phylogenies, as well as ancestors. We also identified and analyzed conserved syntenic blocks among reconstructed ancestral genomes and present yeast species. Next, we analyzed the metabolic level dataset obtained from positive mass spectrometry of human blood samples. We applied machine learning algorithms and feature selection algorithms to construct diagnosis models of Chronic kidney diseases (CKD). We also identified the most critical metabolite features and studied the correlations v among the metabolite features and the developments of CKD stages. The selected metabolite features provided insights into CKD early stage diagnosis, pathophysiological mechanisms, CKD treatments, and medicine development. Finally, we used deep learning techniques to build accurate Down Syndrome (DS) prediction/screening models based on the analysis of newly introduced Illumina human genome genotyping array. We proposed a bi-stream convolutional neural network (CNN) architecture with ten layers and two merged CNN models, which took two input chromosome SNP maps in combination. We evaluated and compared the performances of our CNN DS predictions models with conventional machine learning algorithms. We visualized the feature maps and trained filter weights from intermediate layers of our trained CNN model. We further discussed the advantages of our method and the underlying reasons for the differences of their performances

Phylogenetic assembly of paleogenomes integrating ancient DNA data

Luhmann N. Phylogenetic assembly of paleogenomes integrating ancient DNA data. Bielefeld: Universität Bielefeld; 2017.In comparative genomics, reconstructing the genomes of ancestral species in a given phylogeny is an important problem in order to analyze genome evolution over time. The diversity of present-day genomes in terms of local mutations and genome rearrangements allows to shed light on the dynamics of evolutionary processes that led from a common ancestor to a set of extant genomes. This speciation history is depicted in a phylogenetic tree. Comparative genome reconstruction methods aim to infer genomic features such as an order of markers (e.g. genes) for extinct species at internal nodes of the tree by applying different evolutionary models, relying only on the information available for the extant genomes at the leaves of the phylogenetic tree. Recently, the steady progress in sequencing technologies led to the emergence of the field of paleogenomics, where the study of ancient DNA (aDNA) found in conserved organic material is moving rapidly towards the sequencing and analysis of complete paleogenomes. Such ''genetic time travel'' allows direct insight into specific phases of the evolution of specific genomes that are not only implicitly inferred from extant DNA sequences. However, as DNA is naturally degraded over time after the death of an organism and environmental conditions interfere with the conservation of DNA material, an assembly of these paleogenomes is usually fragmented, preventing a detailed analysis of genome rearrangements along the branches of the phylogenetic tree. In this thesis, we aim to combine the study of aDNA and comparative ancestral reconstruction in a phylogenetic framework. The comparison with extant related genomes can naturally assist in scaffolding a fragmented aDNA assembly, while the aDNA sequencing data can be included as an additional source of information for comparative reconstruction methods to improve the reconstructions of all related genomes in the phylogenetic tree. Our first focus is on integrative methods to reconstruct marker orders globally in a phylogeny under the assumption of parsimony. An underlying rearrangement model can describe the evolutionary operations that occurred along the edges of the tree. However, as much as complex rearrangement scenarios can give insights into underlying biological mechanisms during evolution, from an computational point of view the ancestral reconstruction problem under rearrangement distances is an NP-hard problem. One exception is the Single-Cut-or-Join (SCJ) distance, that uses a marker order-based representation of the involved genomes to model the cut and join of marker adjacencies as evolutionary operations. We build upon this rearrangement model and describe parsimony-based reconstruction methods aiming to minimize the SCJ distance in the tree. In addition, we require the reconstructed solutions to be consistent, such that they represent linear or circular regions of the ancestral genome. Our first polynomial-time method is based on the Sankoff-Rousseau algorithm and directly includes an aDNA assembly graph at one internal node of the tree. We show that including branch lengths in the underlying tree can avoid ambiguity in practice. Our second approach follows a more general strategy and includes the aDNA sequencing data as local weights for adjacencies next to the SCJ distance in the objective. We describe a fixed-parameter-tractable algorithm that also allows to sample co-optimal solutions. Finally, we describe an approach to fill gaps between potentially adjacent markers by aDNA data to reconstruct the complete genome sequence of a paleogenome guided by the related extant genome sequences. In addition, this approach enables us to select the adjacencies that are supported by the sequencing information from sets of conflicting adjacencies. We evaluate our proposed models and algorithms on simulated and biological data. In particular, we integrate two aDNA sequencing data sets for ancient strains of the pathogen Yersinia pestis, that is understood to be the cause of several pandemics in medieval times. We show that the combination of aDNA sequencing reads and a parsimonious reconstruction in the phylogenetic tree reduces the fragmentation of an initial aDNA assembly substantially and explore alternative reconstructions to emphasize reliably reconstructed regions of the ancient genomes

Évolution des génomes par mutations locales et globales : une approche d’alignement

Durant leur évolution, les génomes accumulent des mutations pouvant affecter d’un nucléotide à plusieurs gènes. Les modifications au niveau du nombre et de l’organisation des gènes dans les génomes sont dues à des mutations globales, telles que les duplications, les pertes et les réarrangements. En comparant les ordres de gènes des génomes, il est possible d’inférer les événements évolutifs les plus fréquents, le contenu en gènes des espèces ancestrales ainsi que les histoires évolutives ayant menées aux ordres observés. Dans cette thèse, nous nous intéressons au développement de nouvelles méthodes algorithmiques, par approche d’alignement, afin d’analyser ces différents aspects de l’évolution des génomes. Nous nous intéressons à la comparaison de deux ou d’un ensemble de génomes reliés par une phylogénie, en tenant compte des mutations globales. Pour commencer, nous étudions la complexité théorique de plusieurs variantes du problème de l’alignement de deux ordres de gènes par duplications et pertes, ainsi que de l’approximabilité de ces problèmes. Nous rappelons ensuite les algorithmes exacts, en temps exponentiel, existants, et développons des heuristiques efficaces. Nous proposons, dans un premier temps, DLAlign, une heuristique quadratique pour le problème d’alignement de deux ordres de gènes par duplications et pertes. Ensuite, nous présentons, OrthoAlign, une extension de DLAlign, qui considère, en plus des duplications et pertes, les réarrangements et les substitutions. Nous abordons également le problème de l’alignement phylogénétique de génomes. Pour commencer, l’heuristique OrthoAlign est adaptée afin de permettre l’inférence de génomes ancestraux au noeuds internes d’un arbre phylogénétique. Nous proposons enfin, MultiOrthoAlign, une heuristique plus robuste, basée sur la médiane, pour l’inférence de génomes ancestraux et d’histoires évolutives d’un ensemble de génomes représentés aux feuilles d’un arbre phylogénétique.During the evolution process, genomes accumulate mutations that may affect the genome at different levels, ranging from one base to the overall gene content. Global mutations affecting gene content and organization are mainly duplications, losses and rearrangements. By comparing gene orders, it is possible to infer the most frequent events, the gene content in the ancestral genomes and the evolutionary histories of the observed gene orders. In this thesis, we are interested in developing new algorithmic methods based on an alignment approach for comparing two or a set of genomes represented as gene orders and related through a phylogenetic tree, based on global mutations. We study the theoretical complexity and the approximability of different variants of the two gene orders alignment problem by duplications and losses. Then, we present the existing exact exponential time algorithms, and develop efficient heuristics for these problems. First, we developed DLAlign, a quadratic time heuristic for the two gene orders alignment problem by duplications and losses. Then, we developed OrthoAlign, a generalization of DLAlign, accounting for most genome-wide evolutionary events such as duplications, losses, rearrangements and substitutions. We also study the phylogenetic alignment problem. First, we adapt our heuristic OrthoAlign in order to infer ancestral genomes at the internal nodes of a given phylogenetic tree. Finally, we developed MultiOrthoAlign, a more robust heuristic, based on the median problem, for the inference of ancestral genomes and evolutionary histories of extent genomes labeling leaves of a phylogenetic tree

06201 Abstracts Collection -- Combinatorial and Algorithmic Foundations of Pattern and Association Discovery

From 15.05.06 to 20.05.06, the Dagstuhl Seminar 06201 ``Combinatorial and Algorithmic Foundations of Pattern and Association Discovery\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available