WorldCat Identities

Boeva, Valentina

Works: 16 works in 19 publications in 2 languages and 27 library holdings
Genres: Academic theses 
Roles: Author, Other, Contributor, Opponent, Thesis advisor
Publication Timeline
Most widely held works by Valentina Boeva
Distribution of ammonium nitrate as nitrogen containing nutrient for in-situ biodegradation by means of electrokinetics by Valentina Boeva( Book )

4 editions published between 1995 and 2000 in English and held by 4 WorldCat member libraries worldwide

In-situ bioremediation is a technology, which has recently gained the attention of specialists for the clean-up of hydrocarbons. Organic chemicals are persistent and hard to deal with, because they are usually present in three forms: dissolved into groundwater, as free product over the groundwater surface, and adsorbed onto soil particles. The requirements for the bioremediation process to occur are the availability of microorganisms, a biodegradable pollutant, an electron acceptor, and nutrients. The shortage of nutrients in an available form for the microorganisms is very often a limiting factor for successful bioremediation in-situ. The main difficulties for the supply of nutrients usually come from the low permeability of soils. The feasibility of the application of electrokinetic processes, and, more specifically, the induced electroosmotic flow, for achievement of uniform distribution of nutrients for in-situ bioremediation in a natural clayey silt was investigated. Three different concentrations of ammonium nitrate solution were used. The experiment showed the efficiency of the electrokinetic method for supplying nutrients in a low permeability soil, especially for distribution of solutions with intermediate (1000 mg/L) concentrations. An advantage of the method is the prevention of the leaching of nitrates through the controlled electroosmotic flow
Changes in correlation between promoter methylation and gene expression in cancer by Matahi Moarii( )

1 edition published in 2015 in English and held by 2 WorldCat member libraries worldwide

CHROMATIX: computing the functional landscape of many-body chromatin interactions in transcriptionally active loci from deconvolved single cells by Alan Perez-Rathke( )

1 edition published in 2019 in English and held by 2 WorldCat member libraries worldwide

Learning smoothing models of copy number profiles using breakpoint annotations by Toby Dylan Hocking( )

1 edition published in 2013 in English and held by 2 WorldCat member libraries worldwide

Exact p-value calculation for heterotypic clusters of regulatory motifs and its application in computational annotation of cis-regulatory modules by Valentina Boeva( )

1 edition published in 2007 in English and held by 2 WorldCat member libraries worldwide

Computational pan-genomics: status, promises and challenges by Tobias Marschall( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Activated ALK signals through the ERK-ETV5-RET pathway to drive neuroblastoma oncogenesis by Lucille Lopez-Delisle( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

TBX2 is a neuroblastoma core regulatory circuitry component enhancing MYCN/FOXM1 reactivation of DREAM targets by Bieke Decaesteker( )

1 edition published in 2018 in English and held by 2 WorldCat member libraries worldwide

Nouvelles techniques informatiques pour la localisation et la classification de données de séquençage haut débit by Karel Brinda( )

1 edition published in 2016 in English and held by 2 WorldCat member libraries worldwide

Since their emergence around 2006, Next-Generation Sequencing technologies have been revolutionizing biological and medical research. Obtaining instantly an extensive amount of short or long reads from almost any biological sample enables detecting genomic variants, revealing the composition of species in a metagenome, deciphering cancer biology, decoding the evolution of living or extinct species, or understanding human migration patterns and human history in general. The pace at which the throughput of sequencing technologies is increasing surpasses the growth of storage and computer capacities, which still creates new computational challenges in NGS data processing. In this thesis, we present novel computational techniques for the problems of read mapping and taxonomic classification. With more than a hundred of published mappers, read mapping might be considered fully solved. However, the vast majority of mappers follow the same paradigm and only little attention has been paid to non-standard mapping approaches. Here, we propound the so-called dynamic mapping that we show to significantly improve the resulting alignments compared to traditional mapping approaches. Dynamic mapping is based on exploiting the information from previously computed alignments, helping to improve the mapping of subsequent reads. We provide the first comprehensive overview of this method and demonstrate its qualities using Dynamic Mapping Simulator, a pipeline that compares various dynamic mapping scenarios to static mapping and iterative referencing. An important component of a dynamic mapper is an online consensus caller, i.e., a program collecting alignment statistics and guiding updates of the reference in the online fashion. We provide OCOCO, the first online consensus caller that implements a smart statistics for individual genomic positions using compact bit counters. Beyond its application to dynamic mapping, OCOCO can be employed as an online SNP caller in various analysis pipelines, enabling calling SNPs from a stream without saving the alignments on disk. Metagenomic classification of NGS reads is another major problem studied in the thesis. Having a database of thousands reference genomes placed on a taxonomic tree, the task is to rapidly assign to tree nodes a huge amount of NGS reads, and possibly estimate the relative abundance of involved species. In this thesis, we propose improved computational techniques for this task. In a series of experiments, we show that spaced seeds consistently improve the classification accuracy. We provide Seed-Kraken, a spaced seed extension of Kraken, the most popular classifier at present. Furthermore, we suggest a new indexing strategy based on a BWT-index, obtaining a much smaller and more informative index compared to Kraken. We provide a modified version of BWA that improves the BWT-index for a quick k-mer look-up
Analyse de l'épissage alternatif dans les données RNAseq : développement et comparaison d'outils bioinformatiques by Clara Benoit-Pilven( )

1 edition published in 2016 in French and held by 1 WorldCat member library worldwide

Alternative splicing is the biological process that explain the large diversity of the proteome compared to the limited number of genes. This process allow a qualitative regulation (expressed isoforms) and a quantitative regulation (expression level). The growth of high-trhoughtput sequencing methods enabled the analysis of these two aspects (quantitative and qualitative regulation) with the same experiment (RNA-Seq). During my PhD, I developped a new tool to analyse alternative splicing from RNA-Seq data. I also participated in the automatisation of the complet pipeline of RNA-Seq analysis (expression and splicing). This pipeline has been used to analyse various datasets. Then, we compared our mapping-first tool, FaRLine, with an assembly-first method, KisSplice. We found that the predictions of the two pipelines overlapped (70\% of exon skipping events were common), but with noticeable differences. The mapping-first approach allowed to find more lowly expressed splicing variants, and was better in predicting exons overlapping repeated elements. The assembly-first approach allowed to find more novel variants, including novel unannotated exons and splice sites. It also predicted AS in families of paralog genes. Our work point out where the bioinformatic improvment are still needed. Finally, I participated in the developpement of bioinformatics methods to help biologists to evualuate the fonctionnal impact of splicing alteration : at the level of the protein product by annotating fonctionnal domain at the exon level or at a more global level, by integrating splicing modifications in signaling pathways
The functional and spatial organization of chromatin during Thymocyte development by Yousra Ben Zouari( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

Chromosome folding takes place at different hierarchical levels, with various topologies correlated with control of gene expression. Despite the large number of recent studies describing chromatin topologies and their correlations with gene activity, many questions remain, in particular how these topologies are formed and maintained. To understand better the link between epigenetic marks, chromatin topology and transcriptional control, we use CHi-C technique based on the chromosome conformation capture (3C) method. By using two capture strategies targeting two different chromatin structures (chromatin loops and topological domains), we have been able to decipher the chromatin structure associated with thymocyte differentiation and to highlight mechanisms for the transcriptional control of certain genes. Future experiments of the lab will examine mechanisms other than transcription which may influence chromatin architecture, such as differential binding of CTCF, and how these may interplay with transcriptional control and chromatin architecture
Génomique intégrée des tumeurs bénignes corticosurrénaliennes by Simon Faillot( )

1 edition published in 2017 in French and held by 1 WorldCat member library worldwide

The adrenal cortex produces steroid hormones, mainly cortisol, aldosterone and androgens. The adrenal cortex can be the site of tumors - adenomas or cancers -, hyperplasias and dysplasias. These lesions are in their great majority benign. They may be associated with hypersecretion of steroid hormone, most commonly cortisol (Cushing's syndrome) or aldosterone. There are also non-secreting tumors. Although molecular classifications have been established for carcinomas, to date there is no genome-wide classification of benign adrenocortical tumors, which could provide information on the mechanisms of autonomic secretion and proliferation of these lesions. Finally, the genetic determinism of dysplasia and hyperplasia is only partially known. During my thesis, I analyzed a complete "omics" dataset of benign adrenocortical lesions for more than a hundred samples, including high-throughput sequencing (exome / targeted for mutations, RNA-seq for microRNA analysis), transcriptome and methylome microarrays, and SNP microarrays for chromosomal alterations. I was able to identify a relatively convergent genome-wide molecular classification between the different "omics", which is consistent with the tumor and secretory types, but also identifies new subgroups within these lesions. In particular, it appears that mutations in these lesions are essential determinants of molecular classification. Thus, the lesions are grouped according to the signaling pathway or the altered gene, in particular the PKA / cAMP pathway for lesions producing cortisol, the Wnt / beta-catenin pathway for adenomas that do not secrete little or no cortisol, and ARMC5 for a subgroup of macronodular hyperplasia. These very distinct groups also contain lesions with no identified mutation, presumably with alternative mechanisms of alteration of these signaling pathways. In the group of ARMC5 mutated macronodular hyperplasia, the comparison with all other benign lesions shows a strong ovarian expression signature, marked by the expression of FOXL2 and its targets CYP19A1 and PTHLH. This mark of specifically gonadal differentiation in the adrenal gland causes a development anomaly to be discussed. This integrated genomic analysis also identifies epigenetic alterations of steroidogenesis. In particular, tumors secreting a lot of cortisol are globally hypermethylated in their CpG islands. In addition, hypermethylation of CYP21A2 is probably a mechanism of intratumoral 21-hydroxylase deficiency. MiRNA signatures also appear to have an impact on steroidogenesis. During my thesis I also analyzed the exome of unmutated macronodular hyperplasia ARMC5. I did not identify a new recurrent somatic mutation. At the level of the germinal exome, I identified several recurrent candidate genes, which open the way for complementary genetic analyzes (cohort extension) and cell biology. This work is the first major genomic characterization of benign lesions of the adrenal cortex. Although not all mechanisms are fully elucidated, these data represent an important resource for guiding future research into benign adrenal tumorigenesis and steroidogenesis
Une approche de modélisation de biologie des systèmes sur la spondylarthrite by Emmanuel Chaplais( )

1 edition published in 2015 in French and held by 1 WorldCat member library worldwide

La Spondyloarthrite (SpA) est un rhumatisme inflammatoire chronique fréquent, avec une prévalence de 0,43 % en France. Elle consiste en une atteinte prédominante du squelette axial, mais aussi des articulations périphériques, et peut conduire à une immobilité du rachis et des articulations sacro-iliaques. Des atteintes extra-articulaires sont fréquentes, telles qu'une uvéite, un psoriasis ou une maladie inflammatoire chronique de l'intestin. Les traitements actuels ne sont que symptomatiques, ciblant principalement les manifestations inflammatoires. L'étiologie de la SpA est multifactorielle avec une composante génétique dominée par l'association forte et bien connue avec l'allèle HLA-B27. Cependant, ce facteur génétique n'est clairement pas suffisant pour induire le développement de la maladie. L'objectif de ce projet de thèse était donc d'identifier d'autres facteurs génétiques à l'origine du développement de la SpA.Mon travail a porté sur l'analyse de deux jeux de données complémentaires, dans une perspective de biologie des systèmes. Dans une première partie, j'ai conduit une analyse de liaison dans 210 familles atteintes de la maladie représentant 1310 personnes génotypées avec des puces Affymetrix 250k. Une nouvelle région significativement liée à la SpA a été détectée en 13q13, avec un intervalle de 1,3 Mb défini par des haplotypes recombinants chez les patients.Ensuite, une analyse transcriptomique des cellules dendritiques dérivées des monocytes de 23 patients HLA-B27+, 23 témoins sains HLA-B27+ et 21 témoins sains HLA-B27-, et stimulées ou non par du LPS, a tenté de distinguer les gènes dont l'expression est modifiée par la maladie de ceux influencés par l'allèle HLA-B27 seul. L'annotation fonctionnelle et une analyse par réseau de gènes ont mis en évidence l'inhibition chez les patients des étapes précoces de la biosynthèse du cholestérol
Méthodes bioinformatiques pour l'analyse de données de séquençage dans le contexte du cancer by Justine Rudewicz( )

1 edition published in 2017 in French and held by 1 WorldCat member library worldwide

Le cancer résulte de la prolifération excessive de cellules qui dérivent toutes de la même cellule initiatrice et suivent un processus Darwinien de diversification et de sélection. Ce processus est défini par l'accumulation d'altérations génétiques et épigénétiques dont la caractérisation est un élément majeur pour pouvoir proposer une thérapie ciblant spécifiquement les cellules tumorales. L'avènement des nouvelles technologies de séquençage haut débit permet cette caractérisation à un niveau moléculaire. Cette révolution technologique a entraîné le développement de nombreuses méthodes bioinformatiques. Dans cette thèse, nous nous intéressons particulièrement au développement de nouvelles méthodes computationnelles d'analyse de données de séquençage d'échantillons tumoraux permettant une identification précise d'altérations spécifiques aux tumeurs et une description fine des sous populations tumorales. Dans le premier chapitre, il s'agît d'étudier des méthodes d'identification d'altérations ponctuelles dans le cadre de séquençage ciblé, appliquées à une cohorte de patientes atteintes du cancer du sein. Nous décrivons deux nouvelles méthodes d'analyse, chacune adaptée à une technologie de séquençage, spécifiquement Roche 454 et Pacifique Biosciences.Dans le premier cas, nous avons adapté des approches existantes au cas particulier de séquences de transcrits. Dans le second cas, nous avons été confronté à un bruit de fond élevé entraînant un fort taux de faux positifs lors de l'utilisation d'approches classiques. Nous avons développé une nouvelle méthode, MICADo, basée sur les graphes de De Bruijn et permettant une distinction efficace entre les altérations spécifiques aux patients et les altérations communes à la cohorte, ce qui rend les résultats exploitables dans un contexte clinique. Le second chapitre aborde l'identification d'altérations de nombre de copies. Nous décrivons l'approche mise en place pour leur identification efficace à partir de données de très faible couverture. L'apport principal de ce travail consiste en l'élaboration d'une stratégie d'analyse statistique afin de mettre en évidence des changements locaux et globaux au niveau du génome survenus durant le traitement administré à des patientes atteintes de cancer du sein. Notre méthode repose sur la construction d'un modèle linéaire permettant d'établir des scores de différences entre les échantillons avant et après traitement. Dans le troisième chapitre, nous nous intéressons au problème de reconstruction clonale. Cette problématique récente est actuellement en plein essor, mais manque cependant d'un cadre formel bien établi. Nous proposons d'abord une formalisation du problème de reconstruction clonale. Ensuite nous utilisons ce formalisme afin de mettre en place une méthode basée sur les modèles de mélanges Gaussiens. Cette méthode utilise les altérations ponctuelles et de nombre de copies - comme celles abordées dans les deux chapitres précédents - afin de caractériser et quantifier les différentes populations clonales présentes dans un échantillon tumoral
Evolution sous-clonale dans le neuroblastome by Paul Deveau( )

1 edition published in 2017 in English and held by 1 WorldCat member library worldwide

Neuroblastoma is the most frequent solid extra-cranial cancer of childhood. This cancer displays a high heterogeneity both at clinical and molecular levels. Even though in some patients spontaneous remission can be observed, some others relapse despite treatment and surgical resection. It may be wondered which are the factors that distinguish these two cases. In order to answer this question, identification of populations coexisting at diagnosis and/or relapse in the patients which have relapsed is a prerequisite. This would allow, between other things, to study the pathways differently altered in clones that are specific to each time point. With this in mind, we hereby present QuantumClone, a clonal reconstruction algorithm from sequencing data. In addition, we applied this method to a cohort of patients suffering from neuroblastoma. On these data, our method identified differences in the functional mutation rate, i.e. the number of putative functional variants by total number of variants, between the ancestral clones, clones expanding at relapse, and clones shrinking at relapse
QuantumClone: clonal assessment of functional mutations in cancer based on a genotype-aware method for clonal reconstruction( )

1 edition published in 2018 in English and held by 1 WorldCat member library worldwide

Abstract Motivation In cancer, clonal evolution is assessed based on information coming from single nucleotide variants and copy number alterations. Nonetheless, existing methods often fail to accurately combine information from both sources to truthfully reconstruct clonal populations in a given tumor sample or in a set of tumor samples coming from the same patient. Moreover, previously published methods detect clones from a single set of variants. As a result, compromises have to be done between stringent variant filtering [reducing dispersion in variant allele frequency estimates (VAFs)] and using all biologically relevant variants. Results We present a framework for defining cancer clones using most reliable variants of high depth of coverage and assigning functional mutations to the detected clones. The key element of our framework is QuantumClone, a method for variant clustering into clones based on VAFs, genotypes of corresponding regions and information about tumor purity. We validated QuantumClone and our framework on simulated data. We then applied our framework to whole genome sequencing data for 19 neuroblastoma trios each including constitutional, diagnosis and relapse samples. We confirmed an enrichment of damaging variants within such pathways as MAPK (mitogen-activated protein kinases), neuritogenesis, epithelial-mesenchymal transition, cell survival and DNA repair. Most pathways had more damaging variants in the expanding clones compared to shrinking ones, which can be explained by the increased total number of variants between these two populations. Functional mutational rate varied for ancestral clones and clones shrinking or expanding upon treatment, suggesting changes in clone selection mechanisms at different time points of tumor evolution. Availability and implementation Source code and binaries of the QuantumClone R package are freely available for download at Contact or Supplementary information Supplementary data are available at Bioinformatics online
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Alternative Names
Valentina Boeva researcher

Valentina Boeva wetenschapper

English (15)

French (4)