Дисертації з теми "Cell Annotation"

Single-cell RNA-sequencing makes possible to study the gene expression at the level of individual cells. However, one of the main challenges of the single-cell RNA-sequencing analysis today, is the identification and annotation of cell types. The current method consists in manually checking the expression of genes using top differentially expressed genes and comparing them with related cell-type markers available in scientific publications. It is therefore time-consuming and labour intensive. Nevertheless, in the last two years,numerous automatic cell-type identification and annotation tools which use different strategies have been created. But, the lack of specific comparisons of those tools in the literature and especially for immuno-oncologic and oncologic purposes makes difficult for laboratories and companies to know objectively what are the best tools for annotating cell types. In this project, a review of the current tools and an evaluation of R tools were carried out.The annotation performance, the computation time and the ease of use were assessed. After this preliminary results, the best selected R tools seem to be ClustifyR (fast and rather precise) and SingleR (precise) for the correlation-based tools, and SingleCellNet (precise and rather fast) and scPred (precise but a lot of cell types remains unassigned) for the supervised classificationtools. Finally, for the marker-based tools, MAESTRO and SCINA are rather robust if they are provided with high quality markers.

Euglena gracilis is a species of unicellular photosynthetic flagellate that inhibits aquatic ecosystems. E. gracilis belongs to the supergroup Excavata, and are an important component of the global biosphere, have biotechnological potential and is useful biological model due to their evolutionary history and complex biology. Whilst the evolutionary position of E. gracilis is now clear, their relationship with other protists such as Naegleria, Giardia, and Kinetoplastids, remains to be investigated in detail. Investigating and understanding the biology of this complex organism is a promising way to approach many evolutionary puzzles, including secondary endosymbiotic events and the evolution of parasitism, due to their relationship with Kinetoplastids. Here, I report a draft genome for E. gracilis, together with a high quality transcriptome and proteomic analysis. The estimated genome size is ~ 2 Gbp, with a GC content of ~ 50 % and a protein coding potential predicted at 36,526 Open Reading Frames (ORFs). Less than 25% of the genome is single copy sequence, indicating extensive repeat structure. There are evidences for large number of paralogs amongst specific gene families, indicating expansions and possible polyploidy as well as extensive sharing of genes with other non photosynthetic and photosynthetic eukaryotes: red and green algael genes, together with trypanosomes and other members of the excavates. Functional resolution into several of the biological systems indicates multiple similarities with the trypanosomatids in terms of orthology, paralogy, relatedness and complexity. Several biological systems such as nuclear architecture (e.g. chromosome segregation, nuclear pore complex, nuclear lamins), protein trafficking, translation, surface, consist of conserved and divergent components. For instance, several gene families likely associated with the cell surface and signal transduction possess very large numbers of lineage-specific paralogs, suggesting great flexibility in environmental monitoring and, together with divergent mechanisms for metabolic control, novel solutions to adaptation to extreme environments. I also demonstrate that the majority of control of protein expression levels is post-transcriptional and absence of transcriptional regulation, despite the presence of conventional introns. These data are a major advance in the understanding of the nuclear genome of Euglenids and provide a platform for investigation of the contributions of E. gracilis and relatives to the biosphere.

The proteome and N-terminome of the human odontoblast cell layer was identified for the first time by shotgun proteomic and terminal amine isotopic labeling of substrates (TAILS) N terminomic analyses, respectively, and compared with that of human dental pulp stroma from 3rd molar teeth. After reverse-phase liquid chromatography-tandem mass spectrometry, >170,000 spectra from the shotgun and TAILS analyses were matched by four search engines to 4,888 and 12,063 peptides in the odontoblast cell layer and pulp stroma, respectively. Using the Trans-Proteomic Pipeline, I identified 895 and 2,423 unique proteins in these tissues at an FDR of ≤ 1 %. In the odontoblast cell layer proteome I found proteomic evidence for dentin sialophosphoprotein, which is cleaved into dentin phosphoprotein and dentin sialoprotein, proteins that are important in dentin mineralization. Further, 222 proteins of the odontoblast cell layer were not found in the pulp, suggesting many of these proteins are synthesized preferentially by odontoblasts. I also found minor differences in the odontoblast cell layer between the dental pulp proteomes of older and younger donors. The human dental pulp stroma proteome was expanded by 974 new proteins, not previously identified among the 4,123 proteins identified in our previous dental pulp study (Eckhard et al., 2015). Thus, by exploring the proteome of the odontoblast cell layer and expanding the known dental pulp proteome, we found distinct proteome differences when compared with each other and with dentin. The mass spectrometry raw data and metadata have been deposited to ProteomeXchange with the PXD identifier ˂PXD006557˃.
Dentistry, Faculty of
Graduate

Thesis advisor: David Burgess
This doctoral thesis addresses two central topics divided into separate chapters. In Chapter 1: The cortical response to RhoA is regulated during mitosis, experimental findings using sea urchin embryos are presented that demonstrate that the small GTPase RhoA participates in positive signaling for cell division and that this activity is negatively regulated prior to anaphase. In a second series of experiments, myosin phosphatase is shown to be a central negative regulator of myosin activity during the cell cycle through metaphase of mitosis and experimental findings support the conclusion that myosin phosphatase opposes RhoA signaling until anaphase onset. These experiments also reveal that myosin activation alone is insufficient to stimulate cortical contractions during S phase and during metaphase arrest following activation of the spindle checkpoint. In Chapter 2: Annotation of cytoskeletal and motility proteins in the sea urchin genome assembly, as part of a collaborative project, homologs of cytoskeletal genes and gene families were derived and annotated from the sea urchin genome assembly. In addition, phylogenetic analysis of multiple gene families is presented based on these findings
Thesis (PhD) — Boston College, 2008
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Biology

Les technologies d'analyse de l'expression des gènes sur cellule unique, apparues depuis une dizaine d'années, sont en train de modifier profondément les approches de biologie cellulaire.L'analyse de données de cellules uniques est un processus complexe. Une étape clé est l'annotation cellulaire, qui consiste à assigner un type cellulaire pertinent à chacune des cellules analysées. La bonne annotation des cellules conditionne la qualité des analyses ultérieures. Cette tâche réclame une expertise biologique sur le tissu d'intérêt et une expertise computionnelle en analyse de données. Des initiatives tel que le Human Cell atlas (HCA) permettent de disposer d'atlas de référence de grande taille dotées d'annotations méticuleuses. En tant que telles, elles constituent une opportunité pour développer des modèles d'apprentissage automatique profonds susceptibles d'automatiser le processus d'annotation.Les enjeux de cette thèse étaient de mettre en place des outils d'annotation cellulaire automatiques pouvant fonctionner sur de grands jeux de données. Pour y parvenir, deux axes de travail ont été développés: j'ai tout d'abord réalisé l'annotation d'un atlas des voies aériennes humaines comprenant plus de 400.000 cellules à partir de plusieurs dizaines de biopsies obtenues chez des patients atteints de formes précoces de bronchopneumopathie chronique obstructive (BPCO), qui ont été comparées à autant de biopsies provenant de volontaires sains de même âge. J'ai ensuite mis au point une méthode d'annotation automatique après avoir réalisé un état de l'art des outils existants.Dans une première partie, mon analyse bioinformatique a permis de caractériser le rôle central joué par la fumée de cigarette, principalement au niveau des cellules épithéliales situées à la surface des voies aériennes trachéobronchiques, et dès lors directement en contact avec la fumée de cigarette. Les populations cellulaires affectées sont caractérisées par l'expression de gènes codant pour des enzymes de détoxification ou impliquées dans le métabolisme xénobiotique, l'expression d'aucun de ces gènes n'étant affectée chez d'anciens fumeurs, ni chez des volontaires sains. Cette réversibilité phénotypique lors de l'arrêt de consommation du tabac s'accompagne de modifications moléculaires et cliniques liés à la BPCO. Mes travaux sont actuellement complétés par des approches de transcriptomique spatiale, et d'analyse de l'expression des différentes isoformes de transcrits.La seconde partie de cette thèse explore les méthodes d'annotation automatiques existantes à la lumière des problématiques rencontrées lors de l'annotation de l'atlas BPCO. J'ai d'abord effectué une revue extensive de la littérature, avec un intérêt particulier pour les méthodes utilisant des modèles d'apprentissage profond. J'ai ensuite développé mon propre outil d'annotation automatique, scMusketeers, dont l'architecture favorise la construction d'un espace latent renforçant le type cellulaire tout en minimisant les effets inter-batchs expérimentaux. Des tests menés sur 12 jeux de données différents sur 7 outils actuellement disponibles le positionne favorablement, notamment pour la détection de types cellulaires rares
Single-cell gene expression analysis technologies, which have emerged over the last ten years, are profoundly changing approaches to cell biology. The analysis of single-cell data is a complex process involving many steps. A key step is cell annotation, which involves assigning the most relevant cell type to the different cells analysed. Correct cell annotation determines the quality of subsequent analyses. This complex task requires biological expertise of the tissue of interest and computational expertise to carry out data analysis. Initiatives such as the HCA provide large reference atlases with curated annotation. As such, they represent an opportunity to develop deep learning models capable of automating the annotation process.The aim of this thesis was to set up automatic annotation tools that could operate on large datasets. To achieve this, two lines of work were developed: first, I created an atlas of the human airways comprising more than 400,000 cells based on several dozen biopsies obtained from patients with early forms of chronic obstructive pulmonary disease (COPD), which were compared with as many biopsies from healthy volunteers of the same age. I then developed an automatic annotation method after reviewing the state of the art existing tools.In the first part, I was able to characterise the central role played by cigarette smoke, mainly in the epithelial cells located on the surface of the tracheobronchial airways, and thus directly exposed to cigarette smoke. The populations affected are characterised by the expression of genes coding for detoxification enzymes or enzymes involved in xenobiotic metabolism. None of these genes were affected in either ex-smokers or healthy patients. There appears to be a reversibility of the pathology following cessation of smoking, despite the molecular changes induced during initial exposure to cigarette smoke. My work is now currently extended by spatial transcriptomic approaches and analysis of the expression of different transcript isoforms.In the second part, I explored existing automatic annotation methods in the light of the knowledge acquired when annotating the COPD atlas. I began with an extensive review of the literature, with a particular interest in methods using deep learning models. I then developed an automatic annotation tool, scMusketeers, whose architecture favours the construction of a latent space reinforcing cell type while minimising experimental inter-batch effects. It compared favorably to 7 currently available tools on 12 different datasets, particularly in the task of detecting rare cell types

Ces dernières années, l’émergence des approches en cellules uniques (scRNA-seq) a favorisé la caractérisation de l’hétérogénéité cellulaire avec une précision inégalée. Malgré leur démocratisation, l’analyse de ces données reste complexe, en particulier pour les organismes dont les annotations sont incomplètes. Au cours ma thèse, j’ai observé que les annotations génomiques du poulet sont lacunaires, ce qui engendre la perte d’un grand nombre de lectures de séquençage. J’ai évalué à quel point une annotation améliorée affecte les résultats biologiques et les conclusions issues de ces analyses. Nous proposons une nouvelle approche basée sur la ré-annotation du génome à partir de données scRNA-seq et de RNA-seq bulk en lectures longues. Ce projet de biologie computationnelle s’appuie sur une étroite collaboration avec l’équipe expérimentale de Xavier Morin (IBENS). Le principal objectif biologique est la recherche de signatures de mode de division symétrique et asymétrique au sein de progéniteurs neuronaux. Afin d’identifier les principaux changements transcriptionnels, j’ai mis en place des approches dédiées à la recherche de signatures géniques à partir de données scRNA-seq
In recent years, single-cell RNA-seq (scRNA-seq) has fostered the characterization of cell heterogeneity at a remarkable high resolution. Despite their democratization, the analysis of scRNA-seq remains a challenge, particularly for organisms whose genomic annotations are partial. During my PhD, I observed that the chick genomic annotations are often incomplete, thus resulting in a loss of a large number of sequencing reads. I investigated how an enriched annotation affects the biological results and conclusions from these analyses. We developed a novel approach based on the re-annotation of the genome with scRNA-seq data and long reads bulk RNA-seq. This computational biology project capitalises on a tight collaboration with the experimental team of Xavier Morin (IBENS). The main biological focus is the search for signatures of symmetric versus asymmetric division mode in neural progenitors. In order to identify the key transcriptional switches that occur during the neurogenic transition, I have implemented bioanalysis approaches dedicated to the search for gene signatures from scRNA-seq data

Proteins are indispensable players in virtually all biological events. The functions of proteins are determined by their three dimensional (3D) structure and coordinated through intricate networks of protein-protein interactions (PPIs). Hence, a deep comprehension of such networks turns out to be crucial for understanding the cellular biology. Computational approaches have become critical tools for analysing PPI networks. In silico methods take advantage of the existing PPI knowledge to both predict new interactions and predict the function of proteins. Regarding the task of predicting PPIs, several methods have been already developed. However, recent findings demonstrate that such methods could take advantage of the knowledge on non-interacting protein pairs (NIPs). On the task of predicting the function of proteins,the Guilt-by-Association (GBA) principle can be exploited to extend the functional annotation of proteins over PPI networks. In this thesis, a new algorithm for PPI prediction and a protocol to complete cell signalling networks are presented. iLoops is a method that uses NIP data and structural information of proteins to predict the binding fate of protein pairs. A novel protocol for completing signalling networks –a task related to predicting the function of a protein, has also been developed. The protocol is based on the application of GBA principle in PPI networks.
Les proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.

Stem cells differentiate through an organized hierarchy of intermediate cell types to terminally differentiated cell types. This process is largely guided by master transcriptional regulators, but it also depends on the expression of many other types of genes. The discrete cell types in the differentiation hierarchy are often identified based on the expression or non-expression of certain marker genes. Historically, these have often been various cell-surface proteins, which are fairly easy to assay biochemically but are not necessarily causative of the cell type, in the sense of being master transcriptional regulators. This raises important questions about how gene expression across the whole genome controls or reflects cell state, and in particular, differentiation hierarchies. Traditional approaches to understanding gene expression patterns across multiple conditions, such as principal components analysis or K-means clustering, can group cell types based on gene expression, but they do so without knowledge of the differentiation hierarchy. Hierarchical clustering and maximization of parsimony can organize the cell types into a tree, but in general this tree is different from the differentiation hierarchy. Using hematopoietic differentiation as an example, we demonstrate how many genes other than marker genes are able to discriminate between different branches of the differentiation tree by proposing two models for detecting genes that are up-regulated or down-regulated in distinct lineages. We then propose a novel approach to solving the following problem: Given the differentiation hierarchy and gene expression data at each node, construct a weighted Euclidean distance metric such that the minimum spanning tree with respect to that metric is precisely the given differentiation hierarchy. We provide a set of linear constraints that are provably sufficient for the desired construction and a linear programming framework to identify sparse sets of weights, effectively identifying genes that are most relevant for discriminating different parts of the tree. We apply our method to microarray gene expression data describing 38 cell types in the hematopoiesis hierarchy, constructing a sparse weighted Euclidean metric that uses just 175 genes. These 175 genes are different than the marker genes that were used to identify the 38 cell types, hence offering a novel alternative way of discriminating different branches of the tree. A DAVID functional annotation analysis shows that the 175 genes reflect major processes and pathways active in different parts of the tree. However, we find that there are many alternative sets of weights that satisfy the linear constraints. Thus, in the style of random-forest training, we also construct metrics based on random subsets of the genes and compare them to the metric of 175 genes. Our results show that the 175 genes frequently appear in the random metrics, implicating their significance from an empirical point of view as well. Finally, we show how our linear programming method is able to identify columns that were selected to build minimum spanning trees on the nodes of random variable-size matrices.

Both cDNA tag-based and DNA chip hybridization assays have revealed widespread transcriptional activity across mammalian genomes, providing a rich source of novel protein-coding and non-coding transcripts. Annotation and functional evaluation of this undefined transcriptome space represents a major step towards the comprehensive definition of biomolecules regulating the properties of living cells, including embryonic stem cells (ESCs) and their derivatives. In this study I analysed 87 rare mRNA transcripts from human ESCs that mapped uniquely to the human genome, in regions lacking evidence for known genes or transcripts. In addition, the transcripts appeared enriched in the hESC transcriptome as enumerated by serial analysis of gene expression (SAGE). Full-length transcripts corresponding to twelve novel LongSAGE tags were recovered and evaluated with respect to gene structure, protein-coding potential, and gene regulatory features. In addition, transcript abundance was compared between RNA isolated from undifferentiated hESCs and differentiated cells. Analysis of full-length transcripts revealed that the novel ORFs did not exceed a size of129 amino acids and no matches were observed to well characterized protein domains. Interesting protein level predictions included small disulfide-bonded proteins, known members of which are important in a variety of biological processes. Transcripts evaluated for differential expression by real-time RT-qPCR (Reverse Transcription followed by real-time quantitative Polymerase Chain Reaction) were found to be variably expressed (0.2- to 4.5-fold) in Day-2 orDay-4 retinoic acid-induced differentiation cultures compared to undifferentiated hESCs. Relative quantitation using a universal reference RNA (derived from pooled adult tissues)showed large differences in novel transcript levels (0.002- to 35-fold) compared to hESCs. Collectively, these results provide a detailed analysis of a set of novel hESC transcripts and their abundance in early and adult differentiated cell types, both of which may advance our understanding of the transcriptional events governing stem cell behavior.

Thesis (Ph.D)--Biology, Georgia Institute of Technology, 2009.
Committee Chair: Mark Borodovky; Committee Member: Jung H. Choi; Committee Member: King Jordan; Committee Member: Leonid Bunimovich; Committee Member: Yury Chernoff. Part of the SMARTech Electronic Thesis and Dissertation Collection.

L'incessant augment del nombre de seqüències genòmiques, juntament amb
l'increment del nombre de tècniques experimentals de les que es disposa,
permetrà obtenir el catàleg complet de les funcions cel.lulars de
diferents organismes, incloent-hi la nostra espècie. Aquest catàleg
definirà els fonaments sobre els que es podrà entendre millor com els
organismes funcionen a nivell molecular. Al mateix temps es tindran més
pistes sobre els canvis que estan associats amb les malalties. Per tant,
la seqüència en brut, tal i com s'obté dels projectes de seqüenciació de
genomes, no té cap valor sense les anàlisis i la subsegüent anotació de
les característiques que defineixen aquestes funcions. Aquesta tesi
presenta la nostra contribució en tres aspectes relacionats de
l'anotació dels gens en genomes eucariotes.

Primer, la comparació a nivell de seqüència entre els genomes humà i de
ratolí es va dur a terme mitjançant un protocol semi-automàtic. El
programa de predicció de gens SGP2 es va desenvolupar a partir
d'elements d'aquest protocol. El concepte al darrera de l'SGP2 és que
les regions de similaritat obtingudes amb el programa TBLASTX, es fan
servir per augmentar la puntuació dels exons predits pel programa
geneid, amb el que s obtenen conjunts d'anotacions més acurats
d'estructures gèniques. SGP2 té una especificitat que és prou gran com
per que es puguin validar experimentalment via RT-PCR. La validació de
llocs d'splicing emprant la tècnica de la RT-PCR és un bon exemple de
com la combinació d'aproximacions computacionals i experimentals
produeix millors resultats que per separat.

S'ha dut a terme l'anàlisi descriptiva a nivell de seqüència dels llocs
d'splicing obtinguts sobre un conjunt fiable de gens ortòlegs per humà,
ratolí, rata i pollastre. S'han explorat les diferències a nivell de
nucleòtid entre llocs U2 i U12, pel conjunt d'introns ortòlegs que se'n
deriva d'aquests gens. S'ha trobat que els senyals d'splicing ortòlegs
entre humà i rossegadors, així com entre rossegadors, estan més
conservats que els llocs no relacionats. Aquesta conservació addicional
pot ser explicada però a nivell de conservació basal dels introns.
D'altra banda, s'ha detectat més conservació de l'esperada entre llocs
d'splicing ortòlegs entre mamífers i pollastre. Els resultats obtinguts
també indiquen que les classes intròniques U2 i U12 han evolucionat
independentment des de l'ancestre comú dels mamífers i les aus. Tampoc
s'ha trobat cap cas convincent d'interconversió entre aquestes dues
classes en el conjunt d'introns ortòlegs generat, ni cap cas de
substitució entre els subtipus AT-AC i GT-AG d'introns U12. Al contrari,
el pas de GT-AG a GC-AG, i viceversa, en introns U2 no sembla ser inusual.

Finalment, s'han implementat una sèrie d'eines de visualització per
integrar anotacions obtingudes pels programes de predicció de gens i per
les anàlisis comparatives sobre genomes. Una d'aquestes eines, el
gff2ps, s'ha emprat en la cartografia dels genomes humà, de la mosca del
vinagre i del mosquit de la malària, entre d'altres. El programa
gff2aplot i els filtres associats, han facilitat la tasca d'integrar
anotacions de seqüència amb els resultats d'eines per la cerca
d'homologia, com ara el BLAST. S'ha adaptat també el concepte de
pictograma a l'anàlisi comparativa de llocs d splicing ortòlegs, amb el
desenvolupament del programa compi.
El aumento incesante del número de secuencias genómicas, junto con el
incremento del número de técnicas experimentales de las que se dispone,
permitirá la obtención del catálogo completo de las funciones celulares
de los diferentes organismos, incluida nuestra especie. Este catálogo
definirá las bases sobre las que se pueda entender mejor el
funcionamiento de los organismos a nivel molecular. Al mismo tiempo, se
obtendrán más pistas sobre los cambios asociados a enfermedades. Por
tanto, la secuencia en bruto, tal y como se obtiene en los proyectos de
secuenciación masiva, no tiene ningún valor sin los análisis y la
posterior anotación de las características que definen estas funciones.
Esta tesis presenta nuestra contribución a tres aspectos relacionados de
la anotación de los genes en genomas eucariotas.

Primero, la comparación a nivel de secuencia entre el genoma humano y el
de ratón se llevó a cabo mediante un protocolo semi-automático. El
programa de predicción de genes SGP2 se desarrolló a partir de elementos
de dicho protocolo. El concepto sobre el que se fundamenta el SGP2 es
que las regiones de similaridad obtenidas con el programa TBLASTX, se
utilizan para aumentar la puntuación de los exones predichos por el
programa geneid, con lo que se obtienen conjuntos más precisos de
anotaciones de estructuras génicas. SGP2 tiene una especificidad
suficiente como para validar esas anotaciones experimentalmente vía
RT-PCR. La validación de los sitios de splicing mediante el uso de la
técnica de la RT-PCR es un buen ejemplo de cómo la combinación de
aproximaciones computacionales y experimentales produce mejores
resultados que por separado.

Se ha llevado a cabo el análisis descriptivo a nivel de secuencia de los
sitios de splicing obtenidos sobre un conjunto fiable de genes ortólogos
para humano, ratón, rata y pollo. Se han explorado las diferencias a
nivel de nucleótido entre sitios U2 y U12 para el conjunto de intrones
ortólogos derivado de esos genes. Se ha visto que las señales de
splicing ortólogas entre humanos y roedores, así como entre roedores,
están más conservadas que las no ortólogas. Esta conservación puede ser
explicada en parte a nivel de conservación basal de los intrones. Por
otro lado, se ha detectado mayor conservación de la esperada entre
sitios de splicing ortólogos entre mamíferos y pollo. Los resultados
obtenidos indican también que las clases intrónicas U2 y U12 han
evolucionado independientemente desde el ancestro común de mamíferos y
aves. Tampoco se ha hallado ningún caso convincente de interconversión
entre estas dos clases en el conjunto de intrones ortólogos generado, ni
ningún caso de substitución entre los subtipos AT-AC y GT-AG en intrones
U12. Por el contrario, el paso de GT-AG a GC-AG, y viceversa, en
intrones U2 no parece ser inusual.

Finalmente, se han implementado una serie de herramientas de
visualización para integrar anotaciones obtenidas por los programas de
predicción de genes y por los análisis comparativos sobre genomas. Una
de estas herramientas, gff2ps, se ha utilizado para cartografiar los
genomas humano, de la mosca del vinagre y del mosquito de la malaria. El
programa gff2aplot y los filtros asociados, han facilitado la tarea de
integrar anotaciones a nivel de secuencia con los resultados obtenidos
por herramientas de búsqueda de homología, como BLAST. Se ha adaptado
también el concepto de pictograma al análisis comparativo de los sitios
de splicing ortólogos, con el desarrollo del programa compi.
The constantly increasing amount of available genome sequences, along
with an increasing number of experimental techniques, will help to
produce the complete catalog of cellular functions for different
organisms, including humans. Such a catalog will define the base from
which we will better understand how organisms work at the molecular
level. At the same time it will shed light on which changes are
associated with disease. Therefore, the raw sequence from genome
sequencing projects is worthless without the complete analysis and
further annotation of the genomic features that define those functions.
This dissertation presents our contribution to three related aspects of
gene annotation on eukaryotic genomes.

First, a comparison at sequence level of human and mouse genomes was
performed by developing a semi-automatic analysis pipeline. The SGP2
gene-finding tool was developed from procedures used in this pipeline.
The concept behind SGP2 is that similarity regions obtained by TBLASTX
are used to increase the score of exons predicted by geneid, in order to
produce a more accurate set of gene structures. SGP2 provides a
specificity that is high enough for its predictions to be experimentally
verified by RT-PCR. The RT-PCR validation of predicted splice junctions
also serves as example of how combined computational and experimental
approaches will yield the best results.

Then, we performed a descriptive analysis at sequence level of the
splice site signals from a reliable set of orthologous genes for human,
mouse, rat and chicken. We have explored the differences at nucleotide
sequence level between U2 and U12 for the set of orthologous introns
derived from those genes. We found that orthologous splice signals
between human and rodents and within rodents are more conserved than
unrelated splice sites. However, additional conservation can be
explained mostly by background intron conservation. Additional
conservation over background is detectable in orthologous mammalian and
chicken splice sites. Our results also indicate that the U2 and U12
intron classes have evolved independently since the split of mammals and
birds. We found neither convincing case of interconversion between these
two classes in our sets of orthologous introns, nor any single case of
switching between AT-AC and GT-AG subtypes within U12 introns. In
contrast, switching between GT-AG and GC-AG U2 subtypes does not appear
to be unusual.

Finally, we implemented visualization tools to integrate annotation
features for gene- finding and comparative analyses. One of those tools,
gff2ps, was used to draw the whole genome maps for human, fruitfly and
mosquito. gff2aplot and the accompanying parsers facilitate the task of
integrating sequence annotations with the output of homologybased tools,
like BLAST.We have also adapted the concept of pictograms to the
comparative analysis of orthologous splice sites, by developing compi.

Life scientists often need to count cells in microscopy images, which is very tedious and a time consuming task. Henceforth, automatic approaches can be a solution to this problem. Several works have been devised for this issue, but the majority of these approaches degrade their performance in case of cell overlapping. In this dissertation we propose a method to determine the position of macrophages and parasites in uorescence images of Leishmania-infected macrophages. The proposed strategy is mainly based on blob detection, clustering and separation using concave regions of the cells' contour. By carrying out a comparison with other approaches that also addressed this type of images, we concluded that the proposed methodology achieves better performance in the automatic annotation of Leishmania infections.
A anotação de células é uma tarefa comum a diversas áreas da investigação biomédica. Normalmente, esta tarefa é realizada de forma manual, sendo um processo demorado, cansativo e propício a erros. Neste trabalho, focamos o nosso interesse na anotação de imagens de uorescência com infeções de Leishmania, que representa um destes casos. Leishmania são parasitas unicelulares que infectam mamíferos, sendo responsáveis por um conjunto de doenças conhecidas por leishmanioses. Leishmania usam vertebrados como hospedeiros residindo dentro dos seus macrófagos. Por conseguinte, um modelo adequado para o estudo destes parasitas é infectar in vitro culturas de macrófagos. A capacidade de sobrevivência/replicação da Leishmania nessas condições arti - ciais pode então ser avaliada por parâmetros, como, por exemplo, a percentagem de macrófagos infectados, o número médio de parasitas por macrófagos infectados e o índice de infeção. Essas métricas são geralmente determinadas pela contagem de parasitas e macrófagos ao microscópio. Ambos os tipos de células podem ser facilmente distinguidos com base no seu tamanho e cor, resultante de diferentes a nidades de corantes uorescentes. A passagem desta tarefa do microscópio para o computador já foi conseguida através de aplicações como o CellNote, contudo, apesar de mais fácil e interativa, a anotação continua a ser manual. A evolução da abordagem manual para um processo automático representa um passo natural e lógico, constituindo o principal objetivo deste trabalho. Para isto iniciámos a investigação pela revisão dos principais métodos de deteção e contagem celular. As características das imagens com infeções de Leishmania impossibilitam a utilização dos métodos estudados, de tal modo que optámos por desenvolver uma nova abordagem, capaz de lidar com as várias especi cidades destas imagens. Também durante o processo de revis ão de literatura analisámos os dois métodos previamente propostos para realizar a anotação automática de infeções de Leishmania. Estes revelaram um desempenho abaixo do requerido pelos parasitologistas, justi cando também o desenvolvimento de uma nova abordagem. Durante a concepção do sistema investigámos diversas técnicas de deteção celular, onde a deteção de blobs se destacou pelos resultados positivos. Para segmentar as regiões citoplasmáticas optámos pela utilização de algoritmos de clustering. Estes não foram capazes de solucionar casos em que existia sobreposição de estruturas celulares, motivando assim o método de separação desenvolvido. Este método baseia-se maioritariamente na análise de contorno, sendo as suas concavidades geradoras de separação entre citoplasmas. Através da combinação destas fases foi possível detetar macrófagos e parasitas com mais precisão. Para con rmar esta conclusão testámos não só a nossa abordagem mas também as duas abordagens previamente desenvolvidas para este problema. Os desempenhos alcançados evidenciam não só uma melhoria comparativamente às restantes abordagens como também mostram que a nossa abordagem assegura resultados satisfatórios comparativamente aos obtidos manualmente. Em suma, o trabalho desenvolvido produziu um sistema capaz de realizar a anotação automática de imagens de uorescência com infeções de Leishmania, tendo originado um artigo aceite para publicação na conferência International Conference on Image Analysis and Recognition (ICIAR) 2013.

Oxymonads are a group of poorly studied protists living as intestinal endosymbionts in the gut of insects and vertebrates. In this thesis I focused on the study of phylogeny, genomics and cell biology of oxymonads. Using culture-based approaches, we uncovered the hidden diversity of small oxymonads and described one new genus and six new species. In Monocercomonoides exilis, the only oxymonad with a published genome, we investigated the genome organization using fluorescence in situ hybdridization (FISH) against the telomeric regions and single-copy genes. Our results show that the genome is most probably haploid being organized in 6-7 chromosomes. Annotation of the genome revealed that the DNA replication and repair mechanisms in M. exilis are canonical and they seem more complete than those of other metamonads whose genomes are available. Although M. exilis lacks in any traces of mitochondria, its genome annotation revealed that other cellular systems do not markedly differ from other eukaryotes. Our taxon-rich phylogenetic analyses suggested that the genus Monocercomonoides is closely related to the oxymonad Streblomastix strix, which is found exclusively in the gut of the termites. Streblomastix strix, as opposed to M. exilis, is highly adapted to harbour bacterial ectosymbionts. Since S. strix...

The freshwater planarian Schmidtea mediterranea has been used in research for over 100 years, and is an emerging stem cell model. Exteriorly, planarians are covered in mucous secretions of unknown composition. While the planarian genome has been sequenced, it remains mostly unannotated. The goal my master’s research was to annotate the planarian proteome and mucous sub-proteome. Using a proteogenomics approach, I elucidated the proteome and mucous subproteome via mass spectrometry together with an in silico translated transcript database. I identified 1604 proteins, which were annotated using the Swiss-Prot BLAST algorithm and Gene Ontology analysis. The S. mediterranea proteome is highly similar to that predicted for the trematode Schistosoma mansoni associated with schistosomiasis. Remarkably, orthologs of 119 planarian mucous proteins are present in human mucosal secretions and tear fluid. I suggest planarians have potential to be a model system for parasitic worms and diseases underlined by mucous aberrancies.