Targets Identification and Characterization of Tramp Comples in Mouse

RNA surveillance and degradation play an important role in the development and growth of organisms by eliminating RNA that contains errors, or that is no longer needed by the cell. In some processes, RNAs designated to be degraded are first labeled and then specifically recognized by the exosome, which performs the final degradation. One of the key labeling factors in yeast is the TRAMP complex, a three-subunit complex composed of Air2, Trf4 and Mtr4. Air2 facilitates TRAMP binding of RNA, Trf4 appends a 3′ end polyA tail and Mtr4 regulates the rate of adenylation and modifies RNA structures for ease of degradation through its RNA helicase activity. Though TRAMP has been studied extensively in yeast and its biochemistry and RNA recognition functions well delineated, the recent identification of TRAMP in mammals has made it possible for work to characterize mammalian TRAMP function in tissue culture cells. The mammalian transcriptome is much more complex and diverse compared to yeast in that a large portion is consisted of non-coding RNAs such as lncRNA, snoRNA, miRNA and so on. To understand the role of TRAMP complex in gene expression regulation, we knockdown the SKIV2L2 (mouse Mtr4) subunit and performed a polyA sequencing. With bioinformatics tools such as Bowtie, F-Seqq, MEDIPS, miRCompare, we constructed a data pipeline and identified several categories of targets including snoRNA, rRNA, miRNA and long-noncoding RNA in mouse cells. These data suggests that the targets of TRAMP are widely spread along the genome, and these targets involve a myriad of regulatory pathways. Understanding the relationship between the targets will help reveal the function and effect of this complex. Also, a more accurate and comprehensive target identification method remains to be developed

Network-driven strategies to integrate and exploit biomedical data

[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited. In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca d’una millor comprensió dels sistemes biològics complexos, la comunitat científica ha estat aprofundint en la biologia de les proteïnes, fàrmacs i malalties, poblant les bases de dades biomèdiques amb un gran volum de dades i coneixement. En l’actualitat, el camp de la biomedicina es troba en una era de “dades massives” (Big Data), on la investigació duta a terme per ordinadors se’n pot beneficiar per entendre i caracteritzar millor les entitats químiques i biològiques. No obstant, la heterogeneïtat i complexitat de les dades biomèdiques requereix que aquestes s’integrin i es representin d’una manera idònia, permetent així explotar aquesta informació d’una manera efectiva i eficient. L’objectiu d’aquesta tesis doctoral és desenvolupar noves estratègies que permetin explotar el coneixement biomèdic actual i així extreure informació rellevant per aplicacions biomèdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal d’integrar i explotar el coneixement biomèdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoòmics per tal d’ajudar accelerar el procés de descobriment de nous fàrmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratègia per identificar grups funcionals de gens associats a la resposta de línies cel·lulars als fàrmacs, (ii) creat una col·lecció de descriptors biomèdics capaços, entre altres coses, d’anticipar com les cèl·lules responen als fàrmacs o trobar nous usos per fàrmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biològics corresponen a una associació biològica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors químics i biològics rellevants pel procés de descobriment de nous fàrmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina

Algebraic Topology for Data Scientists

This book gives a thorough introduction to topological data analysis (TDA), the application of algebraic topology to data science. Algebraic topology is traditionally a very specialized field of math, and most mathematicians have never been exposed to it, let alone data scientists, computer scientists, and analysts. I have three goals in writing this book. The first is to bring people up to speed who are missing a lot of the necessary background. I will describe the topics in point-set topology, abstract algebra, and homology theory needed for a good understanding of TDA. The second is to explain TDA and some current applications and techniques. Finally, I would like to answer some questions about more advanced topics such as cohomology, homotopy, obstruction theory, and Steenrod squares, and what they can tell us about data. It is hoped that readers will acquire the tools to start to think about these topics and where they might fit in.Comment: 322 pages, 69 figures, 5 table

Forest landscapes and global change. New frontiers in management, conservation and restoration. Proceedings of the IUFRO Landscape Ecology Working Group International Conference

This volume contains the contributions of numerous participants at the IUFRO Landscape Ecology Working Group International Conference, which took place in Bragança, Portugal, from 21 to 24 of September 2010. The conference was dedicated to the theme Forest Landscapes and Global Change - New Frontiers in Management, Conservation and Restoration. The 128 papers included in this book follow the structure and topics of the conference. Sections 1 to 8 include papers relative to presentations in 18 thematic oral and two poster sessions. Section 9 is devoted to a wide-range of landscape ecology fields covered in the 12 symposia of the conference. The Proceedings of the IUFRO Landscape Ecology Working Group International Conference register the growth of scientific interest in forest landscape patterns and processes, and the recognition of the role of landscape ecology in the advancement of science and management, particularly within the context of emerging physical, social and political drivers of change, which influence forest systems and the services they provide. We believe that these papers, together with the presentations and debate which took place during the IUFRO Landscape Ecology Working Group International Conference – Bragança 2010, will definitively contribute to the advancement of landscape ecology and science in general. For their additional effort and commitment, we thank all the participants in the conference for leaving this record of their work, thoughts and science