Dissertations / Theses on the topic 'EST clustering'

Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.

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Neste trabalho ser?o abordados estudos referentes ? classifica??o de grande quantidade de documentos de conte?do vari?vel. Em tal processo quando um grande n?mero de documentos ? gerado, existe a necessidade de um usu?rio verific?-los um a um com a inten??o de separ?-los em bons (com pouco ou nenhum problema estrutural) ou ruins (que possuem problemas estruturais), processo este considerado lento e oneroso. Considerando este problema, neste trabalho foi desenvolvida uma ferramenta de classifica??o est?tica de documentos que visa reduzir esta interven??o humana. A ferramenta desenvolvida ? baseada em m?tricas que avaliam o quanto um documento automaticamente gerado difere de seu template, criando para cada um destes documentos uma assinatura baseada nas t?cnicas de fingerprint, objetivando primeiramente distingui-los entre si para ent?o utilizar t?cnicas de clusteriza??o criando grupos de documentos com caracter?sticas semelhantes. O algoritmo K-Med?ides ? usado para fazer tal agrupamento, tal algoritmo funciona criando grupos de objetos considerando um destes como base para a cria??o de cada cluster. A id?ia deste trabalho ? reduzir a interven??o humana fazendo com que um usu?rio classifique em bom ou ruim apenas determinados documentos de cada grupo formado pelo algoritmo de clusteriza??o. S?o tamb?m apresentados resultados de quatro experimentos realizados com esta ferramenta avaliando as contribui??es para diminuir a interven??o humana no processo de classifica??o de documentos.

In this work, a new deinterleaving algorithm that can be used as a part of an ESM system and its implementation by using an FPGA is studied. The function of the implemented algorithm is interpreting the complex electromagnetic military field in order to detect and determine different RADARs and their types by using incoming RADAR pulses and their PDWs. It is assumed that RADAR signals in the space are received clearly and PDW of each pulse is generated as an input to the implemented algorithm system. Clustering analysis and a new interpreting process is used to deinterleave the RADAR pulses. In order to implement the algorithm, FPGA is used for achieving a faster and more efficient system. Comparison of the new algorithm and the previous deinterleaving studies is done. The simulation results are shown and discussed in detail.

Master of Science - Science
The field of bioinformatics has been developing steadily, with computational problems related to biology taking on an increased importance as further advances are sought. The large data sets involved in problems within computational biology have dictated a search for good, fast approximations to computationally complex problems. This research aims to improve a method used to discover and understand genes, which are small subsequences of DNA. A difficulty arises because genes contain parts we know to be functional and other parts we assume are non-functional as there functions have not been determined. Isolating the functional parts requires the use of natural biological processes which perform this separation. However, these processes cannot read long sequences, forcing biologists to break a long sequence into a large number of small sequences, then reading these. This creates the computational difficulty of categorizing the short fragments according to gene membership. Expressed Sequence Tag Clustering is a technique used to facilitate the identification of expressed genes by grouping together similar fragments with the assumption that they belong to the same gene. The aim of this research was to investigate the usefulness of distributed memory parallelisation for the Expressed Sequence Tag Clustering problem. This was investigated empirically, with a distributed system tested for speed against a sequential one. It was found that distributed memory parallelisation can be very effective in this domain. The results showed a super-linear speedup for up to 100 processors, with higher numbers not tested, and likely to produce further speedups. The system was able to cluster 500000 ESTs in 641 minutes using 101 processors.

Prediction of functions of genes in a genome is a key step in all genome sequencing projects. Sequences that carry out important functions are likely to be conserved between evolutionarily distant species and can be identified using cross-species comparisons. In the absence of completed genomes and the accompanying high-quality annotations, expressed sequence tags (ESTs) from random cDNA clones are the primary tools for functional genomics. EST datasets are fragmented and redundant, necessitating clustering of ESTs into groups that are likely to have been derived from the same genes. EST clustering helps reduce the search space for sequence homology searching and improves the accuracy of function predictions using EST datasets. This dissertation is a case study that describes clustering of Bos taurus and Sus scrofa EST datasets, and utilizes the EST clusters to make computational function predictions using a comparative genomics approach. We used a novel EST clustering method, TAMUClust, to cluster bovine ESTs and compare its performance to the bovine EST clusters from TIGR Gene Indices (TGI) by using bovine ESTs aligned to the bovine genome assembly as a gold standard. This comparison study reveals that TAMUClust and TGI are similar in performance. Comparisons of TAMUClust and TGI with predicted bovine gene models reveal that both datasets are similar in transcript coverage. We describe here the design and implementation of an annotation pipeline for predicting functions of the Bos taurus (cattle) and Sus scrofa (pig) transcriptomes. EST datasets were clustered into gene families using Ensembl protein family clusters as a framework. Following clustering, the EST consensus sequences were assigned predicted function by transferring annotations of the Ensembl vertebrate protein(s) they are grouped to after sequence homology searches and phylogenetic analysis. The annotations benefit the livestock community by helping narrow down the gamut of direct experiments needed to verify function.

Subspace clustering identifies the attribute support for each cluster as well as identifying the location and number of clusters. In the most general case, attributes associated with each cluster could be unique. A multi-objective evolutionary method is proposed to identify the unique attribute support of each cluster while detecting its data instances. The proposed algorithm, Symbiotic Evolutionary Subspace Clustering (S-ESC) borrows from symbiosis in the sense that each clustering solution is defined in terms of a host, which is formed by a number of co-evolved cluster centroids (or symbionts). Symbionts define clusters and therefore attribute subspaces, whereas hosts define sets of clusters to constitute a non-degenerate clustering solution. The symbiotic representation of S-ESC is the key to making it scalable to high-dimensional datasets, while a subsampling process makes it scalable to large-scale datasets. Performance of the S-ESC algorithm was found to be robust across a common parameterization utilized throughout.

Bandwidth and resource constraints at the server side is a limitation for deployment of streaming media applications. Resource constraints at the server side often leads to saturation of resources during sudden increase in requests. End System Multicast (ESM) is used to overcome the problem of resource saturation. Resources such as storage, bandwidth available at the end systems are utilized to deliver streaming media. In ESM, the end-systems (also known as peers) form a network which is commonly known as Peer-to-Peer (P2P) network. These peers that receive the stream in turn act as routable components and forward the stream to other requests. These peers do not possess server like characteristics. The peers differ from the server in the following ways: (a) they join and exit the system at will (b) unlike servers, they are not reliable source of media. This induces instability in the network. Therefore, streaming media solution over such unstable peer network is a challenging task. Two kinds of media streaming is supported by ESM, namely, live streaming media and on-demand streaming media. ESM is well studied to support live streaming media. In this thesis we explore the effectiveness of using ESM to support on-demand streaming media over P2P network. There are two major issues to support on-demand streaming video.They are: (a)unlike live streaming, every request should be served from the beginning of the stream and (b) instability in the network due to peer characteristics (particularly transience of peers). In our work, late arriving peers can join the existing stream if the initial segments can be served to these peers. In this scheme, a single stream is used to serve multiple requests and therefore the throughput increases. We propose patching mechanism in which the initial segments of media are temporarily cached in the peers as patches. The peers as they join, contribute storage and this storage space is used to cache the initial segments. The patching mechanism is controlled by Expanding Window Control Protocol (EWCP). EWCP defines a “virtual window” that logically represents the aggregated cache contributed by the peers. The window expands as the peer contribute more resources. Larger the window size more is the number of clients that can be served by a single stream. GAP is formed when contiguous segments of media is lost. GAP limits the expansion of the virtual window. We explore the conditions that lead to the formation of GAP. GAP is formed due to the transience and non-cooperation of peers. Transience of peers coupled with real time nature of the application requires fast failure recovery algorithms and methods to overcome loss of media segments. We propose an efficient peer management protocol that provides constant failure recovery time. We explore several redundancy techniques to overcome the problem of loss of video segments during transience of peers. Peer characteristics (duration, resource contribution etc.) have significant impact on performance.The design of peer management protocol must include peer characteristics to increase its effectiveness. In this thesis we present detailed analysis of the relationship between the peer characteristics and performance. Our results indicate that peer characteristics and realtime nature of the application control the performance of the system. Based on our study, we propose algorithms that considers these parameters and increase the performance of the system. Finally, we bring all the pieces of our work together into a comprehensive system architecture for streaming media over P2P networks. We have implemented a prototype Black-Board System (BBS), a distance program utility that reflects the main concepts of our work. We show that algorithms that exploit peer characteristics performs well in P2P networks.