1,169 research outputs found
Book Review: Evolutionary Computation in Bioinformatics
No full textThis course will provide a guided investigation into advanced computational techniques in bioinformatics, especially techniques involving evolutionary computation. The first few weeks will consists of guided in-class discussions reviewing the basic principles of bioinformatics and evolutionary computation. During weeks 3 -6 students will present tutorials on advanced computational topics (see the list of suggested topic areas below). During weeks 7 -10, the students will present papers from the literature in which topics covered in the tutorials have been applied to specific problems in bioinformatics. The class will discuss the quality and findings of these papers
A Proposed Undergraduate Bioinformatics Curriculum for Computer Scientists
No full textBioinformatics is a new and rapidly evolving discipline that has emerged from the fields of experimental molecular biology and biochemistry, and from the the artificial intelligence, database, and algorithms disciplines of computer science. Largely because of the inherently interdisciplinary nature of bioinformatics research, academia has been slow to respond to strong industry and government demands for trained scientists to develop and apply novel bioinformatics techniques to the rapidly-growing, freely-available repositories of genetic and proteomic data. While some institutions are responding to this demand by establishing graduate programs in bioinformatics, the entrance barriers for these programs are high, largely due to the significant amount of prerequisite knowledge in the disparate fields of biochemistry and computer science required to author sophisticated new approaches to the analysis of bioinformatics data. We present a proposal for an undergraduate-level bioinformatics curriculum in computer science that lowers these barriers
Next-generation bioinformatics: Using many-core processor architecture to develop a web service for sequence alignment
No full textMotivation: Bioinformatics algorithms and computing power are the main bottlenecks for analyzing huge amount of data generated by the current technologies, such as the 'next-generation' sequencing methodologies. At the same time, most powerful microprocessors are based on many-core chips, yet most applications cannot exploit such power, requiring parallelized algorithms. As an example of next-generation bioinformatics, we have developed from scratch a new parallelization of the Needleman-Wunsch (NW) sequence alignment algorithm for the 64-core Tile64 microprocessor. The unprecedented performance it offers for a standalone personal computer (PC) is discussed, optimally aligning sequences up to 20 times faster than the non-parallelized version, thus saving valuable time. Availability: This algorithm is available as a free web service for the scientific community at http://www.sicuma.uma.es/multicore. The open source code is also available on such site. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online. © The Author 2010. Published by Oxford University Press.Funding: ‘Ministerio de Ciencia e Innovación’ (AGL2006-12550-C02-01, AGL2006-12550-C02-02); ‘Consejería de Agricultura y Pesca’ of ‘Junta de Andalucía’ (041/C/2007); ‘Grupo PAI’ (AGR-248); ‘Universidad de Córdoba’ (Ayuda a Grupos), Spain.Peer Reviewe
Bioinformatics' approaches to detect genetic variation in whole genome sequencing data
No full textCurrent genetic marker repositories are not sufficient or even are completely lacking for most farm animals. However, genetic markers are essential for the development of a research tool facilitating discovery of genetic factors that contribute to resistance to disease and the overall welfare and performance in farm animals. By large scale identification of Single Nucleotide Polymorphisms (SNPs) and Structural Variants (SVs) we aimed to contribute to the development of a repository of genetic variants for farm animals. For this purpose bioinformatics data pipelines were designed and validated to address the challenge of the cost effective identification of genetic markers in DNA sequencing data even in absence of a fully sequenced reference genome. To find SNPs in pig, we analysed publicly available whole genome shotgun sequencing datasets by sequence alignment and clustering. Sequence clusters were assigned to genomic locations using publicly available BAC sequencing and BAC mapping data. Within the sequence clusters thousands of SNPs were detected of which the genomic location is roughly known. For turkey and duck, species that both were lacking a sufficient sequence data repository for variant discovery, we applied next-generation sequencing (NGS) on a reduced genome representation of a pooled DNA sample. For turkey a genome reference was reconstructed from our sequencing data and available public sequencing data whereas in duck the reference genome constructed by a (NGS) project was used. SNPs obtained by our cost-effective SNP detection procedure still turned out to cover, at intervals, the whole turkey and duck genomes and are of sufficient quality to be used in genotyping studies. Allele frequencies, obtained by genotyping animal panels with a subset our SNPs, correlated well with those observed during SNP detection. The availability of two external duck SNP datasets allowed for the construction of a subset of SNPs which we had in common with these sets. Genotyping turned out that this subset was of outstanding quality and can be used for benchmarking other SNPs that we identified within duck. Ongoing developments in (NGS) allowed for paired end sequencing which is an extension on sequencing analysis that provides information about which pair of reads are coming from the outer ends of one sequenced DNA fragment. We applied this technique on a reduced genome representation of four chicken breeds to detect SVs. Paired end reads were mapped to the chicken reference genome and SVs were identified as abnormally aligned read pairs that have orientation or span sizes discordant from the reference genome. SV detection parameters, to distinguish true structural variants from false positives, were designed and optimized by validation of a small representative sample of SVs using PCR and traditional capillary sequencing. To conclude: we developed SNP repositories which fulfils a requirement for SNPs to perform linkage analysis, comparative genomics QTL studies and ultimately GWA studies in a range of farm animals. We also set the first step in developing a repository for SVs in chicken, a relatively new genetic marker in animal sciences. <br/
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An Overview of the Use of Neural Networks for Data Mining Tasks
No full textIn the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks
Comparing several approaches for hierarchical classification of proteins with decision trees
No full textProteins are the main building blocks of the cell, and perform almost all the functions related to cell activity. Despite the recent advances in Molecular Biology, the function of a large amount of proteins is still unknown. The use of algorithms able to induce classification models is a promising approach for the functional prediction of proteins, whose classes are usually organized hierarchically. Among the machine learning techniques that have been used in hierarchical classification problems, one may highlight the Decision Trees. This paper describes the main characteristics of hierarchical classification models for Bioinformatics problems and applies three hierarchical methods based on the use of Decision Trees to protein functional classification datasets
Microarray data mining using Bioconductor packages
No full textBackground - This paper describes the results of a Gene Ontology (GO) term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure (LAP) was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge. Results - GO enrichment analysis identified significant (raw p-value <0.05) GO terms for all three contrasts included in the analysis. Some of the GO terms linked to, generally, primary immune responses or secondary immune responses indicating the GO enrichment analysis is a useful approach to analyze microarray data. The comparisons of GO enrichment results using chicken gene information and chicken-human orthologous gene information showed more refined GO terms related to immune responses when using chicken-human orthologous gene information, this suggests that using chicken-human orthologous gene information has higher power to detect significant GO terms with more refined functionality. Furthermore, three chromosome regions were identified to be significantly up-regulated in contrast MM8-PM8 (q-value <0.01). Conclusion - Overall, this paper describes a practical approach to analyze microarray data in farm animals where the genome information is still incomplete. For farm animals, such as chicken, with currently limited gene annotation, borrowing gene annotation information from orthologous genes in well-annotated species, such as human, will help improve the pathway analysis results substantially. Furthermore, LAP analysis approach is a relatively new and very useful way to be applied in microarray analysi
Ab initio modeling of the herpesvirus VP26 core domain assessed by CryoEM density
No full textEfforts in structural biology have targeted the systematic determination of all protein structures through experimental determination or modeling. In recent years, 3-D electron cryomicroscopy (CryoEM) has assumed an increasingly important role in determining the structures of these large macromolecular assemblies to intermediate resolutions (6-10 A). While these structures provide a snapshot of the assembly and its components in well-defined functional states, the resolution limits the ability to build accurate structural models. In contrast, sequence-based modeling techniques are capable of producing relatively robust structural models for isolated proteins or domains. In this work, we developed and applied a hybrid modeling approach, utilizing CryoEM density and ab initio modeling to produce a structural model for the core domain of a herpesvirus structural protein, VP26. Specifically, this method, first tested on simulated data, utilizes the CryoEM density map as a geometrical constraint in identifying the most native-like models from a gallery of models generated by ab initio modeling. The resulting model for the core domain of VP26, based on the 8.5-A resolution herpes simplex virus type 1 (HSV-1) capsid cryoEM structure and mutational data, exhibited a novel fold. Additionally, the core domain of VP26 appeared to have a complementary interface to the known upper-domain structure of VP5, its cognate binding partner. While this new model provides for a better understanding of the assembly and interactions of VP26 in HSV-1, the approach itself may have broader applications in modeling the components of large macromolecular assemblies
Genetic algorithm-neural network: feature extraction for bioinformatics data.
No full textWith the advance of gene expression data in the bioinformatics field, the questions which frequently arise,
for both computer and medical scientists, are which genes are significantly involved in discriminating cancer
classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the
misconception of the objectives of microarray study. Furthermore, the application of various preprocessing
techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the
integrity of the findings has been compromised by the improper use of techniques and the ill-conceived
objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has
reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context
of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the
proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data
Extraction of dynamic patterns from static rna expression data: an application to hematological neoplasms
No full textWithin the study of pathological conditions from the analysis of high-throughput data, the usual approach consists in using supervised classification algorithms. Such approach frequently fails due to the initial bias of class definition uncertainty. We used an unsupervised approach to arrange samples according to the progression state of a disease by using a tool, Sample Progression Discovery, developed by Peng Qiu et al. After evaluating its functionality and how to handle its critical aspects, we applied it to two pathologies: chronic lymphocytic leukemia and Waldenström’s macroglobulinemia. The progressions found show good correspondence with clinical parameters under some constraints on the inpu
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