Relevant bibliographies by topics / Computational Genomic

Academic literature on the topic 'Computational Genomic'

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Published: 11 March 2023

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Journal articles on the topic "Computational Genomic"

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Nalbantoglu, Ozkan Ufuk, and Khalid Sayood. "Computational Genomic Signatures." Synthesis Lectures on Biomedical Engineering 6, no. 2 (May 31, 2011): 1–129. http://dx.doi.org/10.2200/s00360ed1v01y201105bme041.

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Yelick, Katherine, Aydın Buluç, Muaaz Awan, Ariful Azad, Benjamin Brock, Rob Egan, Saliya Ekanayake, et al. "The parallelism motifs of genomic data analysis." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2166 (January 20, 2020): 20190394. http://dx.doi.org/10.1098/rsta.2019.0394.

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Genomic datasets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share these data with the research community, but some of these genomic data analysis problems require large-scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high-end parallel systems today and place different requirements on programming support, software libraries and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high-performance genomics analysis, including alignment, profiling, clustering and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or ‘motifs’ that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.
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Hien, Le Thi Thu, Nguyen Tuong Van, Kim Thi Phuong Oanh, Nguyen Dang Ton, Huynh Thi Thu Hue, Nguyen Thuy Duong, Pham Le Bich Hang, and Nguyen Hai Ha. "Genomics and big data: Research, development and applications." Vietnam Journal of Biotechnology 19, no. 3 (October 13, 2021): 393–410. http://dx.doi.org/10.15625/1811-4989/16158.

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Recent years, genomics and big data analytics have been widely applied and have significant impacts in various important areas of social life worldwide. The development of the next-generation sequencing (NGS) technologies, such as whole-genome sequencing (WGS), whole-exome sequencing (WES), transcriptome, and/or targeted sequencing, has enabled quickly generating the genomes of interested living organisms. Around the world many nations have invested in and promoted the development of genomics and big data analytics. A number of well-established projects on sequencing of human, animal, plant, and microorganism genomes to generate vast amounts of genomic data have been conducted independently or as collaborative efforts by national or international research networks of scientists specializing in different technical fields of genomics, bioinformatics, computational and statistical biology, automation, artificial intelligence, etc. Complicated and large genomic datasets have been effectively established, storage, managed, and used. Vietnam supports this new field of study through setting up governmental authorized institutions and conducting genomic research projects of human and other endemic organisms. In this paper, the research, development, and applications of genomic big data are reviewed with focusing on: (i) Available sequencing technologies for generating genomic datasets; (ii) Genomics and big data initiatives worldwide; (iii) Genomics and big data analytics in selected countries and Vietnam; (iv) Genomic data applications in key areas including medicine for human health care, agriculture - forestry, food safety, and environment.
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Lu, Bingxin, and Hon Wai Leong. "Computational methods for predicting genomic islands in microbial genomes." Computational and Structural Biotechnology Journal 14 (2016): 200–206. http://dx.doi.org/10.1016/j.csbj.2016.05.001.

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Salamon, Hugh, Midori Kato-Maeda, Peter M. Small, Jorg Drenkow, and Thomas R. Gingeras. "Detection of Deleted Genomic DNA Using a Semiautomated Computational Analysis of GeneChip Data." Genome Research 10, no. 12 (November 21, 2000): 2044–54. http://dx.doi.org/10.1101/gr.152900.

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Genomic diversity within and between populations is caused by single nucleotide mutations, changes in repetitive DNA systems, recombination mechanisms, and insertion and deletion events. The contribution of these sources to diversity, whether purely genetic or of phenotypic consequence, can only be investigated if we have the means to quantitate and characterize diversity in many samples. With the advent of complete sequence characterization of representative genomes of different species, the possibility of developing protocols to screen for genetic polymorphism across entire genomes is actively being pursued. The large numbers of measurements such approaches yield demand that we pay careful attention to the numerical analysis of data. In this paper we present a novel application of an Affymetrix GeneChip to perform genome-wide screens for deletion polymorphism. A high-density oligonucleotide array formatted for mRNA expression and targeted at a fully sequenced 4.4-million–base pair Mycobacterium tuberculosis standard strain genome was adapted to compare genomic DNA. Hybridization intensities to 111,000 probe pairs (perfect complement and mismatch complement) were measured for genomic DNA from a clinical strain and from a vaccine organism. Because individual probe-pair hybridization intensities exhibit limited sensitivity/specificity characteristics to detect deletions, data-analytical methodology to exploit measurements from multiple probes in tandem locations across the genome was developed. The TSTEP (Tandem Set Terminal Extreme Probability) algorithm designed specifically to analyze the tandem hybridization measurements data was applied and shown to discover genomic deletions with high sensitivity. The TSTEP algorithm provides a foundation for similar efforts to characterize deletions in many hybridization measures in similar-sized and larger genomes. Issues relating to the design of genome content screening experiments and the implications of these methods for studying population genomics and the evolution of genomes are discussed.
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ZUO, GuangHong, and BaiLin HAO. "Computational microbiology in genomic era." SCIENTIA SINICA Vitae 47, no. 2 (January 22, 2017): 159–70. http://dx.doi.org/10.1360/n052016-00312.

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Le, Vinh. "A computational framework to analyze human genomes." Journal of Computer Science and Cybernetics 35, no. 2 (June 3, 2019): 105–18. http://dx.doi.org/10.15625/1813-9663/35/2/13827.

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The advent of genomic technologies has led to the current genomic era. Large-scale human genome projects have resulted in a huge amount of genomic data. Analyzing human genomes is a challenging task including a number of key steps from short read alignment, variant calling, and variant annotating. In this paper, the state-of-the-art computational methods and databases for each step will be analyzed to suggest a practical and efficient guideline for whole human genome analyses. This paper also discusses frameworks to combine variants from various genome analysis pipelines to obtain reliable variants. Finally, we will address advantages as well as discordances of widely-used variant annotation methods to evaluate the clinical significance of variants. The review will empower bioinformaticians to efficiently perform human genome analyses, and more importantly, help genetic consultants understand and properly interpret mutations for clinical purposes.
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Cui, Zhe, Jayaram Kancherla, Kyle W. Chang, Niklas Elmqvist, and Héctor Corrada Bravo. "Proactive visual and statistical analysis of genomic data in Epiviz." Bioinformatics 36, no. 7 (November 29, 2019): 2195–201. http://dx.doi.org/10.1093/bioinformatics/btz883.

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Abstract Motivation Integrative analysis of genomic data that includes statistical methods in combination with visual exploration has gained widespread adoption. Many existing methods involve a combination of tools and resources: user interfaces that provide visualization of large genomic datasets, and computational environments that focus on data analyses over various subsets of a given dataset. Over the last few years, we have developed Epiviz as an integrative and interactive genomic data analysis tool that incorporates visualization tightly with state-of-the-art statistical analysis framework. Results In this article, we present Epiviz Feed, a proactive and automatic visual analytics system integrated with Epiviz that alleviates the burden of manually executing data analysis required to test biologically meaningful hypotheses. Results of interest that are proactively identified by server-side computations are listed as notifications in a feed. The feed turns genomic data analysis into a collaborative work between the analyst and the computational environment, which shortens the analysis time and allows the analyst to explore results efficiently. We discuss three ways where the proposed system advances the field of genomic data analysis: (i) takes the first step of proactive data analysis by utilizing available CPU power from the server to automate the analysis process; (ii) summarizes hypothesis test results in a way that analysts can easily understand and investigate; (iii) enables filtering and grouping of analysis results for quick search. This effort provides initial work on systems that substantially expand how computational and visualization frameworks can be tightly integrated to facilitate interactive genomic data analysis. Availability and implementation The source code for Epiviz Feed application is available at http://github.com/epiviz/epiviz_feed_polymer. The Epiviz Computational Server is available at http://github.com/epiviz/epiviz-feed-computation. Please refer to Epiviz documentation site for details: http://epiviz.github.io/.
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Wan, Peng, and Dongsheng Che. "A Computational Framework for Tracing the Origins of Genomic Islands in Prokaryotes." International Scholarly Research Notices 2014 (October 28, 2014): 1–9. http://dx.doi.org/10.1155/2014/732857.

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Genomic islands (GIs) are chunks of genomic fragments that are acquired from nongenealogical organisms through horizontal gene transfer (HGT). Current researches on studying donor-recipient relationships for HGT are limited at a gene level. As more GIs have been identified and verified, the way of studying donor-recipient relationships can be better modeled by using GIs rather than individual genes. In this paper, we report the development of a computational framework for detecting origins of GIs. The main idea of our computational framework is to identify GIs in a query genome, search candidate genomes that contain genomic regions similar to those GIs in the query genome by BLAST search, and then filter out some candidate genomes if those similar genomic regions are also alien (detected by GI detection tools). We have applied our framework in finding the GI origins for Mycobacterium tuberculosis H37Rv, Herminiimonas arsenicoxydans, and three Thermoanaerobacter species. The predicted results were used to establish the donor-recipient network relationships and visualized by Gephi. Our studies have shown that donor genomes detected by our computational approach were mainly consistent with previous studies. Our framework was implemented with Perl and executed on Windows operating system.
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Chorbadjiev, Lubomir, Jude Kendall, Joan Alexander, Viacheslav Zhygulin, Junyan Song, Michael Wigler, and Alexander Krasnitz. "Integrated Computational Pipeline for Single-Cell Genomic Profiling." JCO Clinical Cancer Informatics, no. 4 (September 2020): 464–71. http://dx.doi.org/10.1200/cci.19.00171.

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PURPOSE Copy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research. MATERIALS AND METHODS The pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda. RESULTS Here we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization. CONCLUSION The pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.
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Dissertations / Theses on the topic "Computational Genomic"

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Mumey, Brendan Marshall. "Some computational problems from genomic mapping /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/6932.

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Alkan, Can. "Computational Studies on Evolution and Functionality of Genomic Repeats." Case Western Reserve University School of Graduate Studies / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=case1120143436.

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Gaspar, Paulo Miguel da Silva. "Computational methods for gene characterization and genomic knowledge extraction." Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13949.

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Doutoramento conjunto MAPi em Ciências da Computação
Motivation: Medicine and health sciences are changing from the classical symptom-based to a more personalized and genetics-based paradigm, with an invaluable impact in health-care. While advancements in genetics were already contributing significantly to the knowledge of the human organism, the breakthrough achieved by several recent initiatives provided a comprehensive characterization of the human genetic differences, paving the way for a new era of medical diagnosis and personalized medicine. Data generated from these and posterior experiments are now becoming available, but its volume is now well over the humanly feasible to explore. It is then the responsibility of computer scientists to create the means for extracting the information and knowledge contained in that data. Within the available data, genetic structures contain significant amounts of encoded information that has been uncovered in the past decades. Finding, reading and interpreting that information are necessary steps for building computational models of genetic entities, organisms and diseases; a goal that in due course leads to human benefits. Aims: Numerous patterns can be found within the human variome and exome. Exploring these patterns enables the computational analysis and manipulation of digital genomic data, but requires specialized algorithmic approaches. In this work we sought to create and explore efficient methodologies to computationally calculate and combine known biological patterns for various purposes, such as the in silico optimization of genetic structures, analysis of human genes, and prediction of pathogenicity from human genetic variants. Results: We devised several computational strategies to evaluate genes, explore genomes, manipulate sequences, and analyze patients’ variomes. By resorting to combinatorial and optimization techniques we were able to create and combine sequence redesign algorithms to control genetic structures; by combining the access to several web-services and external resources we created tools to explore and analyze available genetic data and patient data; and by using machine learning we developed a workflow for analyzing human mutations and predicting their pathogenicity.
Motivação: A medicina e as ciências da saúde estão atualmente num processo de alteração que muda o paradigma clássico baseado em sintomas para um personalizado e baseado na genética. O valor do impacto desta mudança nos cuidados da saúde é inestimável. Não obstante as contribuições dos avanços na genética para o conhecimento do organismo humano até agora, as descobertas realizadas recentemente por algumas iniciativas forneceram uma caracterização detalhada das diferenças genéticas humanas, abrindo o caminho a uma nova era de diagnóstico médico e medicina personalizada. Os dados gerados por estas e outras iniciativas estão disponíveis mas o seu volume está muito para lá do humanamente explorável, e é portanto da responsabilidade dos cientistas informáticos criar os meios para extrair a informação e conhecimento contidos nesses dados. Dentro dos dados disponíveis estão estruturas genéticas que contêm uma quantidade significativa de informação codificada que tem vindo a ser descoberta nas últimas décadas. Encontrar, ler e interpretar essa informação são passos necessários para construir modelos computacionais de entidades genéticas, organismos e doenças; uma meta que, em devido tempo, leva a benefícios humanos. Objetivos: É possível encontrar vários padrões no varioma e exoma humano. Explorar estes padrões permite a análise e manipulação computacional de dados genéticos digitais, mas requer algoritmos especializados. Neste trabalho procurámos criar e explorar metodologias eficientes para o cálculo e combinação de padrões biológicos conhecidos, com a intenção de realizar otimizações in silico de estruturas genéticas, análises de genes humanos, e previsão da patogenicidade a partir de diferenças genéticas humanas. Resultados: Concebemos várias estratégias computacionais para avaliar genes, explorar genomas, manipular sequências, e analisar o varioma de pacientes. Recorrendo a técnicas combinatórias e de otimização criámos e conjugámos algoritmos de redesenho de sequências para controlar estruturas genéticas; através da combinação do acesso a vários web-services e recursos externos criámos ferramentas para explorar e analisar dados genéticos, incluindo dados de pacientes; e através da aprendizagem automática desenvolvemos um procedimento para analisar mutações humanas e prever a sua patogenicidade.
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SINHA, AMIT U. "Discovery and Analysis of Genomic Patterns: Applications to Transcription Factor Binding and Genome Rearrangement." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1204227723.

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Saha, Mandal Arnab. "Computational Analysis of the Evolution of Non-Coding Genomic Sequences." University of Toledo Health Science Campus / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=mco1372349811.

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Danks, Jacob R. "Algorithm Optimizations in Genomic Analysis Using Entropic Dissection." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804921/.

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In recent years, the collection of genomic data has skyrocketed and databases of genomic data are growing at a faster rate than ever before. Although many computational methods have been developed to interpret these data, they tend to struggle to process the ever increasing file sizes that are being produced and fail to take advantage of the advances in multi-core processors by using parallel processing. In some instances, loss of accuracy has been a necessary trade off to allow faster computation of the data. This thesis discusses one such algorithm that has been developed and how changes were made to allow larger input file sizes and reduce the time required to achieve a result without sacrificing accuracy. An information entropy based algorithm was used as a basis to demonstrate these techniques. The algorithm dissects the distinctive patterns underlying genomic data efficiently requiring no a priori knowledge, and thus is applicable in a variety of biological research applications. This research describes how parallel processing and object-oriented programming techniques were used to process larger files in less time and achieve a more accurate result from the algorithm. Through object oriented techniques, the maximum allowable input file size was significantly increased from 200 mb to 2000 mb. Using parallel processing techniques allowed the program to finish processing data in less than half the time of the sequential version. The accuracy of the algorithm was improved by reducing data loss throughout the algorithm. Finally, adding user-friendly options enabled the program to use requests more effectively and further customize the logic used within the algorithm.
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CICCOLELLA, SIMONE. "Practical algorithms for Computational Phylogenetics." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2022. http://hdl.handle.net/10281/364980.

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In questo manoscritto vengono discussi le principali sfide computazionali nel campo della inferenza di filogenesi tumorale a vengono proposte diverse soluzione per i tre principali problemi di (i) ricostruzione dell'evoluzioni di un campione tumorale, (ii) clustering di dati SCS per una piu' pulita e veloce inferenza e (iii) il confronto di diverse filogenesi. Inoltre viene discusso come combinare le diverse soluzioni in una singola pipeline per una piu' rapida analisi.
In this manuscript we described the main computational challenges of the cancer phylogenetic field and we proposed different solutions for the three main problems of (i) the progression reconstruction of a tumor sample, (ii) the clustering of SCS data to allow for a cleaner and faster inference and (iii) the evaluation of different phylogenies. Furthermore we combined them into a usable pipeline to allow for a faster analysis.
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Picard, Colette Lafontaine. "Dynamics of DNA methylation and genomic imprinting in arabidopsis." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122539.

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Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 210-226).
DNA methylation is an epigenetic mark that is highly conserved and important in diverse cellular processes, ranging from transposon silencing to genomic imprinting. In plants, DNA methylation is both mitotically and meiotically heritable, and changes in DNA methylation can be generationally stable and have long-lasting consequences. This thesis aims to improve understanding of DNA methylation dynamics in plants, particularly across generations and during reproduction. In the first project, I present an analysis of the generational dynamics of gene body methylation using recombinant inbred lines derived from differentially methylated parents. I show that while gene body methylation is highly generationally stable, changes in methylation state occur nonrandomly and are enriched in regions of intermediate methylation.
Important DNA methylation changes also occur during seed development in flowering plants, and these changes underlie genomic imprinting, the phenomenon of parent-of-origin specific gene expression. In plants, imprinting occurs in the endosperm, a seed tissue that functions analogously to the mammalian placenta. Imprinted expression is linked to DNA methylation patterns that serve to differentiate the maternally- and paternally-inherited alleles, but the mechanisms used to achieve imprinted expression are often unknown. I next explore imprinted expression and DNA methylation in Arabidopsis lyrata, a close relative of the model plant Arabidopsis thaliana. I find that the majority of imprinted genes in A. lyrata endosperm are also imprinted in A. thaliana, suggesting that imprinted expression is generally conserved. Surprisingly, a subset of A. lyrata imprinted genes are associated with a novel DNA methylation pattern and may be regulated by a different mechanism than their A.
thaliana counterparts. I then explore the genetics of paternal suppression of the seed abortion phenotype caused by mutation of a maternally expressed imprinted gene. Finally, I present the first large single-nuclei RNA-seq dataset generated in plants, reporting data from 1,093 individual nuclei obtained from developing seeds. I find evidence of previously uncharacterized cell states in endosperm, and examine imprinted expression at the single-cell level. Together, these projects contribute to our understanding of DNA methylation and imprinting dynamics during plant development, and highlight the strong generational stability of certain DNA methylation patterns.
by Colette Lafontaine Picard.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Computational and Systems Biology Program
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Rezwan, Faisal Ibne. "Improving computational predictions of Cis-regulatory binding sites in genomic data." Thesis, University of Hertfordshire, 2011. http://hdl.handle.net/2299/7133.

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Cis-regulatory elements are the short regions of DNA to which specific regulatory proteins bind and these interactions subsequently influence the level of transcription for associated genes, by inhibiting or enhancing the transcription process. It is known that much of the genetic change underlying morphological evolution takes place in these regions, rather than in the coding regions of genes. Identifying these sites in a genome is a non-trivial problem. Experimental (wet-lab) methods for finding binding sites exist, but all have some limitations regarding their applicability, accuracy, availability or cost. On the other hand computational methods for predicting the position of binding sites are less expensive and faster. Unfortunately, however, these algorithms perform rather poorly, some missing most binding sites and others over-predicting their presence. The aim of this thesis is to develop and improve computational approaches for the prediction of transcription factor binding sites (TFBSs) by integrating the results of computational algorithms and other sources of complementary biological evidence. Previous related work involved the use of machine learning algorithms for integrating predictions of TFBSs, with particular emphasis on the use of the Support Vector Machine (SVM). This thesis has built upon, extended and considerably improved this earlier work. Data from two organisms was used here. Firstly the relatively simple genome of yeast was used. In yeast, the binding sites are fairly well characterised and they are normally located near the genes that they regulate. The techniques used on the yeast genome were also tested on the more complex genome of the mouse. It is known that the regulatory mechanisms of the eukaryotic species, mouse, is considerably more complex and it was therefore interesting to investigate the techniques described here on such an organism. The initial results were however not particularly encouraging: although a small improvement on the base algorithms could be obtained, the predictions were still of low quality. This was the case for both the yeast and mouse genomes. However, when the negatively labeled vectors in the training set were changed, a substantial improvement in performance was observed. The first change was to choose regions in the mouse genome a long way (distal) from a gene over 4000 base pairs away - as regions not containing binding sites. This produced a major improvement in performance. The second change was simply to use randomised training vectors, which contained no meaningful biological information, as the negative class. This gave some improvement over the yeast genome, but had a very substantial benefit for the mouse data, considerably improving on the aforementioned distal negative training data. In fact the resulting classifier was finding over 80% of the binding sites in the test set and moreover 80% of the predictions were correct. The final experiment used an updated version of the yeast dataset, using more state of the art algorithms and more recent TFBSs annotation data. Here it was found that using randomised or distal negative examples once again gave very good results, comparable to the results obtained on the mouse genome. Another source of negative data was tried for this yeast data, namely using vectors taken from intronic regions. Interestingly this gave the best results.
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Alkhnbashi, Omer S. [Verfasser], and Rolf [Akademischer Betreuer] Backofen. "Computational characterisation of genomic CRISPR-Cas systems in archaea and bacteria." Freiburg : Universität, 2017. http://d-nb.info/1139210904/34.

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Books on the topic "Computational Genomic"

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Nalbantoglu, Ozkan Ufuk, and Khalid Sayood. Computational Genomic Signatures. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01650-9.

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Dassanayake, Ranil S. Genomic and proteomic techniques: In post genomics era. Oxford: Alpha Science International, 2011.

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Bioinformatics in the post-genomic era: Genome, transcriptome, proteome, and information-based medicine. Boston: Addison-Wesley, 2005.

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Masood, Nosheen, and Saima Shakil Malik, eds. 'Essentials of Cancer Genomic, Computational Approaches and Precision Medicine. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1067-0.

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Inc, ebrary, ed. Advances in genomic sequence analysis and pattern discovery. Hackensack, N.J: World Scientific, 2011.

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Dennis, Wigle, Jurisica Igor, and Wong Bill, eds. Cancer informatics in the post genomic era. New York: Springer, 2007.

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Dwyer, Rex A. Genomic Perl: From bioinformatics basics to working code. Cambridge: Cambridge University Press, 2003.

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Y, Galperin Michael, and Koonin Eugene V, eds. Frontiers in computational genomics. Norfolk, U.K: Caister, 2003.

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Shugart, Yin Yao, ed. Applied Computational Genomics. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5558-1.

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Yao, Yin, ed. Applied Computational Genomics. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1071-3.

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Book chapters on the topic "Computational Genomic"

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Nalbantoglu, Ozkan Ufuk, and Khalid Sayood. "Applications: Phylogeny Construction." In Computational Genomic Signatures, 49–60. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01650-9_4.

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Wong, Bill, and Igor Jurisica. "Computational Platforms." In Cancer Informatics in the Post Genomic Era, 85–86. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-69321-7_6.

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Zhang, Jie. "Biostatistics, Data Mining and Computational Modeling." In Genomic Approach to Asthma, 339–47. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8764-6_15.

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Ritter, Otto. "The Integrated Genomic Database (IGD)." In Computational Methods in Genome Research, 57–73. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2451-9_5.

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Jurisica, Igor. "Integrative Computational Biology." In Cancer Informatics in the Post Genomic Era, 129–45. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-69321-7_8.

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Sharma, Abhishek, and Ashok Kumar. "Genomics, Transcriptomics and Proteomics for Computational Biology." In Genomic, Proteomics, and Biotechnology, 1–11. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003220831-1.

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Fertin, Guillaume, and Irena Rusu. "Computing Genomic Distances: An Algorithmic Viewpoint." In Algorithms in Computational Molecular Biology, 773–97. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470892107.ch34.

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Duitama, Jorge. "Genomic Variants Detection and Genotyping." In Computational Methods for Next Generation Sequencing Data Analysis, 133–47. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119272182.ch6.

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Zanetti, João Paulo Pereira, Leonid Chindelevitch, and João Meidanis. "Generalizations of the Genomic Rank Distance to Indels." In Algorithms for Computational Biology, 152–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18174-1_11.

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Tapia, José Juan, Enrique Morett, and Edgar E. Vallejo. "A Clustering Genetic Algorithm for Genomic Data Mining." In Studies in Computational Intelligence, 249–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01088-0_11.

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Conference papers on the topic "Computational Genomic"

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Kanwal, Sehrish, Andrew Lonie, and Richard O. Sinnott. "Digital reproducibility requirements of computational genomic workflows." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217887.

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"Computational Solutions to Explore Genomic 3D Organization." In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995500.

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Ngo, Mathias, and Raphael Labayrade. "Multi-Genomic Algorithms." In 2014 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM). IEEE, 2014. http://dx.doi.org/10.1109/mcdm.2014.7007187.

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Cannataro, Mario. "Session details: Genomic variation." In BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3478666.

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Nabavi, Sheida. "Session details: Genomic variation." In BCB '22: 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3552480.

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"Computational pipeline for genomic epidemiology surveillance of pathogenic bacteria." In Bioinformatics of Genome Regulation and Structure/ Systems Biology. institute of cytology and genetics siberian branch of the russian academy of science, Novosibirsk State University, 2020. http://dx.doi.org/10.18699/bgrs/sb-2020-068.

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Occhipinti, Annalisa, and Claudio Angione. "A Computational Model of Cancer Metabolism for Personalised Medicine." In Building Bridges in Medical Science 2021. Cambridge Medicine Journal, 2021. http://dx.doi.org/10.7244/cmj.2021.03.001.3.

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Cancer cells must rewrite their ‘‘internal code’’ to satisfy the demand for growth and proliferation. Such changes are driven by a combination of genetic (e.g., genes’ mutations) and non-genetic factors (e.g., tumour microenvironment) that result in an alteration of cellular metabolism. For this reason, understanding the metabolic and genomic changes of a cancer cell can provide useful insight on cancer progression and survival outcomes. In our work, we present a computational framework that uses patient-specific data to investigate cancer metabolism and provide personalised survival predictions and cancer development outcomes. The proposed model integrates patient-specific multi-omics data (i.e., genomic, metabolomic and clinical data) into a metabolic model of cancer to produce a list of metabolic reactions affecting cancer progression. Quantitative and predictive analysis, through survival analysis and machine learning techniques, is then performed on the list of selected reactions. Since our model performs an analysis of patient-specific data, the outcome of our pipeline provides a personalised prediction of survival outcome and cancer development based on a subset of identified multi-omics features (genomic, metabolomic and clinical data). In particular, our work aims to develop a computational pipeline for clinicians that relates the omic profile of each patient to their survival probability, based on a combination of machine learning and metabolic modelling techniques. The model provides patient-specific predictions on cancer development and survival outcomes towards the development of personalised medicine.
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Schwartz, Russell. "Computationally resolving heterogeneity in mixed genomic samples." In 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2016. http://dx.doi.org/10.1109/iccabs.2016.7802796.

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Yue, Dong, Yidong Chen, Shou-Jiang Gao, and Yufei Huang. "Computational prediction of microRNA regulatory pathways." In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169469.

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Kim, Sungeun, Li Shen, Andrew J. Saykin, and John D. West. "Data synthesis and tool development for exploring imaging genomic patterns." In 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2009. http://dx.doi.org/10.1109/cibcb.2009.4925742.

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Reports on the topic "Computational Genomic"

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Stevens, Rick. Development of an Extensible Computational Framework for Centralized Storage and Distributed Curation and Analysis of Genomic Data Genome-scale Metabolic Models. Office of Scientific and Technical Information (OSTI), August 2010. http://dx.doi.org/10.2172/1234257.

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Fluhr, Robert, and Volker Brendel. Harnessing the genetic diversity engendered by alternative gene splicing. United States Department of Agriculture, December 2005. http://dx.doi.org/10.32747/2005.7696517.bard.

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Our original objectives were to assess the unexplored dimension of alternative splicing as a source of genetic variation. In particular, we sought to initially establish an alternative splicing database for Arabidopsis, the only plant for which a near-complete genome has been assembled. Our goal was to then use the database, in part, to advance plant gene prediction programs that are currently a limiting factor in annotating genomic sequence data and thus will facilitate the exploitation of the ever increasing quantity of raw genomic data accumulating for plants. Additionally, the database was to be used to generate probes for establishing high-throughput alternative transcriptome analysis in the form of a splicing-specific oligonucleotide microarray. We achieved the first goal and established a database and web site termed Alternative Splicing In Plants (ASIP, http://www.plantgdb.org/ASIP/). We also thoroughly reviewed the extent of alternative splicing in plants (Arabidopsis and rice) and proposed mechanisms for transcript processing. We noted that the repertoire of plant alternative splicing differs from that encountered in animals. For example, intron retention turned out to be the major type. This surprising development was proven by direct RNA isolation techniques. We further analyzed EST databases available from many plants and developed a process to assess their alternative splicing rate. Our results show that the lager genome-sized plant species have enhanced rates of alternative splicing. We did advance gene prediction accuracy in plants by incorporating scoring for non-canonical introns. Our data and programs are now being used in the continuing annotation of plant genomes of agronomic importance, including corn, soybean, and tomato. Based on the gene annotation data developed in the early part of the project, it turned out that specific probes for different exons could not be scaled up to a large array because no uniform hybridization conditions could be found. Therefore, we modified our original objective to design and produce an oligonucleotide microarray for probing alternative splicing and realized that it may be reasonable to investigate the extent of alternative splicing using novel commercial whole genome arrays. This possibility was directly examined by establishing algorithms for the analysis of such arrays. The predictive value of the algorithms was then shown by isolation and verification of alternative splicing predictions from the published whole genome array databases. The BARD-funded work provides a significant advance in understanding the extent and possible roles of alternative splicing in plants as well as a foundation for advances in computational gene prediction.
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Fridman, Eyal, Jianming Yu, and Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597925.bard.

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Heterosis, the enigmatic phenomenon in which whole genome heterozygous hybrids demonstrate superior fitness compared to their homozygous parents, is the main cornerstone of modern crop plant breeding. One explanation for this non-additive inheritance of hybrids is interaction of alleles within the same locus. This proposal aims at screening, identifying and investigating heterosis trait loci (HTL) for different yield traits by implementing a novel integrated mapping approach in Sorghum bicolor as a model for other crop plants. Originally, the general goal of this research was to perform a genetic dissection of heterosis in a diallel built from a set of Sorghum bicolor inbred lines. This was conducted by implementing a novel computational algorithm which aims at associating between specific heterozygosity found among hybrids with heterotic variation for different agronomic traits. The initial goals of the research are: (i) Perform genotype by sequencing (GBS) of the founder lines (ii) To evaluate the heterotic variation found in the diallel by performing field trails and measurements in the field (iii) To perform QTL analysis for identifying heterotic trait loci (HTL) (iv) to validate candidate HTL by testing the quantitative mode of inheritance in F2 populations, and (v) To identify candidate HTL in NAM founder lines and fine map these loci by test-cross selected RIL derived from these founders. The genetic mapping was initially achieved with app. 100 SSR markers, and later the founder lines were genotyped by sequencing. In addition to the original proposed research we have added two additional populations that were utilized to further develop the HTL mapping approach; (1) A diallel of budding yeast (Saccharomyces cerevisiae) that was tested for heterosis of doubling time, and (2) a recombinant inbred line population of Sorghum bicolor that allowed testing in the field and in more depth the contribution of heterosis to plant height, as well as to achieve novel simulation for predicting dominant and additive effects in tightly linked loci on pseudooverdominance. There are several conclusions relevant to crop plants in general and to sorghum breeding and biology in particular: (i) heterosis for reproductive (1), vegetative (2) and metabolic phenotypes is predominantly achieved via dominance complementation. (ii) most loci that seems to be inherited as overdominant are in fact achieving superior phenotype of the heterozygous due to linkage in repulsion, namely by pseudooverdominant mechanism. Our computer simulations show that such repulsion linkage could influence QTL detection and estimation of effect in segregating populations. (iii) A new height QTL (qHT7.1) was identified near the genomic region harboring the known auxin transporter Dw3 in sorghum, and its genetic dissection in RIL population demonstrated that it affects both the upper and lower parts of the plant, whereas Dw3 affects only the part below the flag leaf. (iv) HTL mapping for grain nitrogen content in sorghum grains has identified several candidate genes that regulate this trait, including several putative nitrate transporters and a transcription factor belonging to the no-apical meristem (NAC)-like large gene family. This activity was combined with another BARD-funded project in which several de-novo mutants in this gene were identified for functional analysis.
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Edwards, Jeremy, S. Metabolic engineering of deinococcus radiodurans based on computational analysis and functional genomics. Office of Scientific and Technical Information (OSTI), February 2005. http://dx.doi.org/10.2172/836597.

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Ebrahim, Ali. Development and Dissemination of Computational Methods for Genome-scale Modeling. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1468961.

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Ovcharenko, I. FY06 LDRD Final Report "Development of Computational Techniques For Decoding The Language of Genomes". Office of Scientific and Technical Information (OSTI), January 2007. http://dx.doi.org/10.2172/899447.

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