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Relevant bibliographies by topics / Functional bioinformatics

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Academic literature on the topic 'Functional bioinformatics'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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Journal articles on the topic "Functional bioinformatics"

1

Edwards, Y. J. K. "Bioinformatics and Functional Genomics." Briefings in Functional Genomics and Proteomics 3, no. 2 (2004): 187–90. http://dx.doi.org/10.1093/bfgp/3.2.187.

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Clare, A., A. Karwath, H. Ougham, and R. D. King. "Functional bioinformatics for Arabidopsis thaliana." Bioinformatics 22, no. 9 (2006): 1130–36. http://dx.doi.org/10.1093/bioinformatics/btl051.

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Clare, A., A. Karwath, H. Ougham, and R. D. King. "Functional bioinformatics for Arabidopsis thaliana." Bioinformatics 22, no. 13 (2006): 1674. http://dx.doi.org/10.1093/bioinformatics/btl169.

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Deng, Youping, Hongwei Wang, Ryuji Hamamoto, David Schaffer, and Shiwei Duan. "Functional Genomics, Genetics, and Bioinformatics." BioMed Research International 2015 (2015): 1–3. http://dx.doi.org/10.1155/2015/184824.

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Weichselbaum, David, Bojan Zagrovic, and Anton A. Polyansky. "Fuento: functional enrichment for bioinformatics." Bioinformatics 33, no. 16 (2017): 2604–6. http://dx.doi.org/10.1093/bioinformatics/btx179.

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Sorace, James. "Functional bioinformatics the cellular response database." Frontiers in Bioscience 2, no. 1 (1997): a31–36. http://dx.doi.org/10.2741/a160.

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Deng, Youping, Hongwei Wang, Ryuji Hamamoto, Shiwei Duan, Mehdi Pirooznia, and Yongsheng Bai. "Functional Genomics, Genetics, and Bioinformatics 2016." BioMed Research International 2016 (2016): 1–3. http://dx.doi.org/10.1155/2016/2625831.

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Hatzimaikatis, Vassily. "Bioinformatics and functional genomics: Challenges and opportunities." AIChE Journal 46, no. 12 (2000): 2340–43. http://dx.doi.org/10.1002/aic.690461202.

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Khaldi, Nora. "Bioinformatics approaches for identifying new therapeutic bioactive peptides in food." Functional Foods in Health and Disease 2, no. 10 (2012): 325. http://dx.doi.org/10.31989/ffhd.v2i10.80.

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The traditional methods for mining foods for bioactive peptides are tedious and long. Similar to the drug industry, the length of time to identify and deliver a commercial health ingredient that reduces disease symptoms can take anything between 5 to 10 years. Reducing this time and effort is crucial in order to create new commercially viable products with clear and important health benefits. In the past few years, bioinformatics, the science that brings together fast computational biology, and efficient genome mining, is appearing as the long awaited solution to this problem. By quickly mining food genomes for characteristics of certain food therapeutic ingredients, researchers can potentially find new ones in a matter of a few weeks. Yet, surprisingly, very little success has been achieved so far using bioinformatics in mining for food bioactives. The absence of food specific bioinformatic mining tools, the slow integration of both experimental mining and bioinformatics, and the important difference between different experimental platforms are some of the reasons for the slow progress of bioinformatics in the field of functional food and more specifically in bioactive peptide discovery. In this paper I discuss some methods that could be easily translated, using a rational peptide bioinformatics design, to food bioactive peptide mining. I highlight the need for an integrated food peptide database. I also discuss how to better integrate experimental work with bioinformatics in order to improve the mining of food for bioactive peptides, therefore achieving a higher success rates.Keywords: bioactive peptides, bioinformatics, mining food, therapeutic properties, food proteins, functional food.

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Cai, Y. D., and A. J. Doig. "Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition." Bioinformatics 20, no. 8 (2004): 1292–300. http://dx.doi.org/10.1093/bioinformatics/bth085.

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Dissertations / Theses on the topic "Functional bioinformatics"

1

Bresell, Anders. "Characterization of protein families, sequence patterns, and functional annotations in large data sets." Doctoral thesis, Linköping : Department of Physics, Chemistry and Biology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10565.

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Kemmer, Danielle. "Genomics and bioinformatics approaches to functional gene annotation /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-636-0/.

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Johansson, Annelie. "Identifying gene regulatory interactions using functional genomics data." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-230285.

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Previously studies used correlation of DNase I hypersensitivity sites sequencing (DNase-seq) experiments to predict interactions between enhancers and its target promoter gene. We investigate the correlation methods Pearson’s correlation and Mutual Information, using DNase-seq data for 100 cell-types in regions on chromosome one. To assess the performances, we compared our results of correlation scores to Hi-C data from Jin et al. 2013. We showed that the performances are low when comparing it to the Hi-C data, and there is a need of improved correlation metrics. We also demonstrate that the use of Hi-C data as a gold standard is limited, because of its low resolution, and we suggest using another gold standard in further studies.

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Perkins, J. R. "Functional genomics and bioinformatics protocols for the elucidation of pain." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1384822/.

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Microarray technologies enable us to profile the expression of thousands of gene transcripts within a given cell or tissue. Within pain research they have been used extensively to search for genes that change in expression as a result of the induction of a clinically-relevant pain state, often using an animal model of pain. Studying these genes has led to improvements in our understanding of the genes, pathways and other biological processes involved in pain. These themes are explored further in the first (introductory) chapter of this thesis. These experiments result in large numbers of genes declared differentially expressed between samples, many of which are not directly involved in pain. There is often little overlap of these genes between different pain models. The second chapter of this thesis is concerned with the use of systems biology methods to prioritise these genes based on their likelihood of being pain-related. In the third chapter a web-based software application is described. It allows a pain researcher to combine data from various pain-related microarray experiments with other data sources in order to build their own pain networks. Exemplary usage scenarios are presented. The fourth chapter describes a comparison between microarrays and a new technology, RNA-seq, which uses next generation sequencing technology to quantify the RNA present within a tissue. Samples obtained using a well characterised animal pain model, spinal nerve transection, are used for this purpose. In the fifth chapter the effects of RNA-seq sequencing depth on the detection of differentially expressed genes and the discovery of novel transcribed regions of the genome are investigated. In keeping with the theme of gene expression profiling using animal models of pain, the sixth chapter of this thesis reports a software package for the analysis of high-throughput RT-qPCR data and presents an experiment in which this package was used to analyse cytokine expression.

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Martin, Paul. "Post-GWAS bioinformatics and functional analysis of disease susceptibility loci." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/postgwas-bioinformatics-and-functional-analysis-of-disease-susceptibility-loci(cc0e6cee-5c32-4b75-b3d3-f7c18b6f126d).html.

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Genome-wide association studies (GWAS) have been tremendously successful in identifying genetic variants associated with complex diseases, such as rheumatoid arthritis (RA). However, the majority of these associations lie outside traditional protein coding regions and do not necessarily represent the causal effect. Therefore, the challenges post-GWAS are to identify causal variants, link them to target genes and explore the functional mechanisms involved in disease. The aim of the work presented here is to use high level bioinformatics to help address these challenges. There is now an increasing amount of experimental data generated by several large consortia with the aim of characterising the non-coding regions of the human genome, which has the ability to refine and prioritise genetic associations. However, whilst being publicly available, manually mining and utilising it to full effect can be prohibitive. I developed an automated tool, ASSIMILATOR, which quickly and effectively facilitated the mining and rapid interpretation of this data, inferring the likely functional consequence of variants and informing further investigation. This was used in a large extended GWAS in RA which assessed the functional impact of associated variants at the 22q12 locus, showing evidence that they could affect gene regulation. Environmental factors, such as vitamin D, can also affect gene regulation, increasing the risk of disease but are generally not incorporated into most GWAS. Vitamin D deficiency is common in RA and can regulate genes through vitamin D response elements (VDREs). I interrogated a large, publicly available VDRE ChIP-Seq dataset using a permutation testing approach to test for VDRE enrichment in RA loci. This study was the first comprehensive analysis of VDREs and RA associated variants and showed that they are enriched for VDREs, suggesting an involvement of vitamin D in RA.Indeed, evidence suggests that disease associated variants effect gene regulation through enhancer elements. These can act over large distances through physical interactions. A newly developed technique, Capture Hi-C, was used to identify regions of the genome which physically interact with associated variants for four autoimmune diseases. This study showed the complex physical interactions between genetic elements, which could be mediated by regions associated with disease. This work is pivotal in fully characterising genetic associations and determining their effect on disease. Further work has re-defined the 6q23 locus, a region associated with multiple diseases, resulting in a major re-evaluation of the likely causal gene in RA from TNFAIP3 to IL20RA, a druggable target, illustrating the huge potential of this research. Furthermore, it has been used to study the genetic associations unique to multiple sclerosis in the same region, showing chromatin interactions which support previously implicated genes and identify novel candidates. This could help improve our understanding and treatment of the disease. Bioinformatics is fundamental to fully exploit new and existing datasets and has made many positive impacts on our understanding of complex disease. This empowers researchers to fully explore disease aetiology and to further the discovery of new therapies.

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Reddy, Joseph. "Identification and Analysis of Important Proteins in Protein Interaction Networks Using Functional and Topological Information." Thesis, University of Skövde, School of Life Sciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-2395.

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<p>Studying protein interaction networks using functional and topological information is important for understanding cellular organization and functionality. This study deals with identifying important proteins in protein interaction networks using SWEMODE (Lubovac, et al, 2006) and analyzing topological and functional properties of these proteins with the help of information derived from modular organization in protein interaction networks as well as information available in public resources, in this case, annotation sources describing the functionality of proteins. Multi-modular proteins are short-listed from the modules generated by SWEMODE. Properties of these short-listed proteins are then analyzed using functional information from SGD Gene Ontology(GO) (Dwight, et al., 2002) and MIPS functional categories (Ruepp, et al., 2004). Topological features such as lethality and centrality of these proteins are also investigated, using graph theoretic properties and information on lethal genes from Yeast Hub (Kei-Hoi, et al., 2005). The findings of the study based on GO terms reveal that these important proteins are mostly involved in the biological process of “organelle organization and biogenesis” and a majority of these proteins belong to MIPS “cellular organization” and “transcription” functional categories. A study of lethality reveals that multi-modular proteins are more likely to be lethal than proteins present only in a single module. An examination of centrality (degree of connectivity of proteins) in the network reveals that the ratio of number of important proteins to number of hubs at different hub sizes increases with the hub size (degree).</p>

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Kusnierczyk, Waclaw. "Augmenting Bioinformatics Research with Biomedical Ontologies." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2001.

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<p>The main objective of the reported study was to investigate how biomedical ontologies, logically structured representations of various aspects of the biomedical reality, can help researchers in analyzing experimental data. The dissertation reports two attempts to construct tools for the analysis of high-throughput experimental results using explicit domain knowledge representations. Furthermore, integrative efforts made by the community of Open Biomedical Ontologies (OBO), in which the author has participated, are reported, and a framework for consistently connecting the Gene Ontology (GO) with the Taxonomy of Species is proposed and discussed.</p>

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Jadhav, Trishul. "Knowledge Based Gene Set analysis (KB-GSA) : A novel method for gene expression analysis." Thesis, University of Skövde, School of Life Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4352.

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<p>Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method <strong><em>(Knowledge Based Gene Set Analysis: KB-GSA</em></strong>) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.</p>

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Petri, Eric D. C. "Bioinformatics Tools for Finding the Vocabularies of Genomes." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1213730223.

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Bruskiewich, Richard Michael Maurice. "Genomic mapping, functional analysis and bioinformatics of the Werner syndrome locus (WRN)." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0013/NQ38860.pdf.

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Books on the topic "Functional bioinformatics"

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Bioinformatics and functional genomics. Wiley-Liss, 2004.

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Pevsner, Jonathan. Bioinformatics and Functional Genomics. John Wiley & Sons, Ltd., 2005.

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Pevsner, Jonathan. Bioinformatics and Functional Genomics. John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/047145916x.

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Bioinformatics and functional genomics. 2nd ed. Wiley, 2009.

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Koyutürk, Mehmet, Shankar Subramaniam, and Ananth Grama, eds. Functional Coherence of Molecular Networks in Bioinformatics. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-0320-3.

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Sheng wu xin xi xue ji shu zai shui dao gong neng ji yin yan jiu zhong de ying yong. Zhongguo huan jing ke xue chu ban she, 2008.

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FIMH 2007 (2007 Salt Lake City, Utah). Functional imaging and modeling of the heart: 4th international conference, FIMH 2007, Salt Lake City, UT, USA, June 7-9, 2007 : proceedings. Springer, 2007.

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Gromiha, M. Michael. Protein bioinformatics: From sequence to function. Academic Press/Elsevier, 2010.

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Metaxas, Dimitris N. Functional Imaging and Modeling of the Heart: 6th International Conference, FIMH 2011, New York City, NY, USA, May 25-27, 2011. Proceedings. Springer Berlin Heidelberg, 2011.

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Rigden, Daniel John. From protein structure to function with bioinformatics. Springer, 2009.

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Book chapters on the topic "Functional bioinformatics"

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Chervitz, Stephen A., Helen Parkinson, Jennifer M. Fostel, et al. "Standards for Functional Genomics." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_15.

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Lee, Insuk, and Edward M. Marcotte. "Integrating Functional Genomics Data." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-429-6_14.

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Pushparaj, Peter Natesan. "Introduction to Functional Bioinformatics." In Essentials of Bioinformatics, Volume I. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-02634-9_11.

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Moreno-Hagelsieb, Gabriel. "Inferring Functional Relationships from Conservation of Gene Order." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-429-6_8.

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Selzer, Paul M., Richard J. Marhöfer, and Oliver Koch. "The Functional Analysis of Genomes." In Applied Bioinformatics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68301-0_6.

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Persson, Bengt. "Bioinformatics in protein analysis." In Proteomics in Functional Genomics. Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8458-7_14.

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Abbai, Ragavendran, Sathiyamoorthy Subramaniyam, Ramya Mathiyalagan, and Deok Chun Yang. "Functional Genomic Approaches in Plant Research." In Plant Bioinformatics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67156-7_8.

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Potter, S. Steven. "Functional Genomics-Renal Development and Disease." In Translational Bioinformatics. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1104-7_20.

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Minguez, Pablo, and Peer Bork. "Bioinformatics Analysis of Functional Associations of PTMs." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_14.

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Sameeullah, Muhammad, Noreen Aslam, Faheem Ahmed Khan, and Muhammad Aasim. "Bioinformatics Tools Make Plant Functional Genomics Studies Easy." In Plant Bioinformatics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67156-7_3.

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Conference papers on the topic "Functional bioinformatics"

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Abraham, K. J., Katrin Sameith, and Francesco Falciani. "Improving Functional Module Detection." In 2009 Ohio Collaborative Conference on Bioinformatics (OCCBIO). IEEE, 2009. http://dx.doi.org/10.1109/occbio.2009.11.

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Skarzyńska, Agnieszka, Magdalena Pawełkowicz, Tomasz Krzywkowski, Katarzyna Świerkula, Wojciech Pląder, and Zbigniew Przybecki. "Bioinformatics pipeline for functional identification and characterization of proteins." In XXXVI Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (Wilga 2015), edited by Ryszard S. Romaniuk. SPIE, 2015. http://dx.doi.org/10.1117/12.2205559.

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Paramonova, E. V., L. A. Avakyan, J. Coutinho, and V. S. Bystrov. "Density functional study of magnetic substitutions in hydroxyapatite." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.83.

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Matkarimov, O. O., D. E. Polivoda, and M. S. Poptsova. "Searching for patterns of association between functional genomic elements." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2018. http://dx.doi.org/10.17537/icmbb18.82.

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Onishchenko, P. S., K. Yu Klyshnikov, M. A. Rezvova, and E. A. Ovcharenko. "The Concept of Automated Functional Design of Heart Valve Prostheses." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2020. http://dx.doi.org/10.17537/icmbb20.16.

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Huang, Wentao, and Youceng Feng. "Small-world properties of human brain functional networks based on resting-state functional MRI." In 2011 International Symposium on Bioelectronics and Bioinformatics (ISBB). IEEE, 2011. http://dx.doi.org/10.1109/isbb.2011.6107657.

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Cho, Young-Rae, and Aidong Zhang. "Discovering Frequent Patterns of Functional Associations in Protein Interaction Networks for Function Prediction." In 2008 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2008. http://dx.doi.org/10.1109/bibm.2008.21.

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Shin Teng, Chia-Feng Lu, Yu-Te Wu, et al. "Investigation of differences on functional connectivity in major depressive disorder using functional magnetic resonance imaging." In 2010 International Conference on Bioinformatics and Biomedical Technology. IEEE, 2010. http://dx.doi.org/10.1109/icbbt.2010.5478999.

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Lei, Seak Fei, and Jun Huan. "Towards Site-Based Protein Functional Annotations." In 2008 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2008. http://dx.doi.org/10.1109/bibm.2008.58.

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Alshalalfa, M., A. Qabaja, T. A. Bismar, and R. Alhajj. "Functional characterization of miRNAs in prostate cancer using functional protein networks." In 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2012. http://dx.doi.org/10.1109/cibcb.2012.6217203.

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