Relevant bibliographies by topics / Protein bioinformatics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Protein bioinformatics'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Protein bioinformatics"

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Droit, Arnaud, Guy G. Poirier, and Joanna M. Hunter. "Experimental and bioinformatic approaches for interrogating protein–protein interactions to determine protein function." Journal of Molecular Endocrinology 34, no. 2 (2005): 263–80. http://dx.doi.org/10.1677/jme.1.01693.

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An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. One strategy to determine protein function is to identify the protein–protein interactions. The increasing use of high-throughput and large-scale bioinformatics-based studies has generated a massive amount of data stored in a number of different databases. A challenge for bioinformatics is to explore this disparate data and to uncover biologically relevant interactions and pathways. In parallel, there is clearly a need for the development of approaches that can predict novel protein–protein interaction networks in silico. Here, we present an overview of different experimental and bioinformatic methods to elucidate protein–protein interactions.
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DiTursi, M. K., S. J. Kwon, P. J. Reeder, and J. S. Dordick. "Bioinformatics-driven, rational engineering of protein thermostability." Protein Engineering Design and Selection 19, no. 11 (2006): 517–24. http://dx.doi.org/10.1093/protein/gzl039.

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Laskowski, Roman, and A. W. Chan. "Bioinformatics and Protein Design." Current Pharmaceutical Biotechnology 3, no. 4 (2002): 317–27. http://dx.doi.org/10.2174/1389201023378157.

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Nugent, Timothy, and David T. Jones. "Membrane protein structural bioinformatics." Journal of Structural Biology 179, no. 3 (2012): 327–37. http://dx.doi.org/10.1016/j.jsb.2011.10.008.

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Gibson, James. "Bioinformatics of Protein Allergenicity." Molecular Nutrition & Food Research 50, no. 7 (2006): 591. http://dx.doi.org/10.1002/mnfr.200690020.

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Kopec, Klaus O., Vikram Alva, and Andrei N. Lupas. "Bioinformatics of the TULIP domain superfamily." Biochemical Society Transactions 39, no. 4 (2011): 1033–38. http://dx.doi.org/10.1042/bst0391033.

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Proteins of the BPI (bactericidal/permeability-increasing protein)-like family contain either one or two tandem copies of a fold that usually provides a tubular cavity for the binding of lipids. Bioinformatic analyses show that, in addition to its known members, which include BPI, LBP [LPS (lipopolysaccharide)-binding protein)], CETP (cholesteryl ester-transfer protein), PLTP (phospholipid-transfer protein) and PLUNC (palate, lung and nasal epithelium clone) protein, this family also includes other, more divergent groups containing hypothetical proteins from fungi, nematodes and deep-branching unicellular eukaryotes. More distantly, BPI-like proteins are related to a family of arthropod proteins that includes hormone-binding proteins (Takeout-like; previously described to adopt a BPI-like fold), allergens and several groups of uncharacterized proteins. At even greater evolutionary distance, BPI-like proteins are homologous with the SMP (synaptotagmin-like, mitochondrial and lipid-binding protein) domains, which are found in proteins associated with eukaryotic membrane processes. In particular, SMP domain-containing proteins of yeast form the ERMES [ER (endoplasmic reticulum)-mitochondria encounter structure], required for efficient phospholipid exchange between these organelles. This suggests that SMP domains themselves bind lipids and mediate their exchange between heterologous membranes. The most distant group of homologues we detected consists of uncharacterized animal proteins annotated as TM (transmembrane) 24. We propose to group these families together into one superfamily that we term as the TULIP (tubular lipid-binding) domain superfamily.
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Deng, M., Z. Tu, F. Sun, and T. Chen. "Mapping gene ontology to proteins based on protein-protein interaction data." Bioinformatics 20, no. 6 (2004): 895–902. http://dx.doi.org/10.1093/bioinformatics/btg500.

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LU, Liang, Dong LI, and Fu-Chu HE. "Bioinformatics advances in protein ubiquitination." Hereditas (Beijing) 35, no. 1 (2013): 17–26. http://dx.doi.org/10.3724/sp.j.1005.2013.00017.

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Mohabatkar, Hassan, Mehrnaz Keyhanfar, and Mandana Behbahani. "Protein Bioinformatics Applied to Virology." Current Protein & Peptide Science 13, no. 6 (2012): 547–59. http://dx.doi.org/10.2174/138920312803582988.

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Radivojac, P. "Protein Structure Prediction: Bioinformatics Approach." Briefings in Bioinformatics 5, no. 2 (2004): 207–9. http://dx.doi.org/10.1093/bib/5.2.207.

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Dissertations / Theses on the topic "Protein bioinformatics"

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Simu, Tiberiu. "A method for extracting pathways from Scansite-predicted protein-protein interactions." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-34.

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<p>Protein interaction is an important mechanism for cellular functionality. Predicting protein interactions is available in many cases as computational methods in publicly available resources (for example Scansite). These predictions can be further combined with other information sources to generate hypothetical pathways. However, when using computational methods for building pathways, the process may become time consuming, as it requires multiple iterations and consolidating data from different sources. We have tested whether it is possible to generate graphs of protein-protein interaction by using only domain-motif interaction data and the degree to which it is possible to automate this process by developing a program that is able to aggregate, under user guidance, query results from different information sources. The data sources used are Scansite and SwissProt. Visualisation of the graphs is done with an external program freely available for academic purposes, Osprey. The graphs obtained by running the software show that although it is possible to combine publicly available data and theoretical protein-protein interaction predictions from Scansite, further efforts are needed to increase the biological plausibility of these collections of data. It is possible, however, to reduce the dimensionality of the obtained graphs by focusing the searches on a certain tissue of interest.</p>
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Snøve, Jr Ola. "Hardware-accelerated analysis of non-protein-coding RNAs." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-713.

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<p>A tremendous amount of genomic sequence data of relatively high quality has become publicly available due to the human genome sequencing projects that were completed a few years ago. Despite considerable efforts, we do not yet know everything that is to know about the various parts of the genome, what all the regions code for, and how their gene products contribute in the myriad of biological processes that are performed within the cells. New high-performance methods are needed to extract knowledge from this vast amount of information.</p><p>Furthermore, the traditional view that DNA codes for RNA that codes for protein, which is known as the central dogma of molecular biology, seems to be only part of the story. The discovery of many non-proteincoding gene families with housekeeping and regulatory functions brings an entirely new perspective to molecular biology. Also, sequence analysis of the new gene families require new methods, as there are significant differences between protein-coding and non-protein-coding genes.</p><p>This work describes a new search processor that can search for complex patterns in sequence data for which no efficient lookup-index is known. When several chips are mounted on search cards that are fitted into PCs in a small cluster configuration, the system’s performance is orders of magnitude higher than that of comparable solutions for selected applications. The applications treated in this work fall into two main categories, namely pattern screening and data mining, and both take advantage of the search capacity of the cluster to achieve adequate performance. Specifically, the thesis describes an interactive system for exploration of all types of genomic sequence data. Moreover, a genetic programming-based data mining system finds classifiers that consist of potentially complex patterns that are characteristic for groups of sequences. The screening and mining capacity has been used to develop an algorithm for identification of new non-protein-coding genes in bacteria; a system for rational design of effective and specific short interfering RNA for sequence-specific silencing of protein-coding genes; and an improved algorithmic step for identification of new regulatory targets for the microRNA family of non-protein-coding genes.</p><br>Paper V, VI, and VII are reprinted with kind permision of Elsevier, sciencedirect.com
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Hennerdal, Aron, and Arne Elofsson. "Rapid membrane protein topology prediction." Stockholms universitet, Institutionen för biokemi och biofysik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-61921.

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State-of-the-art methods for topology of α-helical membrane proteins are based on the use of time-consuming multiple sequence alignments obtained from PSI-BLAST or other sources. Here, we examine if it is possible to use the consensus of topology prediction methods that are based on single sequences to obtain a similar accuracy as the more accurate multiple sequence-based methods. Here, we show that TOPCONS-single performs better than any of the other topology prediction methods tested here, but ~6% worse than the best method that is utilizing multiple sequence alignments. AVAILABILITY AND IMPLEMENTATION: TOPCONS-single is available as a web server from http://single.topcons.net/ and is also included for local installation from the web site. In addition, consensus-based topology predictions for the entire international protein index (IPI) is available from the web server and will be updated at regular intervals.
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Björkholm, Patrik. "Method for recognizing local descriptors of protein structures using Hidden Markov Models." Thesis, Linköping University, The Department of Physics, Chemistry and Biology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-11408.

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<p>Being able to predict the sequence-structure relationship in proteins will extend the scope of many bioinformatics tools relying on structure information. Here we use Hidden Markov models (HMM) to recognize and pinpoint the location in target sequences of local structural motifs (local descriptors of protein structure, LDPS) These substructures are composed of three or more segments of amino acid backbone structures that are in proximity with each other in space but not necessarily along the amino acid sequence. We were able to align descriptors to their proper locations in 41.1% of the cases when using models solely built from amino acid information. Using models that also incorporated secondary structure information, we were able to assign 57.8% of the local descriptors to their proper location. Further enhancements in performance was yielded when threading a profile through the Hidden Markov models together with the secondary structure, with this material we were able assign 58,5% of the descriptors to their proper locations. Hidden Markov models were shown to be able to locate LDPS in target sequences, the performance accuracy increases when secondary structure and the profile for the target sequence were used in the models.</p>
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Rao, Aditya. "Tarfetpf: A Plasmodium faciparum protein localization predictor." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-24.

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Rao, Aditya. "TargetPf: A Plasmodium falciparum protein localization predictor." Thesis, University of Skövde, School of Humanities and Informatics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-914.

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<p>Background: In P. falciparum a similarity between the transit peptides of apicoplast and mitochondrial proteins in the context of net positive charge has previously been observed in few proteins. Existing P. falciparum protein localization prediction tools were leveraged in this study to study this similarity in larger sets of these proteins.</p><p>Results: The online public-domain malarial repository PlasmoDB was utilized as the source of apicoplast and mitochondrial protein sequences for the similarity study of the two types of transit peptides. It was found that</p><p>many of the 551 apicoplast-targeted proteins (NEAT proteins) of PlasmoDB may have been wrongly annotated to localize to the apicoplast, as some of these proteins lacked annotations for signal peptides, while others also had annotations for localization to the mitochondrion (NEMT proteins). Also around 50 NEAT proteins could contain signal anchors instead of signal peptides in their N-termini, something that could have an impact on the current theory that explains localization to the apicoplast [1].</p><p>The P. falciparum localization prediction tools were then used to study the similarity in net positive charge between the transit peptides of NEAT and NEMT proteins. It was found that NEAT protein prediction tools like PlasmoAP and PATS could be made to recognize NEMT proteins as NEAT proteins, while the NEMT predicting tool PlasMit could be made to recognize a significant number of NEAT proteins as NEMT. Based on these results a conjecture was proposed that a single technique may be sufficient to predict both apicoplast and mitochondrial transit peptides. An implementation in PERL called TargetPf was implemented to test this conjecture (using PlasmoAP rules), and it reported a total of 408 NEAT</p><p>proteins and 1504 NEMT proteins. This number of predicted NEMT proteins (1504) was significantly higher than the annotated 258 NEMT proteins of plasmoDB, but more in line with the 1200 predictions of the tool PlasMit.</p><p>Conclusions: Some possible ambiguities in the PlasmoDB annotations related to NEAT protein localization were identified in this study. It was also found that existing P. falciparum localization prediction tools can be made to detect transit peptides for which they have not been trained or built for.</p>
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Campbell, M. P. "A bioinformatics approach to protein-protein interactions." Thesis, University of Essex, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426014.

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Elfving, Eric. "Automated annotation of protein families." Thesis, Linköpings universitet, Bioinformatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-69393.

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Introduction: The great challenge in bioinformatics is data integration. The amount of available data is always increasing and there are no common unified standards of where, or how, the data should be stored. The aim of this workis to build an automated tool to annotate the different member families within the protein superfamily of medium-chain dehydrogenases/reductases (MDR), by finding common properties among the member proteins. The goal is to increase the understanding of the MDR superfamily as well as the different member families.This will add to the amount of knowledge gained for free when a new, unannotated, protein is matched as a member to a specific MDR member family. Method: The different types of data available all needed different handling. Textual data was mainly compared as strings while numeric data needed some special handling such as statistical calculations. Ontological data was handled as tree nodes where ancestry between terms had to be considered. This was implemented as a plugin-based system to make the tool easy to extend with additional data sources of different types. Results: The biggest challenge was data incompleteness yielding little (or no) results for some families and thus decreasing the statistical significance of the results. Results show that all the human and mouse MDR members have a Pfam ADH domain (ADH_N and/or ADH_zinc_N) and takes part in an oxidation-reduction process, often with NAD or NADP as cofactor. Many of the proteins contain zinc and are expressed in liver tissue. Conclusions: A python based tool for automatic annotation has been created to annotate the different MDR member families. The tool is easily extendable to be used with new databases and much of the results agrees with information found in literature. The utility and necessity of this system, as well as the quality of its produced results, are expected to only increase over time, even if no additional extensions are produced, as the system itself is able to make further and more detailed inferences as more and more data become available.
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Bottoms, Christopher A. "Bioinformatics of protein bound water." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4188.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2005.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (July 17, 2006) Vita. Includes bibliographical references.
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Wakadkar, Sachin. "Analysis of transmembrane and globular protein depending on their solvent energy." Thesis, University of Skövde, School of Life Sciences, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-2971.

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<p>The number of experimentally determined protein structures in the protein data bank (PDB) is continuously increasing. The common features like; cellular location, function, topology, primary structure, secondary structure, tertiary structure, domains or fold are used to classify them. Therefore, there are various methods available for classification of proteins. In this work we are attempting an additional method for making appropriate classification, i.e. solvent energy. Solvation is one of the most important properties of macromolecules and biological membranes by which they remain stabilized in different environments. The energy required for solvation can be measured in term of solvent energy. Proteins from similar environments are investigated for similar solvent energy. That is, the solvent energy can be used as a measure to analyze and classify proteins. In this project solvent energy of proteins present in the Protein Data Bank (PDB) was calculated by using Jones’ algorithm. The proteins were classified into two classes; transmembrane and globular. The results of statistical analysis showed that the values of solvent energy obtained for two main classes (globular and transmebrane) were from different sets of populations. Thus, by adopting classification based on solvent energy will definitely help for prediction of cellular placement.</p><p> </p>
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Books on the topic "Protein bioinformatics"

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Wu, Cathy H., Cecilia N. Arighi, and Karen E. Ross, eds. Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4.

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Eidhammer, Ingvar, Inge Jonassen, and William R. Taylor. Protein Bioinformatics. John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/9780470092620.

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Pazos, Florencio, and Mónica Chagoyen. Practical Protein Bioinformatics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12727-9.

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Protein modelling with bioinformatics and biophysics. Springer, 2006.

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

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

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1950-, Tsigelny Igor F., ed. Protein structure prediction: Bioinformatic approach. International University Line, 2002.

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Rigden, Daniel John, ed. From Protein Structure to Function with Bioinformatics. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9058-5.

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J. Rigden, Daniel, ed. From Protein Structure to Function with Bioinformatics. Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-024-1069-3.

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Mrozek, Dariusz. Scalable Big Data Analytics for Protein Bioinformatics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98839-9.

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Book chapters on the topic "Protein bioinformatics"

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Lin, Shili, Denise Scholtens, and Sujay Datta. "Protein-Protein Interactions." In Bioinformatics Methods. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315153728-2.

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Clark, Terry. "Protein Sequence Databases." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_10.

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Wu, Sitao, and Yang Zhang. "Protein Structure Prediction." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_11.

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Al-Lazikani, Bissan, Emma E. Hill, and Veronica Morea. "Protein Structure Prediction." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-429-6_2.

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Lin, Shili, Denise Scholtens, and Sujay Datta. "Protein-Protein Interaction Network Analyses." In Bioinformatics Methods. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315153728-3.

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Chen, Chuming, Hongzhan Huang, and Cathy H. Wu. "Protein Bioinformatics Databases and Resources." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_1.

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Wang, Qinghua, Karen E. Ross, Hongzhan Huang, et al. "Analysis of Protein Phosphorylation and Its Functional Impact on Protein–Protein Interactions via Text Mining of the Scientific Literature." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_10.

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Wu, Guanming, and Robin Haw. "Functional Interaction Network Construction and Analysis for Disease Discovery." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_11.

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Tuncbag, Nurcan, Ozlem Keskin, Ruth Nussinov, and Attila Gursoy. "Prediction of Protein Interactions by Structural Matching: Prediction of PPI Networks and the Effects of Mutations on PPIs that Combines Sequence and Structural Information." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_12.

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Pillich, Rudolf T., Jing Chen, Vladimir Rynkov, David Welker, and Dexter Pratt. "NDEx: A Community Resource for Sharing and Publishing of Biological Networks." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_13.

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Conference papers on the topic "Protein bioinformatics"

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Rokde, Chandrayani N., and Manali Kshirsagar. "Bioinformatics: Protein structure prediction." In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, 2013. http://dx.doi.org/10.1109/icccnt.2013.6726753.

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Wang, Jianxin, Wei Peng, Yingjiao Chen, Yu Lu, and Yi Pan. "Identifying essential proteins based on protein domains in protein-protein interaction networks." In 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2013. http://dx.doi.org/10.1109/bibm.2013.6732476.

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Yu Yongkun, Ji Qianlong, and Sun Qingpeng. "Bioinformatics analysis of LeNAC protein." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5689657.

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Hu, Jing, and Yihang Du. "Predicting Moonlighting Proteins from Protein Sequence." In 14th International Conference on Bioinformatics Models, Methods and Algorithms. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011782300003414.

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Sun, Qingpeng, Na Li, Fukuan Zhao, and Yongkun Yu. "Bioinformatics Analysis of Tomato WRKY2 Protein." In 2011 5th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2011. http://dx.doi.org/10.1109/icbbe.2011.5780064.

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SANDER, OLIVER, FRANCISCO S. DOMINGUES, HONGBO ZHU, THOMAS LENGAUER, and INGOLF SOMMER. "STRUCTURAL DESCRIPTORS OF PROTEIN-PROTEIN BINDING SITES." In The 6th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781848161092_0011.

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Sun, Dengdi, and Maolin Hu. "Determining Protein Function by Protein-Protein Interaction Network." In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.12.

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Yibadaiti, Kadier, Yiyi Zu, Jinbao Pan, Qingpeng Sun, and Min Lu. "Isolation and Bioinformatics Analysis of Zma1158 Protein." In 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015). Atlantis Press, 2016. http://dx.doi.org/10.2991/ifeea-15.2016.7.

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Tretyakova, A. V., E. O. Gerasimova, P. A. Krylov, and V. V. Novochadov. "Phylogenetic analysis of the lubricin protein and surfactant-associated proteins B and C." In Mathematical Biology and Bioinformatics. IMPB RAS - Branch of KIAM RAS, 2022. http://dx.doi.org/10.17537/icmbb22.18.

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Yesiltas, Betul, Charlotte Jacobsen, Egon B. Hansen, et al. "Physical and oxidative stability of emulsions stabilized with fractionated potato protein hydrolysates obtained from starch production byproduct: Use of bioinformatics and proteomics." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/xxty9713.

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With the increasing demand for sustainable and functional proteins from alternative sources, it is necessary to use advanced proteomics and bioinformatics tools for more time and cost-efficient research. The identification and release of abundant proteins/peptides from plant-based sources has been gaining significant attention by the food industry in the last decade. Despite its low protein content (1–2%), the magnitude of proteins obtained from the starch industry (~240,000 tons/year) makes potatoes a highly relevant source as a plant-based protein. Previously, we have identified and validated abundant peptides with good emulsifying and antioxidant properties using bioinformatics and proteomics tools as well as in vitro model systems. Using data-driven targeted hydrolysis, we were able to release validated, functional peptides from the potato protein obtained from potato fruit juice, a protein rich by-product of potato starch production. This work focuses on fractionation of potato protein hydrolysates (PPH) obtained through such targeted hydrolysis using trypsin and subsequent fraction characterization. Unfractionated (PPH1) and membrane-fractionated (PPH2 as &gt;10kDa, PPH3 as 10-5kDa, PPH4 as 5-0.8kDa and PPH5 as &lt; 0.8kDa) PPH was characterized for emulsifying and antioxidant properties/potential. Pendant drop technique and dilatational rheology were applied for determining interfacial tension and viscoelasticity of the PPH fractions at the oil-water interface. PPH2 (&gt;10kDa) showed higher decrease of oil-water interfacial tension. All fractions predominantly provided elastic, weak and easily stretchable interfaces. PPH2 provided more rigid interfacial layer than the other fractions. Radical scavenging and metal chelating activities of PPHs were also tested and the best activities were provided by fractions &gt;5kDa. Furthermore, their ability to form physically and oxidatively stable 5% fish oil-in-water emulsions were investigated during 8-day storage and results generally showed that fractions &gt;5kDa provided the best stability followed by the 5–0.8kDa fraction.
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Reports on the topic "Protein bioinformatics"

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.
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Gershoni, Jonathan M., David E. Swayne, Tal Pupko, et al. Discovery and reconstitution of cross-reactive vaccine targets for H5 and H9 avian influenza. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7699854.bard.

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Research objectives: Identification of highly conserved B-cell epitopes common to either H5 or H9 subtypes of AI Reconstruction of conserved epitopes from (1) as recombinantimmunogens, and testing their suitability to be used as universal vaccine components by measuring their binding to Influenza vaccinated sera of birds Vaccination of chickens with reconstituted epitopes and evaluation of successful vaccination, clinical protection and viral replication Development of a platform to investigate the dynamics of immune response towards infection or an epitope based vaccine Estimate our ability to focus the immune response towards an epitope-based vaccine using the tool we have developed in (D) Summary: This study is a multi-disciplinary study of four-way collaboration; The SERPL, USDA, Kimron-Israel, and two groups at TAU with the purpose of evaluating the production and implementation of epitope based vaccines against avian influenza (AI). Systematic analysis of the influenza viral spike led to the production of a highly conserved epitope situated at the hinge of the HA antigen designated “cluster 300” (c300). This epitope consists of a total of 31 residues and was initially expressed as a fusion protein of the Protein 8 major protein of the bacteriophagefd. Two versions of the c300 were produced to correspond to the H5 and H9 antigens respectively as well as scrambled versions that were identical with regard to amino acid composition yet with varied linear sequence (these served as negative controls). The recombinantimmunogens were produced first as phage fusions and then subsequently as fusions with maltose binding protein (MBP) or glutathioneS-transferase (GST). The latter were used to immunize and boost chickens at SERPL and Kimron. Furthermore, vaccinated and control chickens were challenged with concordant influenza strains at Kimron and SEPRL. Polyclonal sera were obtained for further analyses at TAU and computational bioinformatics analyses in collaboration with Prof. Pupko. Moreover, the degree of protection afforded by the vaccination was determined. Unfortunately, no protection could be demonstrated. In parallel to the main theme of the study, the TAU team (Gershoni and Pupko) designed and developed a novel methodology for the systematic analysis of the antibody composition of polyclonal sera (Deep Panning) which is essential for the analyses of the humoral response towards vaccination and challenge. Deep Panning is currently being used to monitor the polyclonal sera derived from the vaccination studies conducted at the SEPRL and Kimron.
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Barkan, Alice, and Zach Adam. The Role of Proteases in Regulating Gene Expression and Assembly Processes in the Chloroplast. United States Department of Agriculture, 2003. http://dx.doi.org/10.32747/2003.7695852.bard.

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Chloroplasts house many biochemical processes that are essential for plant viability. Foremost, among these is photosynthesis, which requires the protein-rich thylakoid membrane system. The activation of chloroplast genes encoding thylakoid membrane proteins and the targeting and assembly of these proteins together with their nuclear-encoded partners are essential for the elaboration of the thylakoid membrane. Several nuclear-encoded proteins that regulate chloroplast gene expression and that mediate the targeting of proteins to the thylakoid membrane have been identified in recent years, and many more remain to be discovered. The abundance of such proteins is critical and is likely to be determined to a significant extent by their stability, which in turn, is influenced by chloroplast protease activities. The primary goal of this project was to link specific proteases to specific substrates, and in particular, to specific regulatory and assembly proteins. We proposed a two-pronged approach, involving genetic analysis of the consequences of the mutational loss of chloroplast proteases, and biochemical analysis of the degradation pathways of specific proteins that have been shown to control chloroplast gene expression. Our initial bioinformatic analysis of chloroplast proteases allowed us to identify the set of pro teases that is targeted to the chloroplast. We used that information to recover three Arabidopsis mutants with T - DNA insertions in specific chloroplast protease genes. We carried out the first analysis of the stability of a regulator of chloroplast gene expression (CRS2), and found that the protein is much less stable than are typical components of the photosynthetic apparatus. Genetic reagents and analytical methods were developed that have set the stage for a rapid advancement of our understanding of chloroplast proteolysis. The results obtained may be useful for manipulating the expression of transgenes in the chloroplast and for engineering plants whose photosynthetic activity is optimized under harsh environmental conditions.
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Elizur, Abigail, Amir Sagi, Gideon Hulata, Clive Jones, and Wayne Knibb. Improving Crustacean Aquaculture Production Efficiencies through Development of Monosex Populations Using Endocrine and Molecular Manipulations. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7613890.bard.

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Background Most of Australian prawn aquaculture production is based on P. monodon. However, the Australian industry is under intense competition from lower priced overseas imports. The availability of all-female monosex populations, by virtue of their large size and associated premium prize, will offer competitive advantage to the industry which desperately needs to counteract competitors within this market. As for the redclaw production in Israel, although it is at its infancy, the growers realized that the production of males is extremely advantageous and that such management strategy will change the economic assumptions and performances of this aquaculture to attract many more growers. Original objectives (as in original proposal) Investigating the sex inheritance mechanism in the tiger prawn. Identification of genes expressed uniquely in the androgenic gland (AG) of prawns and crayfish. The above genes and/or their products will be used to localize the AG in the prawn and manipulate the AG activity in both species. Production of monosex populations through AG manipulation. In the prawn, production of all-female populations and in the crayfish, all-male populations. Achievements In the crayfish, the AG cDNA library was further screened and a third AG specific transcript, designated Cq-AG3, had been identified. Simultaneously the two AG specific genes, which were previously identified, were further characterized. Tissue specificity of one of those genes, termed Cq-AG2, was demonstrated by northern blot hybridization and RNA in-situ hybridization. Bioinformatics prediction, which suggested a 42 amino acid long signal anchor at the N-terminus of the deduced Cq-AG2, was confirmed by immunolocalization of a recombinant protein. Cq-IAG's functionality was demonstrated by dsRNA in-vivo injections to intersex crayfish. Cq-IAGsilencing induced dramatic sex-related alterations, including male feature feminization, reduced sperm production, extensive testicular apoptosis, induction of the vitellogeningene expression and accumulation of yolk proteins in the ovaries. In the prawn, the AG was identified and a cDNA library was created. The putative P. monodonAG hormone encoding gene (Pm-IAG) was identified, isolated and characterized for time of expression and histological localization. Implantation of the AG into prawn post larvae (PL) and juveniles resulted in phenotypic transformation which included the appearance of appendix masculina and enlarged petasma. The transformation however did not result in sex change or the creation of neo males thus the population genetics stage to be executed with Prof. Hulata did not materialized. Repeated AG implantation is currently being trialed. Major conclusions and Implications, both scientific and agricultural Cq-IAG's involvement in male sexual differentiation had been demonstrated and it is strongly suggested that this gene encodes an AG hormone in this crayfish. A thorough screening of the AG cDNA library shows Cq-IAG is the prominent transcript within the library. However, the identification of two additional transcripts hints that Cq-IAG is not the only gene mediating the AG effects. The successful gene silencing of Cq-IAG, if performed at earlier developmental stages, might accomplish full and functional sex reversal which will enable the production of all-male crayfish populations. Pm-IAG is likely to play a similar role in prawns. It is possible that repeated administration of the AG into prawn will lead to the desired full sex reversal, so that WZ neo males, crossed with WZ females can result in WW females, which will form the basis for monosex all-female population.
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Minz, Dror, Stefan J. Green, Noa Sela, Yitzhak Hadar, Janet Jansson, and Steven Lindow. Soil and rhizosphere microbiome response to treated waste water irrigation. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7598153.bard.

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Research objectives : Identify genetic potential and community structure of soil and rhizosphere microbial community structure as affected by treated wastewater (TWW) irrigation. This objective was achieved through the examination soil and rhizosphere microbial communities of plants irrigated with fresh water (FW) and TWW. Genomic DNA extracted from soil and rhizosphere samples (Minz laboratory) was processed for DNA-based shotgun metagenome sequencing (Green laboratory). High-throughput bioinformatics was performed to compare both taxonomic and functional gene (and pathway) differences between sample types (treatment and location). Identify metabolic pathways induced or repressed by TWW irrigation. To accomplish this objective, shotgun metatranscriptome (RNA-based) sequencing was performed. Expressed genes and pathways were compared to identify significantly differentially expressed features between rhizosphere communities of plants irrigated with FW and TWW. Identify microbial gene functions and pathways affected by TWW irrigation*. To accomplish this objective, we will perform a metaproteome comparison between rhizosphere communities of plants irrigated with FW and TWW and selected soil microbial activities. Integration and evaluation of microbial community function in relation to its structure and genetic potential, and to infer the in situ physiology and function of microbial communities in soil and rhizospere under FW and TWW irrigation regimes. This objective is ongoing due to the need for extensive bioinformatics analysis. As a result of the capabilities of the new PI, we have also been characterizing the transcriptome of the plant roots as affected by the TWW irrigation and comparing the function of the plants to that of the microbiome. *This original objective was not achieved in the course of this study due to technical issues, especially the need to replace the American PIs during the project. However, the fact we were able to analyze more than one plant system as a result of the abilities of the new American PI strengthened the power of the conclusions derived from studies for the 1ˢᵗ and 2ⁿᵈ objectives. Background: As the world population grows, more urban waste is discharged to the environment, and fresh water sources are being polluted. Developing and industrial countries are increasing the use of wastewater and treated wastewater (TWW) for agriculture practice, thus turning the waste product into a valuable resource. Wastewater supplies a year- round reliable source of nutrient-rich water. Despite continuing enhancements in TWW quality, TWW irrigation can still result in unexplained and undesirable effects on crops. In part, these undesirable effects may be attributed to, among other factors, to the effects of TWW on the plant microbiome. Previous studies, including our own, have presented the TWW effect on soil microbial activity and community composition. To the best of our knowledge, however, no comprehensive study yet has been conducted on the microbial population associated BARD Report - Project 4662 Page 2 of 16 BARD Report - Project 4662 Page 3 of 16 with plant roots irrigated with TWW – a critical information gap. In this work, we characterize the effect of TWW irrigation on root-associated microbial community structure and function by using the most innovative tools available in analyzing bacterial community- a combination of microbial marker gene amplicon sequencing, microbial shotunmetagenomics (DNA-based total community and gene content characterization), microbial metatranscriptomics (RNA-based total community and gene content characterization), and plant host transcriptome response. At the core of this research, a mesocosm experiment was conducted to study and characterize the effect of TWW irrigation on tomato and lettuce plants. A focus of this study was on the plant roots, their associated microbial communities, and on the functional activities of plant root-associated microbial communities. We have found that TWW irrigation changes both the soil and root microbial community composition, and that the shift in the plant root microbiome associated with different irrigation was as significant as the changes caused by the plant host or soil type. The change in microbial community structure was accompanied by changes in the microbial community-wide functional potential (i.e., gene content of the entire microbial community, as determined through shotgun metagenome sequencing). The relative abundance of many genes was significantly different in TWW irrigated root microbiome relative to FW-irrigated root microbial communities. For example, the relative abundance of genes encoding for transporters increased in TWW-irrigated roots increased relative to FW-irrigated roots. Similarly, the relative abundance of genes linked to potassium efflux, respiratory systems and nitrogen metabolism were elevated in TWW irrigated roots when compared to FW-irrigated roots. The increased relative abundance of denitrifying genes in TWW systems relative FW systems, suggests that TWW-irrigated roots are more anaerobic compare to FW irrigated root. These gene functional data are consistent with geochemical measurements made from these systems. Specifically, the TWW irrigated soils had higher pH, total organic compound (TOC), sodium, potassium and electric conductivity values in comparison to FW soils. Thus, the root microbiome genetic functional potential can be correlated with pH, TOC and EC values and these factors must take part in the shaping the root microbiome. The expressed functions, as found by the metatranscriptome analysis, revealed many genes that increase in TWW-irrigated plant root microbial population relative to those in the FW-irrigated plants. The most substantial (and significant) were sodium-proton antiporters and Na(+)-translocatingNADH-quinoneoxidoreductase (NQR). The latter protein uses the cell respiratory machinery to harness redox force and convert the energy for efflux of sodium. As the roots and their microbiomes are exposed to the same environmental conditions, it was previously hypothesized that understanding the soil and rhizospheremicrobiome response will shed light on natural processes in these niches. This study demonstrate how newly available tools can better define complex processes and their downstream consequences, such as irrigation with water from different qualities, and to identify primary cues sensed by the plant host irrigated with TWW. From an agricultural perspective, many common practices are complicated processes with many ‘moving parts’, and are hard to characterize and predict. Multiple edaphic and microbial factors are involved, and these can react to many environmental cues. These complex systems are in turn affected by plant growth and exudation, and associated features such as irrigation, fertilization and use of pesticides. However, the combination of shotgun metagenomics, microbial shotgun metatranscriptomics, plant transcriptomics, and physical measurement of soil characteristics provides a mechanism for integrating data from highly complex agricultural systems to eventually provide for plant physiological response prediction and monitoring. BARD Report
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Harman, Gary E., and Ilan Chet. Enhancement of plant disease resistance and productivity through use of root symbiotic fungi. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7695588.bard.

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The objectives of the project were to (a) compare effects ofT22 and T-203 on growth promotion and induced resistance of maize inbred line Mol7; (b) follow induced resistance of pathogenesis-related proteins through changes in gene expression with a root and foliar pathogen in the presence or absence of T22 or T-203 and (c) to follow changes in the proteome of Mol? over time in roots and leaves in the presence or absence of T22 or T-203. The research built changes in our concepts regarding the effects of Trichoderma on plants; we hypothesized that there would be major changes in the physiology of plants and these would be reflected in changes in the plant proteome as a consequence of root infection by Trichoderma spp. Further, Trichoderma spp. differ in their effects on plants and these changes are largely a consequence of the production of different elicitors of elicitor mixtures that are produced in the zone of communication that is established by root infection by Trichoderma spp. In this work, we demonstrated that both T22 and T-203 increase growth and induce resistance to pathogens in maize. In Israel, it was shown that a hydrophobin is critical for root colonization by Trichoderma strains, and that peptaibols and an expansin-like protein from Ttrichoderma probably act as elicitors of induced resistance in plants. Further, this fungus induces the jasmonate/ethylene pathway of disease resistance and a specific cucumber MAPK is required for transduction of the resistance signal. This is the first such gene known to be induced by fungal systems. In the USA, extensive proteomic analyses of maize demonstrated a number of proteins are differentially regulated by T. harzianum strain T22. The pattern of up-regulation strongly supports the contention that this fungus induces increases in plant disease resistance, respiratory rates and photosynthesis. These are all very consistent with the observations of effects of the fungus on plants in the greenhouse and field. In addition, the chitinolytic complex of maize was examined. The numbers of maize genes encoding these enzymes was increased about 3-fold and their locations on maize chromosomes determined by sequence identification in specific BAC libraries on the web. One of the chitinolytic enzymes was determined to be a heterodimer between a specific exochitinase and different endochitinases dependent upon tissue differences (shoot or root) and the presence or absence of T. harzianum. These heterodimers, which were discovered in this work, are very strongly antifungal, especially the one from shoots in the presence of the biocontrol fungus. Finally, RNA was isolated from plants at Cornell and sent to Israel for transcriptome assessment using Affymetrix chips (the chips became available for maize at the end of the project). The data was sent back to Cornell for bioinformatic analyses and found, in large sense, to be consistent with the proteomic data. The final assessment of this data is just now possible since the full annotation of the sequences in the maize Affy chips is just now available. This work is already being used to discover more effective strains of Trichoderma. It also is expected to elucidate how we may be able to manipulate and breed plants for greater disease resistance, enhanced growth and yield and similar goals. This will be possible since the changes in gene and protein expression that lead to better plant performance can be elucidated by following changes induced by Trichoderma strains. The work was in, some parts, collaborative but in others, most specifically transcriptome analyses, fully synergistic.
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.
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Wisniewski, Michael E., Samir Droby, John L. Norelli, Noa Sela, and Elena Levin. Genetic and transcriptomic analysis of postharvest decay resistance in Malus sieversii and the characterization of pathogenicity effectors in Penicillium expansum. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7600013.bard.

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Blue mold of apple caused by Penicilliumexpansumis a major postharvest disease. Selection for postharvest disease resistance in breeding programs has been ignored in favor of fruit quality traits such as size, color, taste, etc. The identification of postharvest disease resistance as a heritable trait would represent a significant accomplishment and has not been attempted in apple. Furthermore, insight into the biology of the pathogenicity of P. expansumin apple could provide new approaches to postharvest decay management. Hypothesis: Postharvest resistance of apple to P. expansumcan be mapped to specific genetic loci and significant quantitative-trait-loci (QTLs) can be identified that account for a major portion of the population variance. Susceptibility of apple fruit to P. expansumis dependent on the ability of the pathogen to produce LysM effectors that actively suppress primary and/or secondary resistance mechanisms in the fruit. Objectives: 1) Identify QTL(s) and molecular markers for blue mold resistance in GMAL4593 mapping population (‘Royal Gala’ X MalussieversiiPI613981), 2) Characterize the transcriptome of the host and pathogen (P. expansum) during the infection process 3) Determine the function of LysM genes in pathogenicity of P. expansum. Methods: A phenotypic evaluation of blue mold resistance in the GMAL4593 mapping population, conducted in several different years, will be used for QTL analysis (using MapQTL 6.0) to identify loci associated with blue mold resistance. Molecular markers will be developed for the resistance loci. Transcriptomic analysis by RNA-seq will be used to conduct a time course study of gene expression in resistant and susceptible apple GMAL4593 genotypes in response to P. expansum, as well as fungal responses to both genotypes. Candidate resistance genes identified in the transcriptomic study and or bioinformatic analysis will be positioned in the ‘Golden Delicious’ genome to identify markers that co-locate with the identified QTL(s). A functional analysis of LysM genes on pathogenicity will be conducted by eliminating or reducing the expression of individual effectors by heterologous recombination and silencing technologies. LysMeffector genes will also be expressed in a yeast expression system to study protein function. Expected Results: Identification of postharvest disease resistance QTLs and tightly-linked genetic markers. Increased knowledge of the role of effectors in blue mold pathogenic
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Savaldi-Goldstein, Sigal, and Todd C. Mockler. Precise Mapping of Growth Hormone Effects by Cell-Specific Gene Activation Response. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7699849.bard.

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Plant yield largely depends on a complex interplay and feedback mechanisms of distinct hormonal pathways. Over the past decade great progress has been made in elucidating the global molecular mechanisms by which each hormone is produced and perceived. However, our knowledge of how interactions between hormonal pathways are spatially and temporally regulated remains rudimentary. For example, we have demonstrated that although the BR receptor BRI1 is widely expressed, the perception of BRs in epidermal cells is sufficient to control whole-organ growth. Supported by additional recent works, it is apparent that hormones are acting in selected cells of the plant body to regulate organ growth, and furthermore, that local cell-cell communication is an important mechanism. In this proposal our goals were to identify the global profile of translated genes in response to BR stimulation and depletion in specific tissues in Arabidopsis; determine the spatio-temporal dependency of BR response on auxin transport and signaling and construct an interactive public website that will provide an integrated analysis of the data set. Our technology incorporated cell-specific polysome isolation and sequencing using the Solexa technology. In the first aim, we generated and confirmed the specificity of novel transgenic lines expressing tagged ribosomal protein in various cell types in the Arabidopsis primary root. We next crossed these lines to lines with targeted expression of BRI1 in the bri1 background. All lines were treated with BRs for two time points. The RNA-seq of their corresponding immunopurified polysomal RNA is nearly completed and the bioinformatic analysis of the data set will be completed this year. Followed, we will construct an interactive public website (our third aim). In the second aim we started revealing how spatio-temporalBR activity impinges on auxin transport in the Arabidopsis primary root. We discovered the unexpected role of BRs in controlling the expression of specific auxin efflux carriers, post-transcriptionally (Hacham et al, 2012). We also showed that this regulation depends on the specific expression of BRI1 in the epidermis. This complex and long term effect of BRs on auxin transport led us to focus on high resolution analysis of the BR signaling per se. Taking together, our ongoing collaboration and synergistic expertise (hormone action and plant development (IL) and whole-genome scale data analysis (US)) enabled the establishment of a powerful system that will tell us how distinct cell types respond to local and systemic BR signal. BR research is of special agriculture importance since BR application and BR genetic modification have been shown to significantly increase crop yield and to play an important role in plant thermotolerance. Hence, our integrated dataset is valuable for improving crop traits without unwanted impairment of unrelated pathways, for example, establishing semi-dwarf stature to allow increased yield in high planting density, inducing erect leaves for better light capture and consequent biomass increase and plant resistance to abiotic stresses.
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