Academic literature on the topic 'Bioinformatics tools'
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Journal articles on the topic "Bioinformatics tools"
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Lomberk, Gwen. "Bioinformatics tools." Pancreatology 5, no. 4-5 (2005): 314–15. http://dx.doi.org/10.1159/000086531.
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Moreews, François, Olivier Sallou, Hervé Ménager, et al. "BioShaDock: a community driven bioinformatics shared Docker-based tools registry." F1000Research 4 (December 14, 2015): 1443. http://dx.doi.org/10.12688/f1000research.7536.1.
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Linux container technologies, as represented by Docker, provide an alternative to complex and time-consuming installation processes needed for scientific software. The ease of deployment and the process isolation they enable, as well as the reproducibility they permit across environments and versions, are among the qualities that make them interesting candidates for the construction of bioinformatic infrastructures, at any scale from single workstations to high throughput computing architectures. The Docker Hub is a public registry which can be used to distribute bioinformatic software as Docker images. However, its lack of curation and its genericity make it difficult for a bioinformatics user to find the most appropriate images needed. BioShaDock is a bioinformatics-focused Docker registry, which provides a local and fully controlled environment to build and publish bioinformatic software as portable Docker images. It provides a number of improvements over the base Docker registry on authentication and permissions management, that enable its integration in existing bioinformatic infrastructures such as computing platforms. The metadata associated with the registered images are domain-centric, including for instance concepts defined in the EDAM ontology, a shared and structured vocabulary of commonly used terms in bioinformatics. The registry also includes user defined tags to facilitate its discovery, as well as a link to the tool description in the ELIXIR registry if it already exists. If it does not, the BioShaDock registry will synchronize with the registry to create a new description in the Elixir registry, based on the BioShaDock entry metadata. This link will help users get more information on the tool such as its EDAM operations, input and output types. This allows integration with the ELIXIR Tools and Data Services Registry, thus providing the appropriate visibility of such images to the bioinformatics community.
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Grisham, William, Natalie A. Schottler, Joanne Valli-Marill, Lisa Beck, and Jackson Beatty. "Teaching Bioinformatics and Neuroinformatics by Using Free Web-based Tools." CBE—Life Sciences Education 9, no. 2 (2010): 98–107. http://dx.doi.org/10.1187/cbe.09-11-0079.
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This completely computer-based module's purpose is to introduce students to bioinformatics resources. We present an easy-to-adopt module that weaves together several important bioinformatic tools so students can grasp how these tools are used in answering research questions. Students integrate information gathered from websites dealing with anatomy (Mouse Brain Library), quantitative trait locus analysis (WebQTL from GeneNetwork), bioinformatics and gene expression analyses (University of California, Santa Cruz Genome Browser, National Center for Biotechnology Information's Entrez Gene, and the Allen Brain Atlas), and information resources (PubMed). Instructors can use these various websites in concert to teach genetics from the phenotypic level to the molecular level, aspects of neuroanatomy and histology, statistics, quantitative trait locus analysis, and molecular biology (including in situ hybridization and microarray analysis), and to introduce bioinformatic resources. Students use these resources to discover 1) the region(s) of chromosome(s) influencing the phenotypic trait, 2) a list of candidate genes—narrowed by expression data, 3) the in situ pattern of a given gene in the region of interest, 4) the nucleotide sequence of the candidate gene, and 5) articles describing the gene. Teaching materials such as a detailed student/instructor's manual, PowerPoints, sample exams, and links to free Web resources can be found at http://mdcune.psych.ucla.edu/modules/bioinformatics .
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Zhang, Xiaorong. "Teaching Botany Using Bioinformatics Tools." Creative Education 10, no. 10 (2019): 2137–46. http://dx.doi.org/10.4236/ce.2019.1010155.
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Cantó‐Pastor, Alex, G. Alex Mason, Siobhan M. Brady, and Nicholas J. Provart. "Arabidopsis bioinformatics: tools and strategies." Plant Journal 108, no. 6 (2021): 1585–96. http://dx.doi.org/10.1111/tpj.15547.
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Skuse, Gary R., and Chunguang Du. "Bioinformatics Tools for Plant Genomics." International Journal of Plant Genomics 2008 (June 11, 2008): 1–2. http://dx.doi.org/10.1155/2008/910474.
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Arndt, Timothy. "Visual software tools for bioinformatics." Journal of Visual Languages & Computing 19, no. 2 (2008): 291–301. http://dx.doi.org/10.1016/j.jvlc.2007.06.001.
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Margolis, David J., Nandita Mitra, Bradley Wubbenhorst, and Katherine L. Nathanson. "Filaggrin sequencing and bioinformatics tools." Archives of Dermatological Research 312, no. 2 (2019): 155–58. http://dx.doi.org/10.1007/s00403-019-01956-3.
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Arora, Pankaj Kumar, and Wenxin Shi. "Tools of bioinformatics in biodegradation." Reviews in Environmental Science and Bio/Technology 9, no. 3 (2010): 211–13. http://dx.doi.org/10.1007/s11157-010-9211-x.
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Lomberk, Gwen. "Educational Websites — Bioinformatics Tools II." Pancreatology 9, no. 1-2 (2009): 4–5. http://dx.doi.org/10.1159/000178767.
Full textDissertations / Theses on the topic "Bioinformatics tools"
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Sentausa, Erwin. "Time course simulation replicability of SBML-supporting biochemical network simulation tools." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-33.
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<p>Background: Modelling and simulation are important tools for understanding biological systems. Numerous modelling and simulation software tools have been developed for integrating knowledge regarding the behaviour of a dynamic biological system described in mathematical form. The Systems Biology Markup Language (SBML) was created as a standard format for exchanging biochemical network models among tools. However, it is not certain yet whether actual usage and exchange of SBML models among the tools of different purpose and interfaces is assessable. Particularly, it is not clear whether dynamic simulations of SBML models using different modelling and simulation packages are replicable.</p><p>Results: Time series simulations of published biological models in SBML format are performed using four modelling and simulation tools which support SBML to evaluate whether the tools correctly replicate the simulation results. Some of the tools do not successfully integrate some models. In the time series output of the successful</p><p>simulations, there are differences between the tools.</p><p>Conclusions: Although SBML is widely supported among biochemical modelling and simulation tools, not all simulators can replicate time-course simulations of SBML models exactly. This incapability of replicating simulation results may harm the peer-review process of biological modelling and simulation activities and should be addressed accordingly, for example by specifying in the SBML model the exact algorithm or simulator used for replicating the simulation result.</p>
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Berry, Eric Zachary 1980. "Bioinformatics and database tools for glycans." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/27085.
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Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (leaves 75-76).<br>Recent advances in biology have afforded scientists with the knowledge that polysaccharides play an active role in modulating cellular activities. Glycosaminoglycans (GAGs) are one such family of polysaccharides that play a very important role in regulating the functions of numerous important signaling molecules and enzymes in the cell. Developing bioinformatics tools has been integral to advancing genomics and proteomics. While these tools have been well-developed to store and process sequence and structure information for proteins and DNA, they are very poorly developed for polysaccharides. Glycan structures pose special problems because of their tremendous information density per fundamental unit, their often-branched structures, and the complicated nature of their building blocks. The GlycoBank, an online database of known GAG structures and functions, has been developed to overcome many of these difficulties by developing a common notation for researchers to describe GAG sequences, a common repository to view known structure-function relationships, and the complex tools and searches needed to facilitate their work. This thesis focuses on the development of GlycoBank. In addition, a large, NIGMS-funded consortium, the Consortium for Functional Glycomics, is a larger database that also aims to store polysaccharide structure-function information of a broader collection of polysaccharides. The ideas and concepts implemented in developing GlycoBank were instrumental in developing databases and bioinformatics tools for the Consortium for Functional Glycomics.<br>by Eric Zachary Berry.<br>M.Eng.and S.B.
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Meng, Da. "Bioinformatics tools for evaluating microbial relationships." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Dissertations/Spring2009/d_meng_042209.pdf.
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Thesis (Ph. D.)--Washington State University, May 2009.<br>Title from PDF title page (viewed on June 8, 2009). "School of Electrical Engineering and Computer Science." Includes bibliographical references.
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Martini, Paolo. "Dissecting the transcriptome complexity with bioinformatics tools." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422923.
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Bioinformatics has acquired a lot of importance especially with the advent of genomic approaches. The large amount of data produced by ``omics'' experiments requires appropriate frameworks to handle, store and mine the information and to derive appropriate work hypotheses. Transcriptome is defined as the whole amount of RNA molecules produced by a cell that provides the bridge between the genome and proteins. RNA molecules can be divided in two major classes: protein coding RNAs or messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs). While the first class has been the most studied in the last decades, ncRNAs were recently discovered demonstrating their importance in cell regulatory processes. The most important class of the ncRNAs is composed by the micro RNAs (miRNAs) that have been related to several pathologies, including cancer, because of their ability to regulate oncogenes or oncosuppresors and mRNAs involved in the cell cycle.
Here, I am presenting a work that aims at following and providing the appropriate structure for the interpretation and storage of the transcriptomics data.
In this regard, I devised a tool to integrate expression levels from microarray experiments with gene annotation data like the genome localization and organization in biological pathways. The tool was devised and tuned using two datasets: the first one concerning expression profiles of patients with acute myeloid leukemia (ALL), the second one regarding muscular dystrophies. The application of this new tool to these datasets was very promising, especially regarding meta-analysis studies (muscular dystrophies). For this reason I applied the new tool to analyze public and in-house produced datasets of expression profiles of patients with inflammatory myopathies. This analysis allowed generating the hypothesis of the involvement of JAK-STAT and interferon type I signaling pathways in myopathies. The inferred results were validated using qRT-PCR and the presences of specific proteins produced by validated mRNAs were tested by ELISA and proteomic analysis.
To complete and extend the knowledge of the muscle physiology, I used the pig as a new model organism to develop a framework aiming at the integration of miRNA expression and the regulation of their mRNA-target. It was important to develop the appropriate experimental instruments to perform the expression analyses. I developed two microarray platforms to perform the expression profiles of both miRNA and mRNA purified from the same sample. Then, with the expression data, I computationally analyzed aspects of miRNA biogenesis and performed the data integration leading to the production of regulatory networks specific of the studied tissues, including skeletal-muscle. Our miRNA sequences (mature and hairpin) were crossed with public data from RNA-seq experiments demonstrating that there is an important overlap between our results and the sequences identified by RNA-seq, confirming the goodness of our approach<br>Con l’avvento degli approcci genomici la bioinformatica ha acquisito un importanza sempre maggiore nello studio della biologia. Infatti, gli approcci “omici” permettono di produrre un enorme quantitativo di dati che deve essere archiviato in corrette strutture (database). L’archiviazione del dato comporta la necessità di permettere l’accesso e la manipolazione dello stesso al fine di svolgere gli studi appropriati. Sono quindi richiesti strumenti appropriati che consentano l’ispezione e la manipolazione dei database fine di formulare delle ipotesi coerenti con la problematica biologica che si sta studiando.
Il trascrittoma è definito come l’insieme delle molecole di RNA che sono prodotte da una cellula e rappresentano un passaggio necessario nel processo che dal gene porta alla produzione della proteina. Le molecole di RNA possono essere suddivise in due grandi gruppi: gli RNA codificanti o messaggeri e gli RNA non codificanti. Mentre la prima classe è stata oggetto di ampi studi negli ultimi decenni, gli RNA non codificanti sono stati scoperti solo di recente e associati a funzioni puramente regolative. La classe più importante coinvolta nel processo regolativo degli RNA messaggeri è quella dei micro RNA (miRNA) che sono stati oggetto di un studio intenso che li ha messi in relazione con lo sviluppo di patologie come il cancro in quanto coinvolti nella regolazione fine dell’espressione genica di oncogeni, oncosoppressori o geni del ciclo cellulare.
In questa tesi presento una serie di soluzioni bioinformatiche mirate a fornire le strutture appropriate per condurre gli esperimenti e le analisi dei dati di trascrittomica.
Nel corso del periodo di dottorato, ho sviluppato un metodo che consente l’integrazione dei livelli di espressione genica ottenuti da esperimenti di microarray con informazioni riguardanti la localizzazione degli stessi nei cromosomi o la loro organizzazione in processi biologici. Questo metodo è stato messo a punto e raffinato nel suo funzionamento usando due gruppi di dati disponibili nei database pubblici: il primo riguarda dati di espressione genica ottenuti da esperimenti di microarray su leucemia mieloide acuta; il secondo riguarda l’espressione genica di distrofie muscolari derivanti sempre da dati di microarray. I risultati di questo nuovo metodo si sono dimostrati molto promettenti, in particolare nell’applicazione della meta-analisi che consiste nell’integrare dati provenienti da differenti laboratori.
Forte di questo primo risultato, ho applicato questo metodo di analisi anche all’ispezione dei processi sregolati nelle miopatie infiammatorie affiancando ai dati disponibili prodotti nel laboratorio di Genomica Funzionale diretto dal Prof. G. Lanfranchi quelli depositati nei database pubblici. La meta-analisi da me implementata ha permesso di studiare questa serie di dati sfruttando, per la prima volta, la localizzazione dei geni e raggruppandoli per la funzione permettendo di generare ipotesi sui meccanismi patologici. Grazie a questa tipologia di analisi ho ipotizzato il coinvolgimento nelle miopatie infiammatorie delle vie di segnale che fanno capo a JAK/STAT e agli interferoni. Le ipotesi generate analizzando i dati sono state confermate andando a validare i geni coinvolti nelle vie di segnale appena menzionate usando la qRT-PCR. Inoltre, usando approcci di proteomica, in collaborazione con la Prof. C. Gelfi (Università di Milano) e la tecnica ELISA, è stata anche validata la presenza delle proteine coinvolte in queste vie di segnale nei pazienti affetti da miopatie infiammatorie.
Nella parte conclusiva del mio dottorato, mi sono occupato di completare ed estendere la conoscenza della fisiologia muscolare. Per far questo mi sono spostato sul maiale, un organismo modello molto importante per lo studio di patologie umane e per la produzione di componenti biologiche che possono essere utilizzate per sostituire quelle degradate nell’uomo (valvole aortiche per esempio). Usando il maiale ho sviluppato un sistema per integrare l’espressione dei miRNA e la regolazione che questi esercitano nei messaggeri target. Come prima cosa ho sviluppato le piattaforme di microarray per eseguire l’analisi dell’espressione genica di 14 tessuti di maiale. In particolare ho sviluppato due tipi di piattaforme per eseguire l’analisi dell’espressione dei trascritti e dei miRNA purificati dallo stesso campione. Con questi dati di espressione ho condotto analisi per delucidare alcuni aspetti inerenti la biogenesi dei miRNA. Infine, la completezza dei dati prodotti mi ha permesso di costruire delle reti di regolazione specifiche per ogni tessuto analizzato. Per confermare la validità del nostro approccio ho analizzato il grado di sovrapposizione tra le sequenze derivate dal nostro studio e le sequenze prodotte dai vari esperimenti di RNA-seq. Con questa analisi ho confermato la validità del mio approccio in quanto è stato rivelato una sovrapposizione importante tra le nostre sequenze e quelle derivate da RNA-seq
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Strafford, J. "Docking and bioinformatics tools to guide enzyme engineering." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1339145/.
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The carbon-carbon bond forming ability of transketolase (TK), along with its broad substrate specificity, makes it very attractive as a biocatalyst in industrial organic synthesis. Through the production of saturation mutagenesis libraries focused on individual active site residues, several variants of TK have been discovered with enhanced activities on non-natural substrates. We have used computational and bioinformatics tools to increase our understanding of TK and to guide engineering of the enzyme for further improvements in activity. Computational automated docking is a powerful technique with the potential to identify transient structures along an enzyme reaction pathway that are difficult to obtain by experimental structure determination. We have used the AutoDock algorithm to dock a series of known ketol donor and aldehyde acceptor substrates into the active site of E. coli TK, both in the presence and the absence of reactive intermediates. Comparison of docked conformations with available crystal structure complexes allows us to propose a more complete mechanism at a level of detail not currently possible by experimental structure determination alone. Statistical coupling analysis (SCA) utilises evolutionary sequence data present within multiple sequence alignments to identify energetically coupled networks of residues within protein structures. Using this technique we have identified several coupled networks within the TK enzyme which we have targeted for mutagenesis in multiple mutant variant libraries. Screening of these libraries for increased activity on the non-natural substrate propionaldehyde (PA) has identified combinations of mutations that act synergistically on enzyme activity. Notably, a double variant has been discovered with a 20-fold improvement in kcat relative to wild type on the PA reaction, this is higher than any other TK variant discovered to date.
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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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Mahram, Atabak. "FPGA acceleration of sequence analysis tools in bioinformatics." Thesis, Boston University, 2013. https://hdl.handle.net/2144/11126.
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Thesis (Ph.D.)--Boston University<br>With advances in biotechnology and computing power, biological data are being produced at an exceptional rate. The purpose of this study is to analyze the application of FPGAs to accelerate high impact production biosequence analysis tools. Compared with other alternatives, FPGAs offer huge compute power, lower power consumption, and reasonable flexibility.
BLAST has become the de facto standard in bioinformatic approximate string matching and so its acceleration is of fundamental importance. It is a complex highly-optimized system, consisting of tens of thousands of lines of code and a large number of heuristics. Our idea is to emulate the main phases of its algorithm on FPGA. Utilizing our FPGA engine, we quickly reduce the size of the database to a small fraction, and then use the original code to process the query. Using a standard FPGA-based system, we achieved 12x speedup over a highly optimized multithread reference code.
Multiple Sequence Alignment (MSA)--the extension of pairwise Sequence Alignment to multiple Sequences--is critical to solve many biological problems. Previous attempts to accelerate Clustal-W, the most commonly used MSA code, have directly mapped a portion of the code to the FPGA. We use a new approach: we apply prefiltering of the kind commonly used in BLAST to perform the initial all-pairs alignments. This results in a speedup of from 8Ox to 190x over the CPU code (8 cores). The quality is comparable to the original according to a commonly used benchmark suite evaluated with respect to multiple distance metrics.
The challenge in FPGA-based acceleration is finding a suitable application mapping. Unfortunately many software heuristics do not fall into this category and so other methods must be applied. One is restructuring: an entirely new algorithm is applied. Another is to analyze application utilization and develop accuracy/performance tradeoffs. Using our prefiltering approach and novel FPGA programming models we have achieved significant speedup over reference programs. We have applied approximation, seeding, and filtering to this end. The bulk of this study is to introduce the pros and cons of these acceleration models for biosequence analysis tools.
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Stenberg, Johan. "Software Tools for Design of Reagents for Multiplex Genetic Analyses." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6832.
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Parida, Mrutyunjaya. "Exploring and analyzing omics using bioinformatics tools and techniques." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6244.
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During the Human Genome Project the first hundred billion bases were sequenced in four years, however, the second hundred billion bases were sequenced in four months (NHGRI, 2013). As efforts were made to improve every aspect of sequencing in this project, cost became inversely proportional to the speed (NHGRI, 2013). Human Genome Project ended in April 2003 but research in faster and cheaper ways to sequence the DNA is active to date (NHGRI, 2013). On the one hand, these advancements have allowed the convenient and unbiased generation and interrogation of a variety of omics datasets; on the other hand, they have substantially contributed towards the ever-increasing size of biological data. Therefore, informatics techniques are indispensable tools in the field of biology and medicine due to their ability to efficiently store and probe large datasets. Bioinformatics is a specialized domain under informatics that focusses on biological data storage, organization and analysis (NHGRI, 2013). Here, I have applied informatics approaches such as database designing and web development in the context of biological datasets or bioinformatics, to create a novel web-based resource that allows users to explore the comprehensive transcriptome of common aquatic tunicate named Oikopleura dioica (O .dioica), and access their associated annotations across key developmental time points, conveniently. This unique resource will substantially contribute towards studies on development, evolution and genetics of chordates using O. dioica as a model.
Mendelian or single-gene disorders such as cystic fibrosis, sickle-cell anemia, Huntington’s disease, and Rett’s syndrome run across generations in families (Chial, 2008). Allelic variations associated with Mendelian disorders primarily reside in the protein-coding regions of the genome, collectively called an exome (Stenson et al., 2009). Therefore, sequencing of exome rather than whole genome is an efficient and practical approach to discover etiologic variants in our genome (Bamshad et al., 2011). Renal agenesis (RA) is a severe form of congenital anomalies of the kidney and urinary tract (CAKUT) where children are born with one (unilateral renal agenesis) or no kidneys (bilateral renal agenesis) (Brophy et al., 2017; Yalavarthy & Parikh, 2003). In this study, we have applied exome-sequencing technique to selective human patients in a renal agenesis (RA) pedigree that followed a Mendelian mode of disease transmission. Exome sequencing and molecular techniques combined with my bioinformatics analysis has led to the discovery of a novel RA gene called GREB1L (Brophy et al., 2017). In this study, we have successfully demonstrated the validation of exome sequencing and bioinformatics techniques to narrow down disease-associated mutations in human genome. Additionally, the results from this study has substantially contributed towards understanding the molecular basis of CAKUT. Discovery of novel etiologic variants will enhance our understanding of human diseases and development.
High-throughput sequencing technique called RNA-Seq has revolutionized the field of transcriptome analysis (Z. Wang, Gerstein, & Snyder, 2009). Concisely, a library of cDNA is prepared from a RNA sample using an enzyme called reverse transcriptase (Nottingham et al., 2016). Next, the cDNA is fragmented, sequenced using a sequencing platform of choice and mapped to a reference genome, assembled transcriptome, or assembled de novo to generate a transcriptome (Grabherr et al., 2011; Nottingham et al., 2016). Mapping allows detection of high-resolution transcript boundaries, quantification of transcript expression and identification of novel transcripts in the genome. We have applied RNA-Seq to analyze the gene expression patterns in water flea otherwise known as D. pulex to work out the genetic details underlying heavy metal induced stress (unpublished) and predator induced phenotypic plasticity (PIPP) (Rozenberg et al., 2015), independently. My bioinformatics analysis of the RNA-Seq data has facilitated the discovery of key biological processes participating in metal induced stress response and predator induced defense mechanisms in D. pulex. These studies are great additions to the field of ecotoxicogenomics, phenotypic plasticity and have aided us in gaining mechanistic insight into the impact of toxicant and predator exposure on D. pulex at a bimolecular level.
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Malatras, Apostolos. "Bioinformatics tools for the systems biology of dysferlin deficiency." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066627/document.
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Le but de mon projet est de créer et d’appliquer des outils pour l’analyse de la biologie des systèmes musculaires en utilisant différentes données OMICS. Ce projet s’intéresse plus particulièrement à la dysferlinopathie due la déficience d’une protéine appelée dysferline qui est exprimée principalement dans les muscles squelettiques et cardiaque. La perte du dysferline due à la mutation (autosomique-récessive) du gène DYSF entraîne une dystrophie musculaire progressive (LGMD2B, MM, DMAT). Nous avons déjà développé des outils bio-informatiques qui peuvent être utilisés pour l’analyse fonctionnelle de données OMICS, relative à la dyspherlinopathie. Ces derniers incluent le test dit «gene set enrichment analysis», test comparant les profils OMICS d’intérêts aux données OMICS musculaires préalablement publiées ; et l’analyse des réseaux impliquant les diffèrent(e)s protéines et transcrits entre eux/elles. Ainsi, nous avons analysé des centaines de données omiques publiées provenant d’archives publiques. Les outils informatiques que nous avons développés sont CellWhere et MyoMiner. CellWhere est un outil facile à utiliser, permettant de visualiser sur un graphe interactif à la fois les interactions protéine-protéine et la localisation subcellulaire des protéines. Myominer est une base de données spécialisée dans le tissu et les cellules musculaires, et qui fournit une analyse de co-expression, aussi bien dans les tissus sains que pathologiques. Ces outils seront utilisés dans l'analyse et l'interprétation de données transcriptomiques pour les dyspherlinopathies mais également les autres pathologies neuromusculaires<br>The aim of this project was to build and apply tools for the analysis of muscle omics data, with a focus on Dysferlin deficiency. This protein is expressed mainly in skeletal and cardiac muscles, and its loss due to mutation (autosomal-recessive) of the DYSF gene, results in a progressive muscular dystrophy (Limb Girdle Muscular Dystrophy type 2B (LGMD2B), Miyoshi myopathy and distal myopathy with tibialis anterior onset (DMAT)). We have developed various tools and pipelines that can be applied towards a bioinformatics functional analysis of omics data in muscular dystrophies and neuromuscular disorders. These include: tests for enrichment of gene sets derived from previously published muscle microarray data and networking analysis of functional associations between altered transcripts/proteins. To accomplish this, we analyzed hundreds of published omics data from public repositories. The tools we developed are called CellWhere and MyoMiner. CellWhere is a user-friendly tool that combines protein-protein interactions and protein subcellular localizations on an interactive graphical display (https://cellwhere-myo.rhcloud.com). MyoMiner is a muscle cell- and tissue-specific database that provides co-expression analyses in both normal and pathological tissues. Many gene co-expression databases already exist and are used broadly by researchers, but MyoMiner is the first muscle-specific tool of its kind (https://myominer-myo.rhcloud.com). These tools will be used in the analysis and interpretation of transcriptomics data from dysferlinopathic muscle and other neuromuscular conditions and will be important to understand the molecular mechanisms underlying these pathologies
Books on the topic "Bioinformatics tools"
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Bosu, Orpita. Bioinformatics: Database, tools, algorithms. Oxford University Press, 2007.
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1978-, Stajich Jason Eric, Hansen David, and SpringerLink (Online service), eds. Bioinformatics: Tools and Applications. Springer-Verlag New York, 2009.
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Association, Information Resources Management. Bioinformatics: Concepts, methodologies, tools, and applications. Medical Information Science Reference, 2013.
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Koča, Jaroslav, Radka Svobodová Vařeková, Lukáš Pravda, et al. Structural Bioinformatics Tools for Drug Design. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47388-8.
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Malviya, Rishabha, Pramod Kumar Sharma, Sonali Sundram, Rajesh Kumar Dhanaraj, and Balamurugan Balusamy. Bioinformatics Tools and Big Data Analytics for Patient Care. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003226949.
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Schmitz, Ulf. MicroRNA Cancer Regulation: Advanced Concepts, Bioinformatics and Systems Biology Tools. Springer Netherlands, 2013.
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Srinivasa, K. G., G. M. Siddesh, and S. R. Manisekhar, eds. Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2445-5.
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Paton, Ray. Computation in Cells and Tissues: Perspectives and Tools of Thought. Springer Berlin Heidelberg, 2004.
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Smagula, Cynthia S. Bioinformation on the World Wide Web: An annotated directory of molecular biology tools. BIOTA Publications, 1996.
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Høiriis, Nielsen Jens, and Jewett Michael C, eds. Metabolomics: A powerful tool in systems biology. Springer, 2007.
Find full textBook chapters on the topic "Bioinformatics tools"
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Imelfort, Michael. "Sequence Comparison Tools." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_2.
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Zweigenbaum, Pierre, and Dina Demner-Fushman. "Advanced Literature-Mining Tools." In Bioinformatics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_17.
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Luo, Jingchu. "Applied Bioinformatics Tools." In Basics of Bioinformatics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38951-1_9.
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Gupta, Aeshna, Disha Gangotia, and Indra Mani. "Bioinformatics Tools and Software." In Advances in Bioinformatics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6191-1_2.
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Liebler, Daniel C. "Bioinformatics Tools for Proteomics." In Proteomics for Biological Discovery. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470007745.ch15.
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Raman, Maya. "Bioinformatics and Computational Tools." In Fish Structural Proteins and its Derivatives: Functionality and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2562-5_5.
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Betsy, C. Judith, and C. Siva. "Bioinformatics Tools and Techniques." In Fisheries Biotechnology and Bioinformatics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6991-3_22.
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Bandi, Venkat, Carl Gutwin, Jorge Núñez Siri, Eric Neufeld, Andrew Sharpe, and Isobel Parkin. "Visualization Tools for Genomic Conservation." In Plant Bioinformatics. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2067-0_16.
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Bourque, Guillaume, and Glenn Tesler. "Computational Tools for the Analysis of Rearrangements in Mammalian Genomes." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_20.
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Singh, Gautam B. "Alignment Tools." In Fundamentals of Bioinformatics and Computational Biology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11403-3_8.
Full textConference papers on the topic "Bioinformatics tools"
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Elmakki, Shimaa M., Marwa M. A. Hadhoud, and Vidan F. Ghoneim. "Investigation of Amyotrophic Lateral Sclerosis Using Bioinformatics Tools." In 2025 42nd National Radio Science Conference (NRSC). IEEE, 2025. https://doi.org/10.1109/nrsc65659.2025.11018550.
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Falvo, Fedra Rosita, and Mario Cannataro. "Explainability in Artificial Intelligence: Techniques, Tools and Applications in Medicine and Bioinformatics." In 2024 Fourth International Conference on Digital Data Processing (DDP). IEEE, 2024. https://doi.org/10.1109/ddp64453.2024.00026.
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Li, Xin, Yadong Liu, Zhongbo Yang, Yadong Wang, and Tao Jiang. "Comprehensive Benchmarking of Genotype Imputation Tools Using a Large-Scale Chinese Reference Panel." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822265.
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Yang, Zhongbo, Zhenhao Lu, Xin Li, Tao Jiang, Yadong Wang, and Yadong Liu. "Comprehensive evaluation of haplotype phasing tools with different strategies across diverse sequence technologies." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822156.
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Martins, Inês Branco, Jorge Miguel Silva, and João Rafael Almeida. "A comprehensive study of databases to assess the reliability of metagenomic tools." In 2024 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2024. http://dx.doi.org/10.1109/cibcb58642.2024.10702118.
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Baykal, Pelin Icer, Niko Beerenwinkel, and Serghei Mangul. "Reproducibility of Bioinformatics Tools." In 2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2022. http://dx.doi.org/10.1109/ipdpsw55747.2022.00046.
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Bartlett, Joan C., Yusuke Ishimura, and Lorie A. Kloda. "Scientists' preferences for bioinformatics tools." In the 4th Information Interaction in Context Symposium. ACM Press, 2012. http://dx.doi.org/10.1145/2362724.2362761.
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Cannataro, Mario, and Pietro H. Guzzi. "A taxonomy for bioinformatics tools." In BCB '14: ACM-BCB '14. ACM, 2014. http://dx.doi.org/10.1145/2649387.2660852.
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Skapavets, K., and R. Smolyakova. "BIOINFORMATICS TOOLS FOR METAGENOME SEQUENCING." In SAKHAROV READINGS 2020: ENVIRONMENTAL PROBLEMS OF THE XXI CENTURY. Minsk, ICC of Minfin, 2020. http://dx.doi.org/10.46646/sakh-2020-2-259-262.
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Mohamed, Hisham, and Mohamed Abouelhoda. "WinBioinfTools: Bioinformatics tools for Windows cluster." In 2009 IEEE International Conference on Cluster Computing and Workshops. IEEE, 2009. http://dx.doi.org/10.1109/clustr.2009.5289141.
Full textReports on the topic "Bioinformatics tools"
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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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Beckstrom-Sternberg, Stephen. Bioinformatic Tools for Metagenomic Analysis of Pathogen Backgrounds and Human Microbial Communities. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada581677.
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Yedidia, I., H. Senderowitz, and A. O. Charkowski. Small molecule cocktails designed to impair virulence targets in soft rot Erwinias. United States-Israel Binational Agricultural Research and Development Fund, 2020. http://dx.doi.org/10.32747/2020.8134165.bard.
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Chemical signaling between beneficial or pathogenic bacteria and plants is a central factor in determining the outcome of plant-microbe interactions. Pectobacterium and Dickeya (soft rot Erwinias) are the major cause of soft rot, stem rot, and blackleg formed on potato and ornamentals, currently with no effective control. Our major aim was to establish and study specific bacterial genes/proteins as targets for anti-virulence compounds, by combining drug design tools and bioinformatics with experimental work. The approach allowed us to identify and test compounds (small molecules) that specifically interfere with the activities of these targets, by this impairing bacterial virulence. Two main targets were selected within the frame of the BARD project. The first is the ATP-binding cassette (ABC) transporters and methyl-accepting chemotaxis proteins (MCP) that have been characterized here for the first time in Pectobacteriaceae, and the second is the quorum sensing (QS) machinery of Pectobacterium with its major proteins and in particular, the AHL synthase ExpI that was identified as the preferred target for inhibition. Both systems are strongly associated with bacterial virulence and survival in planta. We found that Pectobacteriaceae, namely Dickeya and Pectobacterium, encode more ABC transporters and MCP in their genomes, compared to other bacteria in the order. For MCP, soft rot Pectobacteriaceae not only contain more than 30 MCP genes per strain, but also have more diverse ligand binding domains than other species in the Enterobacteriales. These findings suggest that both ABC transporters and MCP are important for soft rot Pectobacteriaceae pathogenicity. We now have a selection of mutants in these proteins that may be further explored to understand their direct involvement in virulence. In parallel, we studied the QS central proteins in pectobacteria, the signaling molecule N-acyl-homoserine lactone synthase, ExpI, and the response regulator ExpR, and established their phylogenetic relations within plant pathogenic Gram negative bacteria. Next, these proteins were used for virtual screening of millions of compounds in order to discover new compounds with potential to interfere with the QS machinery. Several natural compounds were tested for their interference with virulence related traits in Pectobacterium and their capability to minimize soft rot infections. Our findings using microcalorimetric binding studies have established for the first time direct interaction between the protein ExpI and two natural ligands, the plant hormone salicylic acid and the volatile compound carvacrol. These results supported a model by which plants interfere with bacterial communication through interkingdom signaling. The collaborative project yielded two research papers and a comprehensive review, which included new computational and bioinformatics data, in Annu. Rev. Phytopathol., the highest ranked journal in phytopathology. Additional two papers are in preparation. In order to transform the fundamental knowledge that have been gained during this collaborative BARD project into agricultural practice, to control soft rot bacteria, we have submitted a continual project.
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Altindis, E., R. Cozzi, B. Di Palo, et al. Protectome analysis: a new selective bioinformatics tool for bacterial vaccine candidate discovery. Cold Spring Harbor Laboratory, 2014. http://dx.doi.org/10.1101/002089.
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Fromm, Hillel, Paul Michael Hasegawa, and Aaron Fait. Calcium-regulated Transcription Factors Mediating Carbon Metabolism in Response to Drought. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7699847.bard.
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Original objectives: The long-term goal of the proposed research is to elucidate the transcription factors, genes and metabolic networks involved in carbon metabolism and partitioning in response to water deficit. The proposed research focuses on the GTLcalcium/calmodulinbindingTFs and the gene and metabolic networks modulated by these TFs in Arabidopsis thaliana. The specific objectives are as follows. Objective-1 (USA): Physiological analyses of GTL1 loss- and gain-of-function plants under water sufficient and drought stress conditions Objective 2 (USA / Israel-TAU): Characterizion of GTL target genes and bioinformatic analysis of data to eulcidate gene-network topology. Objective-3 (Israel-TAU): Regulation of GTLmediated transcription by Ca²⁺/calmodulin: mechanism and biological significance. Objective-4 (Israel-BGU): Metabolic networks and carbon partitioning in response to drought. Additional direction: In the course of the project we added another direction, which was reported in the 2nd annual report, to elucidate genes controlling drought avoidance. The TAU team has isolated a few unhydrotropic (hyd) mutants and are in the process of mapping these mutations (of hyd13 and hyd15; see last year's report for a description of these mutants under salt stress) in the Arabidopsis genome by map-based cloning and deep sequencing. For this purpose, each hyd mutant was crossed with a wild type plant of the Landsberg ecotype, and at the F2 stage, 500-700 seedlings showing the unhydrotropic phenotype were collected separately and pooled DNA samples were subkected to the Illumina deep sequencing technology. Bioinformatics were used to identify the exact genomic positions of the mutations (based on a comparison of the genomic sequences of the two Arabidopsis thaliana ecotypes (Columbia and Landsberg). Background: To feed the 9 billion people or more, expected to live on Earth by the mid 21st century, the production of high-quality food must increase substantially. Based on a 2009 Declaration of the World Summit on Food Security, a target of 70% more global food production by the year 2050 was marked, an unprecedented food-production growth rate. Importantly, due to the larger areas of low-yielding land globally, low-yielding environments offer the greatest opportunity for substantial increases in global food production. Nowadays, 70% of the global available water is used by agriculture, and 40% of the world food is produced from irrigated soils. Therefore, much needs to be done towards improving the efficiency of water use by plants, accompanied by increased crop yield production under water-limiting conditions. Major conclusions, solutions and achievements: We established that AtGTL1 (Arabidopsis thaliana GT-2 LIKE1) is a focal determinant in water deficit (drought) signaling and tolerance, and water use efficiency (WUE). The GTL1 transcription factor is an upstream regulator of stomatal development as a transrepressor of AtSDD1, which encodes a subtilisin protease that activates a MAP kinase pathway that negatively regulates stomatal lineage and density. GTL1 binds to the core GT3 cis-element in the SDD1 promoter and transrepresses its expression under water-sufficient conditions. GTL1 loss-of-function mutants have reduced stomatal number and transpiration, and enhanced drought tolerance and WUE. In this case, higher WUE under water sufficient conditions occurs without reduction in absolute biomass accumulation or carbon assimilation, indicating that gtl1-mediated effects on stomatal conductance and transpiration do not substantially affect CO₂ uptake. These results are proof-of-concept that fine-tuned regulation of stomatal density can result in drought tolerance and higher WUE with maintenance of yield stability. Implications: Accomplishments during the IS-4243-09R project provide unique tools for continued discovery research to enhance plant drought tolerance and WUE.
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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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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
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The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.
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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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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.
Full textAbstract:
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.