Academic literature on the topic 'Biological Sequence Analysis'
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Journal articles on the topic "Biological Sequence Analysis"
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Allison, L., L. Stern, T. Edgoose, and T. I. Dix. "Sequence complexity for biological sequence analysis." Computers & Chemistry 24, no. 1 (January 2000): 43–55. http://dx.doi.org/10.1016/s0097-8485(00)80006-6.
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Li, Hongliang, and Bin Liu. "BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo." PLOS Computational Biology 19, no. 6 (June 20, 2023): e1011214. http://dx.doi.org/10.1371/journal.pcbi.1011214.
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As the key for biological sequence structure and function prediction, disease diagnosis and treatment, biological sequence similarity analysis has attracted more and more attentions. However, the exiting computational methods failed to accurately analyse the biological sequence similarities because of the various data types (DNA, RNA, protein, disease, etc) and their low sequence similarities (remote homology). Therefore, new concepts and techniques are desired to solve this challenging problem. Biological sequences (DNA, RNA and protein sequences) can be considered as the sentences of “the book of life”, and their similarities can be considered as the biological language semantics (BLS). In this study, we are seeking the semantics analysis techniques derived from the natural language processing (NLP) to comprehensively and accurately analyse the biological sequence similarities. 27 semantics analysis methods derived from NLP were introduced to analyse biological sequence similarities, bringing new concepts and techniques to biological sequence similarity analysis. Experimental results show that these semantics analysis methods are able to facilitate the development of protein remote homology detection, circRNA-disease associations identification and protein function annotation, achieving better performance than the other state-of-the-art predictors in the related fields. Based on these semantics analysis methods, a platform called BioSeq-Diabolo has been constructed, which is named after a popular traditional sport in China. The users only need to input the embeddings of the biological sequence data. BioSeq-Diabolo will intelligently identify the task, and then accurately analyse the biological sequence similarities based on biological language semantics. BioSeq-Diabolo will integrate different biological sequence similarities in a supervised manner by using Learning to Rank (LTR), and the performance of the constructed methods will be evaluated and analysed so as to recommend the best methods for the users. The web server and stand-alone package of BioSeq-Diabolo can be accessed at http://bliulab.net/BioSeq-Diabolo/server/.
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Petti, Samantha, and Sean R. Eddy. "Constructing benchmark test sets for biological sequence analysis using independent set algorithms." PLOS Computational Biology 18, no. 3 (March 7, 2022): e1009492. http://dx.doi.org/10.1371/journal.pcbi.1009492.
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Biological sequence families contain many sequences that are very similar to each other because they are related by evolution, so the strategy for splitting data into separate training and test sets is a nontrivial choice in benchmarking sequence analysis methods. A random split is insufficient because it will yield test sequences that are closely related or even identical to training sequences. Adapting ideas from independent set graph algorithms, we describe two new methods for splitting sequence data into dissimilar training and test sets. These algorithms input a sequence family and produce a split in which each test sequence is less than p% identical to any individual training sequence. These algorithms successfully split more families than a previous approach, enabling construction of more diverse benchmark datasets.
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Horton, Robert M. "Biological Sequence Analysis Using Regular Expressions." BioTechniques 27, no. 1 (July 1999): 76–78. http://dx.doi.org/10.2144/99271ir01.
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Yap, T. K., O. Frieder, and R. L. Martino. "Parallel computation in biological sequence analysis." IEEE Transactions on Parallel and Distributed Systems 9, no. 3 (March 1998): 283–94. http://dx.doi.org/10.1109/71.674320.
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Pachter, L., and B. Sturmfels. "Parametric inference for biological sequence analysis." Proceedings of the National Academy of Sciences 101, no. 46 (November 8, 2004): 16138–43. http://dx.doi.org/10.1073/pnas.0406011101.
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Mitrophanov, Alexander Yu, and Mark Borodovsky. "Statistical significance in biological sequence analysis." Briefings in Bioinformatics 7, no. 1 (March 1, 2006): 2–24. http://dx.doi.org/10.1093/bib/bbk001.
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Dwivedi, Vivek Dhar, Indra Prasad Tripathi, Aman Chandra Kaushik, Shiv Bharadwaj, and Sarad Kumar Mishra. "Biological Data Analysis Program (BDAP): a multitasking biological sequence analysis program." Neural Computing and Applications 30, no. 5 (December 17, 2016): 1493–501. http://dx.doi.org/10.1007/s00521-016-2772-z.
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Murad, Taslim, Sarwan Ali, and Murray Patterson. "Exploring the Potential of GANs in Biological Sequence Analysis." Biology 12, no. 6 (June 14, 2023): 854. http://dx.doi.org/10.3390/biology12060854.
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Biological sequence analysis is an essential step toward building a deeper understanding of the underlying functions, structures, and behaviors of the sequences. It can help in identifying the characteristics of the associated organisms, such as viruses, etc., and building prevention mechanisms to eradicate their spread and impact, as viruses are known to cause epidemics that can become global pandemics. New tools for biological sequence analysis are provided by machine learning (ML) technologies to effectively analyze the functions and structures of the sequences. However, these ML-based methods undergo challenges with data imbalance, generally associated with biological sequence datasets, which hinders their performance. Although various strategies are present to address this issue, such as the SMOTE algorithm, which creates synthetic data, however, they focus on local information rather than the overall class distribution. In this work, we explore a novel approach to handle the data imbalance issue based on generative adversarial networks (GANs), which use the overall data distribution. GANs are utilized to generate synthetic data that closely resembles real data, thus, these generated data can be employed to enhance the ML models’ performance by eradicating the class imbalance problem for biological sequence analysis. We perform four distinct classification tasks by using four different sequence datasets (Influenza A Virus, PALMdb, VDjDB, Host) and our results illustrate that GANs can improve the overall classification performance.
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Hanif, Waqar, Hijab Fatima, Muhammad Qasim, Rana Muhammad Atif, and Muhammad Rizwan Javed. "SeqDown: An Efficient Sequence Retrieval Software and Comparative Sequence Retrieval Analysis." Current Trends in OMICS 1, no. 1 (August 2, 2021): 18–29. http://dx.doi.org/10.32350/cto.11.03.
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For any sequence analysis procedure, a single or multiple sequence must be retrieved, stored, organized. One of the most common public databases used for biological sequence retrieval is GenBank which is a comprehensive public database of nucleotide sequences. However, as the length of the sequence to be retrieved increases such as a chromosome, entire genome, scaffold, etc., the elapsed time to download the file gets even elongated due to slower bandwidth to download/retrieve the sequence.[8] In most cases, during sequence analysis, the researcher requires messenger RNA (mRNA), RNA, DNA, protein sequences of the same sequence-of-interest to work with, which consumes a substantial amount of the researcher in finding and retrieving the sequence files. An access to GenBank through JAVA HTTPS protocols is established to request and receive the sequence files associated with the input accessions. SeqDown was shown to be much efficient in terms of retrieval time of the sequences as compared to the other internet browsers and was found to be 15.27% faster than Mozilla Firefox. SeqDown also provides the feature to retrieve coding DNA sequences & protein sequences present in a single chromosome. Sequence retrieval from the most biological databases don’t have proper naming of their files and the user has to deal with the redundantly named sequence files which leads to incorrect and time-consuming analysis and can be solved with SeqDown. SeqDown is available as a free-to-download software at https://bit.ly/3cUwchz
Dissertations / Theses on the topic "Biological Sequence Analysis"
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Yeats, Corin Anthony. "Biological investigations through sequence analysis." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614848.
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Thompson, James. "Genetic algorithms applied to biological sequence analysis /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/2269.
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Parbhane, R. V. "Analysis of DNA sequences: modeling sequence dependent features and their biological roles." Thesis(Ph.D.), CSIR-National Chemical Laboratory, Pune, 2000. http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/2285.
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Verzotto, Davide. "Advanced Computational Methods for Massive Biological Sequence Analysis." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3426282.
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With the advent of modern sequencing technologies massive amounts of biological data, from protein sequences to entire genomes, are becoming increasingly available.
This poses the need for the automatic analysis and classification of such a huge collection of data, in order to enhance knowledge in the Life Sciences.
Although many research efforts have been made to mathematically model this information, for example finding patterns and similarities among protein or genome sequences, these approaches often lack structures that address specific biological issues.
In this thesis, we present novel computational methods for three fundamental problems in molecular biology: the detection of remote evolutionary relationships among protein sequences, the identification of subtle biological signals in related genome or protein functional sites, and the phylogeny reconstruction by means of whole-genome comparisons.
The main contribution is given by a systematic analysis of patterns that may affect these tasks, leading to the design of practical and efficient new pattern discovery tools.
We thus introduce two advanced paradigms of pattern discovery and filtering based on the insight that functional and conserved biological motifs, or patterns, should lie in different sites of sequences.
This enables to carry out space-conscious approaches that avoid a multiple counting of the same patterns.
The first paradigm considered, namely irredundant common motifs, concerns the discovery of common patterns, for two sequences, that have occurrences not covered by other patterns, whose coverage is defined by means of specificity and extension.
The second paradigm, namely underlying motifs, concerns the filtering of patterns, from a given set, that have occurrences not overlapping other patterns with higher priority, where priority is defined by lexicographic properties of patterns on the boundary between pattern matching and statistical analysis.
We develop three practical methods directly based on these advanced paradigms.
Experimental results indicate that we are able to identify subtle similarities among biological sequences, using the same type of information only once.
In particular, we employ the irredundant common motifs and the statistics based on these patterns to solve the remote protein homology detection problem. Results show that our approach, called Irredundant Class, outperforms the state-of-the-art methods in a challenging benchmark for protein analysis.
Afterwards, we establish how to compare and filter a large number of complex motifs (e.g., degenerate motifs) obtained from modern motif discovery tools, in order to identify subtle signals in different biological contexts.
In this case we employ the notion of underlying motifs. Tests on large protein families indicate that we drastically reduce the number of motifs that scientists should manually inspect, further highlighting the actual functional motifs.
Finally, we combine the two proposed paradigms to allow the comparison of whole genomes, and thus the construction of a novel and practical distance function.
With our method, called Unic Subword Approach, we relate to each other the regions of two genome sequences by selecting conserved motifs during evolution.
Experimental results show that our approach achieves better performance than other state-of-the-art methods in the whole-genome phylogeny reconstruction of viruses, prokaryotes, and unicellular eukaryotes, further identifying the major clades of these organisms.Con l'avvento delle moderne tecnologie di sequenziamento, massive quantità di dati biologici, da sequenze proteiche fino a interi genomi, sono disponibili per la ricerca. Questo progresso richiede l'analisi e la classificazione automatica di tali collezioni di dati, al fine di migliorare la conoscenza nel campo delle Scienze della Vita. Nonostante finora siano stati proposti molti approcci per modellare matematicamente le sequenze biologiche, ad esempio cercando pattern e similarità tra sequenze genomiche o proteiche, questi metodi spesso mancano di strutture in grado di indirizzare specifiche questioni biologiche. In questa tesi, presentiamo nuovi metodi computazionali per tre problemi fondamentali della biologia molecolare: la scoperta di relazioni evolutive remote tra sequenze proteiche, l'individuazione di segnali biologici complessi in siti funzionali tra loro correlati, e la ricostruzione della filogenesi di un insieme di organismi, attraverso la comparazione di interi genomi. Il principale contributo è dato dall'analisi sistematica dei pattern che possono interessare questi problemi, portando alla progettazione di nuovi strumenti computazionali efficaci ed efficienti. Vengono introdotti così due paradigmi avanzati per la scoperta e il filtraggio di pattern, basati sull'osservazione che i motivi biologici funzionali, o pattern, sono localizzati in differenti regioni delle sequenze in esame. Questa osservazione consente di realizzare approcci parsimoniosi in grado di evitare un conteggio multiplo degli stessi pattern. Il primo paradigma considerato, ovvero irredundant common motifs, riguarda la scoperta di pattern comuni a coppie di sequenze che hanno occorrenze non coperte da altri pattern, la cui copertura è definita da una maggiore specificità e/o possibile estensione dei pattern. Il secondo paradigma, ovvero underlying motifs, riguarda il filtraggio di pattern che hanno occorrenze non sovrapposte a quelle di altri pattern con maggiore priorità, dove la priorità è definita da proprietà lessicografiche dei pattern al confine tra pattern matching e analisi statistica. Sono stati sviluppati tre metodi computazionali basati su questi paradigmi avanzati. I risultati sperimentali indicano che i nostri metodi sono in grado di identificare le principali similitudini tra sequenze biologiche, utilizzando l'informazione presente in maniera non ridondante. In particolare, impiegando gli irredundant common motifs e le statistiche basate su questi pattern risolviamo il problema della rilevazione di omologie remote tra proteine. I risultati evidenziano che il nostro approccio, chiamato Irredundant Class, ottiene ottime prestazioni su un benchmark impegnativo, e migliora i metodi allo stato dell'arte. Inoltre, per individuare segnali biologici complessi utilizziamo la nozione di underlying motifs, definendo così alcune modalità per il confronto e il filtraggio di motivi degenerati ottenuti tramite moderni strumenti di pattern discovery. Esperimenti su grandi famiglie proteiche dimostrano che il nostro metodo riduce drasticamente il numero di motivi che gli scienziati dovrebbero altrimenti ispezionare manualmente, mettendo in luce inoltre i motivi funzionali identificati in letteratura. Infine, combinando i due paradigmi proposti presentiamo una nuova e pratica funzione di distanza tra interi genomi. Con il nostro metodo, chiamato Unic Subword Approach, relazioniamo tra loro le diverse regioni di due sequenze genomiche, selezionando i motivi conservati durante l'evoluzione. I risultati sperimentali evidenziano che il nostro approccio offre migliori prestazioni rispetto ad altri metodi allo stato dell'arte nella ricostruzione della filogenesi di organismi quali virus, procarioti ed eucarioti unicellulari, identificando inoltre le sottoclassi principali di queste specie.
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Margolin, Yelena 1977. "Analysis of sequence-selective guanine oxidation by biological agents." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42381.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, February 2008.Vita.
Includes bibliographical references.
Oxidatively damaged DNA has been strongly associated with cancer, chronic degenerative diseases and aging. Guanine is the most frequently oxidized base in the DNA, and generation of a guanine radical cation (G'") as an intermediate in the oxidation reaction leads to migration of a resulting cationic hole through the DNA n-stack until it is trapped at the lowest-energy sites. These sites reside at runs of guanines, such as 5'-GG-3' sequences, and are characterized by the lowest sequence-specific ionization potentials (IPs). The charge transfer mechanism suggests that hotspots of oxidative DNA damage induced by electron transfer reagents can be predicted based on the primary DNA sequence. However, preliminary data indicated that nitrosoperoxycarbonate (ONOOCO2"), a mediator of chronic inflammation and a one-electron oxidant, displayed unusual guanine oxidation properties that were the focus of present work. As a first step in our study, we determined relative levels of guanine oxidation, induced by ONOOCO2 in all possible three-base sequence contexts (XGY) within double-stranded oligonucleotides. These levels were compared to the relative oxidation induced within the same guanines by photoactivated riboflavin, a one-electron reagent. We found that, in agreement with previous studies, photoactivated riboflavin was selective for guanines of lowest IPs located within 5'-GG-3' sequences. In contrast, ONOOCO2" preferentially reacted with guanines located within 5'-GC-3' sequences characterized by the highest IPs. This demonstrated that that sequence-specific IP was not a determinant of guanine reactivity with ONOOCO2". Sequence selectivities for both reagents were double-strand specific. Selectivity of ONOOCO2 for 5'-GC-3' sites was also observed in human genomic DNA after ligation-mediated PCR analysis.
(cont.) Relative yields of different guanine lesions produced by both ONOOCO2" and riboflavin varied 4- to 5-fold across all sequence contexts. To assess the role of solvent exposure in mediating guanine oxidation by ONOOCO2", relative reactivities of mismatched guanines with ONOOCO2" were measured. The majority of the mismatches displayed an increased reactivity with ONOOCO2 as compared to the fully matched G-C base-pairs. The extent of reactivity enhancement was sequence context-dependent, and the greatest levels of enhancement were observed for the conformationally flexible guanine- guanine (G-G) mismatches and for guanines located across from a synthetic abasic site. To test the hypothesis that the negative charge of an oxidant influences its reactivity with guanines in DNA, sequence-selective guanine oxidation by a negatively charged reagent, Fe+2-EDTA, was assessed and compared to guanine oxidation produced by a neutral oxidant, y-radiation. Because both of these agents cause high levels of deoxyribose oxidation, a general method to quantify sequence-specific nucleobase oxidation in the presence of direct strand breaks was developed. This method exploited activity of exonuclease III (Exo III), a 3' to 5' exonuclease, and utilized phosphorothioate-modified synthetic oligonucleotides that were resistant to Exo III activity. This method was employed to determine sequence-selective guanine oxidation by Fe+2-EDTA complex and y-radiation and to show that both agents produced identical guanine oxidation pattems and were equally reactive with all guanines, irrespective of their sequence-specific IPs or sequence context.
(cont.) This showed that negative charge was not a determinant of Fe+2-EDTA-mediated guanine oxidation. Finally, the role of oxidant binding on nucleobase damage was assessed by studying sequence-selective oxidation produced by DNA-bound Fe+2 ions in the presence of H202. We found that the major oxidation targets were thymines located within 5'-TGG-3' motifs, demonstrating that while guanines were a required element for coordination of Fe+2 to DNA, they were not oxidized. Our results suggest that factors other than sequence-specific IPs can act as major determinants of sequence-selective guanine oxidation, and that current models of guanine oxidation and charge transfer in DNA cannot be used to adequately predict the location and identity of mutagenic lesions in the genome.
by Yelena Margolin.
Ph.D.
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Kim, Eagu. "Inverse Parametric Alignment for Accurate Biological Sequence Comparison." Diss., The University of Arizona, 2008. http://hdl.handle.net/10150/193664.
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For as long as biologists have been computing alignments of sequences, the question of what values to use for scoring substitutions and gaps has persisted. In practice, substitution scores are usually chosen by convention, and gap penalties are often found by trial and error. In contrast, a rigorous way to determine parameter values that are appropriate for aligning biological sequences is by solving the problem of Inverse Parametric Sequence Alignment. Given examples of biologically correct reference alignments, this is the problem of finding parameter values that make the examples score as close as possible to optimal alignments of their sequences. The reference alignments that are currently available contain regions where the alignment is not specified, which leads to a version of the problem with partial examples.In this dissertation, we develop a new polynomial-time algorithm for Inverse Parametric Sequence Alignment that is simple to implement, fast in practice, and can learn hundreds of parameters simultaneously from hundreds of examples. Computational results with partial examples show that best possible values for all 212 parameters of the standard alignment scoring model for protein sequences can be computed from 200 examples in 4 hours of computation on a standard desktop machine. We also consider a new scoring model with a small number of additional parameters that incorporates predicted secondary structure for the protein sequences. By learning parameter values for this new secondary-structure-based model, we can improve on the alignment accuracy of the standard model by as much as 15% for sequences with less than 25% identity.
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Behr, Jonathan Robert. "Novel tools for sequence and epitope analysis of glycosaminoglycans." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42383.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2007.Includes bibliographical references.
Our understanding of glycosaminoglycan (GAG) biology has been limited by a lack of sensitive and efficient analytical tools designed to deal with these complex molecules. GAGs are heterogeneous and often sulfated linear polys accharides found throughout the extracellular environment, and available to researchers only in limited mixtures. A series of sensitive label-free analytical tools were developed to provide sequence information and to quantify whole epitopes from GAG mixtures. Three complementary sets of tools were developed to provide GAG sequence information. Two novel exolytic sulfatases from Flavobacterium heparinum that degrade heparan/heparan sulfate glycosaminoglycans (HSGAGs) were cloned and characterized. These exolytic enzymes enabled the exo-sequencing of a HSGAG oligosaccharide. Phenylboronic acids (PBAs) were specifically reacted with unsulfated chondroitin sulfate (CS) disaccharides from within a larger mixture. The resulting cyclic esters were easily detected in mass spectrometry (MS) using the distinct isotopic abundance of boron. Electrospray ionization tandem mass spectrometry (ESI-MSn) was employed to determine the fragmentation patterns of HSGAG disaccharides. These patterns were used to quantify relative amounts of isomeric disaccharides in a mixture. Fragmentation information is valuable for building methods for oligosaccharide sequencing, and the general method can be applied to quantify any isomers using MSn. Three other tools were developed to quantify GAG epitopes. Two microfluidic devices were characterized as HSGAG sensors. Sensors were functionalized either with protamine to quantify total HSGAGs or with antithrombin-III (AT-III) to quantify a specific anticoagulant epitope.
(cont.) A charge sensitive silicon field effect sensor accurately quantified clinically relevant anticoagulants including low molecular weight heparins (LMWH), even out of serum. A mass sensitive suspended microchannel resonator (SMR) measured the same clinically relevant HSGAGs. When these two sensors were compared, the SMR proved more robust and versatile. The SMR signal is more stable, it can be reused ad infinitum, and surface modifications can be automated and monitored. The field effect sensor provided an advantage in selectivity by preferentially detecting highly charged HSGAGs instead of any massive, non-specifically bound proteins. Lastly, anti-HSGAG single chain variable fragments (scFv) were evolved using yeast surface display towards generating antibodies for HSGAG epitope sensing and clinical GAG neutralization.
by Jonathan Robert Behr.
Ph.D.
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Tångrot, Jeanette. "Structural Information and Hidden Markov Models for Biological Sequence Analysis." Doctoral thesis, Umeå universitet, Institutionen för datavetenskap, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1629.
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Bioinformatics is a fast-developing field, which makes use of computational methods to analyse and structure biological data. An important branch of bioinformatics is structure and function prediction of proteins, which is often based on finding relationships to already characterized proteins. It is known that two proteins with very similar sequences also share the same 3D structure. However, there are many proteins with similar structures that have no clear sequence similarity, which make it difficult to find these relationships. In this thesis, two methods for annotating protein domains are presented, one aiming at assigning the correct domain family or families to a protein sequence, and the other aiming at fold recognition. Both methods use hidden Markov models (HMMs) to find related proteins, and they both exploit the fact that structure is more conserved than sequence, but in two different ways. Most of the research presented in the thesis focuses on the structure-anchored HMMs, saHMMs. For each domain family, an saHMM is constructed from a multiple structure alignment of carefully selected representative domains, the saHMM-members. These saHMM-members are collected in the so called "midnight ASTRAL set", and are chosen so that all saHMM-members within the same family have mutual sequence identities below a threshold of about 20%. In order to construct the midnight ASTRAL set and the saHMMs, a pipe-line of software tools are developed. The saHMMs are shown to be able to detect the correct family relationships at very high accuracy, and perform better than the standard tool Pfam in assigning the correct domain families to new domain sequences. We also introduce the FI-score, which is used to measure the performance of the saHMMs, in order to select the optimal model for each domain family. The saHMMs are made available for searching through the FISH server, and can be used for assigning family relationships to protein sequences. The other approach presented in the thesis is secondary structure HMMs (ssHMMs). These HMMs are designed to use both the sequence and the predicted secondary structure of a query protein when scoring it against the model. A rigorous benchmark is used, which shows that HMMs made from multiple sequences result in better fold recognition than those based on single sequences. Adding secondary structure information to the HMMs improves the ability of fold recognition further, both when using true and predicted secondary structures for the query sequence.Bioinformatik är ett område där datavetenskapliga och statistiska metoder används för att analysera och strukturera biologiska data. Ett viktigt område inom bioinformatiken försöker förutsäga vilken tredimensionell struktur och funktion ett protein har, utifrån dess aminosyrasekvens och/eller likheter med andra, redan karaktäriserade, proteiner. Det är känt att två proteiner med likande aminosyrasekvenser också har liknande tredimensionella strukturer. Att två proteiner har liknande strukturer behöver dock inte betyda att deras sekvenser är lika, vilket kan göra det svårt att hitta strukturella likheter utifrån ett proteins aminosyrasekvens. Den här avhandlingen beskriver två metoder för att hitta likheter mellan proteiner, den ena med fokus på att bestämma vilken familj av proteindomäner, med känd 3D-struktur, en given sekvens tillhör, medan den andra försöker förutsäga ett proteins veckning, d.v.s. ge en grov bild av proteinets struktur. Båda metoderna använder s.k. dolda Markov modeller (hidden Markov models, HMMer), en statistisk metod som bland annat kan användas för att beskriva proteinfamiljer. Med hjälp en HMM kan man förutsäga om en viss proteinsekvens tillhör den familj modellen representerar. Båda metoderna använder också strukturinformation för att öka modellernas förmåga att känna igen besläktade sekvenser, men på olika sätt. Det mesta av arbetet i avhandlingen handlar om strukturellt förankrade HMMer (structure-anchored HMMs, saHMMer). För att bygga saHMMerna används strukturbaserade sekvensöverlagringar, vilka genereras utifrån hur proteindomänerna kan läggas på varandra i rymden, snarare än utifrån vilka aminosyror som ingår i deras sekvenser. I varje proteinfamilj används bara ett särskilt, representativt urval av domäner. Dessa är valda så att då sekvenserna jämförs parvis, finns det inget par inom familjen med högre sekvensidentitet än ca 20%. Detta urval görs för att få så stor spridning som möjligt på sekvenserna inom familjen. En programvaruserie har utvecklats för att välja ut representanter för varje familj och sedan bygga saHMMer baserade på dessa. Det visar sig att saHMMerna kan hitta rätt familj till en hög andel av de testade sekvenserna, med nästan inga fel. De är också bättre än den ofta använda metoden Pfam på att hitta rätt familj till helt nya proteinsekvenser. saHMMerna finns tillgängliga genom FISH-servern, vilken alla kan använda via Internet för att hitta vilken familj ett intressant protein kan tillhöra. Den andra metoden som presenteras i avhandlingen är sekundärstruktur-HMMer, ssHMMer, vilka är byggda från vanliga multipla sekvensöverlagringar, men också från information om vilka sekundärstrukturer proteinsekvenserna i familjen har. När en proteinsekvens jämförs med ssHMMen används en förutsägelse om sekundärstrukturen, och den beräknade sannolikheten att sekvensen tillhör familjen kommer att baseras både på sekvensen av aminosyror och på sekundärstrukturen. Vid en jämförelse visar det sig att HMMer baserade på flera sekvenser är bättre än sådana baserade på endast en sekvens, när det gäller att hitta rätt veckning för en proteinsekvens. HMMerna blir ännu bättre om man också tar hänsyn till sekundärstrukturen, både då den riktiga sekundärstrukturen används och då man använder en teoretiskt förutsagd.
Jeanette Hargbo.
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Won, Kyoung-Jae. "Exploring the structure of Hidden Markov Models for biological sequence analysis." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.427702.
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Törnkvist, Maria. "Synovial sarcoma : molecular, biological and clinical implications /." Stockholm, 2004. http://diss.kib.ki.se/2004/91-7140-024-9/.
Full textBooks on the topic "Biological Sequence Analysis"
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Ophir, Frieder, and Martino Robert L, eds. High performance computational methods for biological sequence analysis. Boston: Kluwer Academic Publishers, 1996.
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Yap, Tieng K., Ophir Frieder, and Robert L. Martino. High Performance Computational Methods for Biological Sequence Analysis. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1391-5.
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Knut, Reinert, ed. Biological sequence analysis using the SeqAn C++ library. Boca Raton: Chapman & Hall/CRC Taylor & Francis, 2009.
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Gogol-Döring, Andreas. Biological sequence analysis using the SeqAn C++ library. Boca Raton: CRC Press, 2010.
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Gogol-Döring, Andreas. Biological sequence analysis using the SeqAn C++ library. Boca Raton: Chapman & Hall/CRC Taylor & Francis, 2009.
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Yap, Tieng K. High Performance Computational Methods for Biological Sequence Analysis. Boston, MA: Springer US, 1996.
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Richard, Durbin, ed. Biological sequence analysis: Probabalistic models of proteins and nucleic acids. Cambridge, UK: Cambridge University Press, 1998.
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S, Eddy, and Krogh A. et al, eds. Biological Sequence Analysis: Probabilistic Models of Protein & Nucleic Acids. New York: Cambridge University Press, 1998.
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author, Belazzougui Djamal, Cunial Fabio author, and Tomescu Alexandru I. author, eds. Genome-scale algorithm design: Biological sequence analysis in the era of high-throughput sequencing. Cambridge, United Kingdom: University Printing House, 2015.
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Biological microarrays: Methods and protocols. New York: Humana Press, 2011.
Find full textBook chapters on the topic "Biological Sequence Analysis"
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Cawley, Simon E. "Biological Sequence Analysis." In Selected Works of Terry Speed, 563–83. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1347-9_14.
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Chiang, David. "Biological Sequence Analysis: Basics." In Grammars for Language and Genes, 69–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20444-9_5.
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Chiang, David. "Biological Sequence Analysis: Intersection." In Grammars for Language and Genes, 89–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20444-9_6.
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Yap, Tieng K., Ophir Frieder, and Robert L. Martino. "Sequence Analysis Algorithms." In High Performance Computational Methods for Biological Sequence Analysis, 51–97. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1391-5_3.
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Yap, Tieng K., Ophir Frieder, and Robert L. Martino. "Biological Background." In High Performance Computational Methods for Biological Sequence Analysis, 15–49. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1391-5_2.
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Manning, A. M., J. A. Keane, A. Brass, and C. A. Goble. "Clustering techniques in biological sequence analysis." In Principles of Data Mining and Knowledge Discovery, 315–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63223-9_130.
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Liu, Jun S., and T. Logvinenko. "Bayesian Methods in Biological Sequence Analysis." In Handbook of Statistical Genetics, 67–96. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch3.
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Yap, Tieng K., Ophir Frieder, and Robert L. Martino. "Multiprocessor Sequence Alignment." In High Performance Computational Methods for Biological Sequence Analysis, 111–41. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1391-5_5.
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Blanchet, Christophe, Christophe Combet, Vladimir Daric, and Gilbert Deléage. "Web Services Interface to Run Protein Sequence Tools on Grid, Testcase of Protein Sequence Alignment." In Biological and Medical Data Analysis, 240–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11946465_22.
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Srinivasa, K. G., M. Jagadish, K. R. Venugopal, and L. M. Patnaik. "Non-repetitive DNA Sequence Compression Using Memoization." In Biological and Medical Data Analysis, 402–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11946465_36.
Full textConference papers on the topic "Biological Sequence Analysis"
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Nguyen, Thuy-Diem, and Chee-Keong Kwoh. "Efficient agglomerative hierarchical clustering for biological sequence analysis." In TENCON 2015 - 2015 IEEE Region 10 Conference. IEEE, 2015. http://dx.doi.org/10.1109/tencon.2015.7373194.
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Spell, R., R. Brady, and F. Dierich. "BARD: a visualization tool for biological sequence analysis." In IEEE Symposium on Information Visualization 2003. IEEE, 2003. http://dx.doi.org/10.1109/infvis.2003.1249029.
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Schwartz, Daniel. "Invited: “Going viral” with biological sequence analysis." In 2011 IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2011. http://dx.doi.org/10.1109/iccabs.2011.5729861.
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Weindl, J., and J. Hagenauer. "Applying Techniques from Frame Synchronization for Biological Sequence Analysis." In 2007 IEEE International Conference on Communications. IEEE, 2007. http://dx.doi.org/10.1109/icc.2007.142.
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Delibaltov, Diana, S. Karthikeyan, Vignesh Jagadeesh, and B. S. Manjunath. "Robust biological image sequence analysis using graph based approaches." In 2012 46th Asilomar Conference on Signals, Systems and Computers. IEEE, 2012. http://dx.doi.org/10.1109/acssc.2012.6489297.
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Liu, Pei, Ahmed Hemani, and Kolin Paul. "3D-stacked many-core architecture for biological sequence analysis problems." In 2015 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS). IEEE, 2015. http://dx.doi.org/10.1109/samos.2015.7363678.
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Plotz, T., and G. A. Fink. "Feature extraction for improved profile HMM based biological sequence analysis." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334187.
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Nguyen, Khoa Tan, and Timo Ropinski. "Large-scale multiple sequence alignment visualization through gradient vector flow analysis." In 2013 IEEE Symposium on Biological Data Visualization (BioVis). IEEE, 2013. http://dx.doi.org/10.1109/biovis.2013.6664341.
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Liu, Jun-ang, Jian-hong Zhou, and Guo-ying Zhou. "ITS DNA Sequence Analysis of Colletotrichum gloeosporioides and Its Biological Control." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5516794.
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Chengpeng Bi. "A Genetic-Based EM Motif-Finding Algorithm for Biological Sequence Analysis." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221233.
Full textReports on the topic "Biological Sequence Analysis"
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Wang, Ying yuan, Zechang Chen, Luxin Zhang, Shuangyi Chen, Zhuomiao Ye, Tingting Xu, and Yingying Zhang c. A systematic review and network meta-analysis: Role of SNPs in predicting breast carcinoma risk. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2022. http://dx.doi.org/10.37766/inplasy2022.2.0092.
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Review question / Objective: P: Breast cancer patient; I: Single nucleotide polymorphisms associated with breast cancer risk; C: Healthy person; O: By comparing the proportion of SNP mutations in the tumor group and the control group, the effect of BREAST cancer risk-related SNP was investigated; S: Case-control study. Condition being studied: Breast cancer (BC) is one of the most common cancers among women, and its morbidity and mortality have continued to increase worldwide in recent years, reflecting the strong invasiveness and metastasis characteristics of this cancer. BC is a complex disease that involves a sequence of genetic, epigenetic, and phenotypic changes. Polymorphisms of genes involved in multiple biological pathways have been identified as potential risks of BC. These genetic polymorphisms further lead to differences in disease susceptibility and severity among individuals. The development of accurate molecular diagnoses and biological indicators of prognosis are crucial for individualized and precise treatment of BC patients.
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Torney, D. C., W. Bruno, and V. Detours. Nonlinear analysis of biological sequences. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/674921.
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Paran, Ilan, and Molly Jahn. Analysis of Quantitative Traits in Pepper Using Molecular Markers. United States Department of Agriculture, January 2000. http://dx.doi.org/10.32747/2000.7570562.bard.
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Original objectives: The overall goal of the proposal was to determine the genetic and molecular control of pathways leading to the production of secondary metabolites determining major fruit quality traits in pepper. The specific objectives were to: (1) Generate a molecular map of pepper based on simple sequence repeat (SSR) markers. (2) Map QTL for capsaicinoids content (3) Determine possible association between capsaicinoids and carotenoid content and structural genes for capsaicinoid and carotenoid biosynthesis. (4) Map QTL for quantitative traits controlling additional fruit traits. (5) Map fruit-specific ESTs and determine possible association with fruit QTL (6) Map the C locus that determines the presence and absence of capsaicinoids in pepper fruit and identify candidate genes for C. Background: Pungency, color, fruit shape and fruit size are among the most important fruit quality characteristics of pepper. Despite the importance of the pepper crop both in the USA and Israel, the genetic basis of these traits was only little known prior to the studies conducted in the present proposal. In addition, molecular tools for use in pepper improvement were lacking. Major conclusions and achievements: Our studies enabled the development of a saturated genetic map of pepper that includes numerous simple sequence repeat (SSR) markers and the integration of several independent maps into a single resource map that consists of over 2000 markers. Unlike previous maps that consisted mostly of tomato-originated RFLP markers, the SSR-based map consists of largely pepper markers. Therefore, the SSR and integrated maps provide ample of tools for use in marker-assisted selection for diverse targets throughout the Capsicum genome. We determined the genetic and molecular bases of qualitative and quantitative variation of pungency, the most unique characteristics of pepper fruit. We mapped and subsequently cloned the Pun1 gene that serves as a master key for capsaicinoids accumulation and showed that it is an acyltransferase. By sequencing the Pun1 gene in pungent and non-pungent cultivars we identified a deletion that abolishes the expression of the gene in the latter cultivars. We also identified QTLs that control capsaicinoids content and therefore pungency level. These genes will allow pepper breeders to manipulate the level of pungency for specific agricultural and industrial purposes. In addition to pungency we identified genes and QTLs that control other key developmental processes of fruit development such as color, texture and fruit shape. The A gene controlling anthocyanin accumulation in the immature fruit was found as the ortholog of the petunia transcription factor Anthocyanin2. The S gene required for the soft flesh and deciduous fruit nature typical of wild peppers was identified as the ortholog of tomato polygalacturonase. We identified two major QTLs controlling fruit shape, fs3.1 and fs10.1, that differentiate between elongated and blocky and round fruit shapes, respectively. Scientific and agricultural implications: Our studies allowed significant advancement of our understanding at the genetic and molecular levels of important processes of pepper fruit development. Concomitantly to gaining biological knowledge, we were able to develop molecular tools that can be implemented for pepper improvement.
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Zhou, Ting, Roni Shapira, Peter Pauls, Nachman Paster, and Mark Pines. Biological Detoxification of the Mycotoxin Deoxynivalenol (DON) to Improve Safety of Animal Feed and Food. United States Department of Agriculture, July 2010. http://dx.doi.org/10.32747/2010.7613885.bard.
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The trichothecene deoxynivalenol (DON, vomitoxin), one of the most common mycotoxin contaminants of grains, is produced by members of the Fusarium genus. DON poses a health risk to consumers and impairs livestock performance because it causes feed refusal, nausea, vomiting, diarrhea, hemolytic effects and cellular injury. The occurrence of trichothecenes contamination is global and they are very resistant to physical or chemical detoxification techniques. Trichothecenes are absorbed in the small intestine into the blood stream. The overall objective of this project was to develop a protecting system using probiotic bacteria that will express trichothecene 3-O-acetyltransferase (Tri101) that convert T-2 to a less toxic intermediate to reduce ingested levels in-situ. The major obstacle that we had faced during the project is the absence of stable and efficient expression vectors in probiotics. Most of the project period was invested to screen and isolate strong promoter to express high amounts of the detoxify enzyme on one hand and to stabilize the expression vector on the other hand. In order to estimate the detoxification capacity of the isolated promoters we had developed two very sensitive bioassays.The first system was based on Saccharomyces cerevisiae cells expressing the green fluorescent protein (GFP). Human liver cells proliferation was used as the second bioassay system.Using both systems we were able to prove actual detoxification on living cells by probiotic bacteria expressing Tri101. The first step was the isolation of already discovered strong promoters from lactic acid bacteria, cloning them downstream the Tri101 gene and transformed vectors to E. coli, a lactic acid bacteria strain Lactococcuslactis MG1363, and a probiotic strain of Lactobacillus casei. All plasmid constructs transformed to L. casei were unstable. The promoter designated lacA found to be the most efficient in reducing T-2 from the growth media of E. coli and L. lactis. A prompter library was generated from L. casei in order to isolate authentic probiotic promoters. Seven promoters were isolated, cloned downstream Tri101, transformed to bacteria and their detoxification capability was compared. One of those prompters, designated P201 showed a relatively high efficiency in detoxification. Sequence analysis of the promoter region of P201 and another promoter, P41, revealed the consensus region recognized by the sigma factor. We further attempted to isolate an inducible, strong promoter by comparing the protein profiles of L. casei grown in the presence of 0.3% bile salt (mimicking intestine conditions). Six spots that were consistently overexpressed in the presence of bile salts were isolated and identified. Their promoter reigns are now under investigation and characterization.
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Grumet, R., J. Burger, Y. Tadmor, A. Gur, C. Barry, A. Schäffer, and M. Petreikov. Cucumis fruit surface biology: Genetic analysis of fruit exocarp features in melon (C. melo) and cucumber (C. sativus). Israel: United States-Israel Binational Agricultural Research and Development Fund, 2020. http://dx.doi.org/10.32747/2020.8134155.bard.
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The fruit surface (exocarp) is a unique tissue with multiple roles influencing fruit growth and development, disease susceptibility, crop yield, post-harvest treatments, shipping and storage quality, and food safety. Furthermore, highly visible exocarp traits are the consumer's first exposure to the fruit, serving to identify fruit type, variety, attractiveness, and market value. Cucurbit fruit, including the closely related Cucumis species, melon (C. melo) and cucumber (C. sativus), exhibit tremendous diversity for fruit surface properties that are not present in model species. In this project, we identified genetic factors influencing Cucumis fruit surface morphology with respect to important quality determinants such as exocarp and flesh color, cuticle deposition, and surface netting. We employed a combination of approaches including: genome-wide association studies (GWAS) utilizing an extensive melon population and the U.S. Plant Introduction (PI) collection for cucumber to identify genomic regions associated with natural variation in fruit surface traits; bulked segregant RNA-seq (BSR-seq) analysis of bi-parental F2:3 or RIL (recombinant inbred line) populations to genomic regions and candidate genes segregating for fruit surface traits; and comparison of syntenic genomic regions and identification of homologous candidate genes. Candidate genes were examined for sequence and/or expression differences during fruit development that correspond with phenotypic differences. Primary outcomes of the work included identification of candidate genes influencing cuticle deposition, epidermal cell structure, surface netting, and intensity of rind and flesh color. Parallel studies identified mutations within the cucumber and melon homologs of the transcription factor WIN1 (WAX INDUCER1) as a significant factor influencing these surface properties. Additional QTL (quantitative trait loci) were identified in both species, and candidate genes in melon include a novel beta-glucosidase involved in lignin production and an integral membrane protein potentially involved in cuticle metabolism. Genetic resources and biochemical approaches have been developed to study cuticle and wax deposition in both species: segregating populations of melon were developed and sequenced for bulked segregant analysis and samples collected for metabolic analysis; an isolation procedure was developed for lipid droplets from cucumber peel and metabolomic analyses have been initiated. Genetic studies in melon identified mutations in a candidate gene (APRR2), associated with light immature rind, and further indicated that this gene is also associated with color intensity of both mature rinds and flesh, making it a good target for breeding. GWAS studies utilizing the cucumber core diversity population are being performed to identify additional sources of variation for fruit surface properties, map QTL, and examine for synteny with melon. Collectively these studies identified genetic regions associated with important quality traits and contributed to our understanding of underlying biological processes associated with fruit surface development. Knowledge of genetic control of these characteristics can facilitate more efficient breeding for important fruit surface traits.
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Mevarech, Moshe, Jeremy Bruenn, and Yigal Koltin. Virus Encoded Toxin of the Corn Smut Ustilago Maydis - Isolation of Receptors and Mapping Functional Domains. United States Department of Agriculture, September 1995. http://dx.doi.org/10.32747/1995.7613022.bard.
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Ustilago maydis is a fungal pathogen of maize. Some strains of U. maydis encode secreted polypeptide toxins capable of killing other susceptible strains of U. maydis. Resistance to the toxins is conferred by recessive nuclear genes. The toxins are encoded by genomic segments of resident double-strande RNA viruses. The best characterized toxin, KP6, is composed of two polypeptides, a and b, which are not covalently linked. It is encoded by P6M2 dsRNA, which has been cloned, sequenced and expressed in a variety of systems. In this study we have shown that the toxin acts on the membranes of sensitive cells and that both polypeptides are required for toxin activity. The toxin has been shown to function by creating new pores in the cell membrane and disrupting ion fluxes. The experiments performed on artificial phospholipid bilayers indicated that KP6 forms large voltage-independent, cation-selective channels. Experiments leading to the resolution of structure-function relationship of the toxin by in vitro analysis have been initiated. During the course of this research the collaboration also yielded X-ray diffracion data of the crystallized a polypeptide. The effect of the toxin on the pathogen has been shown to be receptor-mediated. A potential receptor protein, identified in membrane fractions of sensitive cells, was subjected to tryptic hydrolysis followed by amino-acid analysis. The peptides obtained were used to isolate a cDNA fragment by reverse PCR, which showed 30% sequence homology to the human HLA protein. Analysis of other toxins secreted by U. maydis, KP1 and KP4, have demonstrated that, unlike KP6, they are composed of a single polypeptide. Finally, KP6 has been expressed in transgenic tobacco plants, indicating that accurate processing by Kex2p-like activity occurs in plants as well. Using tobacco as a model system, we determined that active antifungal toxins can be synthesized and targeted to the outside of transgenic plant cells. If this methodology can be applied to other agronomically crop species, then U. maydis toxins may provide a novel means for biological control of pathogenic fungi.
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Barefoot, Susan F., Bonita A. Glatz, Nathan Gollop, and Thomas A. Hughes. Bacteriocin Markers for Propionibacteria Gene Transfer Systems. United States Department of Agriculture, June 2000. http://dx.doi.org/10.32747/2000.7573993.bard.
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The antibotulinal baceriocins, propionicin PLG-1 and jenseniin G., were the first to be identified, purified and characterized for the dairy propionibaceria and are produced by Propionibacterium thoenii P127 and P. thoenii/jensenii P126, respectively. Objectives of this project were to (a) produce polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1; (b) identify, clone and characterize the propionicin PLG-1 (plg-1) and jenseniin G (jnG) genes; and (3) develop gene transfer systems for dairy propionibacteria using them as models. Polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1 were produced in rabbits. Anti-PLG-1 antiserum had high titers (256,000 to 512,000), neutralized PLG-1 activity, and detected purified PLG-1 at 0.10 mg/ml (indirect ELISA) and 0.033 mg/ml (competitive indirect ELISA). Thirty-nine of 158 strains (most P. thoenii or P. jensenii) yielded cross-reacting material; four strains of P. thoenii, including two previously unidentified bacteriocin producers, showed biological activity. Eight propionicin-negative P127 mutants produced neither ELISA response nor biological activity. Western blot analyses of supernates detected a PLG-1 band at 9.1 kDa and two additional protein bands with apparent molecular weights of 16.2 and 27.5 kDa. PLG-1 polyclonal antibodies were used for detection of jenseniin G. PLG-1 antibodies neutralized jenseniin G activity and detected a jenseniin G-sized, 3.5 kDa peptide. Preliminary immunoprecipitation of crude preparations with PLG-1 antibodies yielded three proteins including an active 3-4 kDa band. Propionicin PLG-1 antibodies were used to screen a P. jensenii/thoenii P126 genomic expression library. Complete sequencing of a cloned insert identified by PLG-1 antibodies revealed a putative response regulator, transport protein, transmembrane protein and an open reading frame (ORF) potentially encoding jenseniin G. PCR cloning of the putative plg-1 gene yielded a 1,100 bp fragment with a 355 bp ORF encoding 118 amino acids; the deduced N-terminus was similar to the known PLG-1 N-terminus. The 118 amino acid sequence deduced from the putative plg-1 gene was larger than PLG-1 possibly due to post-translational processing. The product of the putative plg-1 gene had a calculated molecular weight of 12.8 kDa, a pI of 11.7, 14 negatively charged residues (Asp+Glu) and 24 positively charged residues (Arg+Lys). The putative plg-1 gene was expressed as an inducible fusion protein with a six-histidine residue tag. Metal affinity chromatography of the fused protein yielded a homogeneous product. The fused purified protein sequence matched the deduced putative plg-1 gene sequence. The data preliminarily suggest that both the plg-1 and jnG genes have been identified and cloned. Demonstrating that antibodies can be produced for propionicin PLG-1 and that those antibodies can be used to detect, monitor and compare activity throughout growth and purification was an important step towards monitoring PLG-1 concentrations in food systems. The unexpected but fortunate cross-reactivity of PLG-1 antibodies with jenseniin G led to selective recovery of jenseniin G by immunoprecipitation. Further refinement of this separation technique could lead to powerful affinity methods for rapid, specific separation of the two bacteriocins and thus facilitate their availability for industrial or pharmaceutical uses. Preliminary identification of genes encoding the two dairy propionibacteria bacteriocins must be confirmed; further analysis will provide means for understanding how they work, for increasing their production and for manipulating the peptides to increase their target species. Further development of these systems would contribute to basic knowledge about dairy propionibacteria and has potential for improving other industrially significant characteristics.
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Davidson, Irit, Hsing-Jien Kung, and Richard L. Witter. Molecular Interactions between Herpes and Retroviruses in Dually Infected Chickens and Turkeys. United States Department of Agriculture, January 2002. http://dx.doi.org/10.32747/2002.7575275.bard.
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Tumors in commercial poultry are caused mainly by infection with avian herpes and retroviruses, the herpesvirus Marek's disease virus (MDV) and the retroviruses, reticuloendotheliosis (REV), lymphoid leukosis, subgroups A-I and J (ALV and ALV-J) in chickens, or Iymphoprolipherative disease (LPDV) in turkeys. Infection with one virus aggravates the clinical outcome of birds that are already infected by another oncogenic virus. As these viruses do not interfere for infection, MDV and one or more retroviruses can infect the same flock, the same bird and the same cell. While infecting the same cell, herpes and retroviruses might interact in at least three ways: a) Integration of retrovirus genomes, or genomic fragments (mainly the LTR) into MDV;b) alteration of LTR-driven expression of retroviral genes by MDV immediate- early genes, and c) by herpesvirus induced cellular transcriptional factors. The first type of molecular interaction have been demonstrated to happen efficiently in vitro by Dr. Kung, in cases multiple infection of cell cultures with MDV and REV or MDV and ALV. Moreover, Dr. Witter showed that an in vitro-created recombinant, RM1, had altered in vitro replication and in vivo biological properties. A more comprehensive characterization of RM1 was carried out in the present project. We sought to highlight whether events of such integrations occur also in the bird, in vivo. For that, we had first to determine the prevalence of dually-infected individual birds in commercial flocks, as no systematic survey has been yet reported. Surprisingly, about 25% of the commercial flocks infected with avian oncogenic viruses had a multiple virus infection and 5% of the total samples ana lysed had multiple virus sequences. Then, we aimed to evaluate and characterize biologically and molecularly the resulting recombinants, if formed, and to analyse the factors that affect these events (virus strains, type and age of birds and time interval between the infection with both viruses). The perception of retrovirus insertions into herpesviruses in vivo is not banal, as the in vivo and in vitro systems differ in the viral-target cells, lymphocytes or fibroblasts, in the MDV-replicative type, transforming or productive, and the immune system presence. We realized that previous methods employed to study in vitro created recombinant viruses were not adequate for the study of samples taken directly from the bird. Therefore, the Hot Spot-combined PCR was developed based on the molecularly known RM1 virus. Also, the PFGE that was used for tissue cultured-MDV separation was inefficient for separating MDV from organs, but useful with feather tips as a source of bird original MDV. Much attention was dedicated now to feathers, because if a recombinant virus would be formed in vivo, its biological significance would be evident by horizontal dissemination through the feathers. Major findings were: a) not only in vitro, but also in vivo MDV and retrovirus co-infections lead to LTR integrations into MDV. That was shown by the detection of chimeric molecules. These appeared in low quantities and as quasispecies, thus interfering with sequence analysis of cloned gel-purified chimeric molecules. Mainly inserts were located in the repeat long MDV fragments. In field birds chimeric molecules were detected at a lower frequency (2.5%) than in experimentally infected birds (30-50%). These could be transmitted experimentally to another birds by inoculation with chimeric molecules containing blood. Several types of chimeric molecules were formed, and same types were detected in birds infected by a second round. To reproduce viral integrations, in vivo infection trials were done with field inoculate that contained both viruses, but the chimeric molecule yield was undetectable.
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Zchori-Fein, Einat, Judith K. Brown, and Nurit Katzir. Biocomplexity and Selective modulation of whitefly symbiotic composition. United States Department of Agriculture, June 2006. http://dx.doi.org/10.32747/2006.7591733.bard.
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Whiteflies are sap-sucking insects that harbor obligatory symbiotic bacteria to fulfill their dietary needs, as well as a facultative microbial community with diverse bacterial species. The sweetpotato whitefly Bemisia tabaci (Gennadius) is a severe agricultural pest in many parts of the world. This speciesconsists of several biotypes that have been distinguished largely on the basis of biochemical or molecular diagnostics, but whose biological significance is still unclear. The original objectives of the project were (i) to identify the specific complement of prokaryotic endosymbionts associated with select, well-studied, biologically and phylogeographically representative biotypes of B. tabaci, and (ii) to attempt to 'cure’ select biotypes of certain symbionts to permit assessment of the affect of curing on whitefly fitness, gene flow, host plant preference, and virus transmission competency.To identify the diversity of bacterial community associated with a suite of phylogeographically-diverseB. tabaci, a total of 107 populations were screened using general Bacteria primers for the 16S rRNA encoding gene in a PCR. Sequence comparisons with the available databases revealed the presence of bacteria classified in the: Proteobacteria (66%), Firmicutes (25.70%), Actinobacteria (3.7%), Chlamydiae (2.75%) and Bacteroidetes (<1%). Among previously identified bacteria, such as the primary symbiont Portiera aleyrodidarum, and the secondary symbionts Hamiltonella, Cardinium and Wolbachia, a Rickettsia sp. was detected for the first time in this insect family. The distribution, transmission, and localization of the Rickettsia were studied using PCR and fluorescence in situ hybridization (FISH). Rickettsia was found in all 20 Israeli B. tabaci populations screened as well as some populations screened in the Arizona laboratory, but not in all individuals within each population. FISH analysis of B. tabaci eggs, nymphs and adults, revealed a unique concentration of Rickettsia around the gut and follicle cells as well as its random distribution in the haemolymph, but absence from the primary symbiont housing cells, the bacteriocytes. Rickettsia vertical transmission on the one hand and its partial within-population infection on the other suggest a phenotype that is advantageous under certain conditions but may be deleterious enough to prevent fixation under others.To test for the possible involvement of Wolbachia and Cardiniumin the reproductive isolation of different B. tabacibiotypes, reciprocal crosses were preformed among populations of the Cardinium-infected, Wolbachia-infected and uninfected populations. The crosses results demonstrated that phylogeographically divergent B. tabaci are reproductively competent and that cytoplasmic incompatibility inducer-bacteria (Wolbachia and Cardinium) both interfered with, and/or rescued CI induced by one another, effectively facilitating bidirectional female offspring production in the latter scenario.This knowledge has implications to multitrophic interactions, gene flow, speciation, fitness, natural enemy interactions, and possibly, host preference and virus transmission. Although extensive and creative attempts undertaken in both laboratories to cure whiteflies of non-primary symbionts have failed, our finding of naturally uninfected individuals have permitted the establishment of Rickettsia-, Wolbachia- and Cardinium-freeB. tabaci lines, which are been employed to address various biological questions, including determining the role of these bacteria in whitefly host biology.
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Mawassi, Munir, and Valerian Dolja. Role of RNA Silencing Suppression in the Pathogenicity and Host Specificity of the Grapevine Virus A. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7592114.bard.
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RNA silencing is a defense mechanism that functions against virus infection and involves sequence-specific degradation of viral RNA. Diverse RNA and DNA viruses of plants encode RNA silencing suppressors (RSSs), which, in addition to their role in viral counterdefense, were implicated in the efficient accumulation of viral RNAs, virus transport, pathogenesis, and determination of the virus host range. Despite rapidly growing understanding of the mechanisms of RNA silencing suppression, systematic analysis of the roles played by diverse RSSs in virus biology and pathology is yet to be completed. Our research was aimed at conducting such analysis for two grapevine viruses, Grapevine virus A (GVA) and Grapevine leafroll-associated virus-2 (GLRaV- 2). Our major achievements on the previous cycle of BARD funding are as follows. 1. GVA and GLRaV-2 were engineered into efficient gene expression and silencing vectors for grapevine. The efficient techniques for grapevine infection resulting in systemic expression or silencing of the recombinant genes were developed. Therefore, GVA and GLRaV-2 were rendered into powerful tools of grapevine virology and functional genomics. 2. The GVA and GLRaV-2 RSSs, p10 and p24, respectively, were identified, and their roles in viral pathogenesis were determined. In particular, we found that p10 functions in suppression and pathogenesis are genetically separable. 3. We revealed that p10 is a self-interactive protein that is targeted to the nucleus. In contrast, p24 mechanism involves binding small interfering RNAs in the cytoplasm. We have also demonstrated that p10 is relatively weak, whereas p24 is extremely strong enhancer of the viral agroinfection. 4. We found that, in addition to the dedicated RSSs, GVA and GLRaV-2 counterdefenses involve ORF1 product and leader proteases, respectively. 5. We have teamed up with Dr. Koonin and Dr. Falnes groups to study the evolution and function of the AlkB domain presents in GVA and many other plant viruses. It was demonstrated that viral AlkBs are RNA-specific demethylases thus providing critical support for the biological relevance of the novel process of AlkB-mediated RNA repair.