Relevant bibliographies by topics / Sequenze biologiche

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  2. Dissertations / Theses
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Academic literature on the topic 'Sequenze biologiche'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Sequenze biologiche"

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Willment, J. A., D. P. Martin, E. Van der Walt, and E. P. Rybicki. "Biological and Genomic Sequence Characterization of Maize streak virus Isolates from Wheat." Phytopathology® 92, no. 1 (January 2002): 81–86. http://dx.doi.org/10.1094/phyto.2002.92.1.81.

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Maize streak virus (MSV) is best known as the causal agent of maize streak disease. However, only a genetically uniform subset of the viruses within this diverse species is actually capable of producing severe symptoms in maize. Whereas these “maize-type” viruses all share greater than 95% sequence identity, MSV strains isolated from grasses may share as little as 79% sequence identity with the maize-type viruses. Here, we present the complete genome sequences and biological characterization of two MSV isolates from wheat that share ≈89% sequence identity with the maize-type viruses. Clonal populations of these two isolates, named MSV-Tas and MSV-VW, were leafhopper-transmitted to Digitaria sanguinalis and a range of maize, wheat, and barley genotypes. Whereas the two viruses showed some differences in their pathogenicity in maize, they were both equally pathogenic in D. sanguinalis and the various wheat and barley genotypes tested. Phylogenetic analyses involving the genome sequences of MSV-Tas and MSV-VW, a new maize-type virus also fully sequenced in this study (MSV-VM), and all other available African streak virus sequences, indicated that MSV-Tas and MSV-VW are close relatives that together represent a distinct MSV strain. Sequence analyses revealed that MSV-VM has a recombinant genome containing MSV-Tas/VW-like sequences within its movement protein gene.
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Mechanda, Subbaiah M., Bernard R. Baum, Douglas A. Johnson, and John T. Arnason. "Sequence assessment of comigrating AFLPTM bands in Echinacea — implications for comparative biological studies." Genome 47, no. 1 (January 1, 2004): 15–25. http://dx.doi.org/10.1139/g03-094.

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The extent of sequence identity among clones derived from monomorphic and polymorphic AFLPTM polymorphism bands was quantified. A total of 79 fragments from a monomorphic band of 273 bp and 48 fragments from a polymorphic band of 159 bp, isolated from individuals belonging to different populations, varieties, and species of Echinacea, were cloned and sequenced. The monomorphic fragments exhibited above 90% sequence identity among clones within samples. Sequence identity within variety ranged from 82.78% to 94.87% and within species from 75.82% to 98.9% and was 57.97% in the genus. The polymorphic fragments exhibited much less sequence identity. In some instances, even two clones from the same fragment were different in their size and sequence. Within sample, clone sequence identity ranged from 100% to 51.57%, within variety from 33.33% to 100% in one variety, and from 23.66% to 45% within species and was as low as 1.25% within the genus. In addition, sequences of the same size were aligned to verify the nature of their sequence dissimilarity/similarity. Within each size group, identical sequences were found across species and varieties. In general, comigrating bands cannot be considered homologous. Thus, the use of AFLPTM band data for comparative studies is appropriate only if the results emanating from such analyses are considered as approximations and are interpreted as phenotypic but not genotypic.Key words: AFLP markers, false homologies.
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Venkataraman, Ganesh, Zachary Shriver, Rahul Raman, and Ram Sasisekharan. "Sequencing Complex Polysaccharides." Science 286, no. 5439 (October 15, 1999): 537–42. http://dx.doi.org/10.1126/science.286.5439.537.

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Although rapid sequencing of polynucleotides and polypeptides has become commonplace, it has not been possible to rapidly sequence femto- to picomole amounts of tissue-derived complex polysaccharides. Heparin-like glycosaminoglycans (HLGAGs) were readily sequenced by a combination of matrix-assisted laser desorption ionization mass spectrometry and a notation system for representation of polysaccharide sequences. This will enable identification of sequences that are critical to HLGAG biological activities in anticoagulation, cell growth, and differentiation.
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Song, Bosheng, Zimeng Li, Xuan Lin, Jianmin Wang, Tian Wang, and Xiangzheng Fu. "Pretraining model for biological sequence data." Briefings in Functional Genomics 20, no. 3 (May 2021): 181–95. http://dx.doi.org/10.1093/bfgp/elab025.

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Abstract With the development of high-throughput sequencing technology, biological sequence data reflecting life information becomes increasingly accessible. Particularly on the background of the COVID-19 pandemic, biological sequence data play an important role in detecting diseases, analyzing the mechanism and discovering specific drugs. In recent years, pretraining models that have emerged in natural language processing have attracted widespread attention in many research fields not only to decrease training cost but also to improve performance on downstream tasks. Pretraining models are used for embedding biological sequence and extracting feature from large biological sequence corpus to comprehensively understand the biological sequence data. In this survey, we provide a broad review on pretraining models for biological sequence data. Moreover, we first introduce biological sequences and corresponding datasets, including brief description and accessible link. Subsequently, we systematically summarize popular pretraining models for biological sequences based on four categories: CNN, word2vec, LSTM and Transformer. Then, we present some applications with proposed pretraining models on downstream tasks to explain the role of pretraining models. Next, we provide a novel pretraining scheme for protein sequences and a multitask benchmark for protein pretraining models. Finally, we discuss the challenges and future directions in pretraining models for biological sequences.
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Azha Javed and Muhammad Javed Iqbal. "Classification of Biological Data using Deep Learning Technique." NUML International Journal of Engineering and Computing 1, no. 1 (April 27, 2022): 13–26. http://dx.doi.org/10.52015/nijec.v1i1.10.

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A huge amount of newly sequenced proteins is being discovered on daily basis. The mainconcern is how to extract the useful characteristics of sequences as the input features for thenetwork. These sequences are increasing exponentially over the decades. However, it is veryexpensive to characterize functions for biological experiments and also, it is really necessaryto find the association between the information of datasets to create and improve medicaltools. Recently machine learning algorithms got huge attention and are widely used. Thesealgorithms are based on deep learning architecture and data-driven models. Previous workfailed to properly address issues related to the classification of biological sequences i.e.protein including efficient encoding of variable length biological sequence data andimplementation of deep learning based neural network models to enhance the performance ofclassification/ recognition systems. To overcome these issues, we have proposed a deeplearning based neural network architecture so that classification performance of the systemcan be increased. In our work, we have proposed 1D-convolution neural network whichclassifies the protein sequences to 10 top common classes. The model extracted features fromthe protein sequences labels and learned through the dataset. We have trained and evaluateour model on protein sequences downloaded from protein data bank (PDB). The modelmaximizes the accuracy rate up to 96%.
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Md Isa, Mohd Nazrin, Sohiful Anuar Zainol Murad, Mohamad Imran Ahmad, Muhammad M. Ramli, and Rizalafande Che Ismail. "An Efficient Scheduling Technique for Biological Sequence Alignment." Applied Mechanics and Materials 754-755 (April 2015): 1087–92. http://dx.doi.org/10.4028/www.scientific.net/amm.754-755.1087.

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Computing alignment matrix score to search for regions of homology between biological sequences is time consuming task. This is due to the recursive nature of the dynamic programming-based algorithms such as the Smith-Waterman and the Needleman-Wunsch algorithmns. Typical FPGA-based protein sequencer comprises of two main logic blocks. One for computing alignment scores i.e. the processing element (PE), while another logic block for configuring the PE with coefficients. During alignment matrix computation, the logic block for configuring the PE are left unused until the time consuming alignment matrix computation finished. Therefore, a new technique, known as overlap computation and configuration (OCC) is proposed to minimize the time overhead for performing biological sequence alignment. The OCC technique simultaneously updating substitution matrix in a processing element (PE) systolic array, while computing alignment matrix scores. Results showed that, the sequencer achieves more than two order of magnitude speed-up higher compared to the state of the art, at negligible area overhead, if any.
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Idris, A. M., and J. K. Brown. "Sinaloa Tomato Leaf Curl Geminivirus: Biological and Molecular Evidence for a New Subgroup III Virus." Phytopathology® 88, no. 7 (July 1998): 648–57. http://dx.doi.org/10.1094/phyto.1998.88.7.648.

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The biological and molecular properties of Sinaloa tomato leaf curl virus (STLCV) were investigated in line with the hypothesis that STLCV is a previously uncharacterized, whitefly-transmitted geminivirus from North America. STLCV causes yellow leaf curl symptoms in tomato and yellow-green foliar mottle in pepper. Five species belonging to two plant families were STLCV experimental hosts. STLCV had a persistent relationship with its whitefly vector, Bemisia tabaci. Polymerase chain reaction fragments of STLCV common region (CR) sequences of the A or B genomic components and the viral coat protein gene (AV1) were molecularly cloned and sequenced. The STLCV A- and B-component CR sequences (174 nucleotides each) shared 97.9% identity and contained identical cis elements putatively involved in transcriptional regulation and an origin of replication (the AC cleavage site within the loop of the hairpin structure and two direct repeat sequences thought to constitute the Rep binding motif), which collectively are diagnostic for subgroup III geminiviruses. The STLCV CR sequence shared 23.1 to 77.6% identity with CR sequences of representative geminiviridae, indicating the STLCV CR sequence is unique. Molecular phylogenetic analysis of CR or AV1 sequences of STLCV and the respective sequences of 31 familial members supported the placement of STLCV as a unique bipartite, subgroup III virus most closely related to other viruses from the Western Hemisphere. STLCV is provisionally described as a new species within the genus Begomovirus, family Geminiviridae.
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Lotrakul, Pongtharin, Rodrigo A. Valverde, and Angela D. Landry. "Biological and Molecular Properties of a Begomovirus from Dicliptera sexangularis." Phytopathology® 90, no. 7 (July 2000): 723–29. http://dx.doi.org/10.1094/phyto.2000.90.7.723.

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Sixangle foldwing, Dicliptera sexangularis (Acanthaceae), showing severe yellow mottle and leaf distortion symptoms was collected from the shoreline of Calusa Island (Lee County, FL). The putative virus was transmitted from infected D. sexangularis to healthy seedlings by mechanical, whitefly (Bemisia tabaci biotype B), and graft-inoculations. Different forms of geminivirus-like DNAs were detected in total DNA extracted from infected plants by Southern blot hybridization analyses using DNA-A and -B of Bean golden mosaic virus (BGMV) from Guatemala as probes. Preliminary polymerase chain reaction experiments and sequence comparisons indicated that the virus was a distinct bipartite begomovirus. The virus was designated Dicliptera yellow mottle virus (DiYMV). Replicative dsDNAs of DiYMV were extracted, digested with selected restriction enzymes, and cloned into a plasmid vector. Both DNA-A and -B were sequenced and compared with those of other begomoviruses. Phylogenetic analyses using AV1, AC1, and BV1 nucleotide sequences indicated that DiYMV has a close relationship with the New World begomoviruses, especially those distributed in the nearby geographic areas of the Florida coast and the Caribbean Basin. However, different percent nucleotide sequence identities and phylogenetic relationships were detected when different open reading frames (ORFs) of DiYMV were compared with their counterparts from begomoviruses from the Caribbean Basin. Based on phylogenetic analyses of the AC1 and BV1 ORFs, DiYMV was closely related to BGMV type II isolates, whereas sequence comparisons of the common region and the AC4-derived amino acid sequences indicated its close relationship with Potato yellow mosaic virus from Venezuela.
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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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Liu, Wen-li, and Qing-biao Wu. "Analysis method and algorithm design of biological sequence problem based on generalized k-mer vector." Applied Mathematics-A Journal of Chinese Universities 36, no. 1 (March 2021): 114–27. http://dx.doi.org/10.1007/s11766-021-4033-x.

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AbstractK-mer can be used for the description of biological sequences and k-mer distribution is a tool for solving sequences analysis problems in bioinformatics. We can use k-mer vector as a representation method of the k-mer distribution of the biological sequence. Problems, such as similarity calculations or sequence assembly, can be described in the k-mer vector space. It helps us to identify new features of an old sequence-based problem in bioinformatics and develop new algorithms using the concepts and methods from linear space theory. In this study, we defined the k-mer vector space for the generalized biological sequences. The meaning of corresponding vector operations is explained in the biological context. We presented the vector/matrix form of several widely seen sequence-based problems, including read quantification, sequence assembly, and pattern detection problem. Its advantages and disadvantages are discussed. Also, we implement a tool for the sequence assembly problem based on the concepts of k-mer vector methods. It shows the practicability and convenience of this algorithm design strategy.
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Dissertations / Theses on the topic "Sequenze biologiche"

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Zappala', Domenica. "Espressione di diverse sequenze geniche del Polyomavirus JC nel soggetto immunocompromesso." Doctoral thesis, Università di Catania, 2012. http://hdl.handle.net/10761/1091.

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E' noto che il sistema immunitario rappresenta la base per la protezione dell'organismo dalle infezioni e quindi ogni suo deficit facilita l'insorgenza di malattie infettive e le rende più gravi. Lo stato di immunodepressione, in cui possono trovarsi alcuni soggetti a causa di diversi eventi patologici, diventa il presupposto per la riattivazione di agenti patogeni virali già presenti in forma latente nell organismo. Il JCV è un polyomavirus ubiquitario che infetta l uomo in età pediatrica e permane latente, dopo la prima infezione, nell organismo ospite, alternandosi talvolta ad episodi di attiva replicazione e stati di quiescenza a seconda della capacità reattiva del soggetto infetto. Nonostante la comparsa degli anticorpi, il virus non viene eliminato dall organismo ma rimane latente nel rene, nel midollo osseo, nelle cellule del tessuto nervoso, nei linfonodi e nell epitelio intestinale, rendendo l ospite portatore sano fino ad un eventuale riattivazione. In condizioni di severa immunosoppressione il virus potrebbe riattivare e indurre una fatale malattia demielinizzante conosciuta come Leucoencefalopatia Multifocale Progressiva (PML). Il meccanismo della riattivazione sembra essere strettamente legato ai processi di replicazione e all espressione di particolari sequenze geniche. E stato, quindi, oggetto di questo studio, la valutazione della presenza del DNA di JCV in termini di espressione genica di due differenti regioni del virus: la regione precoce Large-T (early) o l introne late mRNA di mVP1/mVP2 (late) di JCV in cinque distinti gruppi di soggetti immunodepressi. Sono stati analizzati 200 campioni di plasma di pazienti ricoverati presso le U.O. di ematologia, trapianti, gastroenterologia appartenenti a diversi nosocomi catanesi. Inoltre venivano inclusi 55 campioni bioptici a fresco e paraffinati provenienti da un numero corrispondente di pazienti affetti da RCU (mucosa intestinale), appartenenti a soggetti con forme precancerose o cancro del colon (formazione neoplastica) e trapiantati di rene (rene trapiantato). Per lo studio retrospettivo sono state applicate metodiche di Nested-PCR e Real-Time PCR sia per confermare la presenza del DNA virale di JCV sia per la valutazione dell espressione delle due sequenze geniche ricercate. Di tutti i plasma analizzati solo il 26% (52/200) risultava negativo, per gli altri si poteva apprezzare una positiva solo alla regione tardiva del 38,5% (77/200) e una positività per la sola regione precoce del 25% (51/200). La copresenza delle due regioni ricercate si notava nel 10% dei casi (20/200). Data la prevalenza di positività alla regione tardiva, sembra che, nonostante la regione early sia una sequenza di riferimento diagnostico, la sequenza late rivesta un ruolo fondamentale nella diagnosi di tale tipologia di soggetti confermato altresì da un associazione statisticamente significativa tra le due regioni (p<0,05). Per quanto riguarda i campioni bioptici, si aveva positività solo alle sequenza VP1/VP2; ciò potrebbe dipendere dai meccanismi di replicazione che si instaurano in seguito allo stato di latenza o riattivazione del virus nelle cellule per esso non permissive come nel caso delle cellule dell epitelio intestinale. Poiché l espressione di Large-T e VP1/VP2 è strettamente correlata al completamento del ciclo virale, il loro reperimento dipende dalle diverse fasi della replicazione. Il nuovo bersaglio diagnostico, quindi, confrontato ed affiancato a quello tradizionale, potrebbe chiarire l evoluzione delle patologie connesse a questo virus. La ricerca di due regioni differenti del virus potrebbe essere di aiuto nel chiarire la diagnosi e, laddove fosse in corso una terapia, permettere un corretto monitoraggio e l ottimizzazione dell intervento terapeutico.
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Fortino, Vittorio. "Sequence analysis in bioinformatics: methodological and practical aspects." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/985.

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2011 - 2012
My PhD research activities has focused on the development of new computational methods for biological sequence analyses. To overcome an intrinsic problem to protein sequence analysis, whose aim was to infer homologies in large biological protein databases with short queries, I developed a statistical framework BLAST-based to detect distant homologies conserved in transmembrane domains of different bacterial membrane proteins. Using this framework, transmembrane protein domains of all Salmonella spp. have been screened and more than five thousands of significant homologies have been identified. My results show that the proposed framework detects distant homologies that, because of their conservation in distinct bacterial membrane proteins, could represent ancient signatures about the existence of primeval genetic elements (or mini-genes) coding for short polypeptides that formed, through a primitive assembly process, more complex genes. Further, my statistical framework lays the foundation for new bioinformatics tools to detect homologies domain-oriented, or in other words, the ability to find statistically significant homologies in specific target-domains. The second problem that I faced deals with the analysis of transcripts obtained with RNA-Seq data. I developed a novel computational method that combines transcript borders, obtained from mapped RNA-Seq reads, with sequence features based operon predictions to accurately infer operons in prokaryotic genomes. Since the transcriptome of an organism is dynamic and condition dependent, the RNA-Seq mapped reads are used to determine a set of confirmed or predicted operons and from it specific transcriptomic features are extracted and combined with standard genomic features to train and validate three operon classification models (Random Forests - RFs, Neural Networks – NNs, and Support Vector Machines - SVMs). These classifiers have been exploited to refine the operon map annotated by DOOR, one of the most used database of prokaryotic operons. This method proved that the integration of genomic and transcriptomic features improve the accuracy of operon predictions, and that it is possible to predict the existence of potential new operons. An inherent limitation of using RNA-Seq to improve operon structure predictions is that it can be not applied to genes not expressed under the condition studied. I evaluated my approach on different RNA-Seq based transcriptome profiles of Histophilus somni and Porphyromonas gingivalis. These transcriptome profiles were obtained using the standard RNA-Seq or the strand-specific RNA-Seq method. My experimental results demonstrate that the three classifiers achieved accurate operon maps including reliable predictions of new operons. [edited by author]
XI n.s.
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Seth, Pawan. "STUDY OF THE RELATIONSHIP BETWEEN Mus musculus PROTEIN SEQUENCES AND THEIR BIOLOGICAL FUNCTIONS." University of Akron / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=akron1176736255.

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Arvestad, Lars. "Algorithms for biological sequence alignment." Doctoral thesis, KTH, Numerisk analys och datalogi, NADA, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-2905.

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Altschul, Stephen Frank. "Aspects of biological sequence comparison." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/102708.

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Thesis (Ph. D)--Massachusetts Institute of Technology, Dept. of Mathematics, 1987.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Bibliography: leaves 165-168.
by Stephen Frank Altschul.
Ph.D
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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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Pustułka-Hunt, Elżbieta Katarzyna. "Biological sequence indexing using persistent Java." Thesis, University of Glasgow, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270957.

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Xu, Keyuan. "Stochastic modeling of biological sequence evolution." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/32113.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaves 81-86).
Markov models of sequence evolution are a fundamental building block for making inferences in biological research. This thesis reviews several major techniques developed to estimate parameters of Markov models of sequence evolution and presents a new approach for evaluating and comparing estimation techniques. Current methods for evaluating estimation techniques require sequence data from populations with well-known phylogenetic relationships. Such data is not always available since phylogenetic relationships can never be known with certainty. We propose generating sequence data for the purpose of estimation technique evaluation by simulating sequence evolution in a controlled setting. Our elementary simulator uses a Markov model and a binary branching process, which dynamically builds a phylogenetic tree from an initial seed sequence. The sequences at the leaves of the tree can then be used as input to estimation techniques. We demonstrate our evaluation approach on Arvestad and Bruno's estimation method, and show how our approach can reveal performance variations empirically. The results of our simulation can be used as a guide towards improving estimation techniques.
by Keyuan Xu.
M.Eng.
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Murrel, Benjamin. "Improved models of biological sequence evolution." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71870.

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Thesis (PhD)--Stellenbosch University, 2012.
ENGLISH ABSTRACT: Computational molecular evolution is a field that attempts to characterize how genetic sequences evolve over phylogenetic trees – the branching processes that describe the patterns of genetic inheritance in living organisms. It has a long history of developing progressively more sophisticated stochastic models of evolution. Through a probabilist’s lens, this can be seen as a search for more appropriate ways to parameterize discrete state continuous time Markov chains to better encode biological reality, matching the historical processes that created empirical data sets, and creating useful tools that allow biologists to test specific hypotheses about the evolution of the organisms or the genes that interest them. This dissertation is an attempt to fill some of the gaps that persist in the literature, solving what we see as existing open problems. The overarching theme of this work is how to better model variation in the action of natural selection at multiple levels: across genes, between sites, and over time. Through four published journal articles and a fifth in preparation, we present amino acid and codon models that improve upon existing approaches, providing better descriptions of the process of natural selection and better tools to detect adaptive evolution.
AFRIKAANSE OPSOMMING: Komputasionele molekulêre evolusie is ’n navorsingsarea wat poog om die evolusie van genetiese sekwensies oor filogenetiese bome – die vertakkende prosesse wat die patrone van genetiese oorerwing in lewende organismes beskryf – te karakteriseer. Dit het ’n lang geskiedenis waartydens al hoe meer gesofistikeerde waarskynlikheidsmodelle van evolusie ontwikkel is. Deur die lens van waarskynlikheidsleer kan hierdie proses gesien word as ’n soektog na meer gepasde metodes om diskrete-toestand kontinuë-tyd Markov kettings te parametriseer ten einde biologiese realiteit beter te enkodeer – op so ’n manier dat die historiese prosesse wat tot die vorming van biologiese sekwensies gelei het nageboots word, en dat nuttige metodes geskep word wat bioloë toelaat om spesifieke hipotesisse met betrekking tot die evolusie van belanghebbende organismes of gene te toets. Hierdie proefskrif is ’n poging om sommige van die gapings wat in die literatuur bestaan in te vul en bestaande oop probleme op te los. Die oorkoepelende tema is verbeterde modellering van variasie in die werking van natuurlike seleksie op verskeie vlakke: variasie van geen tot geen, variasie tussen posisies in gene en variasie oor tyd. Deur middel van vier gepubliseerde joernaalartikels en ’n vyfde artikel in voorbereiding, bied ons aminosuur- en kodon-modelle aan wat verbeter op bestaande benaderings – hierdie modelle verskaf beter beskrywings van die proses van natuurlike seleksie sowel as beter metodes om gevalle van aanpassing in evolusie te vind.
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Gîrdea, Marta. "New methods for biological sequence alignment." Thesis, Lille 1, 2010. http://www.theses.fr/2010LIL10089/document.

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L'alignement de séquences biologiques est une technique fondamentale en bioinformatique, et consiste à identifier des séries de caractères similaires (conservés) qui apparaissent dans le même ordre dans les deux séquences, et à inférer un ensemble de modifications (substitutions, insertions et suppressions) impliquées dans la transformation d'une séquence en l'autre. Cette technique permet de déduire, sur la base de la similarité de séquence, si deux ou plusieurs séquences biologiques sont potentiellement homologues, donc si elles partagent un ancêtre commun, permettant ainsi de mieux comprendre l'évolution des séquences. Cette thèse aborde les problèmes de comparaison de séquences dans deux cadres différents: la détection d'homologies et le séquençage à haut débit. L'objectif de ce travail est de développer des méthodes d'alignement qui peuvent apporter des solutions aux deux problèmes suivants: i) la détection d'homologies cachées entre des protéines par comparaison de séquences protéiques, lorsque la source de leur divergence sont les mutations qui changent le cadre de lecture, et ii) le mapping de reads SOLiD (séquences de di-nucléotides chevauchantes codés par des couleurs) sur un génome de référence. Dans les deux cas, la même idée générale est appliquée: comparer implicitement les séquences d'ADN pour la détection de changements qui se produisent à ce niveau, en manipulant, en pratique, d'autres représentations (séquences de protéines, séquences de codes di-nucléotides) qui fournissent des informations supplémentaires et qui aident à améliorer la recherche de similarités. Le but est de concevoir et d'appliquer des méthodes exactes et heuristiques d'alignement, ainsi que des systemes de scores, adaptés à ces scénarios
Biological sequence alignment is a fundamental technique in bioinformatics, and consists of identifying series of similar (conserved) characters that appear in the same order in both sequences, and eventually deducing a set of modifications (substitutions, insertions and deletions) involved in the transformation of one sequence into the other. This technique allows one to infer, based on sequence similarity, if two or more biological sequences are potentially homologous, i.e. if they share a common ancestor, thus enabling the understanding of sequence evolution.This thesis addresses sequence comparison problems in two different contexts: homology detection and high throughput DNA sequencing. The goal of this work is to develop sensitive alignment methods that provide solutions to the following two problems: i) the detection of hidden protein homologies by protein sequence comparison, when the source of the divergence are frameshift mutations, and ii) mapping short SOLiD reads (sequences of overlapping di-nucleotides encoded as colors) to a reference genome. In both cases, the same general idea is applied: to implicitly compare DNA sequences for detecting changes occurring at this level, while manipulating, in practice, other representations (protein sequences, sequences of di-nucleotide codes) that provide additional information and thus help to improve the similarity search. The aim is to design and implement exact and heuristic alignment methods, along with scoring schemes, adapted to these scenarios
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Books on the topic "Sequenze biologiche"

1

Ophir, Frieder, and Martino Robert L, eds. High performance computational methods for biological sequence analysis. Boston: Kluwer Academic Publishers, 1996.

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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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Knut, Reinert, ed. Biological sequence analysis using the SeqAn C++ library. Boca Raton: Chapman & Hall/CRC Taylor & Francis, 2009.

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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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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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Yap, Tieng K. High Performance Computational Methods for Biological Sequence Analysis. Boston, MA: Springer US, 1996.

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Gogol-Döring, Andreas. Biological sequence analysis using the SeqAn C++ library. Boca Raton: CRC Press, 2010.

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Nguyen, Ken, Xuan Guo, and Yi Pan. Multiple Biological Sequence Alignment: Scoring Functions, Algorithms and Applications. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119273769.

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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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Van der Kolk, Bessel A., ed. Post-traumatic stress disorder: Psychological and biological sequelae. Washington, D.C: American Psychiatric Press, 1987.

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Book chapters on the topic "Sequenze biologiche"

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Gupta, Amarnath. "Biological Sequences." In Encyclopedia of Database Systems, 1. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_1307-2.

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Gupta, Amarnath. "Biological Sequences." In Encyclopedia of Database Systems, 223–24. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_1307.

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Pappalardo, Elisa, Panos M. Pardalos, and Giovanni Stracquadanio. "Biological Sequences." In SpringerBriefs in Optimization, 1–6. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9053-1_1.

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Gupta, Amarnath. "Biological Sequences." In Encyclopedia of Database Systems, 287–88. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_1307.

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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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Fink, Gernot A. "Analyse biologischer Sequenzen." In Mustererkennung mit Markov-Modellen, 213–15. Wiesbaden: Vieweg+Teubner Verlag, 2003. http://dx.doi.org/10.1007/978-3-322-80065-7_15.

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Bellows, C. "Biological Tissue Graft: Present Status." In Hernia Repair Sequelae, 317–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11541-7_43.

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Hu, Yuh-Jyh. "Biological Sequence Data Mining." In Principles of Data Mining and Knowledge Discovery, 228–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44794-6_19.

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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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Conference papers on the topic "Sequenze biologiche"

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Kang, Tae Ho, Jae Soo Yoo, and Hak Yong Kim. "Mining Frequent Contiguous Sequence Patterns in Biological Sequences." In 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375640.

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Rosa, Marcos P., Jose V. C. Vargas, Vanessa M. Kava, Fernando G. Dias, Daiani Savi, Beatriz Santos, Wellington Balmant, Andre B. Mariano, Andre Servienski, and Juan C. Ordóñez. "Hydrogen and Compounds With Biological Activity From Microalgae." In ASME 2019 13th International Conference on Energy Sustainability collocated with the ASME 2019 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/es2019-3965.

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Abstract Microalgae have a high biotechnological potential as a source of biofuels (biodiesel, biohydrogen) and other high-added value products (e.g., pharmaceuticals, proteins, pigments). However, for microalgae cultivation to be economically competitive with other fuel sources, it is necessary to apply the concept of biorefinery. This seems to be the most ambitious strategy to achieve viability. Therefore, the objectives of this study were to isolate and identify the main microalgae line used to produce biofuels at Federal University of Parana, Brazil, using the rDNA sequence and micromorphological analysis, and to evaluate the potential of this lineage in the production of hydrogen and co-products with biological activity. For the purification of the lineage (LGMM0001), an aliquot was seeded into solid CHU culture medium and an isolated colony was selected. The genomic DNA was purified using a commercial kit (Macherey-Nagel, Düren, Germany) for molecular identification, the ITS region (ITS1, 5.8S and ITS2) (Internal Transcribed Spacer) was amplified and sequenced using primers LS266 and V9G. Morphological characterization was performed as described by Hemschemeier et al. [1]. Finally, for biological activity research, secondary metabolites were extracted by fractionation and evaluated against bacteria of clinical interest. Through microscopic analysis, general characteristics shared by the genus Tetradesmus were observed. The plasticity of the morphological characteristics of this genus reinforces the need for further studies to classify correctly the species in this group, using DNA sequencing. ITS sequence analysis of LGMM0001 showed 100% homology with sequences from the Tetradesmus obliquus species, so, the lineage was classified as belonging to this species. The evaluated microalgae strain was able to produce hydrogen, showing positive results for gas formation. Biological activity was observed with the extract obtained from the residual culture carried out with alternative medium used in the photobioreactors (PBR), against the Staphylococcus aureus pathogenic lineage. In conclusion, the microalgae strain used in this work was identified as Tetradesmus obliquus (= Acutodesmus obliquus), and was able to produce a compound with economic potential in association with the existing biofuel production process.
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Kion, Lee Nung, and Oon Yin Bee. "Potential Perils of Biological Sequence Visualization Using Sequence Logo." In 2013 10th International Conference Computer Graphics, Imaging and Visualization (CGIV). IEEE, 2013. http://dx.doi.org/10.1109/cgiv.2013.26.

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Caragea, Cornelia, Jivko Sinapov, Drena Dobbs, and Vasant Honavar. "Using Global Sequence Similarity to Enhance Biological Sequence Labeling." In 2008 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2008. http://dx.doi.org/10.1109/bibm.2008.54.

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Mhamdi, Faouzi, and Sourour Marai. "Biclustering of Biological Sequences." In 2017 28th International Workshop on Database and Expert Systems Applications (DEXA). IEEE, 2017. http://dx.doi.org/10.1109/dexa.2017.31.

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Miranker, Daniel P. "Evolving models of biological sequence similarity." In 2008 IEEE 24th International Conference on Data Engineeing workshop (ICDE Workshop 2008). IEEE, 2008. http://dx.doi.org/10.1109/icdew.2008.4498339.

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Kimothi, Dhananjay, Ankita Shukla, Pravesh Biyani, Saket Anand, and James M. Hogan. "Metric learning on biological sequence embeddings." In 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2017. http://dx.doi.org/10.1109/spawc.2017.8227769.

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Ravichandran, L., A. Papandreou-Suppappola, A. Spanias, Z. Lacroix, and C. Legendre. "Time-frequency based biological sequence querying." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495708.

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Miranker, Daniel P. "Evolving Models of Biological Sequence Similarity." In 2008 First International Workshop on Similarity Search and Applications (SISAP). IEEE, 2008. http://dx.doi.org/10.1109/sisap.2008.23.

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Battaglia, Giovanni, Roberto Grossi, Roberto Marangoni, and Nadia Pisanti. "Mining Biological Sequences with Masks." In 2009 20th International Workshop on Database and Expert Systems Application. IEEE, 2009. http://dx.doi.org/10.1109/dexa.2009.47.

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Reports on the topic "Sequenze biologiche"

1

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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Gore, Nolan G., Elizabeth W. Edmiston, Joel H. Saltz, and Roger M. Smith. Parallel Processing of Biological Sequence Comparison Algorithms. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada202407.

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Foulser, David E., and Nolan G. Core. Parallel Computation of Multiple Biological Sequence Comparisons. Fort Belvoir, VA: Defense Technical Information Center, July 1989. http://dx.doi.org/10.21236/ada211455.

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Stormo, Gary D. New approaches to recognizing functional domains in biological sequence. Office of Scientific and Technical Information (OSTI), September 2002. http://dx.doi.org/10.2172/804097.

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Lee, Richard, Moshe Bar-Joseph, K. S. Derrick, Aliza Vardi, Roland Brlansky, Yuval Eshdat, and Charles Powell. Production of Antibodies to Citrus Tristeza Virus in Transgenic Citrus. United States Department of Agriculture, September 1995. http://dx.doi.org/10.32747/1995.7613018.bard.

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Citrus tristeza virus (CTV) is the most important virus disease of citrus in the world. CTV causes death of trees on sour orange rootstock and/or stem pitting of scions regardless of rootstock which results in trees of low vigor, reduced yield with reduction in size and quality of fruit. The purpose of this project was to produce monoclonal antibodies (MABs) to CTV coat protein (CP), develop single domain antibodies (dAbs) or Fab fragments which neutralize the infection by binding to the virus, and to produce transformed plants which express the dAbs. The objectives of this research have been met and putative transgenic tobacco and citrus plants have been developed. These putative transgenic plants are presently undergoing evaluation to determine the level of dAbs expression and to determine their resistance to CTV. Additionally, the CTV genome has been sequenced and the CP gene of several biologically characterized CTV strains molecular characterized. This has indicated a correlation between CP sequence homology and biological activity, and the finding of DI RNAs associated with some CTV strains. Several MABs have been produced which enable broad spectrum identification of CTV strains while other MABs enable differentiation between mild and severe strains. The use of selected MAbs and determination of the CP gene sequence has enabled predictions of biological activities of unknown CTV isolates. The epitopes of two MABs, one reacting selectively with severe CTV strains and the other reacting with all strains, have been characterized at the molecular level.
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Stormo, G. D. New approaches to recognizing functional domains in biological sequences. Progress report. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/10111498.

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Harman, Gary E., and Ilan Chet. Discovery and Use of Genes and Gene Combinations Coding for Proteins Useful in Biological Control. United States Department of Agriculture, September 1994. http://dx.doi.org/10.32747/1994.7568787.bard.

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The objectives of the research in this proposal were to (A) identify synergy among proteins that provide enhanced activity over single proteins for control of plant pathogenic fungi, (B) clone and characterize genetic sequences coding for proteins with ability to control pathogenic fungi, (C) produce transgenic organisms with enhanced biocontrol ability using genes and gene combinations and determine their efficiency in protecting plants against plant pathogenic fungi. A related objective was to produce disease-resistant plants. Fungal cell wall degrading enzymes from any source are strongly synergistic with any membrane active compound and, further, different classes of cell wall degrading enzymes are also strongly synergistic. We have cloned and sequenced a number of genes from bacterial and fungal sources including five that are structurally unrelated. We have prepared transgenic fungi that are deficient in production of enzymes and useful in mechanistic studies. Others are hyperproducers of specific enzymes that permit us, for the first time, to produce enzymes from T. harzianum in sufficient quantity to conduct tests of their potential use in commercial agriculture. Finally, genes from these studies have been inserted into several species of crop plants were they produce a high level of resistance to several plant pathogenic fungi.
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Hackett, Kevin, Shlomo Rottem, David L. Williamson, and Meir Klein. Spiroplasmas as Biological Control Agents of Insect Pests. United States Department of Agriculture, July 1995. http://dx.doi.org/10.32747/1995.7613017.bard.

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Toward development of spiroplasmas as novel toxin-delivery systems for biocontrol of beetle pests in the United States (Leptinotarsa decemlineata) and Israel (Maladera matrida), media for cultivating beetle-associated spiroplasmas were improved and surveys of these spiroplasmas were conducted to provide transformable strains. Extensive surveys of spiroplasmas yielded promising extrachromosomal elements for vector constructs. One, plasmid pCT-1, was cloned, characterized, and used as a source of spiroplasma origin of replication in our shuttle vectors. The fibrillin gene was isolated and sequenced and its strong promoter was also used in the constructs. Means for transforming these vectors into spiroplasmas were developed and optimized, with electroporation found to be suitable for most applications. Development and optimization of means for using large unilamellar vesicles (LUVs) in spiroplasma transformation represents a breakthrough that should facilitate insertion of large clusters of virulence genes. With completion of the vector, we should thus be poised to genetically engineer spiroplasmas with genes that will express toxins lethal to our target beetles, thus providing an effective and inexpensive alternative to conventional means of beetle control.
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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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Shoseyov, Oded, Steven A. Weinbaum, Raphael Goren, and Abhaya M. Dandekar. Biological Thinning of Fruit Set by RNAase in Deciduous Fruit Trees. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568110.bard.

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Fruit thinning is a common and necessary practice for commercial fruit production in many deciduous tree fruit species. Fruit thinning in apple may be accomplished with a variety of chemical thinning agents, but the use of these chemicals is a subject of environmental concern. It has been shown recently that RNase enzyme, secreted from the stigma and the style, inhibits pollen germination and pollen tube elongation. In this study we have been able to show that Aspergillus niger B-1 RNase can effectively inhibit peach and apple pollen germination, and tube elongation in-vitro, as well as thin fruit in peach and apple, and reduce the number of seeds in citrus. The objectives of the research were to detrmine the conditions for effective thinning of (USA and Israel), develop fermentation process for cost effective production of RNase from A. niger. (Israel), and clone apple S-RNase cDNA (USA). All the objectives of the research were addressed. We have determined the optimal fermentation conditions for cost effective production of the A. niger at a 20,000 liters scale. TheA. niger B1 RNase was isolated to homogeneity and its kinetic and biochemical properties including its N-terminal sequence were fully characterized. The field test results both in Israel and California have shown variability in effectiveness and more work is needed to define the RNase concentration necessary to completely inhibit pollen development. Plant transformation vectors expressing anti-sense apple S-RNase genes were constructed (USA) with an attempt to produce self compatible transgenic apple trees. Bovine S-Protein cDNA was cloned and successfully expressed in E. coli (Israel). Plant transformation vector expressing the S-Protein gene was constructed (USA) with an attempt to produce transgenic plants expressing S-protein in the style. Exogenous application of S-peptide to these plants will result in active RNase and consequently prevention of fertilization.
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