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Relevant bibliographies by topics / Sequence tags

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Academic literature on the topic 'Sequence tags'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Sequence tags"

1

Cohen, M., and B. Emanuel. "Expressed sequence tags." Science 266, no. 5192 (1994): 1790–91. http://dx.doi.org/10.1126/science.7997870.

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Bürglin, Thomas R., and Thomas M. Barnes. "Introns in sequence tags." Nature 357, no. 6377 (1992): 367. http://dx.doi.org/10.1038/357367a0.

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Adams, Mark D., Chris Fields, and J. Craig Venter. "Introns in sequence tags." Nature 357, no. 6377 (1992): 367–68. http://dx.doi.org/10.1038/357367b0.

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Györgyey, János, Danièle Vaubert, José I. Jiménez-Zurdo, et al. "Analysis of Medicago truncatula Nodule Expressed Sequence Tags." Molecular Plant-Microbe Interactions® 13, no. 1 (2000): 62–71. http://dx.doi.org/10.1094/mpmi.2000.13.1.62.

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Systematic sequencing of expressed sequence tags (ESTs) can give a global picture of the assembly of genes involved in the development and function of organs. Indeterminate nodules representing different stages of the developmental program are especially suited to the study of organogenesis. With the vector λHybriZAP, a cDNA library was constructed from emerging nodules of Medicago truncatula induced by Sinorhizobium meliloti. The 5′ ends of 389 cDNA clones were sequenced, then these ESTs were analyzed both by sequence homology search and by studying their expression in roots and nodules. Two hundred fifty-six ESTs exhibited significant similarities to characterized data base entries and 40 of them represented 26 nodulin genes, while 133 had no similarity to sequences with known function. Only 60 out of the 389 cDNA clones corresponded to previously submitted M. truncatula EST sequences. For 117 cDNAs, reverse Northern (RNA) hybridization with root and nodule RNA probes revealed enhanced expression in the nodule, 48 clones are likely to code for novel nodulins, 33 cDNAs are clones of already known nodulin genes, and 36 clones exhibit similarity to other characterized genes. Thus, systematic analysis of the EST sequences and their expression patterns is a powerful way to identify nodule-specific and nodulation-related genes.

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Wilkins, Marc R., Elisabeth Gasteiger, Jean-Charles Sanchez, Ron D. Appel, and Denis F. Hochstrasser. "Protein identification with sequence tags." Current Biology 6, no. 12 (1996): 1543–44. http://dx.doi.org/10.1016/s0960-9822(02)70764-1.

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MANN, M. "A shortcut to interesting human genes: peptide sequence tags, expressed-sequence tags and computers." Trends in Biochemical Sciences 21, no. 12 (1996): 494–95. http://dx.doi.org/10.1016/s0968-0004(96)30042-x.

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HAN, YONGHUA, BIN MA, and KAIZHONG ZHANG. "SPIDER: SOFTWARE FOR PROTEIN IDENTIFICATION FROM SEQUENCE TAGS WITH DE NOVO SEQUENCING ERROR." Journal of Bioinformatics and Computational Biology 03, no. 03 (2005): 697–716. http://dx.doi.org/10.1142/s0219720005001247.

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For the identification of novel proteins using MS/MS, de novo sequencing software computes one or several possible amino acid sequences (called sequence tags) for each MS/MS spectrum. Those tags are then used to match, accounting amino acid mutations, the sequences in a protein database. If the de novo sequencing gives correct tags, the homologs of the proteins can be identified by this approach and software such as MS-BLAST is available for the matching. However, de novo sequencing very often gives only partially correct tags. The most common error is that a segment of amino acids is replaced by another segment with approximately the same masses. We developed a new efficient algorithm to match sequence tags with errors to database sequences for the purpose of protein and peptide identification. A software package, SPIDER, was developed and made available on Internet for free public use. This paper describes the algorithms and features of the SPIDER software.

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Hisada, Sunao, Tomoya Akihama, Tomoko Endo, Takaya Moriguchi, and Mitsuo Omura. "Expressed Sequence Tags of Citrus Fruit during Rapid Cell Development Phase." Journal of the American Society for Horticultural Science 122, no. 6 (1997): 808–12. http://dx.doi.org/10.21273/jashs.122.6.808.

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A cDNA library was constructed from satsuma mandarin (Citrus unshiu Marc.) fruit tissues during the rapid cell enlargement phase. A total of 950 individual cDNA clones was partially sequenced and compared with GenBank databases for characterizing the gene repertoire expressed during this developmental phase. Among these, 426 cDNA clones (44.8%) showed sequence identity with previously characterized genes with optimized (OPT) scores of ≥200, while 524 clones (55.2%) resulted in low OPT scores (<200) and did not show any significant sequence identity with previously published genes. Based on nucleotide sequence, most clones with OPT scores of ≥200 were assumed to be transcription-, translation-, cell-wall-metabolism-, and stress-response-related genes. Other clones showed homology with published sequences related to housekeeping and stress-response-related genes, including metallothionein-like proteins, late-embryogenesis-abundant (LEA) proteins, and heat-shock proteins. These results suggested that Citrus fruit during rapid cell enlargement were metabolically active and expanding in response to biotic and abiotic stress. For clones with low nucleotide sequence homology, the recurrence was evaluated by aligning nucleotide sequences of each clone and generating contig maps. Expressed sequence tags (ESTs) of 162 clones with OPT scores <200 have not been reported for any other organism. Collectively, randomly sequenced cDNA clones described in this study provided information on types of genes expressed during the rapid cell enlargement phase in Citrus fruit. These genes should be used as candidates for Citrus genome mapping projects.

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Ozdemir Ozgenturk, Nehir, Fatma Oruç, Ugur Sezerman, et al. "Generation and Analysis of Expressed Sequence Tags fromOlea europaeaL." Comparative and Functional Genomics 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/757512.

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Olive (Olea europaeaL.) is an important source of edible oil which was originated in Near-East region. In this study, two cDNA libraries were constructed from young olive leaves and immature olive fruits for generation of ESTs to discover the novel genes and search the function of unknown genes of olive. The randomly selected 3840 colonies were sequenced for EST collection from both libraries. Readable 2228 sequences for olive leaf and 1506 sequences for olive fruit were assembled into 205 and 69 contigs, respectively, whereas 2478 were singletons. Putative functions of all 2752 differentially expressed unique sequences were designated by gene homology based on BLAST and annotated using BLAST2GO. While 1339 ESTs show no homology to the database, 2024 ESTs have homology (under 80%) with hypothetical proteins, putative proteins, expressed proteins, and unknown proteins in NCBI-GenBank. 635 EST's unique genes sequence have been identified by over 80% homology to known function in other species which were not previously described in Olea family. Only 3.1% of total EST's was shown similarity with olive database existing in NCBI. This generated EST's data and consensus sequences were submitted to NCBI as valuable source for functional genome studies of olive.

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McCallum, J., M. J. Havey, J. Lancaster, J. Farrant, and S. Clark. "EXPRESSED SEQUENCE TAGS FROM BULB ONION." Acta Horticulturae, no. 555 (June 2001): 83–86. http://dx.doi.org/10.17660/actahortic.2001.555.8.

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Dissertations / Theses on the topic "Sequence tags"

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Slater, Guy St Clair. "Algorithims for the analysis of expressed sequence tags." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621848.

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Brulliard, Marie. "Infidélité de transcription et carcinogénèse. Analyse bioinformatique et preuves de concept biologiques." Thesis, Vandoeuvre-les-Nancy, INPL, 2009. http://www.theses.fr/2009INPL037N/document.

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L’un des enjeux de la lutte contre le cancer réside dans la compréhension de l’hétérogénéité de la maladie. Le but de notre travail a été d’explorer l’hétérogénéité des cellules cancéreuses du point de vue de la séquence d’ARN messager. Les ESTs (ou Expressed Sequence Tags) d’origine humaine ont été alignées aux séquences de référence ARNm. Les alignements ont été exploités de manière à mesurer les variations de séquence des ESTs issues de tissus tumoraux ou non tumoraux à chaque position de chaque transcrit. L’analyse statistique mise en place a consisté à identifier les positions pour lesquelles les variations de séquence, i.e. substitutions, insertions et délétions, sont différentes entre les ESTs d’origine tumorale et les ESTs d’origine non tumorale. L’étude bioinformatique s’est d’abord concentrée sur 17 transcrits abondamment exprimés avant d’être étendue à l’ensemble du transcriptome. Elle a ensuite été réalisée sur les ESTs murines. Les résultats montrent que l’hétérogénéité des transcrits cancéreux est plus grande que celle des tissus sains. Ainsi, l’infidélité de transcription est augmentée au cours de la carcinogénèse. Ce résultat bioinformatique a été validé par différentes approches biologiques. Tout d’abord, le clonage puis le séquençage d’un ARN provenant d’une tumeur pulmonaire humaine et présentant une délétion prédite de manière bioinformatique ont été réalisés, et ce, en l’absence de mutation somatique. Ensuite, l’identification par spectrométrie de masse d’un variant protéique issu de la traduction d’un ARN dont le codon stop est substitué en triplet codant a été possible. Enfin, l’intérêt de rechercher dans le sérum de patients cancéreux la présence d’anticorps dirigés contre des protéines issues de la traduction d’ARNm infidèles a été démontré. Ainsi, l’infidélité de transcription est un phénomène augmenté dans le cancer et responsable d’une partie de l’hétérogénéité des cellules cancéreuses. L’intérêt de cette découverte réside dans les perspectives nouvelles qu’elle offre en termes de compréhension des mécanismes de carcinogénèse et en termes de diagnostic de la maladie<br>One of the aim of the fight against cancer is to understand the heterogeneity of cancer cells. The goal of our work has been to explore cancer cell mRNA heterogeneity. ESTs (Expressed Sequence Tags) extracted from normal and cancer tissues have been aligned to mRNA reference sequences. This allowed identification of non-random sequence variations that occurred at statistically significant increased rates in cancer compared to normal libraries. This analysis first focused on 17 abundant transcripts and was next extended to whole human genome, as well as to that of Mus musculus. The results show an increase of transcription infidelity events in cancer tissues. Three types of events occur, i.e. base substitutions, deletions and insertions. Bioinformatics results have been validated through different biological methods. First, the cloning and sequencing of mRNA from lung cancer human with a deletion occurring at bioinformatically predicted position in absence of somatic mutation has been achieved. Then, mass spectrometry analysis confirmed the existence of protein variants resulting from translation of mRNA bypassing stop codon. Finally, we showed that transcription infidelity peptides contain specific epitopes of immunoglobulins ; detection of changes in immunoglobulins in patients with cancers opens a novel path toward early stage cancer diagnosis. This increased transcription infidelity in cancer contributes to the heterogeneity of cancer cells. This finding opens novel perspectives and strategies toward understanding carcinogenesis and diagnostic of the disease

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Unneberg, Per. "Computational approaches for in-depth analysis of cDNA sequence tags." Doctoral thesis, KTH, Bioteknologi, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-23.

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Major recent improvements in biotechnology have led to an accelerated production of DNA sequences. The completion of the human genome sequence, along with the genomes of more than two hundred other species, has marked the arrival of the genome era. The ultimate goal is to understand the structure and function of genomes and their genes. This thesis has focused on the computational analysis of complementary DNA (cDNA) sequences. These are copies of mRNA transcripts that correspond to the coding regions of genomes. Studying the expression patterns of genes is essential for understanding gene function. Many gene expression profiling techniques generate short sequence tags that derive from transcripts. A pilot study was performed to assess the feasibility of using the pyrosequencing platform for gene expression analysis. The sequences generated by pyrosequencing in most cases (≈ 85%) were long enough (> 18 nucleotides) to uniquely identify the corresponding transcripts through database searches. Aspects of transcript identification by short sequence tags were further investigated in a number of public databases, revealing that a tag length 16-17 nucleotides was sufficient for unique identifi- cation. Longer transcript representations are obtained from expressed sequence tag (EST) sequencing. Method development for the analysis and maintenance of large EST data sets has been performed on data from poplar, which is a tree of commercial interest to the forest biotechnology industry. In 2003 a large ESTsequencing project reached > 100 000 reads, providing a unique resource for tree biology research. ESTs have been grouped into clusters and singletons that represent potential genes. Preliminary analyses have estimated gene content in Populus to be very similar to that of model organism Arabidopsis thaliana. EST data collections provide a rich source for mining polymorphisms. A software application was developed and applied to EST data from two Populus species, and candidate single nucleotide polymorphisms (SNPs) were recorded. A study of genetic variation between the species revealed a striking similarity, with orthologous pairs being > 98% identical on the protein level. Keywords: cDNA, EST, gene expression, SNP, SAGE, polymorphism, assembly, clustering, DNA sequencing, pyrosequencing, mRNA transcript, orthology, tree biotechnology, restriction enzyme

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Alexsson, Andrei. "Unsupervised hidden Markov model for automatic analysis of expressed sequence tags." Thesis, Linköpings universitet, Bioinformatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-69575.

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This thesis provides an in-depth analyze of expressed sequence tags (EST) that represent pieces of eukaryotic mRNA by using unsupervised hidden Markov model (HMM). ESTs are short nucleotide sequences that are used primarily for rapid identificationof new genes with potential coding regions (CDS). ESTs are made by sequencing on double-stranded cDNA and the synthesizedESTs are stored in digital form, usually in FASTA format. Since sequencing is often randomized and that parts of mRNA contain non-coding regions, some ESTs will not represent CDS.It is desired to remove these unwanted ESTs if the purpose is to identifygenes associated with CDS. Application of stochastic HMM allow identification of region contents in a EST. Softwares like ESTScanuse HMM in which a training of the HMM is done by supervised learning with annotated data. However, because there are not always annotated data at hand this thesis focus on the ability to train an HMM with unsupervised learning on data containing ESTs, both with and without CDS. But the data used for training is not annotated, i.e. the regions that an EST consists of are unknown. In this thesis a new HMM is introduced where the parameters of the HMM are in focus so that they are reasonablyconsistent with biologically important regionsof an mRNA such as the Kozak sequence, poly(A)-signals and poly(A)-tails to guide the training and decoding correctly with ESTs to proper statesin the HMM. Transition probabilities in the HMMhas been adapted so that it represents the mean length and distribution of the different regions in mRNA. Testing of the HMM's specificity and sensitivityhave been performed via BLAST by blasting each EST and compare the BLAST results with the HMM prediction results.A regression analysis test shows that the length of ESTs used when training the HMM is significantly important, the longer the better. The final resultsshows that it is possible to train an HMM with unsupervised machine learning but to be comparable to supervised machine learning as ESTScan, further expansion of the HMM is necessary such as frame-shift correction of ESTs byimproving the HMM's ability to choose correctly positioned start codons or nucleotides. Usually the false positive results are because of incorrectly positioned start codons leadingto too short CDS lengths. Since no frame-shift correction is implemented, short predicted CDS lengths are not acceptable and is hence not counted as coding regionsduring prediction. However, when there is a lack of supervised models then unsupervised HMM is a potential replacement with stable performance and able to be adapted forany eukaryotic organism.

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Norrish, Alan Raymond. "Functional genomics : from expressed sequence tags to DNA vaccines for Leishmania." Thesis, University of Cambridge, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624326.

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Wan, Kiew-Lian. "Expressed sequence tags : an approach to gene discovery in Toxoplasma gondii." Thesis, University of Cambridge, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627602.

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Zhang, Yuan. "MST Based Ab Initio Assembler of Expressed Sequence Tags." Miami University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=miami1273245641.

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Paraoan, Luminita. "Analysis of cDNA libraries and expressed sequence tags of retinal pigment epithelial cells." Thesis, University of Liverpool, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367080.

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Kawaura, Kanako. "Transcriptome analysis of common wheat using a large number of expressed sequence tags." Kyoto University, 2006. http://hdl.handle.net/2433/136637.

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Shah, Bindiya. "Identification of genes encoding secreted proteins of schistosomes." Thesis, University of York, 2000. http://etheses.whiterose.ac.uk/9801/.

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Books on the topic "Sequence tags"

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Parkinson, John, ed. Expressed Sequence Tags (ESTs). Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-136-3.

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Matthiesen, Rune. Mass spectrometry data analysis in proteomics. Humana Press, 2013.

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Expressed Sequence Tags (ESTs). Humana Press, 2008.

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Rune, Matthiesen, ed. Mass spectrometry data analysis in proteomics. Humana Press, 2007.

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Reddy. Group Level Team Assessment: A 10 Step Sequence to a Committed Team, Loose-Leaf Tabs. Pfeiffer Wiley, 1996.

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Book chapters on the topic "Sequence tags"

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Wolfsberg, Tyra G., and David Landsman. "Expressed Sequence Tags (ESTs)." In Methods of Biochemical Analysis. John Wiley & Sons, Inc., 2002. http://dx.doi.org/10.1002/0471223921.ch12.

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Parkinson, John, and Mark Blaxter. "Expressed Sequence Tags: An Overview." In Methods in Molecular Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-136-3_1.

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Cheung, Foo. "Global Assembly of Expressed Sequence Tags." In Methods in Molecular Biology. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-839-9_15.

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Susko, Edward, and Andrew J. Roger. "Statistical Analysis of Expressed Sequence Tags." In Methods in Molecular Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-136-3_13.

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Yu, Zhongtang, and Mark Morrison. "Serial Analysis of V1 Ribosomal Sequence Tags." In Encyclopedia of Metagenomics. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7478-5_806.

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Yu, Zhongtang, and Mark Morrison. "Serial Analysis of V1 Ribosomal Sequence Tags." In Encyclopedia of Metagenomics. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6418-1_806-1.

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Wasmuth, James, and Mark Blaxter. "Obtaining Accurate Translations from Expressed Sequence Tags." In Methods in Molecular Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-136-3_10.

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Soares, Marcelo Bento, Maria de Fatima Bonaldo, Jeremiah D. Hackett, and Debashish Bhattacharya. "Expressed Sequence Tags: Normalization and Subtraction of cDNA Libraries Expressed sequence tags normalization and subtraction of cDNA libraries." In Methods in Molecular Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-136-3_6.

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Grote, Korbinian, and Thomas Werner. "Multidimensional Context of Sequence Tags: Biological Data Integration." In Tag-Based Next Generation Sequencing. Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527644582.ch26.

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Maglott, Donna R., A. Scott Durkin, and William C. Nierman. "259 Human Brain Expressed Sequence Tags (ESTs): Chromosome Localization, Subregional Assignment, and Sequence Analysis." In Identification of Transcribed Sequences. Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2562-2_24.

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Conference papers on the topic "Sequence tags"

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Zhang, Yuan, Hongshen Chen, Yihong Zhao, Qun Liu, and Dawei Yin. "Learning Tag Dependencies for Sequence Tagging." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/637.

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Sequence tagging is the basis for multiple applications in natural language processing. Despite successes in learning long term token sequence dependencies with neural network, tag dependencies are rarely considered previously. Sequence tagging actually possesses complex dependencies and interactions among the input tokens and the output tags. We propose a novel multi-channel model, which handles different ranges of token-tag dependencies and their interactions simultaneously. A tag LSTM is augmented to manage the output tag dependencies and word-tag interactions, while three mechanisms are presented to efficiently incorporate token context representation and tag dependency. Extensive experiments on part-of-speech tagging and named entity recognition tasks show that the proposed model outperforms the BiLSTM-CRF baseline by effectively incorporating the tag dependency feature.

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Reena, N., A. Chandrasekar, A. Riju, P. L. Nima, S. J. Eapen, and M. Anandaraj. "Gene identification inPhytophthora capsicithrough expressed sequence tags." In the International Symposium. ACM Press, 2010. http://dx.doi.org/10.1145/1722024.1722043.

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Alma'aitah, Abdallah Y., Hossam S. Hassanein, and Mohamed Ibnkahla. "RFID tags authentication by unique hash sequence detection." In 2012 IEEE 37th Conference on Local Computer Networks Workshops (LCN Workshops). IEEE, 2012. http://dx.doi.org/10.1109/lcnw.2012.6424063.

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Adams, Mark D., Anthony R. Kerlavage, Mark Dubnick, Ruben F. Moreno, Chris Fields, and J. Craig Venter. "ANALYSIS OF EXPRESSED SEQUENCE TAGS FROM HUMAN BRAIN CDNAS." In Proceedings of the 2nd International Conference. WORLD SCIENTIFIC, 1993. http://dx.doi.org/10.1142/9789814503655_0010.

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Min, Xiangjia J., Gregory Butler, Reginald Storms, and Adrian Tsang. "Comparative Assessment of DNA Assemblers for Assembling Expressed Sequence Tags." In 2009 Ohio Collaborative Conference on Bioinformatics (OCCBIO). IEEE, 2009. http://dx.doi.org/10.1109/occbio.2009.19.

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Hui Li, Chunmei Liu, M. Rwebangira, L. Burge, and W. Southerland. "Rapid generation of peptide sequence tags with a graph search algorithm." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112383.

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Yu, Changyong, Guoren Wang, Yuhai Zhao, Keming Mao, Junjie Wu, and Wendan Zhai. "Generating Peptide Sequence Tags for Peptide Identification via Tandem Mass Spectrometry." In 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2009. http://dx.doi.org/10.1109/bibe.2009.16.

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Sim-Hui Tee. "Identification of the overexpressed genes in hepatocellular carcinoma using Expressed Sequence Tags." In 2012 International Conference on Biomedical Engineering (ICoBE). IEEE, 2012. http://dx.doi.org/10.1109/icobe.2012.6179078.

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Lee, Guo-Hsing, Nai-Yu Chuang, Wen-Dar Lin, Chung-Der Hsiao, Hahn-Ming Lee, and Jan-Ming Ho. "E2D: A Novel Tool for Annotating Protein Domains in Expressed Sequence Tags." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330967.

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Jain, M., H. Holz, J. Shrager, O. Vallon, C. Hauser, and A. Grossman. "A Hybrid, Recursive Algorithm for Clustering Expressed Sequence Tags in Chlamydomonas reinhardtii." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.87.

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