68 research outputs found

    Deep sequencing reveals the complex and coordinated transcriptional regulation of genes related to grain quality in rice cultivars

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    <p>Abstract</p> <p>Background</p> <p>Milling yield and eating quality are two important grain quality traits in rice. To identify the genes involved in these two traits, we performed a deep transcriptional analysis of developing seeds using both massively parallel signature sequencing (MPSS) and sequencing-by-synthesis (SBS). Five MPSS and five SBS libraries were constructed from 6-day-old developing seeds of Cypress (high milling yield), LaGrue (low milling yield), Ilpumbyeo (high eating quality), YR15965 (low eating quality), and Nipponbare (control).</p> <p>Results</p> <p>The transcriptomes revealed by MPSS and SBS had a high correlation co-efficient (0.81 to 0.90), and about 70% of the transcripts were commonly identified in both types of the libraries. SBS, however, identified 30% more transcripts than MPSS. Among the highly expressed genes in Cypress and Ilpumbyeo, over 100 conserved <it>cis </it>regulatory elements were identified. Numerous specifically expressed transcription factor (TF) genes were identified in Cypress (282), LaGrue (312), Ilpumbyeo (363), YR15965 (260), and Nipponbare (357). Many key grain quality-related genes (i.e., genes involved in starch metabolism, aspartate amino acid metabolism, storage and allergenic protein synthesis, and seed maturation) that were expressed at high levels underwent alternative splicing and produced antisense transcripts either in Cypress or Ilpumbyeo. Further, a time course RT-PCR analysis confirmed a higher expression level of genes involved in starch metabolism such as those encoding ADP glucose pyrophosphorylase (AGPase) and granule bound starch synthase I (GBSS I) in Cypress than that in LaGrue during early seed development.</p> <p>Conclusion</p> <p>This study represents the most comprehensive analysis of the developing seed transcriptome of rice available to date. Using two high throughput sequencing methods, we identified many differentially expressed genes that may affect milling yield or eating quality in rice. Many of the identified genes are involved in the biosynthesis of starch, aspartate family amino acids, and storage proteins. Some of the differentially expressed genes could be useful for the development of molecular markers if they are located in a known QTL region for milling yield or eating quality in the rice genome. Therefore, our comprehensive and deep survey of the developing seed transcriptome in five rice cultivars has provided a rich genomic resource for further elucidating the molecular basis of grain quality in rice.</p

    A Fast Corpus-Based Stemmer

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    Relevance Measures Using Geographic Scopes and Types

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    This paper proposes two kinds of relevance measures to rank documents by geographic restriction: scope-based and type-based. The non-geographic and geographic relevance scores are combined using a weighted harmonic mean. The proposed relevance measures and weighting schemes are evaluated on GeoCLEF 2007 dataset with encouraging performance over the standard IR performance. The best performance is achieved when the importance of non-geographic relevance scores outweigh the importance of geographic relevance scores

    Content-Based Image Retrieval Using Combined 2D Attribute Pattern Spectra

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    This work proposes a region-based shape signature that uses a combination of three different types of pattern spectra. The proposed method is inspired by the connected shape filter proposed by Urbach et al. We extract pattern spectra from the red, green and blue color bands of an image then incorporate machine learning techniques for application in photographic image retrieval. Our experiments show that the combined pattern spectrum gives an improvement of approximately 30% in terms of mean average precision and precision at 20 with respect to Urbach et al’s method

    Using centrality to rank web snippets

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    We describe our participation in the WebCLEF 2007 task, targeted at snippet retrieval from web data. Our system ranks snippets based on a simple similarity-based centrality, inspired by the web page ranking algorithms. We experimented with retrieval units (sentences and paragraphs) and with the similarity functions used for centrality computations (word overlap and cosine similarity). We found that using paragraphs with the cosine similarity function shows the best performance with precision around 20% and recall around 25% according to human assessments of the first 7,000 bytes of responses for individual topics

    Translation techniques in cross-language information retrieval

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    Cross-language information retrieval (CLIR) is an active sub-domain of information retrieval (IR). Like IR, CLIR is centered on the search for documents and for information contained within those documents. Unlike IR, CLIR must reconcile queries and documents that are written in different languages. The usual solution to this mismatch involves translating the query and/or the documents before performing the search. Translation is therefore a pivotal activity for CLIR engines. Over the last 15 years, the CLIR community has developed a wide range of techniques and models supporting free text translation. This article presents an overview of those techniques, with a special emphasis on recent developments. © 2012 ACM
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