Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Summary Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST) and Interproscan, re filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry re obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF) using Ruby scripts and Methontology to find associations beten concepts. Relationships beten sequence annotations and semantic concepts re graphically represented through a total of 6845 oriented vectors, which re reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.
['Luis Fernando Castillo', 'Narmer F. Galeano', 'Gustavo A. Isaza', 'Alvaro L. Gaitan']
I am writing a research paper on a idea Construction of coffee transcriptome networks based on gene annotation semantics
[{"from": "human", "value": "I am writing a research paper on a idea Construction of coffee transcriptome networks based on gene annotation semantics"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Summary Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST) and Interproscan, re filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry re obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF) using Ruby scripts and Methontology to find associations beten concepts. Relationships beten sequence annotations and semantic concepts re graphically represented through a total of 6845 oriented vectors, which re reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis."}]