Biology

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Subject: search.filters.subject.Gene Ontology

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    Visualization and analysis of microarray and gene ontology data with treemaps
    (Springer Nature, 2004-06-28) Baehrecke, Eric H; Dang, Niem; Babaria, Ketan; Shneiderman, Ben
    The increasing complexity of genomic data presents several challenges for biologists. Limited computer monitor views of data complexity and the dynamic nature of data in the midst of discovery increase the challenge of integrating experimental results with information resources. The use of Gene Ontology enables researchers to summarize results of quantitative analyses in this framework, but the limitations of typical browser presentation restrict data access. Here we describe extensions to the treemap design to visualize and query genome data. Treemaps are a space-filling visualization technique for hierarchical structures that show attributes of leaf nodes by size and color-coding. Treemaps enable users to rapidly compare sizes of nodes and sub-trees, and we use Gene Ontology categories, levels of RNA, and other quantitative attributes of DNA microarray experiments as examples. Our implementation of treemaps, Treemap 4.0, allows user-defined filtering to focus on the data of greatest interest, and these queried files can be exported for secondary analyses. Links to model system web pages from Treemap 4.0 enable users access to details about specific genes without leaving the query platform. Treemaps allow users to view and query the data from an experiment on a single computer monitor screen. Treemap 4.0 can be used to visualize various genome data, and is particularly useful for revealing patterns and details within complex data sets.
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    A cricket Gene Index: a genomic resource for studying neurobiology, speciation, and molecular evolution
    (Springer Nature, 2007-04-25) Danley, Patrick D; Mullen, Sean P; Liu, Fenglong; Nene, Vishvanath; Quackenbush, John; Shaw, Kerry L
    As the developmental costs of genomic tools decline, genomic approaches to non-model systems are becoming more feasible. Many of these systems may lack advanced genetic tools but are extremely valuable models in other biological fields. Here we report the development of expressed sequence tags (EST's) in an orthopteroid insect, a model for the study of neurobiology, speciation, and evolution. We report the sequencing of 14,502 EST's from clones derived from a nerve cord cDNA library, and the subsequent construction of a Gene Index from these sequences, from the Hawaiian trigonidiine cricket Laupala kohalensis. The Gene Index contains 8607 unique sequences comprised of 2575 tentative consensus (TC) sequences and 6032 singletons. For each of the unique sequences, an attempt was made to assign a provisional annotation and to categorize its function using a Gene Ontology-based classification through a sequence-based comparison to known proteins. In addition, a set of unique 70 base pair oligomers that can be used for DNA microarrays was developed. All Gene Index information is posted at the DFCI Gene Indices web page. Orthopterans are models used to understand the neurophysiological basis of complex motor patterns such as flight and stridulation. The sequences presented in the cricket Gene Index will provide neurophysiologists with many genetic tools that have been largely absent in this field. The cricket Gene Index is one of only two gene indices to be developed in an evolutionary model system. Species within the genus Laupala have speciated recently, rapidly, and extensively. Therefore, the genes identified in the cricket Gene Index can be used to study the genomics of speciation. Furthermore, this gene index represents a significant EST resources for basal insects. As such, this resource is a valuable comparative tool for the understanding of invertebrate molecular evolution. The sequences presented here will provide much needed genomic resources for three distinct but overlapping fields of inquiry: neurobiology, speciation, and molecular evolution.
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    An EST resource for tilapia based on 17 normalized libraries and assembly of 116,899 sequence tags
    (Springer Nature, 2010-04-30) Lee, Bo-Young; Howe, Aimee E; Conte, Matthew A; D'Cotta, Helena; Pepey, Elodie; Baroiller, Jean-Francois; di Palma, Federica; Carleton, Karen L; Kocher, Thomas D
    Large collections of expressed sequence tags (ESTs) are a fundamental resource for analysis of gene expression and annotation of genome sequences. We generated 116,899 ESTs from 17 normalized and two non-normalized cDNA libraries representing 16 tissues from tilapia, a cichlid fish widely used in aquaculture and biological research. The ESTs were assembled into 20,190 contigs and 36,028 singletons for a total of 56,218 unique sequences and a total assembled length of 35,168,415 bp. Over the whole project, a unique sequence was discovered for every 2.079 sequence reads. 17,722 (31.5%) of these unique sequences had significant BLAST hits (e-value < 10-10) to the UniProt database. Normalization of the cDNA pools with double-stranded nuclease allowed us to efficiently sequence a large collection of ESTs. These sequences are an important resource for studies of gene expression, comparative mapping and annotation of the forthcoming tilapia genome sequence.
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    Conserved rules govern genetic interaction degree across species
    (Springer Nature, 2012-07-02) Koch, Elizabeth N; Costanzo, Michael; Bellay, Jeremy; Deshpande, Raamesh; Chatfield-Reed, Kate; Chua, Gordon; D'Urso, Gennaro; Andrews, Brenda J; Boone, Charles; Myers, Chad L
    Synthetic genetic interactions have recently been mapped on a genome scale in the budding yeast Saccharomyces cerevisiae, providing a functional view of the central processes of eukaryotic life. Currently, comprehensive genetic interaction networks have not been determined for other species, and we therefore sought to model conserved aspects of genetic interaction networks in order to enable the transfer of knowledge between species. Using a combination of physiological and evolutionary properties of genes, we built models that successfully predicted the genetic interaction degree of S. cerevisiae genes. Importantly, a model trained on S. cerevisiae gene features and degree also accurately predicted interaction degree in the fission yeast Schizosaccharomyces pombe, suggesting that many of the predictive relationships discovered in S. cerevisiae also hold in this evolutionarily distant yeast. In both species, high single mutant fitness defect, protein disorder, pleiotropy, protein-protein interaction network degree, and low expression variation were significantly predictive of genetic interaction degree. A comparison of the predicted genetic interaction degrees of S. pombe genes to the degrees of S. cerevisiae orthologs revealed functional rewiring of specific biological processes that distinguish these two species. Finally, predicted differences in genetic interaction degree were independently supported by differences in co-expression relationships of the two species. Our findings show that there are common relationships between gene properties and genetic interaction network topology in two evolutionarily distant species. This conservation allows use of the extensively mapped S. cerevisiae genetic interaction network as an orthology-independent reference to guide the study of more complex species.