Allelic Gene Structure Variations in Anopheles gambiae Mosquitoes

Allelic gene structure variations and alternative splicing are responsible for transcript structure variations. More than 75% of human genes have structural isoforms of transcripts, but to date few studies have been conducted to verify the alternative splicing systematically.The present study used expressed sequence tags (ESTs) and EST tagged SNP patterns to examine the transcript structure variations resulting from allelic gene structure variations in the major human malaria vector, Anopheles gambiae. About 80% of 236,004 available A. gambiae ESTs were successfully aligned to A. gambiae reference genomes. More than 2,340 transcript structure variation events were detected. Because the current A. gambiae annotation is incomplete, we re-annotated the A. gambiae genome with an A. gambiae-specific gene model so that the effect of variations on gene coding could be better evaluated. A total of 15,962 genes were predicted. Among them, 3,873 were novel genes and 12,089 were previously identified genes. The gene completion rate improved from 60% to 84%. Based on EST support, 82.5% of gene structures were predicted correctly. In light of the new annotation, we found that approximately 78% of transcript structure variations were located within the coding sequence (CDS) regions, and >65% of variations in the CDS regions have the same open-reading-frame. The association between transcript structure isoforms and SNPs indicated that more than 28% of transcript structure variation events were contributed by different gene alleles in A. gambiae.We successfully expanded the A. gambiae genome annotation. We predicted and analyzed transcript structure variations in A. gambiae and found that allelic gene structure variation plays a major role in transcript diversity in this important human malaria vector

Computer-based tools for decision support in agroforestry: Current state and future needs

Successful design of agroforestry practices hinges on the ability to pull together very diverse and sometimes large sets of information (i.e., biophysical, economic and social factors), and then implementing the synthesis of this information across several spatial scales from site to landscape. Agroforestry, by its very nature, creates complex systems with impacts ranging from the site or practice level up to the landscape and beyond. Computer-based Decision Support Tools (DST) help to integrate information to facilitate the decision-making process that directs development, acceptance, adoption, and management aspects in agroforestry. Computer-based DSTs include databases, geographical information systems, models, knowledge-base or expert systems, and ‘hybrid’ decision support systems. These different DSTs and their applications in agroforestry research and development are described in this paper. Although agroforestry lacks the large research foundation of its agriculture and forestry counterparts, the development and use of computer-based tools in agroforestry have been substantial and are projected to increase as the recognition of the productive and protective (service) roles of these tree-based practices expands. The utility of these and future tools for decision-support in agroforestry must take into account the limits of our current scientific information, the diversity of aspects (i.e. economic, social, and biophysical) that must be incorporated into the planning and design process, and, most importantly, who the end-user of the tools will be. Incorporating these tools into the design and planning process will enhance the capability of agroforestry to simultaneously achieve environmental protection and agricultural production goals