Annotation of Structured Data Project UNC-SILS and Microsoft, 2004-2005

Motivation: Many problems in genome annotation are tackled by using a classification model to predict functional sites such as splice sites, translation start sites or stop codons. Locating the correct position of these sites remains one of the most important but also one of the most difficult issues in the structural annotation of genomes. Most of the software currently in use is written for a very specific problem, thereby limiting the possibilities for reuse. Summary: We developed a software platform that uses a very general approach towards the classification of functional sites in DNA sequences. The program uses an ab initio approach towards the identification of these sites, and extends SpliceMachine, a previously developed splice site predictor that shows state-of-the-art performance for both donor and acceptor splice site recognition in the human and Arabidopsis thaliana genome. Availability: The program is developed as a stand-alone Java application, and is available as GPLv3 open-source software. The program, source and documentation can be obtained from the ‘Software’ section at http://bioinformatics.psb.ugent.be/

Motivation: The sequencing of the Plasmodium yoelii genome, a model rodent malaria parasite, has greatly facilitated research for the development of new drug and vaccine candidates against malaria. Unfortunately, only preliminary gene models were annotated on the partially sequenced genome, mostly by in silico gene prediction, and there has been no major improvement of the annotation since 2002. Results: Here we report on a systematic assessment of the accuracy of the genome annotation based on a detailed analysis of a comprehensive set of cDNA sequences and proteomics data. We found that the coverage of the current annotation tends to be biased toward genes expressed in the blood stages of the parasite life cycle. Based on our proteomic analysis, we estimate that about 15% of the liver stage proteome data we have generated is absent from the current annotation. Through comparative analysis we identified and manually curated a further 510 P. yoelii genes which have clear orthologs in the P. falciparum genome, but were not present or incorrectly annotated in the current annotation. This study suggests that improvements of the current P. yoelii genome annotation should focus on genes expressed in stages other than blood stages. Comparative analysis will be critically helpful for this re-annotation. The addition of newly annotated genes will facilitate the use of P. yoelii as a model system for studying human malaria.

Motivation: We have to cope with both a deluge of new genome sequences and a huge amount of data produced by high-throughput approaches used to exploit these genomic features. Crossing and comparing such heterogeneous and disparate data will help improving functional annotation of genomes. This requires designing elaborate integration systems such as warehouses for storing and querying these data. Results: We have designed a relational genomic warehouse with an original multi-layer architecture made of a databases layer and an entities layer. We describe a new querying module, GenoQuery, which is based on this architecture. We use the entities layer to define mixed queries. These mixed queries allow searching for instances of biological entities and their properties in the different databases, without specifying in which database they should be found. Accordingly, we further introduce the central notion of alternative queries. Such queries have the same meaning as the original mixed queries, while exploiting complementarities yielded by the various integrated databases of the warehouse. We explain how GenoQuery computes all the alternative queries of a given mixed query. We illustrate how useful this querying module is by means of a thorough example. Availability: http://www.lri.fr/~lemoine/GenoQuery/