MOTIVATION: While it is common to refer to 'the genome sequence' as if it were a single, complete and contiguous DNA string, it is in fact an assembly of millions of small, partially overlapping DNA fragments. Sophisticated computer algorithms (assemblers and scaffolders) merge these DNA fragments into contigs, and place these contigs into sequence scaffolds using the paired-end sequences derived from large-insert DNA libraries. Each step in this automated process is susceptible to producing errors; hence, the resulting draft assembly represents (in practice) only a likely assembly that requires further validation. Knowing which parts of the draft assembly are likely free of errors is critical if researchers are to draw reliable conclusions from the assembled sequence data. RESULTS: We develop a machine-learning method to detect assembly errors in sequence assemblies. Several in silico measures for assembly validation have been proposed by various researchers. Using three benchmarking Drosophila draft genomes, we evaluate these techniques along with some new measures that we propose, including the good-minus-bad coverage (GMB), the good-to-bad-ratio (RGB), the average Z-score (AZ) and the average absolute Z-score (ASZ). Our results show that the GMB measure performs better than the others in both its sensitivity and its specificity for assembly error detection. Nevertheless, no single method performs sufficiently well to reliably detect genomic regions requiring attention for further experimental verification. To utilize the advantages of all these measures, we develop a novel machine learning approach that combines these individual measures to achieve a higher prediction accuracy (i.e. greater than 90%). Our combined evidence approach avoids the difficult and often ad hoc selection of many parameters the individual measures require, and significantly improves the overall precisions on the benchmarking data sets.

BACKGROUND: wFleaBase is a database with the necessary infrastructure to curate, archive and share genetic, molecular and functional genomic data and protocols for an emerging model organism, the microcrustacean Daphnia. Commonly known as the water-flea, Daphnia's ecological merit is unequaled among metazoans, largely because of its sentinel role within freshwater ecosystems and over 200 years of biological investigations. By consequence, the Daphnia Genomics Consortium (DGC) has launched an interdisciplinary research program to create the resources needed to study genes that affect ecological and evolutionary success in natural environments. DISCUSSION: These tools include the genome database wFleaBase, which currently contains functions to search and extract information from expressed sequenced tags, genome survey sequences and full genome sequencing projects. This new database is built primarily from core components of the Generic Model Organism Database project, and related bioinformatics tools. SUMMARY: Over the coming year, preliminary genetic maps and the nearly complete genomic sequence of Daphnia pulex will be integrated into wFleaBase, including gene predictions and ortholog assignments based on sequence similarities with eukaryote genes of known function. wFleaBase aims to serve a large ecological and evolutionary research community. Our challenge is to rapidly expand its content and to ultimately integrate genetic and functional genomic information with population-level responses to environmental challenges. URL: http://wfleabase.org/.