We present a novel method for correctly identifying and straightening one dimensional agarose electrophoretic lanes. Our method has been shown to yield comparable accuracy with manual lane tracking results, and to successfully process 98% of DNA fingerprinting gels with no human intervention.

BACKGROUND: Genomic deletions and duplications are important in the pathogenesis of diseases, such as cancer and mental retardation, and have recently been shown to occur frequently in unaffected individuals as polymorphisms. Affymetrix GeneChip whole genome sampling analysis (WGSA) combined with 100 K single nucleotide polymorphism (SNP) genotyping arrays is one of several microarray-based approaches that are now being used to detect such structural genomic changes. The popularity of this technology and its associated open source data format have resulted in the development of an increasing number of software packages for the analysis of copy number changes using these SNP arrays. RESULTS: We evaluated four publicly available software packages for high throughput copy number analysis using synthetic and empirical 100 K SNP array data sets, the latter obtained from 107 mental retardation (MR) patients and their unaffected parents and siblings. We evaluated the software with regards to overall suitability for high-throughput 100 K SNP array data analysis, as well as effectiveness of normalization, scaling with various reference sets and feature extraction, as well as true and false positive rates of genomic copy number variant (CNV) detection. CONCLUSION: We observed considerable variation among the numbers and types of candidate CNVs detected by different analysis approaches, and found that multiple programs were needed to find all real aberrations in our test set. The frequency of false positive deletions was substantial, but could be greatly reduced by using the SNP genotype information to confirm loss of heterozygosity.

MOTIVATION: A considerable amount of human intervention is currently required to produce high-quality fingerprint-based physical maps for genomic studies. RESULTS: An algorithm has been developed and implemented to automatically order fingerprinted clones within contigs. The resulting software, named CORAL (Clone ORdering ALgorithm), has been tested on maps that have previously been manually edited and on maps derived from in silico simulations. The fingerprint map and DNA sequence of the human genome has provided an additional test to CORAL. Measurements suggest that CORAL performs significantly better than the software currently used by most laboratories to order fingerprinted clones at throughputs far exceeding those that can be achieved manually.