MOTIVATION: Copy number abbreviation (CNA) is one type of genomic aberration that is often induced by genome instability and is associated with diseases such as cancer. Determination of the genome-wide CNA profile is an important step in identifying the underlying mutation mechanisms. Genomic data based on next-generation sequencing technology are particularly suitable for determination of high-quality CNA profile. Now is an important time to reevaluate the use of sequencing techniques for CNA analysis, especially with the rapid growth of the different targeted genome and whole-genome sequencing strategies. RESULTS: In this study, we provide a comparison of resequencing strategies, with regard to their utility, applied to the same hepatocellular carcinoma sample for copy number determination. These strategies include whole-genome, exome and restriction site-associated DNA (RAD) sequencing. The last of these strategies is a targeted sequencing technique that involves cutting the genome with a restriction enzyme and isolating the targeted sequences. Our data demonstrate that RAD sequencing is an efficient and comprehensive strategy that allows the cost-effective determination of CNAs. Further investigation of RAD sequencing data led to the finding that a precise measurement of the allele frequency would be a helpful complement to the read depth for CNA analysis for two reasons. First, knowledge of the allele frequency helps to resolve refined calculations of allele-specific copy numbers, which, in turn, identify the functionally important CNAs that are under natural selection on the parental alleles. Second, this knowledge enables deconvolution of CNA patterns in complex genomic regions.

MOTIVATION: The innovation of restriction-site associated DNA sequencing (RAD-seq) method takes full advantage of next-generation sequencing technology. By clustering paired-end short reads into groups with their own unique tags, RAD-seq assembly problem is divided into subproblems. Fast and accurately clustering and assembling millions of RAD-seq reads with sequencing errors, different levels of heterozygosity and repetitive sequences is a challenging question. RESULTS: Rainbow is developed to provide an ultra-fast and memory-efficient solution to clustering and assembling short reads produced by RAD-seq. First, Rainbow clusters reads using a spaced seed method. Then, Rainbow implements a heterozygote calling like strategy to divide potential groups into haplotypes in a top-down manner. And along a guided tree, it iteratively merges sibling leaves in a bottom-up manner if they are similar enough. Here, the similarity is defined by comparing the 2nd reads of a RAD segment. This approach tries to collapse heterozygote while discriminate repetitive sequences. At last, Rainbow uses a greedy algorithm to locally assemble merged reads into contigs. Rainbow not only outputs the optimal but also suboptimal assembly results. Based on simulation and a real guppy RAD-seq data, we show that Rainbow is more competent than the other tools in dealing with RAD-seq data. AVAILABILITY: Source code in C, Rainbow is freely available at http://sourceforge.net/projects/bio-rainbow/files/

Airplane classification is used as an application domain to illustrate how hierarchical reasoning on large knowledge bases can be implemented. The knowledge base is organized as a two-dimensional hierarchy: one dimension corresponds to the levels of complexity often seen in computer vision, and the other dimension corresponds to the complexity of hypothesis used in the reasoning process. Reasoning proceeds top-down, from more abstract levels with fewer details toward levels with more details. Whenever possible, with the help of domain knowledge, decision is taken at a higher level, which significantly reduces processing time. A software package called RuBICS (Rule-Based Image Classification System) is described, and some examples of airplane classification are shown.>