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Unlabelled: Metagenomic sequencing data, obtained from host-associated microbial communities, are usually contaminated with host genome sequence fragments. Prior to performing any downstream analyses, it is necessary to identify and remove such contaminating sequence fragments. The time and memory requirements of available host-contamination detection techniques are enormous. Thus, processing of large metagenomic datasets is a challenging task. This study presents CS-SCORE--a novel algorithm that can rapidly identify host sequences contaminating metagenomic datasets. Validation results indicate that CS-SCORE is 2-6 times faster than the current state-of-the-art methods. Furthermore, the memory footprint of CS-SCORE is in the range of 2-2.5GB, which is significantly lower than other available tools. CS-SCORE achieves this efficiency by incorporating (1) a heuristic pre-filtering mechanism and (2) a directed-mapping approach that utilizes a novel sequence composition metric (cs-score). CS-SCORE is expected to be a handy 'pre-processing' utility for researchers analyzing metagenomic datasets., Availability: For academic users, an implementation of CS-SCORE is freely available at: http://metagenomics.atc.tcs.com/cs-score (or) https://metagenomics.atc.tcs.com/preprocessing/cs-score., (Copyright © 2015 Elsevier Inc. All rights reserved.)

DNA sequencing technology advances have enabled genetic investigation of more samples in a shorter time than has previously been possible. Furthermore, the ability to analyze and understand large sequencing datasets has improved due to concurrent advances in sequence data analysis methods and software tools. Constant improvements to both technology and analytic approaches in this fast moving field are evidenced by many recent publications of computational methods, as well as biological results linking genetic events to human disease. Cancer in particular has been the subject of intense investigation, owing to the genetic underpinnings of this complex collection of diseases. New massively-parallel sequencing (MPS) technologies have enabled the investigation of thousands of samples, divided across tens of different tumor types, resulting in new driver gene identification, mutagenic pattern characterization, and other newly uncovered features of tumor biology. This review will focus both on methods and recent results: current analytical approaches to DNA and RNA sequencing will be presented followed by a review of recent pan-cancer sequencing studies. This overview of methods and results will not only highlight the recent advances in cancer genomics, but also the methods and tools used to accomplish these advancements in a constantly and rapidly improving field.