![]() These tools process data from various platforms using different algorithms (such as Bayesian approaches) and SNP detection procedures. Furthermore, there are open source packages, such as MAQ ( Li et al., 2008), SHRIMP ( Rumble et al., 2009), PASS ( Campagna et al., 2009), BWA ( Li and Durbin, 2009), SAMTOOLS ( Li et al., 2009), BFAST ( Homer et al., 2009), PerM ( Chen et al., 2009), SNVMix ( Goya et al., 2010 Shah et al., 2009), Crossbow ( Langmead et al., 2009) and Atlas-SNP2 ( Shen et al., 2010).įor 1000 Genome Project pilot data bioinformatic analysis, researchers used Mosaik ( ), MAQ, Coronalite (Applied Biosystems, Foster City, CA, USA), and SSAHA2 ( Ning et al., 2001) as aligners, and used Genome Analysis Toolkit (GATK) genotyper ( McKenna et al., 2010), PolyScan ( Chen et al., 2007) and PolyBayes ( Marth et al., 1999) for SNP detection. CLC Genomics Workbench software (CLC bio, Aarhus, Denmark) and NextGENe (SoftGenetics, State College, PA) are among the most popular in the commercial domain. Currently, the scientific community has access to many commercial and open source software packages for analyzing NGS data. In the past few years, tools for the analysis of short/long reads datasets have become available. On one hand, NGS provides unprecedented opportunities for high-throughput genetic research alternatively, it poses problems in rationale data analysis and requires adequate computational strategies to organize, handle and interpret the results ( Shendure and Ji, 2008). Therefore, NGS provides important knowledge about genetic variants in the population and about those genetic variants that are being commonly used in diagnostic studies in clinical settings. Different studies, such as 1000 Genomes Project ( Kaiser, 2008) and the Cancer Genome Atlas ( ), have implemented massive sequencing to more efficiently catalog genetic mutations responsible for cancer and other diseases. These genetic determinants contribute to variation in phenotypes, risk to diseases, and response to drugs or to the environment. A major goal of these studies is the detection of single nucleotide polymorphisms (SNPs), insertions and deletions (InDels), and other rearrangements to identify disease-associated variants in clinical samples. Massively parallel DNA-sequencing technologies, combined with sequence-capture methodologies (targeted re-sequencing) have obvious potential in clinical diagnostics, particularly for complex disorders that may be caused by a combination of several genes. To date, NGS has been applied in various contexts, including whole-genome sequencing ( Bentley, 2006), discovery of transcription factor binding sites, and non-coding RNA expression profiles ( Kato, 2009 Mardis, 2008). Consolidated instruments, such as Illumina Genome Analyzer (Illumina Inc., San Diego, CA, USA) and ABI SOLiD (Applied Biosystems, Foster City, CA, USA) can sequence a full human genome in previously unimagined speed. Next-generation sequencing (NGS), or deep sequencing, methods are rapidly changing the standards in genomics ( Metzker, 2010). Overall GAMES enables an effective complexity reduction in large-scale DNA-sequencing projects.Īvailability: GAMES is available free of charge to academic users and may be obtained from. The prediction of functional mutations is achieved by using different approaches. Variations are matched to known polymorphisms. GAMES attains multiple levels of filtering and annotation, such as aligning the reads to a reference genome, performing quality control and mutational analysis, integrating results with genome annotations and sorting each mismatch/deletion according to a range of parameters. Results: We developed GAMES (Genomic Analysis of Mutations Extracted by Sequencing), a pipeline aiming to serve as an efficient middleman between data deluge and investigators. However, the limitations of these applications include output which is insufficiently annotated and of difficult functional comprehension to end users. Currently, there are many commercial and public software packages that analyze NGS data. Motivation: Next-generation sequencing (NGS) methods have the potential for changing the landscape of biomedical science, but at the same time pose several problems in analysis and interpretation.
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