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Another growing application of NGS is microbial community analysis.
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NGS allows for full genome characterization of other organisms besides the human genome, including known pathogens, and yet-to-be-identified bacterial, viral, or fungal species that may pose a public health threat. Genome-wide association studies and analysis of gene expression, usually made via well-established microarray techniques, can now be done via NGS, e.g. By sequencing the entire genome in targeted patients, it is possible to identify genes and regulatory elements related to pathophysiological conditions. Nowadays, it is possible to sequence any microbial organism or metagenomic sample within hours and to obtain human genomes in weeks. The applications of NGS are almost endless, spanning many ‘–omics’ fields, such as genomics, transcriptomics, and metabolomics. High-throughput or next-generation sequencing (NGS) technologies have become an established and affordable experimental framework for basic and translational research in biomedical sciences and clinical diagnostics. This will eventually facilitate the development of novel diagnostic tools embedded in routine healthcare.
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In the long term, a proper, well-developed (although not necessarily unique) software framework will bridge the current gap between data generation and hypothesis testing. We discuss in detail the potential of a computational framework blending generic template programming and visual programming that addresses all of the current limitations. These characteristics should facilitate both the run of standardized NGS pipelines and the development of new workflows based on technological advancements or users’ needs. serial, multithread, distributed), transparent (platform-independent), interoperable (with external software interface), and usable (via an intuitive graphical user interface). An ideal developing environment for NGS should be modular (with a native library interface), scalable in computational methods (i.e. Here we scrutinize the state-of-the-art low-level software libraries implemented specifically for NGS and graphical tools for NGS analytics. Generic software template libraries specifically developed for NGS can help in dealing with the former problem, whilst coupling template libraries with visual programming may help with the latter. Although a plethora of tools for NGS data analysis has emerged in the past decade, (i) software development is still lagging behind data generation capabilities, and (ii) there is a ‘cultural’ gap between the end user and the developer. NGS data are big, heterogeneous, sparse, and error prone. Processing and analyzing NGS data is challenging. High-throughput or next-generation sequencing (NGS) technologies have become an established and affordable experimental framework in biological and medical sciences for all basic and translational research.