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BioData Min. 2016 Apr 27;9:16. doi: 10.1186/s13040-016-0095-3. eCollection 2016.

Visual programming for next-generation sequencing data analytics.

Author information

1
Department of Engineering, Roma Tre University, Rome, Italy.
2
Bioinfoexperts, LLC, Thibodaux, LA USA.
3
Department of Health Outcomes and Policy, University of Florida, Gainesville, FL USA.
4
Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, 2004 Mowry Road, Gainesville, 32610-0231 FL USA.

Abstract

BACKGROUND:

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. Processing and analyzing NGS data is challenging. NGS data are big, heterogeneous, sparse, and error prone. 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.

TEXT:

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. Here we scrutinize the state-of-the-art low-level software libraries implemented specifically for NGS and graphical tools for NGS analytics. An ideal developing environment for NGS should be modular (with a native library interface), scalable in computational methods (i.e. serial, multithread, distributed), transparent (platform-independent), interoperable (with external software interface), and usable (via an intuitive graphical user interface). These characteristics should facilitate both the run of standardized NGS pipelines and the development of new workflows based on technological advancements or users' needs. We discuss in detail the potential of a computational framework blending generic template programming and visual programming that addresses all of the current limitations.

CONCLUSION:

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. This will eventually facilitate the development of novel diagnostic tools embedded in routine healthcare.

KEYWORDS:

Big data; Generic programming; Graphical user interface; High-throughput sequencing; Next-generation sequencing; Software suite; Template library; Visual programming

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