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Genes 2012, 3(3), 545-575;

Next Generation Sequence Analysis and Computational Genomics Using Graphical Pipeline Workflows

Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92617, USA
Biomedical Informatics Research Network (BIRN), Information Sciences Institute, University of Southern California, Los Angeles, CA 90292, USA
Laboratory of Neuro Imaging (LONI), University of California, Los Angeles, CA 90095, USA
Zilkha Neurogenetic Institute, USC Keck School of Medicine, Los Angeles, CA 90033, USA
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
Functional Genomics Laboratory, Department of Psychiatry And Human Behavior, School of Medicine, University of California, Irvine, CA 92697, USA
Author to whom correspondence should be addressed.
Received: 6 July 2012 / Revised: 15 August 2012 / Accepted: 15 August 2012 / Published: 30 August 2012
(This article belongs to the Special Issue Feature Paper 2012)
PDF [2373 KB, uploaded 30 August 2012]


Whole-genome and exome sequencing have already proven to be essential and powerful methods to identify genes responsible for simple Mendelian inherited disorders. These methods can be applied to complex disorders as well, and have been adopted as one of the current mainstream approaches in population genetics. These achievements have been made possible by next generation sequencing (NGS) technologies, which require substantial bioinformatics resources to analyze the dense and complex sequence data. The huge analytical burden of data from genome sequencing might be seen as a bottleneck slowing the publication of NGS papers at this time, especially in psychiatric genetics. We review the existing methods for processing NGS data, to place into context the rationale for the design of a computational resource. We describe our method, the Graphical Pipeline for Computational Genomics (GPCG), to perform the computational steps required to analyze NGS data. The GPCG implements flexible workflows for basic sequence alignment, sequence data quality control, single nucleotide polymorphism analysis, copy number variant identification, annotation, and visualization of results. These workflows cover all the analytical steps required for NGS data, from processing the raw reads to variant calling and annotation. The current version of the pipeline is freely available at These applications of NGS analysis may gain clinical utility in the near future (e.g., identifying miRNA signatures in diseases) when the bioinformatics approach is made feasible. Taken together, the annotation tools and strategies that have been developed to retrieve information and test hypotheses about the functional role of variants present in the human genome will help to pinpoint the genetic risk factors for psychiatric disorders. View Full-Text
Keywords: Next Generation Sequencing (NGS); LONI pipeline; SNPs; CNVs; workflow; bioinformatics Next Generation Sequencing (NGS); LONI pipeline; SNPs; CNVs; workflow; bioinformatics

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Torri, F.; Dinov, I.D.; Zamanyan, A.; Hobel, S.; Genco, A.; Petrosyan, P.; Clark, A.P.; Liu, Z.; Eggert, P.; Pierce, J.; Knowles, J.A.; Ames, J.; Kesselman, C.; Toga, A.W.; Potkin, S.G.; Vawter, M.P.; Macciardi, F. Next Generation Sequence Analysis and Computational Genomics Using Graphical Pipeline Workflows. Genes 2012, 3, 545-575.

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