Next Generation Sequence Analysis and Computational Genomics Using Graphical Pipeline Workflows

Abstract

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.

Future-Generation Sequencing and Clinical Microbiology

“…Institutions are developing user-friendly analysis that are already being adapted for the clinical laboratory in standard configurations. 108,109 Many of the problems encountered applying existing sequence interpretation methods to the clinical laboratory grow out of the poor programing practices of professional biologists and software designed for ultimate flexibility instead of efficiency, which is a balance struck in the research laboratory but ill-suited to the clinic.…”

FragFlow Automated Fragment Detection in Scientific Workflows

“…A prior study analyzing workflows from different workflow systems and domains showed that almost 20% percent of the analyzed workflows were composed of other workflows [6]. Some systems even allow users to define hierarchical workflows to exploit these commonalities and split workflows into smaller reusable parts [15] [21]. However, there is not much support for the automatic detection of sub-workflows at the moment.…”

“…We evaluate our approach by comparing automatically detected workflow fragments to sub-workflows (groupings) created by users of the LONI Pipeline, a workflow system for neuroimaging analysis [21]. This paper is structured as follows.…”

“…They are usually created for executing research methods and sharing them with a community of users [21] [8], even in cases where the community is small (e.g., a research lab). Scientific workflows can ease data management among different steps [9] [15], facilitate collaborative development through a common interface and simplify access to computational resources (e.g., clusters, grids, etc.…”

Dynamic Crypto Algorithm for Real-Time Applications DCA-RTA, Key Shifting

“…2. Decryption Process (DP), [9] The Index Generation Process (IGP), starts with a fixed announced (16*16) 16 table, then a randomly generated SharedValue is used to mix up the initial table to produce Transformation- Table, then the table is used as a Table-ofIndexes, which is used in both (EP) and (DP) processes. See Index Generation Process (IGP), [9].…”