![]() Using Google Scholar citation, as shown in Table 1, we selected two popular analysis pipelines from public domains and two workflows from commercial products. Here we focus on the computational methods for gene expression quantification by RNA-Seq. How to identify and use the suitable tools for RNA-Seq analysis becomes critical. In the past two years, software applications for RNA-Seq analysis have been flooding the market from public domains as well as commercial organizations. To fully enable RNA-Seq technology to solve biological problems, powerful computational tools are required. Quantification of alternative splicing in tissues, discovery of new fusion genes in cancer, and new transcript identification have all benefited from this new technology. Recently, many studies have applied RNA-Seq to various biological and medical research. Compared to previous technologies for gene mapping with their alternative isoforms and expression detection across diverse cell types, RNA-Seq is more promising in building a complete transcriptome across cell types and states. A massively parallel sequencing technology termed RNA-Seq has made it possible to sequence cDNA derived from cellular RNA. Next-generation sequencing (NGS) platforms have been widely available recently. We then defined a set of criteria and compared the performance of several programs based on these criteria, and we further provided advices on selecting suitable tools for different biological applications. Here we presented an overview of these attempts on quantifying gene expression. In recent years, a number of expression quantification methods have been published from both public and commercial sources. Because sequencing reads from this new technology are shorter than transcripts from which they are derived, expression estimation with RNA-Seq requires increasingly complex computational methods. High-throughput RNA sequencing (RNA-Seq) promises a complete annotation and quantification of all genes and their isoforms across samples. Keywords: RNA-Seq Next Generation Sequencing Received 22 February 2013 revised 27 March 2013 accepted 6 April 2013 This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tested versions include OGE 6.2u6, PBS Pro11.0, LSF 8.3 and 9.1.1R & D Information, AstraZeneca, Shanghai, ChinaĢInnovation Center China, AstraZeneca, Shanghai, ChinaĮmail: © 2013 Yan Ji et al. CLC bio supports the third party scheduling systems OGE, PBS Pro, and IBM Platform LSF. CLC Server jobs can be sent from a master server out to grid nodes, where a grid scheduling system is used to handle job scheduling and submission. These serve as the core execution points of your bioinformatic services based on queue prioritization. Scalability With the built-in Job Node support of CLC Genomics Server, it is possible to attach an array of real or virtualized computers to the solution. The data management architecture offers very flexible built-in tools for restricting access and managing bioinformatics data. There are several approaches and options for customization such as plugin development and integration of external applications.įlexible data management CLC Genomics Server comes with a flexible data management solution for handling large amounts of genomics data. All connections from clients to the server are secured through SSL.Īdvanced customization CLC Genomics Server has been designed for advanced customization. This provides complete choice on the client-side of the system, including an option to design your own client. All are built on our service-oriented SOAP web-services. Multiple client options CLC Genomics Server comes with three client options: CLC Genomics Workbench, CLC Server Command Line Tools, and a web interface.
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