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                            Library & Multimedia
 A Scalable and Memory-Efficient Algorithm for De Novo Transcriptome Assembly of Non-model Organisms
NCJ Number
              255269
Journal

*BMC Genomics* Volume: 18 Dated: 2017
Author(s)
          Sing-Hoi Sze;
                        Meaghan L. Pimsler;
                        Jeffrey K Tomberlin;
                        Corbin D. Jones;
                        Aaron M Tarone
Date Published

2017
Length
              26 pages
Annotation
              This project developed a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while using as many RNA-Seq libraries as possible that contain hundreds of gigabases of data, and new techniques were developed so that computations can be performed on a computing cluster with moderate amount of physical memory.
Abstract
              With the increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. Although algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are memory-intensive, limiting their applications to small data sets with few libraries. The strategy used in the current project minimizes memory consumption while simultaneously obtaining comparable or improved accuracy over existing algorithms. It provides support for incremental updates of assemblies when new libraries become available. 6 figures, 4 tables, and 31 references (publisher abstract modified)
      
  
          Additional Details
Grant Number(s)
        2012-DN-BX-K024


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)
                                Report (Study/Research)
                                Report (Grant Sponsored)
                                Program/Project Description


  
  
        
  Award
        Genomic tools to reduce error in PMI estimates derived from entomological evidence
    
  
  
Date Published: January 1, 2017
    
          Downloads
        
                      DOC

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