ASplice – a scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms

Posted by: RNA-Seq Blog in Transcriptome Assembly Tools June 9, 2017 3,069 Views

With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries.

Texas A&M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory.

Illustration of the iterative algorithm to enumerate k-mer frequencies

For the k ^′-mer $\protect a_{1} \dots a_{k^{'}}$ , its two frequency slots with zero counts for nucleotides c and t are removed to obtain (k ^′+1)-mers $\protect a_{1} \dots a_{k^{'}}$ a and $\protect a_{1} \dots a_{k^{'}}$ g

This strategy 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.

Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice.

Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387. [article]