Review of GPU implementation to process of RNA sequence on cancer

Abstract Nowadays, in the field of research the Graphics Processing Units (GPUs) is possibly the maximum powerful computational hardware. Beside of powerful graphics engine, the modern GPU is also an advanced programmable processor attributes enlarges arithmetic and bandwidth memory which considerably rises with equivalent of Central Processing Unit (CPU). The capacity of GPU is considerably reduce the running time required and expanded invitation for the consideration of mainstream researchers. The used of GPU is to structure a vital area with an advantage of having a minimal effort and prepare for massive parallel power. About this study the researchers and developers become eager to utilize power for general-purpose of computing as identified together of GPGPU (“General-Purpose computing on the GPU”). On other hand, computing on the Graphics Processing Units (GPUs) is exploits parallel that RNA Sequence has effectively been connected to cancer study. RNA Sequence is a strategy created to transcriptome profiling that utilizes profound sequencing technologies. RNA Sequence is used for high-throughput sequencing techniques to convey understanding into the transcriptome of a cell. It has conquered a few disadvantages of already utilized advancements. RNA Sequence preferences are a single test, possibly new genes and splice isoforms, RNA alter, combination transcripts, and allele-particular appearance. This review presents GPU implementation methods, tools and RNA Sequence advantages on cancer.

• Publication year

  2018

• Venue

  Informatics in Medicine Unlocked

• Publication date

  Unknown publication date

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1016/J.IMU.2017.10.008
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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