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Toward the Integration of Mate Pair and RNA Sequencing to Identify Gene Fusions in Cancer Research: A Mini Review

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  • معلومة اضافية
    • بيانات النشر:
      MedCrave
    • الموضوع:
      2017
    • Collection:
      MedCrave
    • نبذة مختصرة :
      Mate pair (MPseq) and RNA sequencing (RNAseq) are important next-generation sequencing (NGS) techniques that are utilized to provide insight into tumorigenesis. Currently, MPseq is being successfully utilized in the clinic to predict chromosomal rearrangements while RNAseq is extensively used in the identification of gene expression, transcript expression and fusion detection. One of the strengths of MPseq is the fact that the fragments are longer than conventional pair-end fragments. This provides better coverage of genomic events such as structural variations. Fusions are structural rearrangements where there is an exchange of DNA sequences between genes. These kind of chromosomal rearrangements have great clinical importance. They are considered important biomarkers in neoplasia as well as therapeutic targets. However, as previously reported, fusion prediction tends to be difficult. This has been attributed to the large number of false positives due to sequencing errors. There are other factors such as poor alignment, library preparation, and insufficient depth of coverage. In addition, fusion predictions based purely on DNA technologies do not include gene expression information. Although, multiple software packages have been developed for fusions prediction, in many cases a consensus approach is required to eliminate false positives. MPseq’s capabilities to detect genomic structural rearrangements can provide an unbiased orthogonal approach to predicting fusions when combined with RNAseq. In this mini-review we explore the benefits of MPseq and RNAseq as two complementary tools in the prediction of gene fusions.
    • File Description:
      application/pdf
    • Relation:
      http://medcraveonline.com/MOJPB/MOJPB-05-00175.pdf
    • Rights:
      http://creativecommons.org/licenses/by/4.0/
    • الرقم المعرف:
      edsbas.9B4D3AEE