RNA-sequencing is a high throughput sequencing technology that sequences cDNA to obtain information from a particular sample of RNA. RNA-seq has already been proven to be an important factor to research numerous incurable diseases like cancers. Like other high throughput technologies, it also produces a huge number of accurate but relatively short reads. So it remains a concerning issue for De Novo assembly tools to assemble this massive amount of short reads. Existing De Novo assembly tools to assemble RNA-seq data are developed mainly based on three different algorithms; Greedy, OLC, and Euler-path. Recent research has revealed that Euler-path approach works best for RNA-seq data. A few De Novo assembly tools are available nowadays that were developed using Euler-path approach. Most of these tools based on Euler-path can be used free of cost for non commercial purposes. However, the performance of these non-commercial usable tools varies under different criteria. Again, their performance has not been measured in all possible conditions. So, it is always a matter of concern, which tool should be used for a particular data. Main objective of this thesis work is to consider four Euler-path De Novo assembly tools available for non commercial use and find out their performance for EST data of eukaryote. Criteria of their performance would be assembly accuracy and integrity.
ACM computing Classification system
Applied computing
Life and medical science-> Computational Biology-> Molecular Sequence Analysis
