Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the advent of next-generation high throughput sequencing instruments, large volumes of short sequence data are generated at an unprecedented rate. Processing and analyzing these massive data requires overcoming several challenges including mapping of generated short sequences to a reference genome. This computationally intensive process takes time on the order of days using existing sequential techniques on large scale datasets. In this work,  propose six parallelization methods to speedup short sequence mapping and to reduce the execution time under just a few hours for such large datasets.  comparatively present these methods and give theoretical cost models for each method. Experimental results on real datasets demonstrate the effectiveness of the parallel methods and indicate that the cost models help accurate estimation of parallel execution time. Based on these cost models  implemented a selection function to predict the best method for a given scenario. To the best of our knowledge this is the first study on parallelization of short sequence mapping problem.
['Doruk Bozdag', 'Catalin Barbacioru', 'Ümit V. Çatalyürek']
I am writing a research paper on a idea Parallel short sequence mapping for high throughput genome sequencing
[{"from": "human", "value": "I am writing a research paper on a idea Parallel short sequence mapping for high throughput genome sequencing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the advent of next-generation high throughput sequencing instruments, large volumes of short sequence data are generated at an unprecedented rate. Processing and analyzing these massive data requires overcoming several challenges including mapping of generated short sequences to a reference genome. This computationally intensive process takes time on the order of days using existing sequential techniques on large scale datasets. In this work,  propose six parallelization methods to speedup short sequence mapping and to reduce the execution time under just a few hours for such large datasets.  comparatively present these methods and give theoretical cost models for each method. Experimental results on real datasets demonstrate the effectiveness of the parallel methods and indicate that the cost models help accurate estimation of parallel execution time. Based on these cost models  implemented a selection function to predict the best method for a given scenario. To the best of our knowledge this is the first study on parallelization of short sequence mapping problem."}]