Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep sequencing has become a popular tool for novel miRNA detection but its data must be vied carefully as the state of the field is still undeveloped. Using three different programs, miRDeep (v1, 2), miRanalyzer and DSAP,  have analyzed seven data sets (six biological and one simulated) to provide a critical evaluation of the programs performance.  selected these software based on their popularity and overall approach toward the detection of novel and known miRNAs using deep-sequencing data. The program comparisons suggest that, despite differing stringency levels they all identify a similar set of known and novel predictions. Comparisons beten the first and second version of miRDeep suggest that the stringency level of each of these programs may, in fact, be a result of the algorithm used to map the reads to the target. Different stringency levels are likely to affect the number of possible novel candidates for functional verification, causing undue strain on resources and time. With that in mind,  propose that an intersection across multiple programs be taken, especially if considering novel candidates that will be targeted for additional analysis. Using this approach,  identify and performed initial validation of 12 novel predictions in our in-house data with real-time PCR, six of which have been previously unreported.
['Vernell S. Williamson', 'Albert Kim', 'Bin Xie', 'G. Omari McMichael', 'Yuan Gao', 'Vladimir I. Vladimirov']
I am writing a research paper on a idea Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation
[{"from": "human", "value": "I am writing a research paper on a idea Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Deep sequencing has become a popular tool for novel miRNA detection but its data must be vied carefully as the state of the field is still undeveloped. Using three different programs, miRDeep (v1, 2), miRanalyzer and DSAP,  have analyzed seven data sets (six biological and one simulated) to provide a critical evaluation of the programs performance.  selected these software based on their popularity and overall approach toward the detection of novel and known miRNAs using deep-sequencing data. The program comparisons suggest that, despite differing stringency levels they all identify a similar set of known and novel predictions. Comparisons beten the first and second version of miRDeep suggest that the stringency level of each of these programs may, in fact, be a result of the algorithm used to map the reads to the target. Different stringency levels are likely to affect the number of possible novel candidates for functional verification, causing undue strain on resources and time. With that in mind,  propose that an intersection across multiple programs be taken, especially if considering novel candidates that will be targeted for additional analysis. Using this approach,  identify and performed initial validation of 12 novel predictions in our in-house data with real-time PCR, six of which have been previously unreported."}]