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Saini, Sangharsh
- A Statistical Method for Analyzing Low Quality Scores in DNA Sequencing Reads
Authors
1 Department of Computer Science and Applications, S.D. College, Ambala Cantt., Kurukshetra University, Kurukshetra, IN
Source
Biometrics and Bioinformatics, Vol 7, No 7 (2015), Pagination: 179-182Abstract
The exponential growth of new DNA sequencing technologies is changing biological sciences by allowing scientific investigators to sequence large amounts raw DNA bases previously requiring a major genome sequencing efforts. Next-generation sequencing produces much higher output with significantly lower cost, because of the millions of reactions running in parallel and much smaller reaction volumes [1]. These new Techniques come with unmatched amount of data - but this sequencing data comes with errors. A better knowledge of the error profiles is essential for sequence analysis and absolutely necessary in order to make substantial decisions [19]. Unterminated bases in sequencing cycles have been reported to be the major source of errors. In this paper we perform an analysis on sequencing reads data from a real human being for sequence quality scores. Here, we compute quality scores and detect low quality clusters in DNA sequencing reads and produce a graphical analysis. We also infer the factors that lead to the presence of many low quality clusters in the sample. This statistical analysis allows us to study and compare various errors introduced by different next generation sequencers. Having the ability to analyze error profiles for sequencing reads has the potential to significantly enhance our ability to perform accurate sequence analysis.
Keywords
Next Generation Sequencing, DNA Bases, Sequencing Errors, Quality Scores, Base Caller, Sequencing Reads.- An Improved Branch and Bound Algorithm for Cyclopeptide Sequencing
Authors
1 Department of Computer Science and Applications, Government College, Naraingarh, Kurukshetra University, Kurukshetra, IN
2 Department of Computer Science and Applications, Kurukshetra University, Kurukshetra, IN
Source
Biometrics and Bioinformatics, Vol 6, No 6 (2014), Pagination: 154-158Abstract
The mass-production of antibiotics and medication has started an evolutionary race between antibiotics and bacteria. Pharmaceutical companies proved helpful to create new antibiotics, while pathogens developed a new level of resistance to these antibiotics. Growth of drug-resistant disease increases the challenge of searching for new, more effective antibiotics. The isolation and sequencing of cyclic peptide antibiotics is time-consuming and error-prone, compared with the linear peptides. Given these facts, there is a need for new tools to sequence cyclic non-ribosomal proteins (NRPs). In this paper we show how to improve the cyclic peptide sequencing method further to reduce its running time considerably in its expansion (branching) step. Our results suggest that instead of extending the k-mers over the full span of 18 to more than 100 amino acids, we could extend the k-mers just over the candidate amino acids measured in the first step of the cyclic peptide sequencing method. This strategy improves the running time and space requirement of the method significantly.