Above are papers related to the following paper: [Paper_ID] Paper #228992. 	[Title] AMASS: A Structured Pattern Matching Approach to Shotgun Sequence Assembly [Abstract] In this paper, we propose an efficient, reliable shotgun sequence assembly algorithm based on a fingerprinting scheme that is robust to both noise and repetitive sequences in the data. Our algorithm uses exact matches of short patterns randomly selected from fragment data to identify fragment overlaps, construct an overlap map, and finally deliver a consensus sequence. We show how statistical clues made explicit in our approach can easily be exploited to correctly assemble results even in the presence of extensive repetitive sequences. Our approach is exceptionally fast in practice: e.g., we have successfully assembled a whole Mycoplasma genitalium genome (approximately 580 kbps) in roughly 8 minutes of 64MB 200MHz Pentium Pro CPU time from real shotgun data, where most existing algorithms can be expected to run for several hours to a day on the same data. Moreover, experiments with shotgun data synthetically prepared from real DNA sequences from a wide range of organisms (including human DNA) and containing extensive repeating regions demonstrate our algorithm's robustness to noise and the presence of repetitive sequences. For example, we have correctly assembled a 238kbp Human DNA sequence in less than 3 minutes of 64MB 200MHz Pentium Pro CPU time. fl Support for this research was provided in part by the Office of Naval Research through grant N0014-94-1-1178. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #228990. 	[Title] A New Algorithm for DNA Sequence Assembly  Running Title: A New Algorithm for DNA Sequence Assembly [Abstract]  Please summarise all the given information.

Paper #228992 has the title `AMASS: A Structured Pattern Matching Approach to Shotgun Sequence Assembly  
` and the abstract `In this paper, we propose an efficient, reliable shotgun sequence assembly algorithm based on a fingerprinting scheme that is robust to both noise and repetitive sequences in the data. Our algorithm uses exact matches of short patterns randomly selected from fragment data to identify fragment overlaps, construct an overlap map, and finally deliver a consensus sequence. We show how statistical clues made explicit in our approach can easily be exploited to correctly assemble results even in the presence of extensive repetitive sequences. Our approach is exceptionally fast in practice: e.g., we have successfully assembled a whole Mycoplasma genitalium genome (approximately 580 kbps) in roughly 8 minutes of 64MB 200MHz Pentium Pro CPU time from real shotgun data, where most existing algorithms can be expected to run for several hours to a day on the same data. Moreover, experiments with shotgun data synthetically prepared from real DNA sequences from a wide range of organisms (including human DNA) and containing extensive repeating regions demonstrate our algorithm's robustness to noise and the presence of repetitive sequences. For example, we have correctly assembled a 238kbp Human DNA sequence in less than 3 minutes of 64MB 200MHz Pentium Pro CPU time. fl Support for this research was provided in part by the Office of Naval Research through grant N0014-94-1-1178.
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Question: Which category should Paper #228992 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #228992 has the title `AMASS: A Structured Pattern Matching Approach to Shotgun Sequence Assembly  
` and the abstract `In this paper, we propose an efficient, reliable shotgun sequence assembly algorithm based on a fingerprinting scheme that is robust to both noise and repetitive sequences in the data. Our algorithm uses exact matches of short patterns randomly selected from fragment data to identify fragment overlaps, construct an overlap map, and finally deliver a consensus sequence. We show how statistical clues made explicit in our approach can easily be exploited to correctly assemble results even in the presence of extensive repetitive sequences. Our approach is exceptionally fast in practice: e.g., we have successfully assembled a whole Mycoplasma genitalium genome (approximately 580 kbps) in roughly 8 minutes of 64MB 200MHz Pentium Pro CPU time from real shotgun data, where most existing algorithms can be expected to run for several hours to a day on the same data. Moreover, experiments with shotgun data synthetically prepared from real DNA sequences from a wide range of organisms (including human DNA) and containing extensive repeating regions demonstrate our algorithm's robustness to noise and the presence of repetitive sequences. For example, we have correctly assembled a 238kbp Human DNA sequence in less than 3 minutes of 64MB 200MHz Pentium Pro CPU time. fl Support for this research was provided in part by the Office of Naval Research through grant N0014-94-1-1178.
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Question: Which category should Paper #228992 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks