@proceedings {38374,
	title = {MetaPhyler: Taxonomic profiling for metagenomic sequences},
	year = {2010},
	month = {2010},
	publisher = {IEEE},
	type = {10.1109/BIBM.2010.5706544},
	abstract = {A major goal of metagenomics is to characterize the microbial diversity of an environment. The most popular approach relies on 16S rRNA sequencing, however this approach can generate biased estimates due to differences in the copy number of the 16S rRNA gene between even closely related organisms, and due to PCR artifacts. The taxonomic composition can also be determined from whole-metagenome sequencing data by matching individual sequences against a database of reference genes. One major limitation of prior methods used for this purpose is the use of a universal classification threshold for all genes at all taxonomic levels. We propose that better classification results can be obtained by tuning the taxonomic classifier to each matching length, reference gene, and taxonomic level. We present a novel taxonomic profiler MetaPhyler, which uses marker genes as a taxonomic reference. Results on simulated datasets demonstrate that MetaPhyler outperforms other tools commonly used in this context (CARMA, Megan and PhymmBL). We also present interesting results obtained by applying MetaPhyler to a real metagenomic dataset.},
	keywords = {Bioinformatics, CARMA comparison, Databases, Genomics, Linear regression, marker genes, matching length, Megan comparison, metagenomic sequences, metagenomics, MetaPhyler, microbial diversity, microorganisms, molecular biophysics, molecular configurations, Pattern classification, pattern matching, phylogenetic classification, Phylogeny, PhymmBL comparison, reference gene database, Sensitivity, sequence matching, taxonomic classifier, taxonomic level, taxonomic profiling, whole metagenome sequencing data},
	isbn = {978-1-4244-8306-8},
	author = {Liu, Bo and Gibbons, T. and Ghodsi, M. and M. Pop}
}
@proceedings {38502,
	title = {A SIMD solution to the sequence comparison problem on the MGAP},
	year = {1994},
	month = {1994},
	publisher = {IEEE},
	type = {10.1109/ASAP.1994.331791},
	abstract = {Molecular biologists frequently compare an unknown biosequence with a set of other known biosequences to find the sequence which is maximally similar, with the hope that what is true of one sequence, either physically or functionally, could be true of its analogue. Even though efficient dynamic programming algorithms exist for the problem, when the size of the database is large, the time required is quite long, even for moderate length sequences. In this paper, we present an efficient pipelined SIMD solution to the sequence alignment problem on the Micro-Grain Array Processor (MGAP), a fine-grained massively parallel array of processors with nearest-neighbor connections. The algorithm compares K sequences of length O(M) with the actual sequence of length N, in O(M+N+K) time with O(MN) processors, which is AT-optimal. The implementation on the MGAP computes at the rate of about 0.1 million comparisons per second for sequences of length 128},
	keywords = {AT-optimal algorithm, Biological information theory, biology computing, biosequence comparison problem, computational complexity, Computer science, Costs, database size, Databases, DNA computing, dynamic programming, dynamic programming algorithms, fine-grained massively parallel processor array, Genetics, Heuristic algorithms, maximally similar sequence, MGAP parallel computer, Micro-Grain Array Processor, Military computing, molecular biology, molecular biophysics, Nearest neighbor searches, nearest-neighbor connections, Parallel algorithms, pipeline processing, pipelined SIMD solution, sequence alignment problem, sequences},
	isbn = {0-8186-6517-3},
	author = {Borah, M. and Bajwa, R. S. and Sridhar Hannenhalli and Irwin, M. J.}
}
