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23rd International Joint Conference on Artificial Intelligence (IJCAI 2013); 08/2013
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13th SIAM International Conference on Data Mining (SDM 2013); 05/2013
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ABSTRACT: This work uses thermo-gravimetric, differential thermo-gravimetric and differential thermal analyses to evaluate the kinetics of pyrolysis (in inert/N(2) atmosphere) and (oxidative) combustion of microalgae Chlorella vulgaris by heating from 50 to 800°C at heating rates of 5-40°C/min. This study shows that combustion produces higher biomass conversion than pyrolysis, and that three stages of decomposition occur in both cases, of which, the second one - consisting of two temperature zones - is the main stage of devolatization. Proteins and carbohydrates are decomposed in the first of the two zones at activation energies of 51 and 45kJ/mol for pyrolysis and combustion, respectively, while lipids are decomposed in its second zone at higher activation energies of 64 and 63kJ/mol, respectively. The kinetic expressions of the reaction rates in the two zones for pyrolysis and combustion have been obtained and it has been shown that increased heating rates result in faster and higher conversion.
Bioresource technology 10/2012; 128C:72-80. · 4.25 Impact Factor
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ABSTRACT: RNAsnap™ is a simple and novel method that recovers all intracellular RNA quantitatively (>99%), faster (<15 min) and less expensively (∼3 cents/sample) than any of the currently available RNA isolation methods. In fact, none of the bacterial RNA isolation methods, including the commercial kits, are effective in recovering all species of intracellular RNAs (76-5700 nt) with equal efficiency, which can lead to biased results in genome-wide studies involving microarray or RNAseq analysis. The RNAsnap™ procedure yields ∼60 µg of RNA from 10(8) Escherichia coli cells that can be used directly for northern analysis without any further purification. Based on a comparative analysis of specific transcripts ranging in size from 76 to 5700 nt, the RNAsnap™ method provided the most accurate measure of the relative amounts of the various intracellular RNAs. Furthermore, the RNAsnap™ RNA was successfully used in enzymatic reactions such as RNA ligation, reverse transcription, primer extension and reverse transcriptase-polymerase chain reaction, following sodium acetate/ethanol precipitation. The RNAsnap™ method can be used to isolate RNA from a wide range of Gram-negative and Gram-positive bacteria as well as yeast.
Nucleic Acids Research 07/2012; · 8.03 Impact Factor
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ABSTRACT: Pairwise statistical significance has been recognized to be able to accurately identify related sequences, which is a very important cornerstone procedure in numerous bioinformatics applications. However, it is both computationally and data intensive, which poses a big challenge in terms of performance and scalability.
We present a GPU implementation to accelerate pairwise statistical significance estimation of local sequence alignment using standard substitution matrices. By carefully studying the algorithm's data access characteristics, we developed a tile-based scheme that can produce a contiguous data access in the GPU global memory and sustain a large number of threads to achieve a high GPU occupancy. We further extend the parallelization technique to estimate pairwise statistical significance using position-specific substitution matrices, which has earlier demonstrated significantly better sequence comparison accuracy than using standard substitution matrices. The implementation is also extended to take advantage of dual-GPUs. We observe end-to-end speedups of nearly 250 (370) × using single-GPU Tesla C2050 GPU (dual-Tesla C2050) over the CPU implementation using Intel Corei7 CPU 920 processor.
Harvesting the high performance of modern GPUs is a promising approach to accelerate pairwise statistical significance estimation for local sequence alignment.
BMC Bioinformatics 01/2012; 13 Suppl 5:S3. · 2.75 Impact Factor
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Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011; 01/2011
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Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011; 01/2011
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IJKDB. 01/2011; 2:34-54.
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Concurrency and Computation: Practice and Experience. 01/2011; 23:2269-2279.
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IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011, Orlando, FL, USA, February 3-5, 2011; 01/2011
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IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011, Orlando, FL, USA, February 3-5, 2011; 01/2011
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IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2011, Orlando, FL, USA, February 3-5, 2011; 01/2011
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Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011; 01/2011
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Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011; 01/2011
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Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011; 01/2011
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Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on, Vancouver, BC, Canada, December 11, 2011; 01/2011
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25th IEEE International Symposium on Parallel and Distributed Processing, IPDPS 2011, Anchorage, Alaska, USA, 16-20 May 2011 - Workshop Proceedings; 01/2011
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ABSTRACT: Thresholding is always critical and decisive in many bioinformatics problems. In this paper, we propose and apply a fuzzy-logic-based adaptive thresholding approach to a well-known solution for the exon prediction problem, which uses a threshold on the frequency component at f = 1/3 in the nucleotide sequence. The proposed approach allows the thresholds to vary along the data set based on the local statistical properties. Experiments and results on the nucleotide data of Saccharomyces cerevisiae (Bakers yeast) illustrate the advantage of our approach. A user-friendly GUI in MATLAB is freely available for academic use at www.cs.iastate.edu/˜ankitag/FATBEP.html.
International Journal of Computational Biology and Drug Design 01/2011; 3(4):311-33.
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ABSTRACT: There has been a deluge of biological sequence data in the public domain, which makes sequence comparison one of the most fundamental computational problems in bioinformatics. The biologists routinely use pairwise alignment programs to identify similar, or more specifically, related sequences (having common ancestor). It is a well-known fact that almost everything in bioinformatics depends on the inter-relationship between sequence, structure, and function (all encapsulated in the term relatedness), which is far from being well understood. The potential relatedness of two sequences is better judged by statistical significance of the alignment score rather than by the alignment score alone. This chapter presents a summary of recent advances in accurately estimating statistical significance of pairwise local alignment for the purpose of identifying related sequences, by making the sequence comparison process more sequence specific. Comparison of using pairwise statistical significance to rank database sequences, with well-known database search programs like BLAST, PSI-BLAST, and SSEARCH, is also presented. As expected, the sequence-comparison performance (evaluated in terms of retrieval accuracy) improves significantly as the sequence comparison process is made more and more sequence specific. Shortcomings of currently used approaches and some potentially useful directions for future work are also presented.
Advances in experimental medicine and biology 01/2011; 696:297-306. · 1.09 Impact Factor
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ABSTRACT: Recently, a number of programs have been proposed for mapping short reads to a reference genome. Many of them are heavily optimized for short-read mapping and hence are very efficient for shorter queries, but that makes them inefficient or not applicable for reads longer than 200 bp. However, many sequencers are already generating longer reads and more are expected to follow. For long read sequence mapping, there are limited options; BLAT, SSAHA2, FANGS and BWA-SW are among the popular ones. However, resequencing and personalized medicine need much faster software to map these long sequencing reads to a reference genome to identify SNPs or rare transcripts.
We present AGILE (AliGnIng Long rEads), a hash table based high-throughput sequence mapping algorithm for longer 454 reads that uses diagonal multiple seed-match criteria, customized q-gram filtering and a dynamic incremental search approach among other heuristics to optimize every step of the mapping process. In our experiments, we observe that AGILE is more accurate than BLAT, and comparable to BWA-SW and SSAHA2. For practical error rates (< 5%) and read lengths (200-1000 bp), AGILE is significantly faster than BLAT, SSAHA2 and BWA-SW. Even for the other cases, AGILE is comparable to BWA-SW and several times faster than BLAT and SSAHA2.
http://www.ece.northwestern.edu/~smi539/agile.html.
Bioinformatics 11/2010; 27(2):189-95. · 5.47 Impact Factor
