A review on multiple sequence alignment from the perspective of genetic algorithm.

Sequence alignment is an active research area in the field of bioinformatics. It is also a crucial task as it guides many other tasks like phylogenetic analysis, function, and/or structure prediction of biological macromolecules like DNA, RNA, and Protein. Proteins are the building blocks of every living organism. Although protein alignment problem has been studied for several decades, unfortunately, every available method produces alignment results differently for a single alignment problem. Multiple sequence alignment is characterized as a very high computational complex problem. Many stochastic methods, therefore, are considered for improving the accuracy of alignment. Among them, many researchers frequently use Genetic Algorithm. In this study, we have shown different types of the method applied in alignment and the recent trends in the multiobjective genetic algorithm for solving multiple sequence alignment. Many recent studies have demonstrated considerable progress in finding the alignment accuracy.

An optimized approach for annotation of large eukaryotic genomic sequences using genetic algorithm.

BACKGROUND: Detection of important functional and/or structural elements and identification of their positions in a large eukaryotic genomic sequence are an active research area. Gene is an important functional and structural unit of DNA. The computation of gene prediction is, therefore, very essential for detailed genome annotation. RESULTS: In this paper, we propose a new gene prediction technique based on Genetic Algorithm (GA) to determine the optimal positions of exons of a gene in a chromosome or genome. The correct identification of the coding and non-coding regions is difficult and computationally demanding. The proposed genetic-based method, named Gene Prediction with Genetic Algorithm (GPGA), reduces this problem by searching only one exon at a time instead of all exons along with its introns. This representation carries a significant advantage in that it breaks the entire gene-finding problem into a number of smaller sub-problems, thereby reducing the computational complexity. We tested the performance of the GPGA with existing benchmark datasets and compared the results with well-known and relevant techniques. The comparison shows the better or comparable performance of the proposed method. We also used GPGA for annotating the human chromosome 21 (HS21) using cross-species comparisons with the mouse orthologs. CONCLUSION: It was noted that the GPGA predicted true genes with better accuracy than other well-known approaches.

Minimum turbulence assumptions and u* and L estimation for dispersion models during low-wind stable conditions.

UNLABELLED: The U.S. Environmental Protection Agency (EPA) short-distance dispersion model, AERMOD, has been shown to overpredict by a factor of as much as 10 when compared with observed concentrations from continuous releases at the Oak Ridge, TN (OR), and Idaho Falls, ID (IF), field experiments during stable periods when wind speeds often dropped below 1 m/sec. Some of this overprediction tendency can be reduced by revising AERMOD's meteorological preprocessor's parameterizations of the friction velocity, u*, during low-wind stable conditions, thus increasing the calculated sigma(v) and sigma(w) and hence the lateral and vertical dispersion rates. Observations show that as the mean wind speed approaches zero at night, there is always significant sigma(v) and sigma(w) over time periods of 15 to 60 min, while standard Monin-Obukhov Similarity Theory (MOST) predicts that sigma(v) and sigma(w) will approach zero. This paper focuses on the u* estimation methods and the minimum turbulence (sigma(v) and sigma(w)) assumptions in AERMOD (beta option 4) and two widely used U.S. operational dispersion models, AERMOD (v12345) and SCICHEM. The U.S. EPA has provided results of its tests with the OR and IF data, with its base AERMOD version and its December 2012 modified versions, which assume adjustments to the low-wind u* and increases in the minimum sigma(v) parameterization. SCICHEM has relatively small mean bias for both data sets. The revised AERMOD shows much less mean bias, agreeing more with SCICHEM. IMPLICATIONS: Suggestions are made for improvements to dispersion models such as AERMOD to correct overpredictions during light-wind stable conditions. Methods for estimating u*, L, and the minimum turbulence parameters (sigma(v) and sigma(w)) are reviewed and compared. SCICHEM and the current operational version and an optional beta version (December 2012) of AERMOD are evaluated with tracer data from low-wind stable field experiments in Idaho Falls and Oak Ridge. It is seen that the operational version of AERMOD overpredicts by a factor of 2 to 10, while the optional beta version of AERMOD and SCICHEM have much less bias.

Breakage fusion bridge cycles drive high oncogene copy number, but not intratumoral genetic heterogeneity or rapid cancer genome change.

Oncogene amplification is a major driver of cancer pathogenesis. Breakage fusion bridge (BFB) cycles, like extrachromosomal DNA (ecDNA), can lead to high copy numbers of oncogenes, but their impact on intratumoral heterogeneity, treatment response, and patient survival are not well understood due to difficulty in detecting them by DNA sequencing. We describe a novel algorithm that detects and reconstructs BFB amplifications using optical genome maps (OGMs), called OM2BFB. OM2BFB showed high precision (>93%) and recall (92%) in detecting BFB amplifications in cancer cell lines, PDX models and primary tumors. OM-based comparisons demonstrated that short-read BFB detection using our AmpliconSuite (AS) toolkit also achieved high precision, albeit with reduced sensitivity. We detected 371 BFB events using whole genome sequences from 2,557 primary tumors and cancer lines. BFB amplifications were preferentially found in cervical, head and neck, lung, and esophageal cancers, but rarely in brain cancers. BFB amplified genes show lower variance of gene expression, with fewer options for regulatory rewiring relative to ecDNA amplified genes. BFB positive (BFB (+)) tumors showed reduced heterogeneity of amplicon structures, and delayed onset of resistance, relative to ecDNA(+) tumors. EcDNA and BFB amplifications represent contrasting mechanisms to increase the copy numbers of oncogene with markedly different characteristics that suggest different routes for intervention.

Modeling atmospheric volatile organic compound concentrations resulting from a deepwater oil well blowout - Mitigation by subsea dispersant injection.

The atmospheric concentrations of volatile organic compounds (VOCs) generated by surface slicks during an oil spill have not been extensively studied. We modeled oil transport and fate, air emissions, and atmospheric dispersion of VOCs from a hypothetical deepwater well blowout in De Soto Canyon of the Gulf of Mexico assuming no intervention and use of SubSea Dispersant Injection (SSDI) at the source during three week-long periods representing different atmospheric mixing conditions. Spatially varying time histories of atmospheric VOCs within ~2 km from the release site were estimated. As compared to the no-intervention case, SSDI dispersed the discharged oil over a larger water volume at depth and enhanced VOC dissolution and biodegradation, thereby reducing both the total mass of VOCs released to the atmosphere and the concentration of VOCs within 2 km from the release site. Atmospheric conditions also influenced the VOC concentrations, although to a lesser degree than SSDI.