Loai M. Alnemer

Overexpression of two ATNAC3 -related genes improves drought and salt tolerance in tomato ( Solanum lycopersicum L.)

The NAC family is a multigene family that present uniquely in plants and whose members are involved in many important cellular processes including abiotic stress tolerance. In this study, sequences of two ATNAC3-related genes (SlNAC3) were identified in the tomato genome using different bioinformatics approaches. Phylogenetic analysis clustered 84 tomato identified NAC proteins into 19 different subfamilies that included 5 subfamilies for stress-related NAC genes with SlNAC3 members clustered with previously characterized ATNAC3 members from Arabidopsis. Gene expression analysis of SlNAC3 genes indicated that both of them are expressed in response to drought and salinity stress conditions. The over-expression of two stress-related SlNAC3 in tomato plants resulted in enhanced drought and salt tolerance when compared with wild type plants. The identified stress-related NAC genes could be a useful tool to improve tomato productivity under stress conditions.

Wheat Zapper: a flexible online tool for colinearity studies in grass genomes

In the course of evolution, the genomes of grasses have maintained an observable degree of gene order conservation. The information available for already sequenced genomes can be used to predict the gene order of nonsequenced species by means of comparative colinearity studies. The “Wheat Zapper” application presented here performs on-demand colinearity analysis between wheat, rice, Sorghum, and Brachypodium in a simple, time efficient, and flexible manner. This application was specifically designed to provide plant scientists with a set of tools, comprising not only synteny inference, but also automated primer design, intron/exon boundaries prediction, visual representation using the graphic tool Circos 0.53, and the possibility of downloading FASTA sequences for downstream applications. Quality of the “Wheat Zapper” prediction was confirmed against the genome of maize, with good correlation (r > 0.83) observed between the gene order predicted on the basis of synteny and their actual position on the genome. Further, the accuracy of “Wheat Zapper” was calculated at 0.65 considering the “Genome Zipper” application as the “gold” standard. The differences between these two tools are amply discussed, making the point that “Wheat Zapper” is an accurate and reliable on-demand tool that is sure to benefit the cereal scientific community. The Wheat Zapper is available at http://wge.ndsu.nodak.edu/wheatzapper/.

Accelerated evolution of the mitochondrial genome in an alloplasmic line of durum wheat

BackgroundWheat is an excellent plant species for nuclear mitochondrial interaction studies due to availability of large collection of alloplasmic lines. These lines exhibit different vegetative and physiological properties than their parents. To investigate the level of sequence changes introduced into the mitochondrial genome under the alloplasmic condition, three mitochondrial genomes of the Triticum-Aegilops species were sequenced: 1) durum alloplasmic line with the Ae. longissima cytoplasm that carries the T. turgidum nucleus designated as (lo) durum, 2) the cytoplasmic donor line, and 3) the nuclear donor line.ResultsThe mitochondrial genome of the T. turgidum was 451,678 bp in length with high structural and nucleotide identity to the previously characterized T. aestivum genome. The assembled mitochondrial genome of the (lo) durum and the Ae. longissima were 431,959 bp and 399,005 bp in size, respectively. The high sequence coverage for all three genomes allowed analysis of heteroplasmy within each genome. The mitochondrial genome structure in the alloplasmic line was genetically distant from both maternal and paternal genomes. The alloplasmic durum and the Ae. longissima carry the same versions of atp6, nad6, rps19-p, cob and cox2 exon 2 which are different from the T. turgidum parent. Evidence of paternal leakage was also observed by analyzing nad9 and orf359 among all three lines. Nucleotide search identified a number of open reading frames, of which 27 were specific to the (lo) durum line.ConclusionsSeveral heteroplasmic regions were observed within genes and intergenic regions of the mitochondrial genomes of all three lines. The number of rearrangements and nucleotide changes in the mitochondrial genome of the alloplasmic line that have occurred in less than half a century was significant considering the high sequence conservation between the T. turgidum and the T. aestivum that diverged from each other 10,000 years ago. We showed that the changes in genes were not limited to paternal leakage but were sufficiently significant to suggest that other mechanisms, such as recombination and mutation, were responsible. The newly formed ORFs, differences in gene sequences and copy numbers, heteroplasmy, and substoichiometric changes show the potential of the alloplasmic condition to accelerate evolution towards forming new mitochondrial genomes.

Network-Based Filtering of Unreliable Markers in Genome Mapping

Genome mapping, or the experimental determination of the ordering of DNA markers on a chromosome, is an important step in genome sequencing and ultimate assembly of sequenced genomes. The presented research addresses the problem of identifying markers that cannot be placed reliably. If such markers are included in standard mapping procedures they can result in an overall poor mapping. Traditional techniques for identifying markers that cannot be placed consistently are based on resampling, which requires an already computationally expensive process to be done for a large ensemble of resampled populations. We propose a network-based approach that uses pair wise similarities between markers and demonstrate that the results from this approach largely match the more computationally expensive conventional approaches. The evaluation of the proposed approach is done on data from the radiation hybrid mapping of the wheat genome.

Frequent Substring-Based Sequence Classification with an Ensemble of Support Vector Machines Trained Using Reduced Amino Acid Alphabets

We propose a frequent pattern-based algorithm for predicting functions and localizations of proteins from their primary structure (amino acid sequence). We use reduced alphabets that capture the higher rate of substitution between amino acids that are physiochemically similar. Frequent sub strings are mined from the training sequences, transformed into different alphabets, and used as features to train an ensemble of SVMs. We evaluate the performance of our algorithm using protein sub-cellular localization and protein function datasets. Pair-wise sequence-alignment-based nearest neighbor and basic SVM k-gram classifiers are included as comparison algorithms. Results show that the frequent sub string-based SVM classifier demonstrates better performance compared with other classifiers on the sub-cellular localization datasets and it performs competitively with the nearest neighbor classifier on the protein function datasets. Our results also show that the use of reduced alphabets provides statistically significant performance improvements for half of the classes studied.

Hybrid SMOTE-Ensemble Approach for Software Defect Prediction

Software defect prediction is the process of identifying new defects/bugs in software modules. Software defect presents an error in a computer program, which is caused by incorrect code or incorrect programming logic. As a result, undiscovered defects lead to a poor quality software products. In recent years, software defect prediction has received a considerable amount of attention from researchers. Most of the previous defect detection algorithms are marred by low defect detection ratios. Furthermore, software defect prediction is very challenging problem due to the high imbalanced distribution, where the bug-free codes are much higher than defective ones. In this paper, the software defect prediction problem is formulated as a classification task, and then it examines the impact of several ensembles methods on the classification effectiveness. In addition, the best ensemble classifier will be selected to be trained again on an over-sampled datasets using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm to tackle imbalanced distribution problem. The proposed hybrid method is evaluated using four software defects datasets. Experimental results demonstrate that the proposed method can effectively enhance the defect prediction accuracy.

Parallel coordinates metrics for classification visualization

The high dimensionality of data presents a major issue in understanding and interpreting the results of classification learning. Among the various approaches that address this issue, parallel coordinates visualization has proven its capabilities to enhance investigation and comprehension of data dimension features especially when the number of dimensions is high and there are numerous output classes. We propose several parallel coordinates metrics, namely entropy, class ordering, and edge crossing, to further facilitate inspection of data features and their relevance to output class. Experiments on real world datasets are presented to show the effectiveness of the proposed approach.

Multiple Sources Classification of Gene Position on Chromosomes Using Statistical Significance of Individual Classification Results

In data mining applications it is common to have more than one data source available to describe the same record. For example, in biological sciences, the same genes may be characterized through many types of experiments. Which of the data sources proves to be most reliable in predictions may depend on the record in question. For some records pieces of information may be unavailable because an experiment has not yet been done, or certain type of inferences may not be applicable, such as when a gene does not have a homologue in some species. We demonstrate how multi-classifier systems can allow classification in cases where any individual source is scarce or unreliable to provide an accurate prediction model by itself. We propose a method to predict a class label using statistical significance of individual classification results. We show that the proposed approach increases the accuracy of results compared with conventional techniques in a problem related to gene mapping in wheat.

Morphology Based Arabic Sentiment Analysis of Book Reviews

Sentiment analysis is a fundamental natural language processing task that automatically analyzes raw textual data and infer from it semantic meaning. The inferred information focuses on the author’s attitude or opinion towards a written text. Although there is extensive research done on sentiment analysis on English language, there has been little work done that targets the morphologically rich structure of the Arabic language. In addition, most of the research done on Arabic either focus on introducing new datasets or new sentiment lexicons. We propose a supervised sentiment analysis approach for two tasks: positive/negative classification and positive/negative/neutral classification. We focus on the morphological structure of the Arabic language by introducing filtering, segmentation and morphological processing specifically for this language. We also manually create an emoticon sentiment lexicon in order to stress the expressed emotions and improve on the sentiment analyzer.

Microsatellites based algorithm for cross flanking regions identification in grass species

This research aims at exploring the evolutionary path of selected five grass species to understand how they evolve through time from a computer science perspective, by designing a new algorithm that employs raw data sequences to extract the information necessary for explaining the hypothesis that claims crossing over regions on genomes are responsible of pushing the evolutionary path of plant species. Basic Local Alignment Search Tool (BLAST) and Clustal Omega application were used to perform local alignment and global multiple alignments between species and define the relativity between them in order to approximately determine what species falls in the evolutionary path over time.