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Background: Due to overdispersion in the RNA-Seq data and its discrete structure, clustering samples based on gene expression profiles remains a challenging problem, and several clustering approaches have been developed so far. However, there is no “gold standard” strategy for clustering RNA-Seq data, so alternative approaches are needed. Objective: In this study, we presented a new clustering approach, which incorporates two powerful methods, i.e., voom and self-organizing maps, into the frequently used clustering algorithms such as kmeans, k-medoid and hierarchical clustering algorithms for RNA-seq data clustering. Methods: We first filter and normalize the raw RNA-seq count data. Then to transform counts into continuous data, we apply the voom method, which outputs the log-cpm matrix and sample quality weights. After the voom transformation, we apply the SOM algorithm to log-cpm values to get the codebook used in the downstream analysis. Next, we calculate the weighted distance matrices using the sample quality weights obtained from voom transformation and codebooks from the SOM algorithm. Finally, we apply k-means, k-medoid and hierarchical clustering algorithms to cluster samples. Results: The performances of the presented approach and existing methods are compared over simulated and real datasets. The results show that the new clustering approach performs similarly or better than other methods in the Rand index and adjusted Rand index. Since the voom method accurately models the observed mean-variance relationship of RNA-seq data and SOM is an efficient algorithm for modeling high dimensional data, integrating these two powerful methods into clustering algorithms increases the performance of clustering algorithms in overdispersed RNA-seq data. Conclusion: The proposed algorithm, voomSOM, is an efficient and novel clustering approach that can be applied to RNA-Seq data clustering problems.

Gene-expression studies have been studied using microarray data for many years, and numerous methods have been developed for these data. However, microarray technology is old technology and has some limitations. RNA-sequencing (RNA-seq) is a new transcriptomics technique capable of coping with these limitations, using the capabilities of new generation sequencing technologies, and performing operations quickly and cheaply based on the principle of high-throughput sequencing technology. Compared to microarrays, RNA-seq offers several advantages: (1) having less noisy data, (2) being able to detect new transcripts and coding regions, (3) not requiring pre-determination of the transcriptomes of interest. However, RNA-seq data has several features that pose statistical challenges. Thus, one cannot directly use methods developed for microarray analyses, which has a discrete and overdispersed nature of data, quite different from the continuous data structure of microarrays. This article aims to provide an overview and practical guidance to researchers working with RNA-seq data for different purposes.

With the recent advances in molecular biology techniques such as next-generation sequencing, mass-spectrometry, etc., a large omic data is produced. Using such data, the expression levels of thousands of molecular features (genes, proteins, metabolites, etc.) can be quantified and associated with diseases. The fact that multiple omics data contains different types of data and the number of analyzed variables increases the complexity of the models created with machine learning methods. In addition, due to many variables, the investigation of molecular variables associated with diseases is very costly. Therefore, selecting the informative and disease-related molecular features is applicable before model training and evaluation. This feature selection step is essential for obtaining accurate and generalizable models in minimum time with minimum cost. Some current methods used for feature selection are as follows: recursive feature elimination, information gain, minimum redundancy maximum relevance (mRMR), boruta, altmann, and lasso.

The present study evaluated fatty acid digestibility, histology, blood biochemistry and gene expression in the gut of juvenile gilthead sea bream, Sparus aurata (initial mean weight, 60.4 ± 4.9 g) fed with iso-proteic (48%) and iso-lipidic (20.7%) diets in which fish oil was partially (60%) or totally (100%) replaced with either camelina or chia oil. Each of the experimental diets was fed to triplicate groups of fish stocked at 10 fish/tank in 150 L tanks for 90 days. Although inclusion of camelina or chia oil altered dietary fatty acid (FA) profile, major differences in digestibility in the individual FAs were not observed. Total replacement of fish oil with camelina or chia oil did not have any effect on blood glucose levels, whereas cholesterol and triglyceride levels were reduced. Results of messenger ribonucleic acid (mRNA) transcription showed elevated levels in the expression of the elongases and desaturases in gilthead seabream fed with diets devoid of fish oil. Fish fed with camelina or chia oil based diets had increased expression in lipolytic and lipogenic genes, resulting in accumulation of lipid droplets in the gut of fish. Histological examination revealed no pathological disorders in the guts of the fish.

As the most readily adopted molecular screening test, low-pass WGS of maternal plasma cell-free DNA for aneuploidy detection generates a vast amount of genomic data. This large-scale method also allows for high-throughput virome screening. NIPT sequencing data, yielding 6.57 terabases of data from 187.8 billion reads, from 12,951 pregnant Turkish women was used to investigate the prevalence and abundance of viral DNA in plasma. Among the 22 virus sequences identified in 12% of participants were human papillomavirus, herpesvirus, betaherpesvirus and anellovirus. We observed a unique pattern of circulating viral DNA with a high prevalence of papillomaviruses. The prevalence of herpesviruses/anellovirus was similar among Turkish, European and Dutch populations. Hepatitis B prevalence was remarkably low in Dutch, European and Turkish populations, but higher in China. WGS data revealed that herpesvirus/anelloviruses are naturally found in European populations. This represents the first comprehensive research on the plasma virome of pregnant Turkish women.

Crocus istanbulensis (B.Mathew) Rukšāns is one of the most endangered Crocus species in the world and has an extremely limited distribution range in Istanbul. Our recent field work indicates that no more than one hundred individuals remain in the wild. In the present study, we used genome skimming to determine the complete chloroplast (cp) genome sequences of six C. istanbulensis individuals collected from the locus classicus. The cp genome of C. istanbulensis has 151,199 base pairs (bp), with a large single-copy (LSC) (81,197 bp), small single copy (SSC) (17,524 bp) and two inverted repeat (IR) regions of 26,236 bp each. The cp genome contains 132 genes, of which 86 are protein-coding (PCGs), 8 are rRNA and 38 are tRNA genes. Most of the repeats are found in intergenic spacers of Crocus species. Mononucleotide repeats were most abundant, accounting for over 80% of total repeats. The cp genome contained four palindrome repeats and one forward repeat. Comparative analyses among other Iridaceae species identified one inversion in the terminal positions of LSC region and three different gene (psbA, rps3 and rpl22) arrangements in C. istanbulensis that were not reported previously. To measure selective pressure in the exons of chloroplast coding sequences, we performed a sequence analysis of plastome-encoded genes. A total of seven genes (accD, rpoC2, psbK, rps12, ccsA, clpP and ycf2) were detected under positive selection in the cp genome. Alignment-free sequence comparison showed an extremely low sequence diversity across naturally occurring C. istanbulensis specimens. All six sequenced individuals shared the same cp haplotype. In summary, this study will aid further research on the molecular evolution and development of ex situ conservation strategies of C. istanbulensis.

Earthworms belong to the main groups of soil-dwelling invertebrates and are an important element for soil biota. The ecological importance of earthworms on terrestrial ecosystem is mainly attributed to their close synergistic interaction with soil biota. Among earthworms, Eisenia fetida has been considered as preferred for vermiculture and vermicomposting practices due to their reproductive potentials, short life cycle and wide temperature and density pressure tolerances. Although the genome and transcriptome data for E. fetida are available, the EST-SSRs composition and frequency of this species, which used for phylogenetic studies, genus-level taxonomy and population genetics, remain poorly defined. In current study, we mined publicly available transcriptome data and characterised genome-wide EST-SSR markers for this epigeic species. A total of 13,060 EST-SSRs were identified from 162,609 contigs. The most abundant EST-SSR types were found to be trimeric repeats (5,998, 46%) followed by dimeric (4,762, 36%), tetrameric (1,716, 13%), pentameric (478, 4%) and hexameric repeats (106, 1%). For SSR motifs, the most prevalent motifs were AC/GT (14.72%), followed by AT/AT (13.20%), ATC/ATG (13.03%), AAT/ATT (12.30%) and AG/CT (8.46%). Following validation of the species using COI sequences and to test the designed primer pairs, the eight primer pairs were designed, but only six of them were successfully amplified. Although EST-SSR information is scarce for annelids, the EST-SSR patterns of E. fetida seem to be similar to annelids. Overall, the EST-SSR markers help in taxonomic resolution of Eisenia genus from other earthworm genera and in studying the population structure and geographic distributions of E. fetida.

Arsenic is a broad-spectrum environmental contaminant with mutagenic, teratogenic and carcinogenic effects. Due to its widespread distribution in nature, drinking water is the most common source of arsenic exposure for the general population. In this study, we aimed to determine the effect of sodium arsenite on the viability and expression profile of steroidogenic genes in TM3 Leydig cells, responsible for testicular steroidogenesis. The TM3 Leydig cells were treated with sodium arsenic (384,8 µM or 7,6 mM) for 24 hours with LH (Luteinizing hormone) stimulation. The MTT assay was used for measuring cell viability, the expression level of key genes of the steroidogenesis was evaluated using RT-qPCR.The MTT assay showed that cell viability was decreased dose-dependently. RT-qPCR demonstrated that the expression level of CYP11A1, CYP17A1 were decreased as compared to the untreated control while StAR gene expression was found to be surprisingly high in the cell exposed to high-dose arsenic (p

The present study evaluated fatty acid (FA) metabolism in juvenile gilthead sea bream (Sparus aurata, 60.4 ± 4.9 g) fed diets with camelina seed oil and chia oil which totally (CSO and CO diets, respectively) or 60% (60CSO and 60CO diets, respectively) replaced fish oil (FO diet) for 90 days. Analysis of the expression of genes was carried out to determine the effects of the diets on some of the major genes involved in fatty acid metabolism in fish. Good growth performance was recorded in all fish although lower growth (P

Background and Objective: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data. Although there are many algorithms proposed for microarray data, the number of available algorithms and programs are limited for classification of RNA-sequencing data. For this reason, we developed MLSeq, to bring not only frequently used classification algorithms but also novel approaches together and make them available to be used for classification of RNA sequencing data. This package is developed using R language environment and distributed through BIOCONDUCTOR network. Methods: Classification of RNA-sequencing data is not straightforward since raw data should be preprocessed before downstream analysis. With MLSeq package, researchers can easily preprocess (normalization, filtering, transformation etc.) and classify raw RNA-sequencing data using two strategies: (i) to perform algorithms which are directly proposed for RNA-sequencing data structure or (ii) to transform RNA-sequencing data in order to bring it distributionally closer to microarray data structure, and perform algorithms which are developed for microarray data. Moreover, we proposed novel algorithms such as voom (an acronym for variance modelling at observational level) based nearest shrunken centroids (voomNSC), diagonal linear discriminant analysis (voomDLDA), etc. through MLSeq. Materials: Three real RNA-sequencing datasets (i.e cervical cancer, lung cancer and aging datasets) were used to evalute model performances. Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) were selected as algorithms based on dicrete distributions, and voomNSC, nearest shrunken centroids (NSC) and support vector machines (SVM) were selected as algorithms based on continuous distributions for model comparisons. Each algorithm is compared using classification accuracies and sparsities on an independent test set. Results: The algorithms which are based on discrete distributions performed better in cervical cancer and aging data with accuracies above 0.92. In lung cancer data, the most of algorithms performed similar with accuracies of 0.88 except that SVM achieved 0.94 of accuracy. Our voomNSC algorithm was the most sparse algorithm, and able to select 2.2% and 6.6% of all features for cervical cancer and lung cancer datasets respectively. However, in aging data, sparse classifiers were not able to select an optimal subset of all features. Conclusion: MLSeq is comprehensive and easy-to-use interface for classification of gene expression data. It allows researchers perform both preprocessing and classification tasks through single platform. With this property, MLSeq can be considered as a pipeline for the classification of RNA-sequencing data. Erciyes Üniversitesi: TDK-2015-5468 We would like to thank S. Anders, M.I. Love and the BIOCONDUCTOR team for their useful comments, suggestions and support to improve the usefulness of the package. This study was supported by the Research Fund of Erciyes University [TDK-2015-5468]. We also would like to thank Sumeet Pal Singh for sharing the aging dataset which is used in the illustration of MLSeq package. Finally, we would like to thank anonymous reviewers for their contribution and valuable comments. 2-s2.0-85064937612 PubMed: 31104710

WOS: 000443307900005, PubMed ID: 30155366, Background. Extraintestinal pathogenic Escherichia coli (ExPEC) is an important bacterium and responsible for many bloodstream infections, including urinary tract infections and even fatal bacteremia. The aim of this research was to investigate whether ExPEC strains isolated from Turkish blood cultures have a relationship between 16S rRNA based phylogenetic clusters and antibiotic resistance profiles, virulence factors or clonal lineages. Methods. Phenotypically identified ExPEC blood culture isolates (n = 104) were included in this study. The 16S rRNA partial sequence analysis was performed for genotypic identification of ExPEC isolates. Antibiotic susceptibility and Extended Spectrum beta-Lactamase testing of isolates were performed. Phylogenetic classification (A, B1, B2 and D), Multi Locus Sequence Typing analysis and virulence-associated genes were investigated. Results. Based on 16S rRNA partial sequence analysis, 97 out of 104 (93.26%) ExPEC isolates were confirmed as E. coli. Ampicillin (74.22%) and cefuroxime axetil (65.97%) resistances had the highest frequencies among the ExPEC isolates. In terms of phylogenetic classification of ExPEC, D (38.14%, 37/97) was the most prevalent group after A (29.89%, 29/97), B2 (20.61%, 20/97), and B1 (11.34%, 11/97). The sequence types of the 20 ExPEC isolates belonging to the B2 phylogenetic group were analyzed by Multi Locus Sequence Typing. Ten isolates out of 20 (50.0%) were identified as ST131. The other STs were ST95 (n=1), ST14 (n=1), ST10 (n=1), ST69 (n=1), ST1722 (n=2), ST141 (n=1), ST88 (n=1), ST80 (n=1), and ST998 (n=1). Of the ST131 strains, six (60%, 6/10) represented serogroup O25. The most common virulence factor genes were serum resistance factor gene, traT (55.7%) aerobactin siderophore receptor and yersiniabactin encoding genes iutA (45.3%) and fyuA (50.5%), respectively. In addition, PAI (41.2%), iroN (23.7%), hlyA (15.4%), kpsII (13.4%), ompT (13.4%), papG (12.4%), iss (9.3%), cnf1 (7.2%), ibeA (2.06%), and sfaS (2.06%) genes were present in the ExPEC isolates. Conclusion. The 16S rRNA-based phylogenetic relationship tree analysis showed that a large cluster was present among 97 ExPEC isolates along with related reference strains. There were 21 main clusters with 32 closely related subclusters. Based on our findings, different clonal lineages of ExPEC can display different antibiotic susceptibilities and virulence properties. We also concluded that virulence factors were not distributed depending on phylogenetic groups (A, B1, B2, and D). The ExPEC isolates belonging to the same phylogenetic group and sequence type could display different resistance and virulence characteristics., Scientific Research Unit of the Istanbul University [FUA-2016-20386, BYP-2016-2305], This study was supported by The Scientific Research Unit of the Istanbul University with project ID: FUA-2016-20386 and ID: BYP-2016-2305. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Background RNA sequencing (RNA-Seq) is a powerful technique for transcriptome profiling of the organisms that uses the capabilities of next-generation sequencing (NGS) technologies. Recent advances in NGS let to measure the expression levels of tens to thousands of transcripts simultaneously. Using such information, developing expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of disease. Microarray based classifiers cannot be directly applied due to the discrete nature of RNA-Seq data. One way is to develop count-based classifiers, such as poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA). Other way is to transform the data hierarchically closer to microarrays and apply microarray-based classifiers. In most of the studies, the data overdispersion seems to be an another challenge in modeling RNA-Seq data. In this study, we aimed to examine the effect of dispersion parameter and classification algorithms on RNA-Seq classification. We also considered the effect of other parameters (i) sample size, (ii) number of genes, (iii) number of class, (iv) DE (differential expression) rate, (v) transformation method on classification performance. Methods We designed a comprehensive simulation study, also used two miRNA and two mRNA experimental datasets. Simulated datasets are generated from negative binomial distribution under different scenarios and real datasets are obtained from publicly available resources. We compared the results of several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). Results Results from the simulated and real datasets revealed that increasing the sample size, differential expression rate, number of genes and decreasing the dispersion parameter and number of groups lead to an increase in the classification accuracy. To make an overall assessment, power transformed PLDA, RF and SVM classifiers performed the highest classification accuracies. Discussion Overdispersion seems to be an important challenge in RNA-Seq classification studies. Similar with differential expression studies, classification of RNA-Seq data requires careful attention on handling data overdispersion. We conclude that, as a count-based classifier, power transformed PLDA; as a microarray based classifier vst or rlog transformed RF and SVM (bagSVM for high sample sized data) classifiers may be a good choice for classification. However, there is still a need to develop novel classifiers or transformation approaches for classification of RNA-Seq data. An R/BIOCONDUCTOR package MLSeq with a vignette is freely available at http://www.bioconductor.org/packages/2.14/bioc/html/MLSeq.html .

Objective: As in adults, hypertension is also an important risk factor for cardiovascular disease in children. We aimed to evaluate the effect of sleep duration on blood pressure in normal weight Turkish children aged between 11-17 years. Methods: This cross-sectional study was conducted in the primary and secondary schools of the two central and ten outlying districts of Kayseri, Turkey. Subjects were 2860 children and adolescents (1385 boys, 1475 girls). Systolic and diastolic blood pressures were measured according to the recommendations of the Fourth Report of the National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. Sleep duration was classified as follows: ≤8 hours, 8.1-8.9 hours, 9.0-9.9 hours or ≥10 hours. Results: For short sleeper boys and girls (participants with a sleep duration ≤8 h) the prevalence of prehypertension and hypertension was 35.0% and 30.8%, respectively. In univariate binary logistic regression analyses (age-adjusted), each unit increment in sleep duration (hours) in boys and girls, decreased the prehypertension and hypertension risk by 0.89 [odds ratio (OR)] [confidance interval (CI); 0.82-0.98] and 0.88 (OR) (CI; 0.81-0.97), respectively (p

European anchovy has considerable economic and ecological importance due to its high reproduction capacity and growth rate. As one of the largest source of wild marine protein, an increasing muscle mass strength has a major contribution to this growth rate during transition from subadult to adult stage. In the present study, using Illumina sequencing technology (HiSeq2000) accompanied with appropriate bioinformatics softwares; we have sequenced, assembled and annotated the transcriptome of wild subadult and adult anchovy muscles. A total of 131,081,776 high-quality reads were assembled into 125,506 contigs with an average length of 709.35 bp and N50 length of 1159 bp. Functional annotations of assembled contigs have been summarized according to 3325 GO terms, 3370 PFAM domains and 378 predicted KEGG metabolic pathways. About 11% of all contigs had at least one type of SSR motif in their sequences. According to the sequence homology analysis by BlasTN it was concluded that the assembled contigs include 16 skeletal muscle-expressed miRNAs, 14 ncRNAs and most of sarcomeric/myofibrillar genes. We hope that the sequence information regarding the muscle transcriptome of anchovy can provide some insight into the understanding of genome-wide transcriptome profile of teleost muscle tissue and give useful information in fish muscle development. (C) 2015 Elsevier B.V. All rights reserved.

RNA sequencing (RNA-Seq) is a powerful technique for the gene-expression profiling of organisms that uses the capabilities of next-generation sequencing technologies. Developing gene-expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of diseases. Most of the statistical methods proposed for the classification of gene-expression data are either based on a continuous scale (eg. microarray data) or require a normal distribution assumption. Hence, these methods cannot be directly applied to RNASeq data since they violate both data structure and distributional assumptions. However, it is possible to apply these algorithms with appropriate modifications to RNA-Seq data. One way is to develop count-based classifiers, such as Poisson linear discriminant analysis and negative binomial linear discriminant analysis. Another way is to bring the data closer to microarrays and apply microarray-based classifiers. In this study, we compared several classifiers including PLDA with and without power transformation, NBLDA, single SVM, bagging SVM (bagSVM), classification and regression trees (CART), and random forests (RF). We also examined the effect of several parameters such as overdispersion, sample size, number of genes, number of classes, differential-expression rate, and the transformation method on model performances. A comprehensive simulation study is conducted and the results are compared with the results of two miRNA and two mRNA experimental datasets. The results revealed that increasing the sample size, differential-expression rate and decreasing the dispersion parameter and number of groups lead to an increase in classification accuracy. Similar with differential-expression studies, the classification of RNA-Seq data requires careful attention when handling data overdispersion. We conclude that, as a count-based classifier, the power transformed PLDA and, as a microarray-based classifier, vst or rlog transformed RF and SVM classifiers may be a good choice for classification. An R/BIOCONDUCTOR package, MLSeq, is freely available at https://www. bioconductor. org/packages/release/bioc/ html/MLSeq. html.

Hyperhydricity is a morphophysiological disorder of plants in tissue culture characterized morphologically by the presence of translucent, thick, curled, and fragile leaves as a result of excessive water intake. Since clonal propagation is a major in vitro technique for multiplying plants vegetatively, the emergence of hyperhydricity-related symptoms causes significant economic losses to agriculture and horticulture. Although numerous efforts have been hitherto devoted to the morphological and anatomical responses of plants to hyperhydricity, the underlying molecular mechanism remains largely unknown. Here, a genome-wide transcriptome analysis was performed to identify differentially expressed genes in hyperhydric and nonhyperhydric leaves of peach [Prunus persica (L.) Batsch]. The RNA sequencing (RNA-Seq) analysis showed that the expression of >300 transcripts was altered between control and hyperhydric leaf cells. The top 30 differentially expressed transcripts (DETs) were related to the posttranscriptional regulators of organelle gene expression and photosynthesis, cellular elimination, plant cuticle development, and abiotic stress response processes. The expression of 10 DETs was also conformed by quantitative real-time polymerase chain reaction (RT-qPCR) in hyperhydric and nonhyperhydric leaves. As a complex biological process, hyperhydricity alters the expression of various transcripts including transcription factor (Myb2), RNA binding protein (pentatricopeptide, PPR), transporter protein (ABC), and Laccase3. Thus, this genome-wide transcriptome profiling study may help elucidate the molecular mechanism of hyperhydricity.

RNA-Seq is a recent and efficient technique that uses the capabilities of next-generation sequencing technology for characterizing and quantifying transcriptomes. One important task using gene-expression data is to identify a small subset of genes that can be used to build diagnostic classifiers particularly for cancer diseases. Microarray based classifiers are not directly applicable to RNA-Seq data due to its discrete nature. Overdispersion is another problem that requires careful modeling of mean and variance relationship of the RNA-Seq data. In this study, we present voomDDA classifiers: variance modeling at the observational level (voom) extensions of the nearest shrunken centroids (NSC) and the diagonal discriminant classifiers. VoomNSC is one of these classifiers and brings voom and NSC approaches together for the purpose of gene-expression based classification. For this purpose, we propose weighted statistics and put these weighted statistics into the NSC algorithm. The VoomNSC is a sparse classifier that models the mean-variance relationship using the voom method and incorporates voom’s precision weights into the NSC classifier via weighted statistics. A comprehensive simulation study was designed and four real datasets are used for performance assessment. The overall results indicate that voomNSC performs as the sparsest classifier. It also provides the most accurate results together with power-transformed Poisson linear discriminant analysis, rlog transformed support vector machines and random forests algorithms. In addition to prediction purposes, the voomNSC classifier can be used to identify the potential diagnostic biomarkers for a condition of interest. Through this work, statistical learning methods proposed for microarrays can be reused for RNA-Seq data. An interactive web application is freely available at http://www.biosoft.hacettepe.edu.tr/voomDDA/.

Alburnus istanbulensis (Thracian shemaya) is an endemic fish species to the northwestern region of Turkey. Alburnus istanbulensis has a remarkably limited geographic distribution, and no genetic data have been available on this species until now. The mitogenome (16,612 bp) had the typical mitochondrial characteristics of other Leuciscinae fishes. This mitogenome sequence can help to further resolve phylogenetic relationships among Cyprinidae.

Background RNA sequencing (RNA-Seq) is a powerful technique for transcriptome profiling of the organisms that uses the capabilities of next-generation sequencing (NGS) technologies. Recent advances in NGS let to measure the expression levels of tens to thousands of transcripts simultaneously. Using such information, developing expression-based classification algorithms is an emerging powerful method for diagnosis, disease classification and monitoring at molecular level, as well as providing potential markers of disease. Here, we present the bagging support vector machines (bagSVM), a machine learning approach and bagged ensembles of support vector machines (SVM), for classification of RNA-Seq data. The bagSVM basically uses bootstrap technique and trains each single SVM separately; next it combines the results of each SVM model using majority-voting technique. Results We demonstrate the performance of the bagSVM on simulated and real datasets. Simulated datasets are generated from negative binomial distribution under different scenarios and real datasets are obtained from publicly available resources. A deseq normalization and variance stabilizing transformation (vst) were applied to all datasets. We compared the results with several classifiers including Poisson linear discriminant analysis (PLDA), single SVM, classification and regression trees (CART), and random forests (RF). In slightly overdispersed data, all methods, except CART algorithm, performed well. Performance of PLDA seemed to be best and RF as second best for very slightly and substantially overdispersed datasets. While data become more spread, bagSVM turned out to be the best classifier. In overall results, bagSVM and PLDA had the highest accuracies. Conclusions According to our results, bagSVM algorithm after vst transformation can be a good choice of classifier for RNA-Seq datasets mostly for overdispersed ones. Thus, we recommend researchers to use bagSVM algorithm for the purpose of classification of RNA-Seq data. PLDA algorithm should be a method of choice for slight and moderately overdispersed datasets. An R/BIOCONDUCTOR package MLSeq with a vignette is freely available at http://www.bioconductor.org/packages/2.14/bioc/html/MLSeq.html Keywords: Bagging, machine learning, RNA-Seq classification, support vector machines, transcriptomics

MicroRNAs (miRNAs) are small, endogenously expressed, and nonprotein coding RNAs that regulate gene expression via post-transcriptional inhibition and cleavage. To date, several plant miRNAs have been identified via direct cloning, high-throughput sequencing, and bioinformatics analyses. The miRNAs participate in RNA-induced gene silencing complex, and specifically repress the target gene transcripts. Thus, miRNAs regulate the expression of genes playing diverse roles in plants, such as root initiation, leaf morphology, flower development, and response to environmental stimuli. A number of miRNAs have been identified and functionally characterized in eukaryotes. In this review, we discuss the functional roles of miRNAs in plant development as well as stress response to biotic and abiotic environmental factors. Additionally, we present brief information about miRNA detection and discovery techniques.

bZIP proteins are one of the largest transcriptional regulators playing crucial roles in plant development, physiological processes, and biotic/abiotic stress responses. Despite the availability of recently published draft genome sequence of Cucumis sativus, no comprehensive investigation of these family members has been presented for cucumber. We have identified 64 bZIP transcription factor-encoding genes in the cucumber genome. Based on structural features of their encoded proteins, CsbZIP genes could be classified into 6 groups. Cucumber bZIP genes were expanded mainly by segmental duplication rather than tandem duplication. Although segmental duplication rate of the CsbZIP genes was lower than that of Arabidopsis, rice and sorghum, it was observed as a common expansion mechanism. Some orthologous relationships and chromosomal rearrangements were observed according to comparative mapping analysis with other species. Genome-wide expression analysis of bZIP genes indicated that 64 CsbZIP genes were differentially expressed in at least one of the ten sampled tissues. A total of 4 CsbZIP genes displayed higher expression values in leaf, flowers and root tissues. The in silico micro-RNA (miRNA) and target transcript analyses identified that a total of 21 CsbZIP genes were targeted by 38 plant miRNAs. CsbZIP20 and CsbZIP22 are the most targeted by miR165 and miR166 family members, respectively. We also analyzed the expression of ten CsbZIP genes in the root and leaf tissues of drought-stressed cucumber using quantitative RT-PCR. All of the selected CsbZIP genes were measured as increased in root tissue at 24th h upon PEG treatment. Contrarily, the down-regulation was observed in leaf tissues of all analyzed CsbZIP genes. CsbZIP12 and CsbZIP44 genes showed gradual induction of expression in root tissues during time points. This genome-wide identification and expression profiling provides new opportunities for cloning and functional analyses, which may be used in further studies for improving stress tolerance in plants.

Boron stress is an environmental factor affecting plant development and production. Recently, microRNAs (miRNAs) have been found to be involved in several plant processes such as growth regulation and stress responses. In this study, miRNAs associated with boron stress were identified and characterized in barley. miRNA profiles were also comparatively analyzed between root and leave samples. A total of 31 known and 3 new miRNAs were identified in barley; 25 of them were found to respond to boron treatment. Several miRNAs were expressed in a tissue specific manner; for example, miR156d, miR171a, miR397, and miR444a were only detected in leaves. Additionally, a total of 934 barley transcripts were found to be specifically targeted and degraded by miRNAs. In silico analysis of miRNA target genes demonstrated that many miRNA targets are conserved transcription factors such as Squamosa promoter-binding protein, Auxin response factor (ARF), and the MYB transcription factor family. A majority of these targets were responsible for plant growth and response to environmental changes. We also propose that some of the miRNAs in barley such as miRNA408 might play critical roles against boron exposure. In conclusion, barley may use several pathways and cellular processes targeted by miRNAs to cope with boron stress.

Peach (Prunus persica L.) is one of the most important worldwide fresh fruits. Since fruit growth largely depends on adequate water supply, drought stress is considered as the most important abiotic stress limiting fleshy fruit production and quality in peach. Plant responses to drought stress are regulated both at transcriptional and post-transcriptional level. As post-transcriptional gene regulators, miRNAs (miRNAs) are small (19-25 nucleotides in length), endogenous, non-coding RNAs. Recent studies indicate that miRNAs are involved in plant responses to drought. Therefore, Illumina deep sequencing technology was used for genome-wide identification of miRNAs and their expression profile in response to drought in peach. In this study, four sRNA libraries were constructed from leaf control (LC), leaf stress (LS), root control (RC) and root stress (RS) samples. We identified a total of 531, 471, 535 and 487 known mature miRNAs in LC, LS, RC and RS libraries, respectively. The expression level of 262 (104 up-regulated, 158 down-regulated) of the 453 miRNAs changed significantly in leaf tissue, whereas 368 (221 up-regulated, 147 down-regulated) of the 465 miRNAs had expression levels that changed significantly in root tissue upon drought stress. Additionally, a total of 197, 221, 238 and 265 novel miRNA precursor candidates were identified from LC, LS, RC and RS libraries, respectively. Target transcripts (137 for LC, 133 for LS, 148 for RC and 153 for RS) generated significant Gene Ontology (GO) terms related to DNA binding and catalytic activites. Genome-wide miRNA expression analysis of peach by deep sequencing approach helped to expand our understanding of miRNA function in response to drought stress in peach and Rosaceae. A set of differentially expressed miRNAs could pave the way for developing new strategies to alleviate the adverse effects of drought stress on plant growth and development. Citation: Eldem V, Celikkol Akcay U, Ozhuner E, Bakir Y, Uranbey S, et al. (2012) Genome-Wide Identification of miRNAs Responsive to Drought in Peach (Prunus persica) by High-Throughput Deep Sequencing. PLoS ONE 7(12): e50298. doi:10.1371/journal.pone.0050298

Alburnus tarichi is the only vertebrate species that can survive in Lake Van, the largest soda lake on Earth, which is characterized by extremely high pH and salinity. The circular mitogenomes of A. tarichi was 16,602 base pairs in size, containing 13 protein-coding genes (PCGs), 2 rRNA genes, 22 tRNA genes, and 2 major non-coding control regions (D-Loop and OL). Congurence was observed between the Alburnus mtDNAs in terms of genome organization, base composition, gene arrangement, and tRNA structure. Mitogenome sequences may be useful for conservation efforts of this endangered species.