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Background In social groups, dominant individuals may socially inhibit reproduction of subordinates using aggressive interactions or, in the case of highly eusocial insects, pheromonal communication. It has been hypothesized these two modes of reproductive inhibition utilize conserved pathways. Here, we use a comparative framework to investigate the chemical and genomic underpinnings of reproductive dominance in the primitively eusocial wasp Polistes metricus. Our goals were to first characterize transcriptomic and chemical correlates of reproductive dominance and second, to test whether dominance-associated mechanisms in paper wasps overlapped with aggression or pheromone-related gene expression patterns in other species. To explore whether conserved molecular pathways relate to dominance, we compared wasp transcriptomic data to previous studies of gene expression associated with pheromonal communication and queen-worker differences in honey bees, and aggressive behavior in bees, Drosophila, and mice. Results By examining dominant and subordinate females from queen and worker castes in early and late season colonies, we found that cuticular hydrocarbon profiles and genome-wide patterns of brain gene expression were primarily associated with season/social environment rather than dominance status. In contrast, gene expression patterns in the ovaries were associated primarily with caste and ovary activation. Comparative analyses suggest genes identified as differentially expressed in wasp brains are not related to queen pheromonal communication or caste in bees, but were significantly more likely to be associated with aggression in other insects (bees, flies), and even a mammal (mice). Conclusions This study provides the first comprehensive chemical and molecular analysis of reproductive dominance in paper wasps. We found little evidence for a chemical basis for reproductive dominance in P. metricus, and our transcriptomic analyses suggest that different pathways regulate dominance in paper wasps and pheromone response in bees. Furthermore, there was a substantial impact of season/social environment on gene expression patterns, indicating the important role of external cues in shaping the molecular processes regulating behavior. Interestingly, genes associated with dominance in wasps were also associated with aggressive behavior in bees, solitary insects and mammals. Thus, genes involved in social regulation of reproduction in Polistes may have conserved functions associated with aggression in insects and other taxa. [ABSTRACT FROM AUTHOR]

The article presents papers from the Asia Pacific Bioinformatics Network (APBioNet) Eighth International Conference on Bioinformatics (InCoB2009) in Singapore from September 7-11, 2009 including "Genome-Wide Analysis of Alternative Splicing in Cow: Implications in Bovine as a Model for Human Diseases," "MapNext: A Software Tool for Spliced and Unspliced Alignments and SNP Detection of Short Sequence Reads," and "Measuring Similarity Between Gene Expression Profiles: A Bayesian Approach."

While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose challenges for most existing feature selection methods. In this paper, we present a feature selection method based on neural network (scFSNN) to solve classification problem for the scRNA-seq data. scFSNN is an embedded method that can automatically select features (genes) during model training, control the false discovery rate of selected features and adaptively determine the number of features to be eliminated. Extensive simulation and real data studies demonstrate its excellent feature selection ability and predictive performance. [ABSTRACT FROM AUTHOR]

Background: Northern blotting is still used as a gold standard for validation of the data obtained from high-throughput whole transcriptome-based methods. However, its disadvantages of lower sensitivity, labor-intensive operation, and higher quality of RNA required limit its utilization in a routine molecular biology laboratory to monitor gene expression at RNA level. Therefore, it is necessary to optimize the traditional Northern protocol to make the technique more applicable for standard use. Results: In this paper, we report modifications and tips used to improve the traditional Northern protocol for the detection of mRNAs in total RNA. To maximize the retention of specifically bound radiolabeled probes on the blot, posthybridization washes were performed under only with moderate-stringency until the level of radioactivity retained on the filter decreased to 20~50 counts per second, rather than normally under high and low stringency sequentially for scheduled time or under only high stringent condition. Successful detection of the low-expression gene using heterologous DNA probes in 20 µg of total RNA after a two-day exposure suggested an improvement in detection sensitivity. Quantitatively controlled posthybridization washes combined with an ethidium bromide-prestaining RNA procedure to directly visualize prestained RNA bands at any time during electrophoresis or immediately after electrophoresis, which made the progress of the Northern procedure to be monitored and evaluated step by step, thereby making the experiment reliable and controllable. We also report tips used in the modified Northern protocol, including the moderate concentration of formaldehyde in the gel, the accessory capillary setup, and the staining jar placed into an enamel square tray with a lid used for hybridization. Using our modified Northern protocol, eight rounds of rehybridization could be performed on a single blot. The modification made and tips used ensured the efficient proceeding of the experiment and the resulting good performance, but without using special reagents or equipment. Conclusions: The modified Northern protocol improved detection sensitivity and made the experiment easy, less expensive, reliable, and controllable, and can be employed in a routine molecular biology laboratory to detect low-expressed mRNAs with heterologous DNA probes in total RNA. [ABSTRACT FROM AUTHOR]

This paper aims to explore the role of circRNA expression profiles and circRNA-associated ceRNA networks in the regulation of myogenesis in the longissimus dorsi of cattle breeds surviving under subtropical conditions in southern China by RNA sequencing and bioinformatics analysis. It also aims to provide comprehensive understanding of the differences in muscle fibers in subtropical cattle breeds and to expand the knowledge of the molecular networks that regulate myogenesis. With regard to meat quality indicators, results showed that the longissimus dorsi of LQC had lower pH (P < 0.0001), lower redness (P < 0.01), lower shear force (P < 0.05), and higher brightness (P < 0.05) than the longissimus dorsi of LFC. With regard to muscle fiber characteristics, the longissimus dorsi of LQC had a smaller diameter (P < 0.0001) and higher density of muscle fibers (P < 0.05). The analysis results show that the function of many circRNA-targeted mRNAs was related to myogenesis and metabolic regulation. Furthermore, in the analysis of the function of circRNA source genes, we hypothesized that btacirc_00497 and btacirc_034497 may regulate the function and type of myofibrils by affecting the expression of MYH6, MYH7, and NEB through competitive linear splicing. [ABSTRACT FROM AUTHOR]

Background: Paeonia ludlowii (Stern & G. Taylor D.Y. Hong) belongs to the peony group of the genus Paeonia in the Paeoniaceae family and is now classified as a "critically endangered species" in China. Reproduction is important for this species, and its low fruiting rate has become a critical factor limiting both the expansion of its wild population and its domestic cultivation. Results: In this study, we investigated possible causes of the low fruiting rate and ovule abortion in Paeonia ludlowii. We clarified the characteristics of ovule abortion and the specific time of abortion in Paeonia ludlowii, and used transcriptome sequencing to investigate the mechanism of abortion of ovules in Paeonia ludlowii. Conclusions: In this paper, the ovule abortion characteristics of Paeonia ludlowii were systematically studied for the first time and provide a theoretical basis for the optimal breeding and future cultivation of Paeonia ludlowii. [ABSTRACT FROM AUTHOR]

This cannot be reconciled with the betalain biosynthetic pathway, and also not supported by the metabolite profile provided in Table S9 in [[1]], i.e. RR pulp has more than 800 times total betacyanins than GW pulp, and RR peel has more than 5 times than GW peel. 3 [[1]] have lower or similar expressions in YW pulp and GW pulp compared to YW peel and GW peel. For example, RR peel has about ten times more total anthocyanins (all the differentially expressed anthocyanins taken together) than RR pulp (Table S9 in [[1]]), however, the I ANS i expression in both of them was not significantly different (Fig. Keywords: Anthocyanins; Betalains; Gene expression; Amaranthin; Gomphrenin-I EN Anthocyanins Betalains Gene expression Amaranthin Gomphrenin-I 1 5 5 11/10/22 20221109 NES 221109 Background The premise of the paper authored by Zhou et al. [[1]] published in BMC Genomics is that, in pitayas I Hylocereus undatus i (red peel-red pulp or RR; green peel-white pulp or GW) and I H. megalanthus i (yellow peel-white pulp or YW, also called I Selenicereus megalanthus i , http://legacy.tropicos.org/Name/50251405?tab=acceptednames, accessed on 14/11/2020) [[2]]), anthocyanins and betalains co-accumulate, and hence both contribute to peel and pulp colour formation. [Extracted from the article]

Background: Social insects exhibit striking phenotypic plasticity in the form of distinct reproductive (queen) and non-reproductive (worker) castes, which are typically driven by differences in the environment during early development. Nutritional environment and nourishment during development has been shown to be broadly associated with caste determination across social insect taxa such as bees, wasps, and termites. In primitively social insects such as Polistes paper wasps, caste remains flexible throughout adulthood, but there is evidence that nourishment inequalities can bias caste development with workers receiving limited nourishment compared to queens. Dominance and vibrational signaling are behaviors that have also been linked to caste differences in paper wasps, suggesting that a combination of nourishment and social factors may drive caste determination. To better understand the molecular basis of nutritional effects on caste determination, we used RNA-sequencing to investigate the gene expression changes in response to proteinaceous nourishment deprivation in Polistes metricus larvae. Results: We identified 285 nourishment-responsive transcripts, many of which are related to lipid metabolism and oxidation-reduction activity. Via comparisons to previously identified caste-related genes, we found that nourishment restriction only partially biased wasp gene expression patterns toward worker caste-like traits, which supports the notion that nourishment, in conjunction with social environment, is a determinant of developmental caste bias. In addition, we conducted cross-species comparisons of nourishment-responsive genes, and uncovered largely lineage-specific gene expression changes, suggesting few shared nourishment-responsive genes across taxa. Conclusion: Overall, the results from this study highlight the complex and multifactorial nature of environmental effects on the gene expression patterns underlying plastic phenotypes. [ABSTRACT FROM AUTHOR]

Background: Various analytical methods exist that first quantify gene expression and then analyze differentially expressed genes from Affymetrix GeneChip® gene expression analysis array data. These methods differ in the choice of probe measure (quantification of probe hybridization), summarizing multiple probe intensities into a gene expression value, and analysis of differential gene expression. Research papers that describe these methods focus on performance, and how their approaches differ from others. To better understand the common features and differences between various methods, and to evaluate their impact on the results of gene expression analysis, we describe a class of models, referred to as generalized probe models (GPMs), which encompass various currently available methods. Results: Using an empirical dataset, we compared different formulations of GPMs, and GPMs with three other commonly used methods, i.e. MAS 5.0, dChip, and RMA. The comparison shows that, on a genome-wide scale , different methods yield similar results if the same probe measures are chosen. Conclusion: In this paper we present a general framework, i.e. GPMs, which encompasses various methods. GPMs permit the use of a wide range of probe measures and facilitate appropriate comparison between commonly used methods. We demonstrate that the dissimilar results stem primarily from different choice of probe measures, rather than other factors. [ABSTRACT FROM AUTHOR]

Background: With single-cell RNA sequencing (scRNA-seq) methods, gene expression patterns at the single-cell resolution can be revealed. But as impacted by current technical defects, dropout events in scRNA-seq lead to missing data and noise in the gene-cell expression matrix and adversely affect downstream analyses. Accordingly, the true gene expression level should be recovered before the downstream analysis is carried out. Results: In this paper, a novel low-rank tensor completion-based method, termed as scLRTC, is proposed to impute the dropout entries of a given scRNA-seq expression. It initially exploits the similarity of single cells to build a third-order low-rank tensor and employs the tensor decomposition to denoise the data. Subsequently, it reconstructs the cell expression by adopting the low-rank tensor completion algorithm, which can restore the gene-to-gene and cell-to-cell correlations. ScLRTC is compared with other state-of-the-art methods on simulated datasets and real scRNA-seq datasets with different data sizes. Specific to simulated datasets, scLRTC outperforms other methods in imputing the dropouts closest to the original expression values, which is assessed by both the sum of squared error (SSE) and Pearson correlation coefficient (PCC). In terms of real datasets, scLRTC achieves the most accurate cell classification results in spite of the choice of different clustering methods (e.g., SC3 or t-SNE followed by K-means), which is evaluated by using adjusted rand index (ARI) and normalized mutual information (NMI). Lastly, scLRTC is demonstrated to be also effective in cell visualization and in inferring cell lineage trajectories. Conclusions: a novel low-rank tensor completion-based method scLRTC gave imputation results better than the state-of-the-art tools. Source code of scLRTC can be accessed at https://github.com/jianghuaijie/scLRTC. [ABSTRACT FROM AUTHOR]

Background: Integrating transcriptomic experiments within drug development is increasingly advocated for the early detection of toxicity. This is partly to reduce costs related to drug failures in the late, and expensive phases of clinical trials. Such an approach has proven useful both in the study of toxicology and carcinogenicity. However, general lack of translation of in vitro findings to in vivo systems remains one of the bottle necks in drug development. This paper proposes a method for identifying disconnected genes between in vitro and in vivo toxicogenomic rat experiments. The analytical framework is based on the joint modeling of dose-dependent in vitro and in vivo data using a fractional polynomial framework and biclustering algorithm. Results: Most disconnected genes identified belonged to known pathways, such as drug metabolism and oxidative stress due to reactive metabolites, bilirubin increase, glutathion depletion and phospholipidosis. We also identified compounds that were likely to induce disconnect in gene expression between in vitro and in vivo toxicogenomic rat experiments. These compounds include: sulindac and diclofenac (both linked to liver damage), naphtyl isothiocyanate (linked to hepatoxocity), indomethacin and naproxen (linked to gastrointestinal problem and damage of intestines). Conclusion: The results confirmed that there are important discrepancies between in vitro and in vivo toxicogenomic experiments. However, the contribution of this paper is to provide a tool to identify genes that are disconnected between the two systems. Pathway analysis of disconnected genes may improve our understanding of uncertainties in the mechanism of actions of drug candidates in humans, especially concerning the early detection of toxicity. [ABSTRACT FROM AUTHOR]

Background: Gmelina arborea Roxb is a fast-growing tree species of commercial importance for tropical countries due to multiple industrial uses of its wood. Wood is primarily composed of thick secondary cell walls of xylem cells which imparts the strength to the wood. Identification of the genes involved in the secondary cell wall biosynthesis as well as their cognate regulators is crucial to understand how the production of wood occurs and serves as a starting point for developing breeding strategies to produce varieties with improved wood quality, better paper pulping or new potential uses such as biofuel production. In order to gain knowledge on the molecular mechanisms and gene regulation related with wood development in white teak, a de novo sequencing and transcriptome assembly approach was used employing secondary cell wall synthesizing cells from young white teak trees. Results: For generation of transcriptome, RNA-seq reads were assembled into 110,992 transcripts and 49,364 genes were functionally annotated using plant databases; 5071 GO terms and 25,460 SSR markers were identified within xylem transcripts and 10,256 unigenes were assigned to KEGG database in 130 pathways. Among transcription factor families, C2H2, C3H, bLHLH and MYB were the most represented in xylem. Differential gene expression analysis using leaves as a reference was carried out and a total of 20,954 differentially expressed genes were identified including monolignol biosynthetic pathway genes. The differential expression of selected genes (4CL, COMT, CCoAOMT, CCR and NST1) was validated using qPCR. Conclusions: We report the very first de novo transcriptome of xylem-related genes in this tropical timber species of commercial importance and constitutes a valuable extension of the publicly available transcriptomic resource aimed at fostering both basic and breeding studies. [ABSTRACT FROM AUTHOR]

Background: In post-partum dairy cows, the energy needs to satisfy high milk production induces a status of more or less pronounced Negative Energy Balance (NEB). NEB associated with fat mobilization impairs reproductive function. In a companion paper, we described constitutive gene expression in the three main endometrial cell types (stromal, glandular and luminal epithelial cells) isolated by laser capture micro-dissection (LCM) showing the specificities of their transcriptomic profiles. This study investigates the specific impact of NEB on gene expression in these cells around 80 days after parturition at day 15 of the oestrus cycle and describes their specific response to NEB. Results: Following the description of their constitutive expression, the transcriptome profiles obtained by RNA sequencing of the three cells types revealed that differences related to the severity of NEB altered mainly specific patterns of expression related to individual cell types. Number of differentially expressed genes between severe NEB (SNEB) and mild NEB (MNEB) cows was higher in ST than in LE and GE, respectively. SNEB was associated with differential expression of genes coding for proteins involved in metabolic processes and embryo-maternal interactions in ST. Under-expression of genes encoding proteins with functions related to cell structure was found in GE whereas genes encoding proteins participating in pro-inflammatory pathways were over-expressed. Genes associated to adaptive immunity were under-expressed in LE. Conclusion: The severity of NEB after calving is associated with changes in gene expression around 80 days after parturition corresponding to the time of breeding. Specific alterations in GEs are associated with activation of pro-inflammatory mechanisms. Concomitantly, changes in the expression of genes encoding proteins involved in cell interactions and maternal recognition of pregnancy takes place in ST. The combination of these effects possibly altering the uterine environment and embryo maternal interactions may negatively influence the establishment of pregnancy. [ABSTRACT FROM AUTHOR]

Background: The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules. Results: This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification. Conclusions: The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better. [ABSTRACT FROM AUTHOR]

Background: RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope. STRING database analysis of the identified genes characterize gene affiliations involved in physiological regulatory networks that contribute to biological variability. Individuals with divergent gene groupings within network pathways can thereby be identified and judiciously evaluated prior to standard differential analysis. Results: A three-step process is presented for evaluating biological variability within a group in RNA sequencing data in which gene counts were: (1) scaled to minimize heteroscedasticity; (2) rank-ordered to detect potentially divergent "trendlines" for every gene in the data set; and (3) tested with the STRING database to identify statistically significant pathway associations among the genes displaying marked trendline variability and dispersion. This approach was used to identify the "trendline" profile of every gene in three test data sets. Control data from an in-house data set and two archived samples revealed that 65–70% of the sequenced genes displayed trendlines with minimal variation and dispersion across the sample group after rank-ordering the samples; this is referred to as a linear trendline. Smaller subsets of genes within the three data sets displayed markedly skewed trendlines, wide dispersion and variability. STRING database analysis of these genes identified interferon-mediated response networks in 11–20% of the individuals sampled at the time of blood collection. For example, in the three control data sets, 14 to 26 genes in the defense response to virus pathway were identified in 7 individuals at false discovery rates ≤1.92 E-15. Conclusions: This analysis provides a rationale for identifying and characterizing notable gene expression variability within a study group. The identification of highly variable genes and their network associations within specific individuals empowers more judicious inspection of the sample group prior to differential gene expression analysis. [ABSTRACT FROM AUTHOR]

Background: Researchers discover LncRNA–miRNA regulatory paradigms modulate gene expression patterns and drive major cellular processes. Identification of lncRNA-miRNA interactions (LMIs) is critical to reveal the mechanism of biological processes and complicated diseases. Because conventional wet experiments are time-consuming, labor-intensive and costly, a few computational methods have been proposed to expedite the identification of lncRNA-miRNA interactions. However, little attention has been paid to fully exploit the structural and topological information of the lncRNA-miRNA interaction network. Results: In this paper, we propose novel lncRNA-miRNA prediction methods by using graph embedding and ensemble learning. First, we calculate lncRNA-lncRNA sequence similarity and miRNA-miRNA sequence similarity, and then we combine them with the known lncRNA-miRNA interactions to construct a heterogeneous network. Second, we adopt several graph embedding methods to learn embedded representations of lncRNAs and miRNAs from the heterogeneous network, and construct the ensemble models using two ensemble strategies. For the former, we consider individual graph embedding based models as base predictors and integrate their predictions, and develop a method, named GEEL-PI. For the latter, we construct a deep attention neural network (DANN) to integrate various graph embeddings, and present an ensemble method, named GEEL-FI. The experimental results demonstrate both GEEL-PI and GEEL-FI outperform other state-of-the-art methods. The effectiveness of two ensemble strategies is validated by further experiments. Moreover, the case studies show that GEEL-PI and GEEL-FI can find novel lncRNA-miRNA associations. Conclusion: The study reveals that graph embedding and ensemble learning based method is efficient for integrating heterogeneous information derived from lncRNA-miRNA interaction network and can achieve better performance on LMI prediction task. In conclusion, GEEL-PI and GEEL-FI are promising for lncRNA-miRNA interaction prediction. [ABSTRACT FROM AUTHOR]

Background: Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. Results: In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. Conclusion: The effectiveness of proposed algorithm is validated on both small and large protein interaction networks. [ABSTRACT FROM AUTHOR]

Background: Microarray experiments often measure expressions of genes taken from sample tissues in the presence of external perturbations such as medication, radiation, or disease. The external perturbation can change the expressions of some genes directly or indirectly through gene interaction network. In this paper, we focus on an important class of such microarray experiments that inherently have two groups of tissue samples. When such different groups exist, the changes in expressions for some of the genes after the perturbation can be different between the two groups. It is not only important to identify the genes that respond differently across the two groups, but also to mine the reason behind this differential response. In this paper, we aim to identify the cause of this differential behavior of genes, whether because of the perturbation or due to interactions with other genes. Results: We propose a new probabilistic Bayesian method CMRF based on Markov Random Field to identify such genes. CMRF leverages the information about gene interactions as the prior of the model. We compare the accuracy of CMRF with SSEM and Student's t test and our old method SMRF on semi-synthetic dataset generated from microarray data. CMRF obtains high accuracy and outperforms all the other three methods. We also conduct a statistical significance test using a parametric noise based experiment to evaluate the accuracy of our method. In this experiment, CMRF generates significant regions of confidence for various parameter settings. Conclusions: In this paper, we solved the problem of finding primarily differentially regulated genes in the presence of external perturbations when the data is sampled from two groups. The probabilistic Bayesian method CMRF based on Markov Random Field incorporates dependency structure of the gene networks as the prior to the model. Experimental results on synthetic and real datasets demonstrated the superiority of CMRF compared to other simple techniques. [ABSTRACT FROM AUTHOR]

Background: Analysing gene expression data from microarray technologies is a very important task in biology and medicine, and particularly in cancer diagnosis. Different from most other popular methods in high dimensional biomedical data analysis, such as microarray gene expression or proteomics mass spectroscopy data analysis, fuzzy rule-based models can not only provide good classification results, but also easily be explained and interpreted in human understandable terms, by using fuzzy rules. However, the advantages offered by fuzzy-based techniques in microarray data analysis have not yet been fully explored in the literature. Although some recently developed fuzzy-based modeling approaches can provide satisfactory classification results, the rule bases generated by most of the reported fuzzy models for gene expression data are still too large to be easily comprehensible. Results: In this paper, we develop some Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) methods for analysing high dimensional bio-medical data sets, such as microarray gene expression data and proteomics mass spectroscopy data. We mainly focus on evaluating our proposed models on microarray gene expression cancer data sets, i.e., the lung cancer data set and the colon cancer data set, but we extend our investigations to other type of cancer data set, such as the ovarian cancer data set. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, can be successfully obtained for challenging microarray gene expression datasets. Conclusions: We believe that fuzzy-based techniques, and in particular the methods proposed in this paper, can be very useful tools in dealing with high dimensional cancer data. We also argue that the potential of applying fuzzy-based techniques to microarray data analysis need to be further explored. [ABSTRACT FROM AUTHOR]

Background: High-throughput microarray experiments now permit researchers to screen thousands of genes simultaneously and determine the different expression levels of genes in normal or cancerous tissues. In this paper, we address the challenge of selecting a relevant and manageable subset of genes from a large microarray dataset. Currently, most gene selection methods focus on identifying a set of genes that can further improve classification accuracy. Few or none of these small sets of genes, however, are biologically relevant (i.e. supported by medical evidence). To deal with this critical issue, we propose two novel methods that can identify biologically relevant genes concerning cancers. Results: In this paper, we propose two novel techniques, entitled random forest gene selection (RFGS) and support vector sampling technique (SVST). Compared with results from six other methods developed in this paper, we demonstrate experimentally that RFGS and SVST can identify more biologically relevant genes in patients with leukemia or prostate cancer. Among the top 25 genes selected using SVST method, 15 genes were biologically relevant genes in patients with leukemia and 13 genes were biologically relevant genes in patients with prostate cancer. Meanwhile, the RFGS method, while less effective than SVST, still identified an average of 9 biologically relevant genes in both leukemia and prostate cancers. In contrast to traditional statistical methods, which only identify less than 8 genes in patients with leukemia and less than 8 genes in patients with prostate cancer, our methods yield significantly better results. Conclusions: Our proposed SVST and RFGS methods are novel approaches that can identify a greater number of biologically relevant genes. These methods have been successfully applied to both leukemia and prostate cancers. Research in the fields of biology and medicine should benefit from the identification of biologically relevant genes by confirming recent discoveries in cancer research or suggesting new avenues for exploration. [ABSTRACT FROM AUTHOR]

Background: It has been a long-standing biological challenge to understand the molecular regulatory mechanisms behind mammalian ageing. Harnessing the availability of many ageing microarray datasets, a number of studies have shown that it is possible to identify genes that have age-dependent differential expression (DE) or differential variability (DV) patterns. The majority of the studies identify "interesting" genes using a linear regression approach, which is known to perform poorly in the presence of outliers or if the underlying age-dependent pattern is non-linear. Clearly a more robust and flexible approach is needed to identify genes with various age-dependent gene expression patterns. Results: Here we present a novel model selection approach to discover genes with linear or nonlinear age-dependent gene expression patterns from microarray data. To identify DE genes, our method fits three quantile regression models (constant, linear and piecewise linear models) to the expression profile of each gene, and selects the least complex model that best fits the available data. Similarly, DV genes are identified by fitting and comparing two quantile regression models (non-DV and the DV models) to the expression profile of each gene. We show that our approach is much more robust than the standard linear regression approach in discovering age-dependent patterns. We also applied our approach to analyze two human brain ageing datasets and found many biologically interesting gene expression patterns, including some very interesting DV patterns, that have been overlooked in the original studies. Furthermore, we propose that our model selection approach can be extended to discover DE and DV genes from microarray datasets with discrete class labels, by considering different quantile regression models. Conclusion: In this paper, we present a novel application of quantile regression models to identify genes that have interesting linear or non-linear age-dependent expression patterns. One important contribution of this paper is to introduce a model selection approach to DE and DV gene identification, which is most commonly tackled by null hypothesis testing approaches. We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks. [ABSTRACT FROM AUTHOR]

Background: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. Results: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 404 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis. Conclusion: In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification. [ABSTRACT FROM AUTHOR]

Background: Gene expression data extracted from microarray experiments have been used to study the difference between mRNA abundance of genes under different conditions. In one of such experiments, thousands of genes are measured simultaneously, which provides a high-dimensional feature space for discriminating between different sample classes. However, most of these dimensions are not informative about the between-class difference, and add noises to the discriminant analysis. Results: In this paper we propose and study feature selection methods that evaluate the "informativeness" of a set of genes. Two measures of information based on multigene expression profiles are considered for a backward information-driven screening approach for selecting important gene features. By considering multigene expression profiles, we are able to utilize interaction information among these genes. Using a breast cancer data, we illustrate our methods and compare them to the performance of existing methods. Conclusion: We illustrate in this paper that methods considering gene-gene interactions have better classification power in gene expression analysis. In our results, we identify important genes with relative large p-values from single gene tests. This indicates that these are genes with weak marginal information but strong interaction information, which will be overlooked by strategies that only examine individual genes. [ABSTRACT FROM AUTHOR]

As gene expression profile data from DNA microarrays accumulate rapidly, there is a natural need to compare data across labs and platforms. Comparisons of microarray data can be quite challenging due to data complexity and variability. Different labs may adopt different technology platforms. One may ask about the degree of agreement we can expect from different labs and different platforms. To address this question, we conducted a study of inter-lab and inter-platform agreement of microarray data across three platforms and three labs. The statistical measures of consistency and agreement used in this paper are the Pearson correlation, intraclass correlation, kappa coefficients, and a measure of intra-transcript correlation. The three platforms used in the present paper were Affymetrix GeneChip, custom cDNA arrays, and custom oligo arrays. Using the within-platform variability as a benchmark, we found that these technology platforms exhibited an acceptable level of agreement, but the agreement between two technologies within the same lab was greater than that between two labs using the same technology. The consistency of replicates in each experiment varies from lab to lab. When there is high consistency among replicates, different technologies show good agreement within and across labs using the same RNA samples. On the other hand, the lab effect, especially when confounded with the RNA sample effect, plays a bigger role than the platform effect on data agreement. [ABSTRACT FROM AUTHOR]

Background: High-density DNA microarrays require automatic feature extraction methodologies and softwares. These can be a potential source of non-reproducibility of gene expression measurements. Variation in feature location or in signal integration methodology may be a significant contribution to the observed variance in gene expression levels. Results: We explore sources of variability in feature extraction from DNA microarrays on Nylon membrane with radioactive detection. We introduce a mathematical model of the signal emission and derive methods for correcting biases such as overshining, saturation or variation in probe amount. We also provide a quality metric which can be used qualitatively to flag weak or untrusted signals or quantitatively to modulate the weight of each experiment or gene in higher level analyses (clustering or discriminant analysis). Conclusions: Our novel feature extraction methodology, based on a mathematical model of the radioactive emission, reduces variability due to saturation, neighbourhood effects and variable probe amount. Furthermore, we provide a fully automatic feature extraction software, BZScan, which implements the algorithms described in this paper. [ABSTRACT FROM AUTHOR]

Background: Grapes are highly valued for their nutritional and economic benefits, and have been widely studied for their biological attributes such as fruit development, quality formation, and stress resistance. One significant threat to grape quality is gray mold, caused by Botrytis cinerea, which can infect the flowers, fruits, leaves, and stems. The quantitative real-time PCR (qRT-PCR), known for its high sensitivity and quantitative accuracy, is an essential tool for analyzing gene expression related to the pathogenesis of gray mold, thereby providing deeper insights into the disease. Result: In this study, we aim to identify stable internal reference genes crucial for accurate gene expression analysis via qRT-PCR. Utilizing transcriptome data from grapes under various disease stresses, we identified twelve candidate reference genes with consistently high expression levels. The stability of these genes was assessed through delta-CT, geNorm, NormFinder, BestKeeper, and RefFinder analyses after establishing the cycling thresholds (Ct) in different grape varieties treated with Botrytis cinerea. Conclusions: Our findings reveal that VIT-17s0000g02750 and VIT-06s0004g04280 exhibit stable expression and are suitable as new reference genes. This foundational work supports further research into the molecular mechanisms of grape biological processes. [ABSTRACT FROM AUTHOR]

Background: Calmodulin-binding transcription activator (CAMTA) is comprised of a group of transcription factors and plays an important role in the Ca2+ signaling pathway, mediating various molecular responses via interactions with other transcription factors and binding to the promoter region of specific genes. Mung beans (Vigna radiata) are one of the most commonly consumed commodities in Asia. To date, CAMTA proteins have not been characterized in this important crop plant. Results: Eight paralogous VrCAMTA genes were identified and found to be distributed on five of the 11 chromosomes. The proteins possessed CG-1 DNA-binding domains with bipartite NLS signals, ankyrin domains, CaM-binding IQ motifs, and CaM-binding domain (CaMBD). The 2 kb upstream regions of VrCAMTA genes contained sequence motifs of abscisic acid-responsive elements (ABRE) and ethylene-responsive elements (ERE), and binding sites for transcription factors of the bZIP and bHLH domains. Analysis of RNA-seq data from a public repository revealed ubiquitous expression of the VrCAMTA genes, as VrCAMTA1 was expressed at the highest level in seedling leaves, whereas VrCAMTA8 was expressed at the lowest level, which agreed with the RT-qPCR analysis performed on the first true leaves. On day four after leaf emergence, all VrCAMTA genes were upregulated, with VrCAMTA1 exhibiting the highest degree of upregulation. In darkness on day 4, upregulation was not observed in most VrCAMTA genes, except VrCAMTA7, for which a low degree of upregulation was found, whereas no difference was found in VrCAMTA8 expression between light and dark conditions. Treatment with calcium ionophores enhanced VrCAMTA expression under light and/or dark conditions at different times after leaf emergence, suggesting that calcium signaling is involved in the light-induced upregulation of VrCAMTA gene expression. Conclusions: The expression dependence of nearly all VrCAMTA genes on light and calcium signaling suggests their possible differential but likely complementary roles during the early stages of mung bean growth and development. [ABSTRACT FROM AUTHOR]

Background: African cattle represent a unique resource of genetic diversity in response to adaptation to numerous environmental challenges. Characterising the genetic landscape of indigenous African cattle and identifying genomic regions and genes of functional importance can contribute to targeted breeding and tackle the loss of genetic diversity. However, pinpointing the adaptive variant and determining underlying functional mechanisms of adaptation remains challenging. Results: In this study, we use selection signatures from whole-genome sequence data of eight indigenous African cattle breeds in combination with gene expression and quantitative trait loci (QTL) databases to characterise genomic targets of artificial selection and environmental adaptation and to identify the underlying functional candidate genes. In general, the trait-association analyses of selection signatures suggest the innate and adaptive immune system and production traits as important selection targets. For example, a large genomic region, with selection signatures identified for all breeds except N'Dama, was located on BTA27, including multiple defensin DEFB coding-genes. Out of 22 analysed tissues, genes under putative selection were significantly enriched for those overexpressed in adipose tissue, blood, lung, testis and uterus. Our results further suggest that cis-eQTL are themselves selection targets; for most tissues, we found a positive correlation between allele frequency differences and cis-eQTL effect size, suggesting that positive selection acts directly on regulatory variants. Conclusions: By combining selection signatures with information on gene expression and QTL, we were able to reveal compelling candidate selection targets that did not stand out from selection signature results alone (e.g. GIMAP8 for tick resistance and NDUFS3 for heat adaptation). Insights from this study will help to inform breeding and maintain diversity of locally adapted, and hence important, breeds. [ABSTRACT FROM AUTHOR]

Background: Cold is a significant limiting factor in productivity, particularly in northwestern and eastern China. Calcium-Dependent Protein Kinases (CDPKs), a primary calcium signaling sensor in plants, play an important role in their response to cold. Snow lotus (Sasussured involucrata Kar L) is a plant that thrives in harsh climates and grows in northwest China. However, there were no reports on the transcriptome of OE-SikCDPK1 transgenic tobacco in response to cold. Results: When exposed to cold stress, OE-SikCDPK1 plants displayed a cold-tolerant phenotype compared to non-transgenic tobacco. Under cold conditions, relative water content reduced, relative conductivity increased, malondialdehyde levels rose, and cold-responsive gene expression increased. The OE-SikCDPK1 gene and non-transgenic tobacco were employed for research purposes. The transcriptome of leaves was sequenced using the HISAT2 sequencing platform, and the data were used to examine gene function annotation and differentially expressed genes (DEGs). 53,022 DEGs in tobacco leaves under cold treatment were obtained. The GO enrichment results revealed that it was enriched for biological-process, defense response and other processes under cold stress. The KEGG pathway enrichment analysis revealed that the metabolic pathways of significant enrichment of DEGs under cold stress mainly involved MAPK signaling pathway transduction. The transcription factor identification results showed that the transcription factors with the largest number of differential expressions under cold treatment were mainly from WRKY, AP2, MYB, bHLH, NAC and other transcription factor families. Conclusion: The cold tolerance mechanism of snow lotus SikCDPK1 was comprehensively analyzed at the transcriptional level for the first time using RNA-seq technology. This study demonstrates that SikCDPK1 can respond to cold by participating in the MAPK signaling pathway and regulating the expression levels of transcription factors, including WRKY, AP2, MYB, bHLH, and NAC. These results offer valuable insights for further exploration of the cold tolerance mechanism associated with SikCDPK1. [ABSTRACT FROM AUTHOR]

Background: Transcription factors (TFs) bind to different parts of the genome in different types of cells, but it is usually assumed that the inherent DNA-binding preferences of a TF are invariant to cell type. Yet, there are several known examples of TFs that switch their DNA-binding preferences in different cell types, and yet more examples of other mechanisms, such as steric hindrance or cooperative binding, that may result in a "DNA signature" of differential binding. Results: To survey this phenomenon systematically, we developed a deep learning method we call SigTFB (Signatures of TF Binding) to detect and quantify cell-type specificity in a TF's known genomic binding sites. We used ENCODE ChIP-seq data to conduct a wide scale investigation of 169 distinct TFs in up to 14 distinct cell types. SigTFB detected statistically significant DNA binding signatures in approximately two-thirds of TFs, far more than might have been expected from the relatively sparse evidence in prior literature. We found that the presence or absence of a cell-type specific DNA binding signature is distinct from, and indeed largely uncorrelated to, the degree of overlap between ChIP-seq peaks in different cell types, and tended to arise by two mechanisms: using established motifs in different frequencies, and by selective inclusion of motifs for distint TFs. Conclusions: While recent results have highlighted cell state features such as chromatin accessibility and gene expression in predicting TF binding, our results emphasize that, for some TFs, the DNA sequences of the binding sites contain substantial cell-type specific motifs. [ABSTRACT FROM AUTHOR]

Background: Two strains of the endoparasitoid Cotesia typhae (Hymenoptera: Braconidae) present a differential parasitism success on the host, Sesamia nonagrioides (Lepidoptera: Noctuidae). One is virulent on both permissive and resistant host populations, and the other only on the permissive host. This interaction provides a very interesting frame for studying virulence factors. Here, we used a combination of comparative transcriptomic and proteomic analyses to unravel the molecular basis underlying virulence differences between the strains. Results: First, we report that virulence genes are mostly expressed during the pupal stage 24 h before adult emergence of the parasitoid. Especially, 55 proviral genes are up-regulated at this stage, while their expression is only expected in the host. Parasitoid gene expression in the host increases from 24 to 96 h post-parasitism, revealing the expression of 54 proviral genes at early parasitism stage and the active participation of teratocytes to the parasitism success at the late stage. Secondly, comparison between strains reveals differences in venom composition, with 12 proteins showing differential abundance. Proviral expression in the host displays a strong temporal variability, along with differential patterns between strains. Notably, a subset of proviral genes including protein-tyrosine phosphatases is specifically over-expressed in the resistant host parasitized by the less virulent strain, 24 h after parasitism. This result particularly hints at host modulation of proviral expression. Combining proteomic and transcriptomic data at various stages, we identified 8 candidate genes to support the difference in reproductive success of the two strains, one proviral and 7 venom genes, one of them being also produced within the host by the teratocytes. Conclusions: This study sheds light on the temporal expression of virulence factors of Cotesia typhae, both in the host and in the parasitoid. It also identifies potential molecular candidates driving differences in parasitism success between two strains. Together, those findings provide a path for further exploration of virulence mechanisms in parasitoid wasps, and offer insights into host-parasitoid coevolution. [ABSTRACT FROM AUTHOR]

Background: Plant-specific TIFY proteins play crucial roles in regulating plant growth, development, and various stress responses. However, there is no information available about this family in Artemisia argyi, a well-known traditional medicinal plant with great economic value. Results: A total of 34 AaTIFY genes were identified, including 4 TIFY, 22 JAZ, 5 PPD, and 3 ZML genes. Structural, motif scanning, and phylogenetic relationships analysis of these genes revealed that members within the same group or subgroup exhibit similar exon-intron structures and conserved motif compositions. The TIFY genes were unevenly distributed across the 15 chromosomes. Tandem duplication events and segmental duplication events have been identified in the TIFY family in A. argyi. These events have played a crucial role in the gene multiplication and compression of different subfamilies within the TIFY family. Promoter analysis revealed that most AaTIFY genes contain multiple cis-elements associated with stress response, phytohormone signal transduction, and plant growth and development. Expression analysis of roots and leaves using RNA-seq data revealed that certain AaTIFY genes showed tissue-specific expression patterns, and some AaTIFY genes, such as AaTIFY19/29, were found to be involved in regulating salt and saline-alkali stresses. In addition, RT-qPCR analysis showed that TIFY genes, especially AaTIFY19/23/27/29, respond to a variety of hormonal treatments, such as MeJA, ABA, SA, and IAA. This suggested that TIFY genes in A. argyi regulate plant growth and respond to different stresses by following different hormone signaling pathways. Conclusion: Taken together, our study conducted a comprehensive identification and analysis of the TIFY gene family in A. argyi. These findings suggested that TIFY might play an important role in plant development and stress responses, which laid a valuable foundation for further understanding the function of TIFY genes in multiple stress responses and phytohormone crosstalk in A. argyi. [ABSTRACT FROM AUTHOR]

Mastication trait of citrus significantly influences the fruit's overall quality and consumer preference. The accumulation of cellulose in fruits significantly impacts the mastication trait of citrus fruits, and the glycoside hydrolase 9 (GH9) family plays a crucial role in cellulose metabolism. In this study, we successfully identified 32 GH9 genes from the Citrus sinensis genome and subsequently conducted detailed bioinformatics analyses of the GH9 family. Additionally, we profiled the spatiotemporal expression patterns of CsGH9 genes across four distinct fruit tissue types and six crucial developmental stages of citrus fruits, leveraging transcriptome data. Parallel to this, we undertook a comparative analysis of transcriptome profiles and cellulose content among diverse fruit tissues spanning six developmental stages. Furthermore, to identify the pivotal genes involved in cellulose metabolism within the GH9 family during fruit maturity, we employed correlation analysis between cellulose content and gene expression in varying tissues across diverse citrus varieties. This analysis highlighted key genes such as CsGH9A2/6 and CsGH9B12/13/14/22. Collectively, this study provides an in-depth analysis of the GH9 gene family in citrus and offers novel molecular insights into the underlying mechanisms governing the mastication trait formation in citrus fruits. [ABSTRACT FROM AUTHOR]

DNA methylation is an epigenetic mechanism that regulates gene expression, and for mammals typically occurs on cytosines within CpG dinucleotides. A significant challenge for methylation detection methods is accurately measuring methylation levels within GC-rich regions such as gene promoters, as inaccuracies compromise downstream biological interpretation of the data. To address this challenge, we compared methylation levels assayed using four different Methods Enzymatic Methyl-seq (EM-seq), whole genome bisulphite sequencing (WGBS), Infinium arrays (Illumina MethylationEPIC, "EPIC"), and Oxford Nanopore Technologies nanopore sequencing (ONT) applied to human DNA. Overall, all methods produced comparable and consistent methylation readouts across the human genome. The flexibility offered by current gold standard WGBS in interrogating genome-wide cytosines is surpassed technically by both EM-seq and ONT, as their coverages and methylation readouts are less prone to GC bias. These advantages are tempered by increased laboratory time (EM-seq) and higher complexity (ONT). We further assess the strengths and weaknesses of each method, and provide recommendations in choosing the most appropriate methylation method for specific scientific questions or translational needs. [ABSTRACT FROM AUTHOR]

Background: Myelocytomatosis (MYC) transcription factors are crucial mediators of the response of plants to environmental stresses through via binding to DNA regulatory regions. However, few systematic characterizations of MYC genes are available in Cucurbitaceae species. Results: In this study, we identified 10, 8, 12, and 10 MYC genes in Cucumis sativus, Cucumis melo, Citrullus lanatus, and Benincasa hispida, respectively. Characterization revealed that all of the MYC proteins contain a highly conserved H4-V5-E6-E8-R9-R11-R12 sequence, which is essential for the binding of DNA regulatory regions. Evolutionary analysis enabled us to categorize 40 predicted MYC proteins from seven species into five distinct groups and revealed that the expansion of the MYC genes occurred before the divergence of monocots and dicots. The upstream promoter regions of the MYC genes contain a variety of developmental, stress, and hormone-responsive regulatory elements. The expression of cucumber MYC genes varies significantly across organs, with particularly high expression of CsaV3_3G001710 observed across all organs. Transcriptomic analysis revealed that certain cucumber MYC genes undergo specific upregulation or downregulation in response to both biotic and abiotic stressors. In particular, under temperature stress, the cucumber genes CsaV3_3G007980 and CsaV3_3G001710 were significantly upregulated. Interestingly, the homologs of these two genes in C. lanatus presented a similar expression pattern to that in C. sativus, whereas in B. hispida, they presented the opposite pattern, i.e., significant downregulation. These findings indicated that these two genes indeed respond to temperature stress but with different expression patterns, highlighting the divergent functions of homologous genes across different species. Conclusions: This study analyzed the size and composition of the MYC gene family in four Cucurbitaceae species and investigated stress-responsive expression profiles, especially under temperature stress. All the results showed that MYC genes play important roles in development and stress responses, laying a theoretical foundation for further investigations of these response mechanisms. [ABSTRACT FROM AUTHOR]

Background: Black spot disease in tree peony caused by the fungal necrotroph A. alternata, is a primary limiting factor in the production of the tree peony. The intricate molecular mechanisms underlying the tree peony resistance to A. alternata have not been thoroughly investigated. Results: The present study utilized high-throughput RNA sequencing (RNA-seq) technology to conduct global expression profiling, revealing an intricate network of genes implicated in the interaction between tree peony and A. alternata. RNA-Seq libraries were constructed from leaf samples and high-throughput sequenced using the BGISEQ-500 sequencing platform. Six distinct libraries were characterized. M1, M2 and M3 were derived from leaves that had undergone mock inoculation, while I1, I2 and I3 originated from leaves that had been inoculated with the pathogen. A range of 10.22–11.80 gigabases (Gb) of clean bases were generated, comprising 68,131,232 − 78,633,602 clean bases and 56,677 − 68,996 Unigenes. A grand total of 99,721 Unigenes were acquired, boasting a mean length of 1,266 base pairs. All these 99,721 Unigenes were annotated in various databases, including NR (Non-Redundant, 61.99%), NT (Nucleotide, 45.50%), SwissProt (46.32%), KEGG (Kyoto Encyclopedia of Genes and Genomes, 49.33%), KOG (clusters of euKaryotic Orthologous Groups, 50.18%), Pfam (Protein family, 47.16%), and GO (Gene Ontology, 34.86%). In total, 66,641 (66.83%) Unigenes had matches in at least one database. By conducting a comparative transcriptome analysis of the mock- and A. alternata-infected sample libraries, we found differentially expressed genes (DEGs) that are related to phytohormone signalling, pathogen recognition, active oxygen generation, and circadian rhythm regulation. Furthermore, multiple different kinds of transcription factors were identified. The expression levels of 10 selected genes were validated employing qRT-PCR (quantitative real-time PCR) to confirm RNA-Seq data. Conclusions: A multitude of transcriptome sequences have been generated, thus offering a valuable genetic repository for further scholarly exploration on the immune mechanisms underlying the tree peony infected by A. alternata. While the expression of most DEGs increased, a few DEGs showed decreased expression. [ABSTRACT FROM AUTHOR]

Background: The WD40 domain, one of the most abundant in eukaryotic genomes, is widely involved in plant growth and development, secondary metabolic biosynthesis, and mediating responses to biotic and abiotic stresses. WD40 repeat (WD40) protein has been systematically studied in several model plants but has not been reported in the Capsicum annuum (pepper) genome. Results: Herein, 269, 237, and 257 CaWD40 genes were identified in the Zunla, CM334, and Zhangshugang genomes, respectively. CaWD40 sequences from the Zunla genome were selected for subsequent analysis, including chromosomal localization, phylogenetic relationships, sequence characteristics, motif compositions, and expression profiling. CaWD40 proteins were unevenly distributed on 12 chromosomes, encompassing 19 tandem duplicate gene pairs. The 269 CaWD40s were divided into six main branches (A to F) with 17 different types of domain distribution. The CaWD40 gene family exhibited diverse expression patterns, and several genes were specifically expressed in flowers and seeds. Yeast two-hybrid (Y2H) and dual-luciferase assay indicated that CaWD40-91 could interact with CaAN1 and CaDYT1, suggesting its involvement in anthocyanin biosynthesis and male sterility in pepper. Conclusions: In summary, we systematically characterized the phylogeny, classification, structure, and expression of the CaWD40 gene family in pepper. Our findings provide a valuable foundation for further functional investigations on WD40 genes in pepper. [ABSTRACT FROM AUTHOR]

Background: Lanping black-boned sheep (LPB) represent a distinctive mammalian species characterized by hyperpigmentation, resulting in black bone and muscle features, in contrast to their conventional counterparts exhibiting red muscle and white bone. The genetic basis underlying LPB hyperpigmentation has remained enigmatic. Methods: In this study, we conducted whole-genome sequencing of 100 LPB and 50 Lanping normal sheep (LPN), and integrated this data with 421 sequenced datasets from wild and domestic sheep, shedding light on the genetic backdrop and genomic variations associated with LPB. Furthermore, we performed comparative RNA-Seq analysis using liver sample to pinpoint genes implicated in the pigmentation process. We generated a comprehensive dataset comprising 97,944,357 SNPs from 571 sheep, facilitating an in-depth exploration of genetic factors. Results: Population genetic structure analysis revealed that the LPB breed traces its origin back to LPN, having evolved into a distinct breed. The integration of positively selected genes with differentially expressed genes identified two candidates, ERBB4 and ROR1, potentially linked to LPB hyperpigmentation. Comparative analysis of ERBB4 and ROR1 mRNA relative expression levels in liver, spleen, and kidney tissues of LPB, in comparison to Diqing sheep, revealed significant upregulation, except for ERBB4 in the liver. Gene expression heatmaps further underscored marked allelic frequency disparities in different populations. Conclusion: Our findings establish the evolutionary lineage of the LPB breed from LPN and underscore the involvement of ERBB4 and ROR1 genes in melanin synthesis. These results enhance our comprehension of the molecular basis of hyperpigmentation and contribute to a more comprehensive depiction of sheep diversity. [ABSTRACT FROM AUTHOR]

Background: Squamosa promoter-binding protein-like (SPL) is a plant-specific transcription factor that is widely involved in the regulation of plant growth and development, including flower and grain development, stress responses, and secondary metabolite synthesis. However, this gene family has not been comprehensively evaluated in barley, the most adaptable cereal crop with a high nutritional value. Results: In this study, a total of 15 HvSPL genes were identified based on the Hordeum vulgare genome. These genes were named HvSPL1 to HvSPL15 based on the chromosomal distribution of the HvSPL genes and were divided into seven groups (I, II, III, V, VI, VII, and VIII) based on the phylogenetic tree analysis. Chromosomal localization revealed one pair of tandem duplicated genes and one pair of segmental duplicated genes. The HvSPL genes exhibited the highest collinearity with the monocotyledonous plant, Zea mays (27 pairs), followed by Oryza sativa (18 pairs), Sorghum bicolor (16 pairs), and Arabidopsis thaliana (3 pairs), and the fewest homologous genes with Solanum lycopersicum (1 pair). The distribution of the HvSPL genes in the evolutionary tree was relatively scattered, and HvSPL proteins tended to cluster with SPL proteins from Z. mays and O. sativa, indicating a close relationship between HvSPL and SPL proteins from monocotyledonous plants. Finally, the spatial and temporal expression patterns of the 14 HvSPL genes from different subfamilies were determined using quantitative real-time polymerase chain reaction (qRT-PCR). Based on the results, the HvSPL gene family exhibited tissue-specific expression and played a regulatory role in grain development and abiotic stress. HvSPL genes are highly expressed in various tissues during seed development. The expression levels of HvSPL genes under the six abiotic stress conditions indicated that many genes responded to stress, especially HvSPL8, which exhibited high expression under multiple stress conditions, thereby warranting further attention. Conclusion: In this study, 15 SPL gene family members were identified in the genome of Hordeum vulgare, and the phylogenetic relationships, gene structure, replication events, gene expression, and potential roles of these genes in millet development were studied. Our findings lay the foundation for exploring the HvSPL genes and performing molecular breeding of barley. [ABSTRACT FROM AUTHOR]

Pectobacterium carotovorum and Pectobacterium aroidearum represent the primary pathogens causing variable soft rot disease. However, the fundamental defense responses of Pinellia ternata to pathogens remain unclear. Our investigation demonstrated that the disease produced by P. carotovorum is more serious than P. aroidearum. RNA-seq analysis indicated that many cell wall-related genes, receptor-like kinase genes, and resistance-related genes were induced by P. aroidearum and P. carotovorum similarly. But many different regulatory pathways exert a crucial function in plant immunity against P. aroidearum and P. carotovorum, including hormone signaling, whereas more auxin-responsive genes were responsive to P. carotovorum, while more ethylene and gibberellin-responsive genes were responsive to P. aroidearum. 12 GDSL esterase/lipase genes and 3 fasciclin-like arabinogalactan protein genes were specifically upregulated by P. carotovorum, whereas 11 receptor-like kinase genes and 8 disease resistance genes were up-regulated only by P. aroidearum. Among them, a lectin gene (part1transcript/39001) was induced by P. carotovorum and P. aroidearum simultaneously. Transient expression in N. benthamiana demonstrated that the lectin gene improves plant resistance to P. carotovorum. This study offers a comprehensive perspective on P. ternata immunity produced by different soft rot pathogens and reveals the importance of lectin in anti-soft rot of P. ternata for the first time. [ABSTRACT FROM AUTHOR]

Background: In eukaryotic cells, transcription factors (TFs) are thought to act in a combinatorial way, by competing and collaborating to regulate common target genes. However, several questions remain regarding the conservation of these combinations among different gene classes, regulatory regions and cell types. Results: We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a target TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the nucleotide content of the sequences and of the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combinations involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for predicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers. First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in promoters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and enhancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the different targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately distinguish promoters associated with specific biological processes. Conclusions: TFcoop appears as an accurate approach for studying TF combinations. Its use on ENCODE and FANTOM data allowed us to discover important properties of human TF combinations in different promoter classes and in enhancers. The R code for learning a TFcoop model and for reproducing the main experiments described in the paper is available in an R Markdown file at address https://gite.lirmm.fr/brehelin/TFcoop. [ABSTRACT FROM AUTHOR]

Background: Next-Generation Sequencing (NGS) has been widely accepted as an essential tool in molecular biology. Reduced costs and automated analysis pipelines make the use of NGS data feasible even for small labs, yet the methods for interpreting the data are not sophisticated enough to account for the amount of information. Results: We propose s ·nr, a Visual Analytics tool that provides simple yet powerful visual interfaces for displaying and querying NGS data. It allows researchers to explore their own data in the context of experimental data deposited in public repositories, as well as to extract specific data sets with similar gene expression signatures. We tested s ·nr on 1543 RNA-Seq based mouse differential expression profiles derived from the public ArrayExpress platform. We provide the repository of processed data with this paper. Conclusion: s ·nr, easily deployable utilizing its containerized implementation, empowers researchers to analyze and relate their own RNA-Seq as well as to provide interactive and contextual crosstalk with data from public repositories. This allows users to deduce novel and unbiased hypotheses about the underlying molecular processes. Demo: Login demo/demo: snr.sf.mpg.de (Tested with Google Chrome) [ABSTRACT FROM AUTHOR]

Background: The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. Results: We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. Conclusions: In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance. [ABSTRACT FROM AUTHOR]