Origin and evolution of the bread wheat D genome.

Bread wheat (Triticum aestivum) is a globally dominant crop and major source of calories and proteins for the human diet. Compared with its wild ancestors, modern bread wheat shows lower genetic diversity, caused by polyploidisation, domestication and breeding bottlenecks1,2. Wild wheat relatives represent genetic reservoirs, and harbour diversity and beneficial alleles that have not been incorporated into bread wheat. Here we establish and analyse extensive genome resources for Tausch's goatgrass (Aegilops tauschii), the donor of the bread wheat D genome. Our analysis of 46 Ae. tauschii genomes enabled us to clone a disease resistance gene and perform haplotype analysis across a complex disease resistance locus, allowing us to discern alleles from paralogous gene copies. We also reveal the complex genetic composition and history of the bread wheat D genome, which involves contributions from genetically and geographically discrete Ae. tauschii subpopulations. Together, our results reveal the complex history of the bread wheat D genome and demonstrate the potential of wild relatives in crop improvement.

msRepDB: a comprehensive repetitive sequence database of over 80 000 species.

Repeats are prevalent in the genomes of all bacteria, plants and animals, and they cover nearly half of the Human genome, which play indispensable roles in the evolution, inheritance, variation and genomic instability, and serve as substrates for chromosomal rearrangements that include disease-causing deletions, inversions, and translocations. Comprehensive identification, classification and annotation of repeats in genomes can provide accurate and targeted solutions towards understanding and diagnosis of complex diseases, optimization of plant properties and development of new drugs. RepBase and Dfam are two most frequently used repeat databases, but they are not sufficiently complete. Due to the lack of a comprehensive repeat database of multiple species, the current research in this field is far from being satisfactory. LongRepMarker is a new framework developed recently by our group for comprehensive identification of genomic repeats. We here propose msRepDB based on LongRepMarker, which is currently the most comprehensive multi-species repeat database, covering >80 000 species. Comprehensive evaluations show that msRepDB contains more species, and more complete repeats and families than RepBase and Dfam databases. (https://msrepdb.cbrc.kaust.edu.sa/pages/msRepDB/index.html).

FARNA: knowledgebase of inferred functions of non-coding RNA transcripts.

Non-coding RNA (ncRNA) genes play a major role in control of heterogeneous cellular behavior. Yet, their functions are largely uncharacterized. Current available databases lack in-depth information of ncRNA functions across spectrum of various cells/tissues. Here, we present FARNA, a knowledgebase of inferred functions of 10,289 human ncRNA transcripts (2,734 microRNA and 7,555 long ncRNA) in 119 tissues and 177 primary cells of human. Since transcription factors (TFs) and TF co-factors (TcoFs) are crucial components of regulatory machinery for activation of gene transcription, cellular processes and diseases in which TFs and TcoFs are involved suggest functions of the transcripts they regulate. In FARNA, functions of a transcript are inferred from TFs and TcoFs whose genes co-express with the transcript controlled by these TFs and TcoFs in a considered cell/tissue. Transcripts were annotated using statistically enriched GO terms, pathways and diseases across cells/tissues based on guilt-by-association principle. Expression profiles across cells/tissues based on Cap Analysis of Gene Expression (CAGE) are provided. FARNA, having the most comprehensive function annotation of considered ncRNAs across widest spectrum of human cells/tissues, has a potential to greatly contribute to our understanding of ncRNA roles and their regulatory mechanisms in human. FARNA can be accessed at: http://cbrc.kaust.edu.sa/farna.

DESM: portal for microbial knowledge exploration systems.

Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about molecular and genetic basis of functioning of different microorganisms and their ability for bioproduction. To enable such exploration, we compiled 45 topic-specific knowledgebases (KBs) accessible through DESM portal (www.cbrc.kaust.edu.sa/desm). The KBs contain information derived through text-mining of PubMed information and complemented by information data-mined from various other resources (e.g. ChEBI, Entrez Gene, GO, KOBAS, KEGG, UniPathways, BioGrid). All PubMed records were indexed using 4,538,278 concepts from 29 dictionaries, with 1 638 986 records utilized in KBs. Concepts used are normalized whenever possible. Most of the KBs focus on a particular type of microbial activity, such as production of biocatalysts or nutraceuticals. Others are focused on specific categories of microorganisms, e.g. streptomyces or cyanobacteria. KBs are all structured in a uniform manner and have a standardized user interface. Information exploration is enabled through various searches. Users can explore statistically most significant concepts or pairs of concepts, generate hypotheses, create interactive networks of associated concepts and export results. We believe DESM will be a useful complement to the existing resources to benefit microbiology and biotechnology research.

DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining.

Noncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine. To support ncRNA research and facilitate retrieval of relevant information regarding miRNAs and lncRNAs from the plethora of published ncRNA-related research, we developed DES-ncRNA ( www.cbrc.kaust.edu.sa/des_ncrna ). DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc. DES-ncRNA contains approximately 878,000 associations of terms from these dictionaries of which 36,222 (5,373) are with regards to miRNAs (lncRNAs). We provide several ways to explore information regarding ncRNAs to users including controlled generation of association networks as well as hypotheses generation. We show an example how DES-ncRNA can aid research on Alzheimer disease and suggest potential therapeutic role for Fasudil. DES-ncRNA is a powerful tool that can be used on its own or as a complement to the existing resources, to support research in human ncRNA. To our knowledge, this is the only knowledgebase dedicated to human miRNAs and lncRNAs derived primarily through literature-mining enabling exploration of a broad spectrum of associated biomedical entities, not paralleled by any other resource.

Flooding in semi-unformal urban areas in North Africa: Environmental and psychosocial drivers.

Urban flooding is recognized as a nature-driven disaster shaped by inherent factors such as climate, morphology, and hydrology, affecting vulnerability and flood exposure. While these factors play a paramount role, significant psychosocial intricate drivers are acknowledged, though they are challenging for prediction and assessment. This study delves into these drivers in a specific context, aiming to draw conclusions that extend beyond. It undertakes a comprehensive approach, integrating cloud-based Radar flood detection, analysis of flood causation patterns, and geostatistical analysis of a social survey based on cross-synthesis, contingency analysis, and structural equation modeling. In particular, we characterize the case of the coastal city of Tetouan in Morocco, which is representative in its environmental and socioeconomic settings to most cities in North Africa. It unraveled the nuanced interplay of psychosocial, economic, and territorial dynamics influencing flood exposure. The findings reveal how watershed location molds unique environmental exposures, steering nuanced, emotional, and behavioral responses among residents. Gender and education differentials reveal diverse perceptions and awareness of flood risks. Psychosocial intricacies come to the forefront, portraying education, income, and awareness as crucial mediators influencing cognitive and affective responses. Elevated education, increased income, and heightened awareness correlate with heightened perception and coping strategies. Findings reveal that risk perception significantly and differently influences risk acceptance, coping, and aversion through an array of identified key factors influencing coping strategies, mediating elements in flood damage relationships, and underscoring the pivotal role of perception in shaping responses to risk. Moreover, it found that lower risk acceptance leads to higher coping and aversion, and the latter positively affects coping, indicating that acceptance reduces the motivation to avoid the risk and decreases the willingness to adopt coping strategies to reduce the exposure. The outcomes carry critical implications for comprehending individual and collective social behaviors, informing strategies, and mitigating flood risk that apply at a wider context. It accentuates the inadequacy of relying solely on structural engineering for risk management, citing spatial constraints, misinformation, and lapses in prior-risk memory as compounding exposure challenges. This recognition catalyzes action, advocating tailored awareness campaigns, educational initiatives, and capacity-building programs, spotlighting the need for heightened individual profiles to enhance social understanding, engagement, and resilience. We anticipate profound insights, fostering a richer comprehension of urban flooding complexities and informing adaptive strategies on a broader scale.

Soil erosion and hydroclimatic hazards in major African port cities: the case study of Tangier.

Land degradation and soil erosion are becoming increasingly problematic in Africa's rapidly developing urban areas, particularly in Major Port Cities. Uncontrolled expansion and human pressures are hindering planning, adaptation, and conservation efforts. To understand the extent of these issues, this study combined morphometric analysis, soil loss calculation, field monitoring, and remote sensing and GIS tools to assess soil erosion in the Metropolis of Tangier (Morocco) located at the confluence of the Mediterranean Sea and the Atlantic Ocean at the Strait of Gibraltar. The study relied on data from 13 rain gauge stations, official reports, and remote sensing acquisitions, as well as field observations. Results showed an average soil erosion rate of 24.2 t/ha/year, equivalent to an annual soil loss of 588,051 t/year. This high rate was largely due to areas with a high erosion risk (99.8%), covering only 8.3% of the territory, which were characterized by recently burned topsoil, fallow land, and steep slopes. These areas included both uncontrolled neighbourhoods and areas for planned urban and industrial expansion, posing a threat to the landscape's sustainability and socio-economic prospects. The morphometric analysis revealed its high vulnerability to erosion and degradation, with the highest soil loss rates observed in the eastern and western regions. The study also found that flash floods caused by hydroclimatic hazards can lead to significant damage to infrastructure and equipment, particularly in western sub-basins and mountainous regions. In conclusion, the use of remote sensing and GIS technologies provided valuable insights into the physical characteristics and vulnerability of the Tangier Metropolis to land degradation and soil erosion. These findings emphasize the need for effective land management practices and conservation measures to mitigate the impacts of land degradation and soil erosion in the face of climate change. This information is crucial for decision-makers and stakeholders to develop strategies to address these pressing issues.

DES-Amyloidoses "Amyloidoses through the looking-glass": A knowledgebase developed for exploring and linking information related to human amyloid-related diseases.

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer's disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities' influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

Pan-cancer pervasive upregulation of 3' UTR splicing drives tumourigenesis.

Most mammalian genes generate messenger RNAs with variable untranslated regions (UTRs) that are important post-transcriptional regulators. In cancer, shortening at 3' UTR ends via alternative polyadenylation can activate oncogenes. However, internal 3' UTR splicing remains poorly understood as splicing studies have traditionally focused on protein-coding alterations. Here we systematically map the pan-cancer landscape of 3' UTR splicing and present this in SpUR ( http://www.cbrc.kaust.edu.sa/spur/home/ ). 3' UTR splicing is widespread, upregulated in cancers, correlated with poor prognosis and more prevalent in oncogenes. We show that antisense oligonucleotide-mediated inhibition of 3' UTR splicing efficiently reduces oncogene expression and impedes tumour progression. Notably, CTNNB1 3' UTR splicing is the most consistently dysregulated event across cancers. We validate its upregulation in hepatocellular carcinoma and colon adenocarcinoma, and show that the spliced 3' UTR variant is the predominant contributor to its oncogenic functions. Overall, our study highlights the importance of 3' UTR splicing in cancer and may launch new avenues for RNA-based anti-cancer therapeutics.

Comparative assessment of different reference evapotranspiration models towards a fit calibration for arid and semi-arid areas.

The enhancement of water efficiency requires controlling the high demand for irrigated agriculture which depends on improving the capabilities to accurately simulate the water cycle and its components. Among these, evapotranspiration is widely studied to estimate reference evapotranspiration (ET0) but the performance and accuracy of the estimates vary. Moreover, these estimates require some hardly available or misrepresentative meteorological data which lead, mainly in arid and semi-arid areas, to errors and inaccuracies. Here, ET0 of five empirical temperature-based estimates are compared to the standard FAO Penman-Monteith estimate (ET0-PM) under the representative and wide-ranging settings of 22 weather stations of Morocco. We found a significant positive correlation between ET0-PM and solar radiation, average and maximum air temperatures. We have determined that the Dorji estimate shows relatively better precision and stability while it requires advanced calibration to accommodate arid and semi-arid conditions. After hundreds of calibration repetitions, we concluded a new estimate (ET0-Hadria) which demonstrates an overall improvement in the quality and precision of ET0 assessment, mainly in flat areas. This estimate improved the precision and enhanced the precision in almost 68% of the stations. This simple calibrated estimate is an accurate, improved, and transferable tool achieved through a precise methodical process of selection and configuration.

DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network.

Analysis and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, as well as drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, while providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by a number of features, namely, pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad composition, transition and distribution, Moreau-Broto autocorrelation, and structural features. The drug compounds are subsequently encoded using substructure fingerprints. Next, eXtreme gradient boosting (XGBoost) is used to determine the subset of non-redundant features of importance. The optimal balanced set of sample vectors is obtained by applying the synthetic minority oversampling technique (SMOTE). Finally, a DTIs predictor, DNN-DTIs, is developed based on a deep neural network (DNN) via a layer-by-layer learning scheme. Experimental results indicate that DNN-DTIs achieves better performance than other state-of-the-art predictors with ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's datasets. Therefore, the accurate prediction performance of DNN-DTIs makes it a favored choice for contributing to the study of DTIs, especially drug repositioning.

Prediction of protein-protein interaction sites through eXtreme gradient boosting with kernel principal component analysis.

Predicting protein-protein interaction sites (PPI sites) can provide important clues for understanding biological activity. Using machine learning to predict PPI sites can mitigate the cost of running expensive and time-consuming biological experiments. Here we propose PPISP-XGBoost, a novel PPI sites prediction method based on eXtreme gradient boosting (XGBoost). First, the characteristic information of protein is extracted through the pseudo-position specific scoring matrix (PsePSSM), pseudo-amino acid composition (PseAAC), hydropathy index and solvent accessible surface area (ASA) under the sliding window. Next, these raw features are preprocessed to obtain more optimal representations in order to achieve better prediction. In particular, the synthetic minority oversampling technique (SMOTE) is used to circumvent class imbalance, and the kernel principal component analysis (KPCA) is applied to remove redundant characteristics. Finally, these optimal features are fed to the XGBoost classifier to identify PPI sites. Using PPISP-XGBoost, the prediction accuracy on the training dataset Dset186 reaches 85.4%, and the accuracy on the independent validation datasets Dtestset72, PDBtestset164, Dset_448 and Dset_355 reaches 85.3%, 83.9%, 85.8% and 85.4%, respectively, which all show an increase in accuracy against existing PPI sites prediction methods. These results demonstrate that the PPISP-XGBoost method can further enhance the prediction of PPI sites.

Impacts and social implications of landuse-environment conflicts in a typical Mediterranean watershed.

In coastal watersheds, services and landuse favour coastal tourism and urbanization, depriving rural upstream of infrastructure and attention. This unbalanced management leads to an intensification of socioeconomic changes that generate a structural heterogeneity of the landscape and a reduction in the livelihoods of the rural population. The incessant dissociation between the objectives of the stakeholders triggers landuse-environment-economy conflicts which threaten to mutate large-scale development programs. Here, we used multi-assessment techniques in a Mediterranean watershed from Morocco to evaluate the effects of landuse change on water, vegetation, and perception of the rural population towards environmental issues. We combined complementary vegetation indexes (NDVI and EVI) to study long-term landuse change and phenological statistical pixel-based trends. We assessed the exposure of rural households to the risk of groundwater pollution through a water analysis supplemented by the calculation of an Integrated Water Quality Index. Later, we contrasted the findings with the results of a social survey with a representative sample of 401 households from 7 villages. We found that rapid coastal linear urbanization has resulted in a 12-fold increase in construction over the past 35 years, to the detriment of natural spaces and the lack of equipment and means in rural areas upstream. We show that the worst water qualities are linked to the negative impact of anthropogenic activities on immediately accessible water points. We observe that rural households are aware of the existence and gravity of environmental issues but act confusedly because of their low education level which generates a weak capacity to understand cause and effect relationships. We anticipate the pressing need to improve the well-being and education of the population and synergistically correct management plans to target the watershed as a consolidated system. Broadly, stakeholders should restore lost territorial harmony and reallocate landuse according to a sustainable environment-socioeconomic vision.

DES-Tcell is a knowledgebase for exploring immunology-related literature.

T-cells are a subtype of white blood cells circulating throughout the body, searching for infected and abnormal cells. They have multifaceted functions that include scanning for and directly killing cells infected with intracellular pathogens, eradicating abnormal cells, orchestrating immune response by activating and helping other immune cells, memorizing encountered pathogens, and providing long-lasting protection upon recurrent infections. However, T-cells are also involved in immune responses that result in organ transplant rejection, autoimmune diseases, and some allergic diseases. To support T-cell research, we developed the DES-Tcell knowledgebase (KB). This KB incorporates text- and data-mined information that can expedite retrieval and exploration of T-cell relevant information from the large volume of published T-cell-related research. This KB enables exploration of data through concepts from 15 topic-specific dictionaries, including immunology-related genes, mutations, pathogens, and pathways. We developed three case studies using DES-Tcell, one of which validates effective retrieval of known associations by DES-Tcell. The second and third case studies focuses on concepts that are common to Grave's disease (GD) and Hashimoto's thyroiditis (HT). Several reports have shown that up to 20% of GD patients treated with antithyroid medication develop HT, thus suggesting a possible conversion or shift from GD to HT disease. DES-Tcell found miR-4442 links to both GD and HT, and that miR-4442 possibly targets the autoimmune disease risk factor CD6, which provides potential new knowledge derived through the use of DES-Tcell. According to our understanding, DES-Tcell is the first KB dedicated to exploring T-cell-relevant information via literature-mining, data-mining, and topic-specific dictionaries.

DES-ROD: Exploring Literature to Develop New Links between RNA Oxidation and Human Diseases.

Normal cellular physiology and biochemical processes require undamaged RNA molecules. However, RNAs are frequently subjected to oxidative damage. Overproduction of reactive oxygen species (ROS) leads to RNA oxidation and disturbs redox (oxidation-reduction reaction) homeostasis. When oxidation damage affects RNA carrying protein-coding information, this may result in the synthesis of aberrant proteins as well as a lower efficiency of translation. Both of these, as well as imbalanced redox homeostasis, may lead to numerous human diseases. The number of studies on the effects of RNA oxidative damage in mammals is increasing by year due to the understanding that this oxidation fundamentally leads to numerous human diseases. To enable researchers in this field to explore information relevant to RNA oxidation and effects on human diseases, we developed DES-ROD, an online knowledgebase that contains processed information from 298,603 relevant documents that consist of PubMed abstracts and PubMed Central full-text articles. The system utilizes concepts/terms from 38 curated thematic dictionaries mapped to the analyzed documents. Researchers can explore enriched concepts, as well as enriched pairs of putatively associated concepts. In this way, one can explore mutual relationships between any combinations of two concepts from used dictionaries. Dictionaries cover a wide range of biomedical topics, such as human genes and proteins, pathways, Gene Ontology categories, mutations, noncoding RNAs, enzymes, toxins, metabolites, and diseases. This makes insights into different facets of the effects of RNA oxidation and the control of this process possible. The usefulness of the DES-ROD system is demonstrated by case studies on some known information, as well as potentially novel information involving RNA oxidation and diseases. DES-ROD is the first knowledgebase based on text and data mining that focused on the exploration of RNA oxidation and human diseases.

Bridging the gap of perception is the only way to align soil protection actions.

Science is the seed of a decent life, with which we sow hope in the present and which we irrigate with the perfecting of good deeds. It is even crucial in the Mediterranean southern frontiers where the cultural erosion dissolves the structure of a society abandoned by the arms and brains of its youth. Soil-water-vegetation crisis should not be underestimated; coupled with socioeconomic congestion it would lead to an irremediable crash. Here, we show that the first and most difficult step to face soil degradation is to cultivate the right idea and develop it into a well-established community culture. We found in northern Morocco that 94.5% of farmers have no qualification and 82.6% of them act in a way that worsens soil degradation even if they are aware of the severity of the problem. This confused perception of ideas originates inappropriate labour behaviours non-aligned with public actions. Our results show that the impact of this is a high potential regional erosion rate of 27.7 t/ha/year which is equivalent to a massive potential gross amount of soil loss of 44.3 Mt/year. We show that this leads to an overall vegetation decrease related mainly to the anthropogenic pressure then to climate and lithology. We anticipate that the solution must be comprehensive, participatory, strategic and innovative, led by education and scientific research (Citizen Science) and involving all actors equally. In its broad context, the only path to achieve the coordination and alignment of actions would be through a gradual change of perception and involvement based on a time-consuming culture of assimilation and acceptance rather than a culture of rapid reform.

Literature-Based Enrichment Insights into Redox Control of Vascular Biology.

In cellular physiology and signaling, reactive oxygen species (ROS) play one of the most critical roles. ROS overproduction leads to cellular oxidative stress. This may lead to an irrecoverable imbalance of redox (oxidation-reduction reaction) function that deregulates redox homeostasis, which itself could lead to several diseases including neurodegenerative disease, cardiovascular disease, and cancers. In this study, we focus on the redox effects related to vascular systems in mammals. To support research in this domain, we developed an online knowledge base, DES-RedoxVasc, which enables exploration of information contained in the biomedical scientific literature. The DES-RedoxVasc system analyzed 233399 documents consisting of PubMed abstracts and PubMed Central full-text articles related to different aspects of redox biology in vascular systems. It allows researchers to explore enriched concepts from 28 curated thematic dictionaries, as well as literature-derived potential associations of pairs of such enriched concepts, where associations themselves are statistically enriched. For example, the system allows exploration of associations of pathways, diseases, mutations, genes/proteins, miRNAs, long ncRNAs, toxins, drugs, biological processes, molecular functions, etc. that allow for insights about different aspects of redox effects and control of processes related to the vascular system. Moreover, we deliver case studies about some existing or possibly novel knowledge regarding redox of vascular biology demonstrating the usefulness of DES-RedoxVasc. DES-RedoxVasc is the first compiled knowledge base using text mining for the exploration of this topic.

BioPS: System for screening and assessment of biofuel-production potential of cyanobacteria.

BACKGROUND: Cyanobacteria are one of the target groups of organisms explored for production of free fatty acids (FFAs) as biofuel precursors. Experimental evaluation of cyanobacterial potential for FFA production is costly and time consuming. Thus, computational approaches for comparing and ranking cyanobacterial strains for their potential to produce biofuel based on the characteristics of their predicted proteomes can be of great importance. RESULTS: To enable such comparison and ranking, and to assist biotechnology developers and researchers in selecting strains more likely to be successfully engineered for the FFA production, we developed the Biofuel Producer Screen (BioPS) platform (http://www.cbrc.kaust.edu.sa/biops). BioPS relies on the estimation of the predicted proteome makeup of cyanobacterial strains to produce and secrete FFAs, based on the analysis of well-studied cyanobacterial strains with known FFA production profiles. The system links results back to various external repositories such as KEGG, UniProt and GOLD, making it easier for users to explore additional related information. CONCLUSION: To our knowledge, BioPS is the first tool that screens and evaluates cyanobacterial strains for their potential to produce and secrete FFAs based on strain's predicted proteome characteristics, and rank strains based on that assessment. We believe that the availability of such a platform (comprising both a prediction tool and a repository of pre-evaluated stains) would be of interest to biofuel researchers. The BioPS system will be updated annually with information obtained from newly sequenced cyanobacterial genomes as they become available, as well as with new genes that impact FFA production or secretion.

DES-Mutation: System for Exploring Links of Mutations and Diseases.

During cellular division DNA replicates and this process is the basis for passing genetic information to the next generation. However, the DNA copy process sometimes produces a copy that is not perfect, that is, one with mutations. The collection of all such mutations in the DNA copy of an organism makes it unique and determines the organism's phenotype. However, mutations are often the cause of diseases. Thus, it is useful to have the capability to explore links between mutations and disease. We approached this problem by analyzing a vast amount of published information linking mutations to disease states. Based on such information, we developed the DES-Mutation knowledgebase which allows for exploration of not only mutation-disease links, but also links between mutations and concepts from 27 topic-specific dictionaries such as human genes/proteins, toxins, pathogens, etc. This allows for a more detailed insight into mutation-disease links and context. On a sample of 600 mutation-disease associations predicted and curated, our system achieves precision of 72.83%. To demonstrate the utility of DES-Mutation, we provide case studies related to known or potentially novel information involving disease mutations. To our knowledge, this is the first mutation-disease knowledgebase dedicated to the exploration of this topic through text-mining and data-mining of different mutation types and their associations with terms from multiple thematic dictionaries.

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species.

Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and published literature. We developed DES-TOMATO knowledgebase (KB) for exploration of information related to tomato. Information exploration is enabled through terms from 26 dictionaries and combination of these terms. To illustrate the utility of DES-TOMATO, we provide several examples how one can efficiently use this KB to retrieve known or potentially novel information. DES-TOMATO is free for academic and nonprofit users and can be accessed at http://cbrc.kaust.edu.sa/des_tomato/, using any of the mainstream web browsers, including Firefox, Safari and Chrome.