Explainable deep-learning-based ischemia detection using hybrid O-15 H2O perfusion positron emission tomography and computed tomography imaging with clinical data.

BACKGROUND: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 H2O perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings. METHODS: A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image-and data-based classifier considering 35 clinical, CTA, and PET variables. Data from invasive coronary angiography were used as reference. Performance was evaluated with clinical classification using accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE), precision (PRE), net benefit, and Cohen's Kappa. Statistical testing was conducted using McNemar's test. RESULTS: The DL model had a median ACC = 0.8478, AUC = 0.8481, F1S = 0.8293, SEN = 0.8500, SPE = 0.8846, and PRE = 0.8500. Improved detection of true-positive and false-negative cases, increased net benefit in thresholds up to 34%, and comparable Cohen's kappa was seen, reaching similar performance to clinical reading. Statistical testing revealed no significant differences between DL model and clinical reading. CONCLUSIONS: The combined DL model is a feasible and an effective method in detection of CAD, allowing to highlight important data findings individually in interpretable manner.

On finding natural antibiotics based on TCM formulae.

CONTEXT: Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, redundant, and noise-involved chemical space of NPs. In silico approaches are needed to select NPs as antibiotic candidates. OBJECTIVE: This study screens out NPs with antibacterial efficacy guided by both TCM and modern medicine and constructed a dataset aiming to serve the new antibiotic design. METHOD: A knowledge-based network is proposed in this study involving NPs, herbs, the concepts of TCM, and the treatment protocols (or etiologies) of infectious in modern medicine. Using this network, the NPs candidates are screened out and compose the dataset. Feature selection of machine learning approaches is conducted to evaluate the constructed dataset and statistically validate the im- portance of all NPs candidates for different antibiotics by a classification task. RESULTS: The extensive experiments prove the constructed dataset reaches a convincing classification performance with a 0.9421 weighted accuracy, 0.9324 recall, and 0.9409 precision. The further visu- alizations of sample importance prove the comprehensive evaluation for model interpretation based on medical value considerations.

Sleep Staging Framework with Physiologically Harmonized Sub-Networks.

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic stage scoring by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the medical criterion of the sleep scoring task on top of EEG features. We argue that capturing stage-specific features that satisfy the criterion of sleep medicine is non-trivial for automatic sleep scoring. This paper considers two criteria: Transient stage marker and Overall profile of EEG features, then we propose a physiologically meaningful framework for sleep stage scoring via mixed deep neural networks. The framework consists of two sub-networks: feature extraction networks, constructed in consideration of the physiological characteristics of sleep, and an attention-based scoring decision network. Moreover, we quantize the framework for potential use under an IoT setting. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets with the largest comprising 42,560 h recorded from 5,793 subjects. From the experiment results, the proposed method achieves a competitive stage scoring performance, especially for Wake, N2, and N3, with higher F1 scores of 0.92, 0.86, and 0.88, respectively. Moreover, the feasibility analysis of framework quantization provides a potential for future implementation in the edge computing field and clinical settings.

Evaluation of intercellular lipid lamellae in the stratum corneum by polarized microscopy.

BACKGROUND: Intercellular lipids contain a lamellar structure that glows in polarized images. It could be expected that the intercellular lipid content be estimated from the luminance values calculated from polarized images of stratum corneum strips. Therefore, we attempted to develop a method for simple and rapid evaluation of the intercellular lipid content through a procedure. Herein, we demonstrated a relationship between the luminance value and the amount of ceramides, one of the main components of intercellular lipids. MATERIALS AND METHODS: The stratum corneum was collected from the forearm using slides with a pure rubber-based adhesive, which did not produce unnecessary luminescence under polarizing conditions. Images were analyzed using luminance indices. The positive secondary ion peak images were obtained using the time of flight-secondary ion mass spectrometry; the polarized and brightfield images were obtained using a polarized microscope. The ceramide and protein amount was measured by high-performance liquid chromatography and bicinchoninic acid protein assay after microscope imaging. Images and quantitative values were used to construct evaluation models based on a convolutional neural network (CNN). RESULTS: There was a correlation between the highlighted areas of the polarized image to overlap with the area where ceramide-derived peak was detected. Evaluation of the CNN-based model of the polarized images predicted the amount of ceramides per unit of stratum corneum. CONCLUSION: The method proposed in the study enabled a large number of specimens to provide a simple, rapid, and efficient evaluation of the intercellular lipid content.

Kinematics approach with neural networks for early detection of sepsis (KANNEDS).

BACKGROUND: Sepsis is a severe illness that affects millions of people worldwide, and its early detection is critical for effective treatment outcomes. In recent years, researchers have used models to classify positive patients or identify the probability for sepsis using vital signs and other time-series variables as input. METHODS: In our study, we analyzed patients' conditions by their kinematics position, velocity, and acceleration, in a six-dimensional space defined by six vital signs. The patient is affected by the disease after a period if the position gets "near" to a calculated sepsis position in space. We imputed these kinematics features as explanatory variables of long short-term memory (LSTM), convolutional neural network (CNN) and linear neural network (LNN) and compared the prediction accuracies with only the vital signs as input. The dataset used contained information of approximately 4800 patients, each with 48 hourly registers. RESULTS: We demonstrated that the kinematics features models had an improved performance compared with vital signs models. The kinematics features model of LSTM achieved the best accuracy, 0.803, which was nine points higher than the vital signs model. Although with lesser accuracies, the kinematics features models of the CNN and LNN showed better performances than vital signs models. CONCLUSION: Applying our novel approach for early detection of sepsis using neural networks will prove to be an invaluable, more accurate method than considering only simple vital signs as input variables. We expect that other researchers with similar objectives can use the model presented in this innovative approach to improve their results.

Divergence of metabolites in three phylogenetically close Monascus species (M. pilosus, M. ruber, and M. purpureus) based on secondary metabolite biosynthetic gene clusters.

BACKGROUND: Species of the genus Monascus are considered to be economically important and have been widely used in the production of yellow and red food colorants. In particular, three Monascus species, namely, M. pilosus, M. purpureus, and M. ruber, are used for food fermentation in the cuisine of East Asian countries such as China, Japan, and Korea. These species have also been utilized in the production of various kinds of natural pigments. However, there is a paucity of information on the genomes and secondary metabolites of these strains. Here, we report the genomic analysis and secondary metabolites produced by M. pilosus NBRC4520, M. purpureus NBRC4478 and M. ruber NBRC4483, which are NBRC standard strains. We believe that this report will lead to a better understanding of red yeast rice food. RESULTS: We examined the diversity of secondary metabolite production in three Monascus species (M. pilosus, M. purpureus, and M. ruber) at both the metabolome level by LCMS analysis and at the genome level. Specifically, M. pilosus NBRC4520, M. purpureus NBRC4478 and M. ruber NBRC4483 strains were used in this study. Illumina MiSeq 300 bp paired-end sequencing generated 17 million high-quality short reads in each species, corresponding to 200 times the genome size. We measured the pigments and their related metabolites using LCMS analysis. The colors in the liquid media corresponding to the pigments and their related metabolites produced by the three species were very different from each other. The gene clusters for secondary metabolite biosynthesis of the three Monascus species also diverged, confirming that M. pilosus and M. purpureus are chemotaxonomically different. M. ruber has similar biosynthetic and secondary metabolite gene clusters to M. pilosus. The comparison of secondary metabolites produced also revealed divergence in the three species. CONCLUSIONS: Our findings are important for improving the utilization of Monascus species in the food industry and industrial field. However, in view of food safety, we need to determine if the toxins produced by some Monascus strains exist in the genome or in the metabolome.

Graph cluster approach in identifying novel proteins and significant pathways involved in polycystic ovary syndrome.

RESEARCH QUESTION: Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with diverse clinical implications, such as infertility, metabolic disorders, cardiovascular diseases and psychological problems among others. The heterogeneity of conditions found in PCOS contribute to its various phenotypes, leading to difficulties in identifying proteins involved in this abnormality. Several studies, however, have shown the feasibility in identifying molecular evidence underlying other diseases using graph cluster analysis. Therefore, is it possible to identify proteins and pathways related to PCOS using the same approach? METHODS: Known PCOS-related proteins (PCOSrp) from PCOSBase and DisGeNET were integrated with protein-protein interactions (PPI) information from Human Integrated Protein-Protein Interaction reference to construct a PCOS PPI network. The network was clustered with DPClusO algorithm to generate clusters, which were evaluated using Fisher's exact test. Pathway enrichment analysis using gProfileR was conducted to identify significant pathways. RESULTS: The statistical significance of the identified clusters has successfully predicted 138 novel PCOSrp with 61.5% reliability and, based on Cronbach's alpha, this prediction is acceptable. Androgen signalling pathway and leptin signalling pathway were among the significant PCOS-related pathways corroborating the information obtained from the clinical observation, where androgen signalling pathway is responsible in producing male hormones in women with PCOS, whereas leptin signalling pathway is involved in insulin sensitivity. CONCLUSIONS: These results show that graph cluster analysis can provide additional insight into the pathobiology of PCOS, as the pathways identified as statistically significant correspond to earlier biological studies. Therefore, integrative analysis can reveal unknown mechanisms, which may enable the development of accurate diagnosis and effective treatment in PCOS.

Classification of alkaloids according to the starting substances of their biosynthetic pathways using graph convolutional neural networks.

BACKGROUND: Alkaloids, a class of organic compounds that contain nitrogen bases, are mainly synthesized as secondary metabolites in plants and fungi, and they have a wide range of bioactivities. Although there are thousands of compounds in this class, few of their biosynthesis pathways are fully identified. In this study, we constructed a model to predict their precursors based on a novel kind of neural network called the molecular graph convolutional neural network. Molecular similarity is a crucial metric in the analysis of qualitative structure-activity relationships. However, it is sometimes difficult for current fingerprint representations to emphasize specific features for the target problems efficiently. It is advantageous to allow the model to select the appropriate features according to data-driven decisions for extracting more useful information, which influences a classification or regression problem substantially. RESULTS: In this study, we applied a neural network architecture for undirected graph representation of molecules. By encoding a molecule as an abstract graph and applying "convolution" on the graph and training the weight of the neural network framework, the neural network can optimize feature selection for the training problem. By incorporating the effects from adjacent atoms recursively, graph convolutional neural networks can extract the features of latent atoms that represent chemical features of a molecule efficiently. In order to investigate alkaloid biosynthesis, we trained the network to distinguish the precursors of 566 alkaloids, which are almost all of the alkaloids whose biosynthesis pathways are known, and showed that the model could predict starting substances with an averaged accuracy of 97.5%. CONCLUSION: We have showed that our model can predict more accurately compared to the random forest and general neural network when the variables and fingerprints are not selected, while the performance is comparable when we carefully select 507 variables from 18000 dimensions of descriptors. The prediction of pathways contributes to understanding of alkaloid synthesis mechanisms and the application of graph based neural network models to similar problems in bioinformatics would therefore be beneficial. We applied our model to evaluate the precursors of biosynthesis of 12000 alkaloids found in various organisms and found power-low-like distribution.

UC2 search: using unique connectivity of uncharged compounds for metabolite annotation by database searching in mass spectrometry-based metabolomics.

Summary: For metabolite annotation in metabolomics, variations in the registered states of compounds (charged molecules and multiple components, such as salts) and their redundancy among compound databases could be the cause of misannotations and hamper immediate recognition of the uniqueness of metabolites while searching by mass values measured using mass spectrometry. We developed a search system named UC2 (Unique Connectivity of Uncharged Compounds), where compounds are tentatively neutralized into uncharged states and stored on the basis of their unique connectivity of atoms after removing their stereochemical information using the first block in the hash of the IUPAC International Chemical Identifier, by which false-positive hits are remarkably reduced, both charged and uncharged compounds are properly searched in a single query and records having a unique connectivity are compiled in a single search result. Availability and implementation: The UC2 search tool is available free of charge as a REST web service (http://webs2.kazusa.or.jp/mfsearcher) and a Java-based GUI tool. Contact: sakurai@kazusa.or.jp. Supplementary information: Supplementary data are available at Bioinformatics online.

A Novel CNN-Based Framework for Classification of Signal Quality and Sleep Position from a Capacitive ECG Measurement.

The further exploration of the capacitive ECG (cECG) is hindered by frequent fluctuations in signal quality from body movement and changes in sleep position. The processing framework must be fundamentally adapted to make full use of this signal. Therefore, we propose a new signal-processing framework that determines the signal quality for short signal segments (2 and 4 seconds) using a multi-class classification model (qua_model) based on a convolutional neural network (CNN). We built another independent deep CNN classifier (pos_model) to classify the sleep position. In the validation, 12 subjects were recruited for a 30-minute experiment, which required the subjects to lie on a bed in different sleeping positions. The short segments, classified as clear (C1 class) by the qua_model, were used to determine sleep positions with the pos_model. In 10-fold cross-validation, the qua_model for signals of 4-second length could recognize the signal of the C1 class at a 0.99 precision and a 0.99 recall; the pos_model could recognize the supine sleep position, the left, and right lateral sleep positions at a 0.99 averaged precision and a 0.99 averaged recall. Given the amount of data accumulated per night and the instability in the signal quality, this fully automatic processing framework is indispensable for a personal healthcare system. Therefore, this study could serve as an important step for cECG technique trying to explore the cECG for unconstrained heart monitoring.

Curcumin Derivatives Verify the Essentiality of ROS Upregulation in Tumor Suppression.

BACKGROUND: Curcumin has been shown to exert pleiotropic biological effects, including anti-tumorigenic activity. We previously showed that curcumin controls reactive oxygen species (ROS) levels through the ROS metabolic enzymes, to prevent tumor cell growth. In this study, we synthesized 39 novel curcumin derivatives and examined their anti-proliferative and anti-tumorigenic properties. METHODS AND RESULTS: Thirty-nine derivatives exhibited anti-proliferative activity toward human cancer cell lines, including CML-derived K562 leukemic cells, in a manner sensitive to an antioxidant, N-acetyl-cysteine (NAC). Some compounds exhibited lower GI50 values than curcumin, some efficiently induced cell senescence, and others markedly increased ROS levels, efficiently induced cell death and suppressed tumor formation in a xenograft mouse model, without any detectable side effects. A clustering analysis of the selected compounds and their measurement variables revealed that anti-tumorigenic activity was most well-correlated with an increase in ROS levels. Pulldown assays and a molecular docking analysis showed that curcumin derivatives competed with co-enzymes to bind to the respective ROS metabolic enzymes and inhibited their enzymatic activities. CONCLUSIONS: The analysis of novel curcumin derivatives established the importance of ROS upregulation in suppression of tumorigenesis, and these compounds are potentially useful for the development of an anti-cancer drug with few side effects.

An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease.

BACKGROUND: There are different and complicated associations between genes and diseases. Finding the causal associations between genes and specific diseases is still challenging. In this work we present a method to predict novel associations of genes and pathways with inflammatory bowel disease (IBD) by integrating information of differential gene expression, protein-protein interaction and known disease genes related to IBD. RESULTS: We downloaded IBD gene expression data from NCBI's Gene Expression Omnibus, performed statistical analysis to determine differentially expressed genes, collected known IBD genes from DisGeNet database, which were used to construct a IBD related PPI network with HIPPIE database. We adapted our graph-based clustering algorithm DPClusO to cluster the disease PPI network. We evaluated the statistical significance of the identified clusters in the context of determining the richness of IBD genes using Fisher's exact test and predicted novel genes related to IBD. We showed 93.8% of our predictions are correct in the context of other databases and published literatures related to IBD. CONCLUSIONS: Finding disease-causing genes is necessary for developing drugs with synergistic effect targeting many genes simultaneously. Here we present an approach to identify novel disease genes and pathways and discuss our approach in the context of IBD. The approach can be generalized to find disease-associated genes for other diseases.

Molecular Components of Arabidopsis Intact Vacuoles Clarified with Metabolomic and Proteomic Analyses.

We analyzed the metabolites and proteins contained in pure intact vacuoles isolated from Arabidopsis suspension-cultured cells using capillary electrophoresis-mass spectrometry (CE-MS), Fourier transform-ion cyclotron resonance (FT-ICR)-MS and liquid chromatography (LC)-MS. We identified 21 amino acids and five organic acids as major primary metabolites in the vacuoles with CE-MS. Further, we identified small amounts of 27 substances including well-known vacuolar molecules, but also some unexpected substances (e.g. organic phosphate compounds). Non-target analysis of the vacuolar sample with FT-ICR-MS suggested that there are 1,106 m/z peaks that could predict the 5,090 molecular formulae, and we have annotated 34 compounds in these peaks using the KNapSAck database. By conducting proteomic analysis of vacuolar sap, we found 186 proteins in the same vacuole samples. Since the vacuole is known as a major degradative compartment, many of these were hydrolases, but we also found various oxidoreductases and transferases. The relationships between the proteins and metabolites in the vacuole are discussed.

Co-ordinated Regulations of mRNA Synthesis and Decay during Cold Acclimation in Arabidopsis Cells.

Plants possess a cold acclimation system to acquire freezing tolerance through pre-exposure to non-freezing low temperatures. The transcriptional cascade of C-repeat-binding factors (CBFs)/dehydration response element-binding factors (DREBs) is considered a major transcriptional regulatory pathway during cold acclimation. However, little is known regarding the functional significance of mRNA stability regulation in the response of gene expression to cold stress. The actual level of individual mRNAs is determined by a balance between mRNA synthesis and degradation. Therefore, it is important to assess the regulatory steps to increase our understanding of gene regulation. Here, we analyzed temporal changes in mRNA amounts and half-lives in response to cold stress in Arabidopsis cell cultures based on genome-wide analysis. In this mRNA decay array method, mRNA half-life measurements and microarray analyses were combined. In addition, temporal changes in the integrated value of transcription rates were estimated from the above two parameters using a mathematical approach. Our results showed that several cold-responsive genes, including Cold-regulated 15a, were relatively destabilized, whereas the mRNA amounts were increased during cold treatment by accelerating the transcription rate to overcome the destabilization. Considering the kinetics of mRNA synthesis and degradation, this apparently contradictory result supports that mRNA destabilization is advantageous for the swift increase in CBF-responsive genes in response to cold stress.

Finding an appropriate equation to measure similarity between binary vectors: case studies on Indonesian and Japanese herbal medicines.

BACKGROUND: The binary similarity and dissimilarity measures have critical roles in the processing of data consisting of binary vectors in various fields including bioinformatics and chemometrics. These metrics express the similarity and dissimilarity values between two binary vectors in terms of the positive matches, absence mismatches or negative matches. To our knowledge, there is no published work presenting a systematic way of finding an appropriate equation to measure binary similarity that performs well for certain data type or application. A proper method to select a suitable binary similarity or dissimilarity measure is needed to obtain better classification results. RESULTS: In this study, we proposed a novel approach to select binary similarity and dissimilarity measures. We collected 79 binary similarity and dissimilarity equations by extensive literature search and implemented those equations as an R package called bmeasures. We applied these metrics to quantify the similarity and dissimilarity between herbal medicine formulas belonging to the Indonesian Jamu and Japanese Kampo separately. We assessed the capability of binary equations to classify herbal medicine pairs into match and mismatch efficacies based on their similarity or dissimilarity coefficients using the Receiver Operating Characteristic (ROC) curve analysis. According to the area under the ROC curve results, we found Indonesian Jamu and Japanese Kampo datasets obtained different ranking of binary similarity and dissimilarity measures. Out of all the equations, the Forbes-2 similarity and the Variant of Correlation similarity measures are recommended for studying the relationship between Jamu formulas and Kampo formulas, respectively. CONCLUSIONS: The selection of binary similarity and dissimilarity measures for multivariate analysis is data dependent. The proposed method can be used to find the most suitable binary similarity and dissimilarity equation wisely for a particular data. Our finding suggests that all four types of matching quantities in the Operational Taxonomic Unit (OTU) table are important to calculate the similarity and dissimilarity coefficients between herbal medicine formulas. Also, the binary similarity and dissimilarity measures that include the negative match quantity d achieve better capability to separate herbal medicine pairs compared to equations that exclude d.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer.

Targeted Integration of RNA-Seq and Metabolite Data to Elucidate Curcuminoid Biosynthesis in Four Curcuma Species.

Curcuminoids, namely curcumin and its analogs, are secondary metabolites that act as the primary active constituents of turmeric (Curcuma longa). The contents of these curcuminoids vary among species in the genus Curcuma. For this reason, we compared two wild strains and two cultivars to understand the differences in the synthesis of curcuminoids. Because the fluxes of metabolic reactions depend on the amounts of their substrate and the activity of the catalysts, we analyzed the metabolite concentrations and gene expression of related enzymes. We developed a method based on RNA sequencing (RNA-Seq) analysis that focuses on a specific set of genes to detect expression differences between species in detail. We developed a 'selection-first' method for RNA-Seq analysis in which short reads are mapped to selected enzymes in the target biosynthetic pathways in order to reduce the effect of mapping errors. Using this method, we found that the difference in the contents of curcuminoids among the species, as measured by gas chromatography-mass spectrometry, could be explained by the changes in the expression of genes encoding diketide-CoA synthase, and curcumin synthase at the branching point of the curcuminoid biosynthesis pathway.

Evaluation of structural and thermophysical effects on the measurement accuracy of deep body thermometers based on dual-heat-flux method.

To help pave a path toward the practical use of continuous unconstrained noninvasive deep body temperature measurement, this study aims to evaluate the structural and thermophysical effects on measurement accuracy for the dual-heat-flux method (DHFM). By considering the thermometer's height, radius, conductivity, density and specific heat as variables affecting the accuracy of DHFM measurement, we investigated the relationship between those variables and accuracy using 3-D models based on finite element method. The results of our simulation study show that accuracy is proportional to the radius but inversely proportional to the thickness of the thermometer when the radius is less than 30.0mm, and is also inversely proportional to the heat conductivity of the heat insulator inside the thermometer. The insights from this study would help to build a guideline for design, fabrication and optimization of DHFM-based thermometers, as well as their practical use.

KNApSAcK Metabolite Activity Database for retrieving the relationships between metabolites and biological activities.

Databases (DBs) are required by various omics fields because the volume of molecular biology data is increasing rapidly. In this study, we provide instructions for users and describe the current status of our metabolite activity DB. To facilitate a comprehensive understanding of the interactions between the metabolites of organisms and the chemical-level contribution of metabolites to human health, we constructed a metabolite activity DB known as the KNApSAcK Metabolite Activity DB. It comprises 9,584 triplet relationships (metabolite-biological activity-target species), including 2,356 metabolites, 140 activity categories, 2,963 specific descriptions of biological activities and 778 target species. Approximately 46% of the activities described in the DB are related to chemical ecology, most of which are attributed to antimicrobial agents and plant growth regulators. The majority of the metabolites with antimicrobial activities are flavonoids and phenylpropanoids. The metabolites with plant growth regulatory effects include plant hormones. Over half of the DB contents are related to human health care and medicine. The five largest groups are toxins, anticancer agents, nervous system agents, cardiovascular agents and non-therapeutic agents, such as flavors and fragrances. The KNApSAcK Metabolite Activity DB is integrated within the KNApSAcK Family DBs to facilitate further systematized research in various omics fields, especially metabolomics, nutrigenomics and foodomics. The KNApSAcK Metabolite Activity DB could also be utilized for developing novel drugs and materials, as well as for identifying viable drug resources and other useful compounds.

An application of a relational database system for high-throughput prediction of elemental compositions from accurate mass values.

SUMMARY: High-accuracy mass values detected by high-resolution mass spectrometry analysis enable prediction of elemental compositions, and thus are used for metabolite annotations in metabolomic studies. Here, we report an application of a relational database to significantly improve the rate of elemental composition predictions. By searching a database of pre-calculated elemental compositions with fixed kinds and numbers of atoms, the approach eliminates redundant evaluations of the same formula that occur in repeated calculations with other tools. When our approach is compared with HR2, which is one of the fastest tools available, our database search times were at least 109 times shorter than those of HR2. When a solid-state drive (SSD) was applied, the search time was 488 times shorter at 5 ppm mass tolerance and 1833 times at 0.1 ppm. Even if the search by HR2 was performed with 8 threads in a high-spec Windows 7 PC, the database search times were at least 26 and 115 times shorter without and with the SSD. These improvements were enhanced in a low spec Windows XP PC. We constructed a web service 'MFSearcher' to query the database in a RESTful manner. AVAILABILITY AND IMPLEMENTATION: Available for free at http://webs2.kazusa.or.jp/mfsearcher. The web service is implemented in Java, MySQL, Apache and Tomcat, with all major browsers supported. CONTACT: sakurai@kazusa.or.jp SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.