Potential anti-Pythium insidiosum therapeutics identified through screening of agricultural fungicides

Pythiosis is a life-threatening infectious disease caused by the oomycete Pythium insidiosum. Clinical manifestations of pythiosis include an eye, blood vessel, skin, or gastrointestinal tract infection. Pythiosis has been increasingly reported worldwide, with an overall mortality rate of 28%. Radical surgery is required to save patients' lives due to the limited efficacy of antimicrobial drugs. Effective medical treatments are urgently needed for pythiosis. This study aims to find anti-P. insidiosum agents by screening 17 agricultural fungicides that inhibit plant-pathogenic oomycetes and validating their efficacy and safety. Cyazofamid outperformed other fungicides as it can potently inhibit genetically diverse P. insidiosum isolates while exhibiting minimal cellular toxicities. The calculated therapeutic scores determined that the concentration of cyazofamid causing significant cellular toxicities was eight times greater than the concentration of the drug effectively inhibiting P. insidiosum. Furthermore, other studies showed that cyazofamid exhibits low-to-moderate toxicities in animals. The mechanism of cyazofamid action is likely the inhibition of cytochrome b, an essential component in ATP synthesis. Molecular docking and dynamic analyses depicted a stable binding of cyazofamid to the Qi site of the P. insidiosum's cytochrome b orthologous protein. In conclusion, our search for an effective anti-P. insidiosum drug indicated that cyazofamid is a promising candidate for treating pythiosis. With its high efficacy and low toxicity, cyazofamid is a potential chemical for treating pythiosis, reducing the need for radical surgeries, and improving recovery rates. Our findings could pave the way for the development of new and effective treatments for pythiosis.IMPORTANCEPythiosis is a severe infection caused by Pythium insidiosum. The disease is prevalent in tropical/subtropical regions. This infectious condition is challenging to treat with antifungal drugs and often requires surgical removal of the infected tissue. Pythiosis can be fatal if not treated promptly. There is a need for a new treatment that effectively inhibits P. insidiosum. This study screened 17 agricultural fungicides that target plant-pathogenic oomycetes and found that cyazofamid was the most potent in inhibiting P. insidiosum. Cyazofamid showed low toxicity to mammalian cells and high affinity to the P. insidiosum's cytochrome b, which is involved in energy production. Cyazofamid could be a promising candidate for the treatment of pythiosis, as it could reduce the need for surgery and improve the survival rate of patients. This study provides valuable insights into the biology and drug susceptibility of P. insidiosum and opens new avenues for developing effective therapies for pythiosis.

iEdgeDTA: integrated edge information and 1D graph convolutional neural networks for binding affinity prediction

Artificial intelligence has become more prevalent in broad fields, including drug discovery, in which the process is costly and time-consuming when conducted through wet experiments. As a result, drug repurposing, which tries to utilize approved and low-risk drugs for a new purpose, becomes more attractive. However, screening candidates from many drugs for specific protein targets is still expensive and tedious. This study aims to leverage computational resources to aid drug discovery by utilizing drug-protein interaction data and estimating their interaction strength, so-called binding affinity. Our estimation approach addresses multiple challenges encountered in the field. First, we employed a graph-based deep learning technique to overcome the limitations of drug compounds represented in string format by incorporating background knowledge of node and edge information as separate multi-dimensional features. Second, we tackled the complexities associated with extracting the representation and structure of proteins by utilizing a pre-trained model for feature extraction. Also, we employed graph operations over the 1D representation of a protein sequence to overcome the fixed-length problem typically encountered in language model tasks. In addition, we conducted a comparative analysis with a baseline model that creates a protein graph from a contact map prediction model, giving valuable insights into the performance and effectiveness of our proposed method. We evaluated the performance of our model using the same benchmark datasets with a variety of matrices as other previous work, and the results show that our model achieved the best prediction results while requiring no contact map information compared to other graph-based methods.

STRategy: A support system for collecting and analyzing next-generation sequencing data of short tandem repeats for forensic science

Short tandem repeats (STRs) are short repeated sequences commonly found in the human genome and valuable in forensic science, used for human identity and relatedness markers. Next-generation sequencing (NGS) technologies, e.g., ForenSeq Signature Prep, can sequence STRs, inferring length-based alleles and single nucleotide polymorphisms (SNPs) and providing valuable insights into population and sub-population structures. Despite the potential benefits of NGS for STRs, no open-source software platform integrates the collection, management, and analysis of STR data from NGS into one place. Users must use multiple programs to process their STR data and then collect the results into a separate database or a file system folder. Moreover, analyzing repeat structures (STR repeat motifs) may require learning multiple software tools, making the process inefficient and cumbersome. To address this gap, we introduce the STRategy, a standalone web-based application supporting essential STR data management and analysis capabilities. The STRategy allows users to collect their data into its database, automatically calculates forensic parameters, and visualizes the analyzed data in various forms. Users can search the database using different options, such as by profile, loci, and genotypes, with and without a specific test kit. Moreover, users can also find the nucleotide variants of a locus among the samples. We designed the STRategy for internal use in a laboratory or an organization. Hence, our system includes role-based access control that allows users to search for or access specific data based on their responsibilities. The administrator role can customize the system, for example, configure maps according to the samples' geographic data, and manage reference STR repeat motifs. A laboratory or an organization can download and install a copy of STRategy on their local system using Docker, as described in https://github.com/cucpbioinfo/STRategy. In summary, the STRategy is an end-to-end system that provides users with a database to collect the analyzed STR data from NGS, the dynamic analyses of forensic parameters, and the variants of STR patterns according to the newly added samples, which are then explorable via various search options and visualizations. The system is helpful for both forensic investigations and forensic genetics.

Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-Coding RNA Identification

Long non-coding RNAs (lncRNAs) play crucial roles in many biological processes and are implicated in several diseases. With the next-generation sequencing technologies, substantial unannotated transcripts have been discovered. Classifying unannotated transcripts using biological experiments are more time-consuming and expensive than computational approaches. Several tools are available for identifying long non-coding RNAs. These tools, however, did not explain the features in their tools that contributed to the prediction results. Here, we present Xlnc1DCNN, a tool for distinguishing long non-coding RNAs (lncRNAs) from protein-coding transcripts (PCTs) using a one-dimensional convolutional neural network with prediction explanations. The evaluation results of the human test set showed that Xlnc1DCNN outperformed other state-of-the-art tools in terms of accuracy and F1-score. The explanation results revealed that lncRNA transcripts were mainly identified as sequences with no conserved regions, short patterns with unknown functions, or only regions of transmembrane helices while protein-coding transcripts were mostly classified by conserved protein domains or families. The explanation results also conveyed the probably inconsistent annotations among the public databases, lncRNA transcripts which contain protein domains, protein families, or intrinsically disordered regions (IDRs). Xlnc1DCNN is freely available at https://github.com/cucpbioinfo/Xlnc1DCNN.

The Thai reference exome (T-REx) variant database

To maximize the potential of genomics in medicine, it is essential to establish databases of genomic variants for ethno-geographic groups that can be used for filtering and prioritizing candidate pathogenic variants. Populations with non-European ancestry are poorly represented among current genomic variant databases. Here, we report the first high-density survey of genomic variants for the Thai population, the Thai Reference Exome (T-REx) variant database. T-REx comprises exome sequencing data of 1092 unrelated Thai individuals. The targeted exome regions common among four capture platforms cover 30.04 Mbp on autosomes and chromosome X. 345 681 short variants (18.27% of which are novel) and 34 907 copy number variations were found. Principal component analysis on 38 469 single nucleotide variants present worldwide showed that the Thai population is most genetically similar to East and Southeast Asian populations. Moreover, unsupervised clustering revealed six Thai subpopulations consistent with the evidence of gene flow from neighboring populations. The prevalence of common pathogenic variants in T-REx was investigated in detail, which revealed subpopulation-specific patterns, in particular variants associated with erythrocyte disorders such as the HbE variant in HBB and the Viangchan variant in G6PD. T-REx serves as a pivotal addition to the current databases for genomic medicine.

Norcycloartocarpin targets Akt and suppresses Akt-dependent survival and epithelial-mesenchymal transition in lung cancer cells

In searching for novel targeted therapeutic agents for lung cancer treatment, norcycloartocarpin from Artocarpus gomezianus was reported in this study to promisingly interacted with Akt and exerted the apoptosis induction and epithelial-to-mesenchymal transition suppression. Selective cytotoxic profile of norcycloartocarpin was evidenced with approximately 2-fold higher IC50 in normal dermal papilla cells (DPCs) compared with human lung cancer A549, H460, H23, and H292 cells. We found that norcycloartocarpin suppressed anchorage-independent growth, cell migration, invasion, filopodia formation, and decreased EMT in a dose-dependent manner at 24 h, which were correlated with reduced protein levels of N-cadherin, Vimentin, Slug, p-FAK, p-Akt, as well as Cdc42. In addition, norcycloartocarpin activated apoptosis caspase cascade associating with restoration of p53, down-regulated Bcl-2 and augmented Bax in A549 and H460 cells. Interestingly, norcycloartocarpin showed potential inhibitory role on protein kinase B (Akt) the up-stream dominant molecule controlling EMT and apoptosis. Computational molecular docking analysis further confirmed that norcycloartocarpin has the best binding affinity of -12.52 kcal/mol with Akt protein at its critical active site. As Akt has recently recognized as an attractive molecular target for therapeutic approaches, these findings support its use as a plant-derived anticancer agent in cancer therapy.

inCNV: An Integrated Analysis Tool for Copy Number Variation on Whole Exome Sequencing

The detection of copy number variations (CNVs) on whole-exome sequencing (WES) represents a cost-effective technique for the study of genetic variants. This approach, however, has encountered an obstacle with high false-positive rates due to biases from exome sequencing capture kits and GC contents. Although plenty of CNV detection tools have been developed, they do not perform well with all types of CNVs. In addition, most tools lack features of genetic annotation, CNV visualization, and flexible installation, requiring users to put much effort into CNV interpretation. Here, we present “inCNV,” a web-based application that can accept multiple CNV-tool results, then integrate and prioritize them with user-friendly interfaces. This application helps users analyze the importance of called CNVs by generating CNV annotations from Ensembl, Database of Genomic Variants (DGV), ClinVar, and Online Mendelian Inheritance in Man (OMIM). Moreover, users can select and export CNVs of interest including their flanking sequences for primer design and experimental verification. We demonstrated how inCNV could help users filter and narrow down the called CNVs to a potentially novel CNV, a common CNV within a group of samples of the same disease, or a de novo CNV of a sample within the same family. Besides, we have provided in CNV as a docker image for ease of installation (https://github.com/saowwapark/inCNV).

Four novel mutations of FAM20A in amelogenesis imperfecta type IG and review of literature for its genotype and phenotype spectra

Amelogenesis imperfecta type IG (AI1G) is caused by mutations in FAM20A. Genotypic and phenotypic features of AI1G are diverse and their full spectra remain to be characterized. The aim of this study was to identify and summarize variants in FAM20A in a broad population of patients with AI1G. We identified a Thai female (Pt-1) and a Saudi male (Pt-2) affected with AI1G. Both had hypoplastic enamel, gingival hyperplasia, and intrapulpal calcification. Pt-1 also had rapidly progressive embedding of unerupted teeth, early eruption of permanent teeth, and spontaneous dental infection. Uniquely, Pt-2 had all permanent teeth erupted which was uncommon in AI1G patients. Whole exome sequencing (WES) identified that Pt-1 was heterozygous for FAM20A, c.758A > G (p.Tyr253Cys), inherited from her father. The mutation on maternal allele was not detected by WES. Pt-2 possessed compound heterozygous mutations, c.1248dupG (p.Phe417Valfs*7); c.1081C > T (p.Arg361Cys) in FAM20A. Array comparative genomic hybridization (aCGH), cDNA sequencing, and whole genome sequencing successfully identified 7531 bp deletion on Pt-1's maternal allele. This was the largest FAM20A deletion ever found. A review of all 70 patients from 50 independent families with AI1G (including two families in this study) showed that the penetrance of hypoplastic enamel and gingival hyperplasia was complete. Unerupted permanent teeth were found in all 70 patients except Pt-2. Exons 1 and 11 were mutation-prone. Most mutations were frameshift. Certain variants showed founder effect. To conclude, this study reviews and expands phenotypic and genotypic spectra of AI1G. A large deletion missed by WES can be detected by WGS. Hypoplastic enamel, gingival hyperplasia, and unerupted permanent teeth prompt genetic testing of FAM20A. Screening of nephrocalcinosis, early removal of embedded teeth, and monitoring of dental infection are recommended.

Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual representations

BACKGROUND

The Bacteria Biotope (BB) task is a biomedical relation extraction (RE) that aims to study the interaction between bacteria and their locations. This task is considered to pertain to fundamental knowledge in applied microbiology. Some previous investigations conducted the study by applying feature-based models; others have presented deep-learning-based models such as convolutional and recurrent neural networks used with the shortest dependency paths (SDPs). Although SDPs contain valuable and concise information, some parts of crucial information that is required to define bacterial location relationships are often neglected. Moreover, the traditional word-embedding used in previous studies may suffer from word ambiguation across linguistic contexts.

RESULTS

Here, we present a deep learning model for biomedical RE. The model incorporates feature combinations of SDPs and full sentences with various attention mechanisms. We also used pre-trained contextual representations based on domain-specific vocabularies. To assess the model's robustness, we introduced a mean F1 score on many models using different random seeds. The experiments were conducted on the standard BB corpus in BioNLP-ST'16. Our experimental results revealed that the model performed better (in terms of both maximum and average F1 scores; 60.77% and 57.63%, respectively) compared with other existing models.

CONCLUSIONS

We demonstrated that our proposed contributions to this task can be used to extract rich lexical, syntactic, and semantic features that effectively boost the model's performance. Moreover, we analyzed the trade-off between precision and recall to choose the proper cut-off to use in real-world applications.

Transcriptome-based discovery of pathways and genes related to reproduction of the black tiger shrimp (Penaeus monodon)

The black tiger shrimp (Penaeus monodon) is an aquatic animal with considerable economic importance. Poor reproductive maturation in captivity impedes sustainable aquaculture production of this species. This study aims to provide transcriptomic information on reproductive organs using 454 pyrosequencing technology. The transcriptome analysis of ovaries and testes revealed 41,136 transcripts with 20,192 contigs. We found novel sets of transcripts completing several important reproductive pathways such as gonadotropin-releasing hormone (GnRH) signaling and progesterone-mediated oocyte maturation. In addition, we found transcripts encoding for receptors crucial for initiation of the maturation process, such as GnRH receptor (GnRHR), voltage-dependent calcium channel L type alpha-1C (CACNA1C) and epidermal growth factor receptor (EGFR). Moreover, we found a putative novel vigillin encoding for an estrogen-induced polysome-associated protein, which has not been reported in penaeid shrimp. These results suggest that the regulatory mechanism of the pathways important to reproductive maturation might be similar to those in the vertebrate. The obtained data will consequently accelerate the study of reproductive biology of this important species to ensure a sustainable shrimp farming industry.

A Bioinformatics Method for the Design of Live Attenuated Virus Vaccine Utilizing Host MicroRNA Response Elements

The host microRNA machinery has been employed to control viral replication. To improve safety for live attenuated virus vaccines, the binding sites of the host microRNAs, so-called microRNA response elements (MREs), were incorporated into the virus sequences. These MREs were typically designed for a specific host microRNA and virus sequence with the effectiveness evaluated by experimental trials. Here, we describe a computational flow that can be used to simultaneously design and prioritize the effective MREs in large-scale.

Insights from the genome of Ophiocordyceps polyrhachis-furcata to pathogenicity and host specificity in insect fungi

Background

Ophiocordyceps unilateralis is an outstanding insect fungus for its biology to manipulate host ants’ behavior and for its extreme host-specificity. Through the sequencing and annotation of Ophiocordyceps polyrhachis-furcata, a species in the O. unilateralis species complex specific to the ant Polyrhachis furcata, comparative analyses on genes involved in pathogenicity and virulence between this fungus and other fungi were undertaken in order to gain insights into its biology and the emergence of host specificity.

Results

O. polyrhachis-furcata possesses various genes implicated in pathogenicity and virulence common with other fungi. Overall, this fungus possesses protein-coding genes similar to those found on other insect fungi with available genomic resources (Beauveria bassiana, Metarhizium robertsii (formerly classified as M. anisopliae s.l.), Metarhizium acridum, Cordyceps militaris, Ophiocordyceps sinensis). Comparative analyses in regard of the host ranges of insect fungi showed a tendency toward contractions of various gene families for narrow host-range species, including cuticle-degrading genes (proteases, carbohydrate esterases) and some families of pathogen-host interaction (PHI) genes. For many families of genes, O. polyrhachis-furcata had the least number of genes found; some genes commonly found in other insect fungi are even absent (e.g. Class 1 hydrophobin). However, there are expansions of genes involved in 1) the production of bacterial-like toxins in O. polyrhachis-furcata, compared with other entomopathogenic fungi, and 2) retrotransposable elements.

Conclusions

The gain and loss of gene families helps us understand how fungal pathogenicity in insect hosts evolved. The loss of various genes involved throughout the pathogenesis for O. unilateralis would result in a reduced capacity to exploit larger ranges of hosts and therefore in the different level of host specificity, while the expansions of other gene families suggest an adaptation to particular environments with unexpected strategies like oral toxicity, through the production of bacterial-like toxins, or sophisticated mechanisms underlying pathogenicity through retrotransposons.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2101-4) contains supplementary material, which is available to authorized users.

ChemEx: information extraction system for chemical data curation

Background

Manual chemical data curation from publications is error-prone, time consuming, and hard to maintain up-to-date data sets. Automatic information extraction can be used as a tool to reduce these problems. Since chemical structures usually described in images, information extraction needs to combine structure image recognition and text mining together.

Results

We have developed ChemEx, a chemical information extraction system. ChemEx processes both text and images in publications. Text annotator is able to extract compound, organism, and assay entities from text content while structure image recognition enables translation of chemical raster images to machine readable format. A user can view annotated text along with summarized information of compounds, organism that produces those compounds, and assay tests.

Conclusions

ChemEx facilitates and speeds up chemical data curation by extracting compounds, organisms, and assays from a large collection of publications. The software and corpus can be downloaded from http://www.biotec.or.th/isl/ChemEx.

A computational tool for the design of live attenuated virus vaccine based on microRNA-mediated gene silencing

BACKGROUND

The microRNA-based gene-silencing machinery has been recognized as a promising approach to control viral replication and used for improving safety for the live attenuated virus vaccines. The effective host microRNA response elements (MREs) have been incorporated into a virus sequence mainly based on the experimental trials for identifying both microRNA binding sites and effective mutations. The design of MREs for viral genomes or with multiple host microRNAs of interest, then, will be time and cost consuming.

RESULTS

In this paper, we introduced a computational flow that could be used to design MREs of human microRNAs within Influenza A H1N1 virus gene segments. The main steps of the flow includes locating possible binding sites; MREs, of human microRNAs within the viral sequences using a miRNA target prediction tool (miranda), performing various mutations among mismatched binding positions, calculating the binding energy, score, identity, and the effects of changed physical properties of amino acids according to the changed bases in RNA level, and prioritizing the mutated binding sites. The top ranked MREs of human microRNA hsa-miR-93 is consistent with previous literature while other results waited to be experimentally verified. To make the computational flow easily accessible by virologists, we also developed MicroLive, a web server version of the MRE design flow together with the database of miranda-predicted MREs within gene sequences of seven RNA viruses including Influenza A, dengue, hepatitis C, measles, mumps, poliovirus, and rabies. Users may design MREs of specific human microRNAs for their input viral sequences using MRE design tool or optimize the miranda-predicted MREs of seven viruses available on the system. Also, users could design varied number of MREs for multiple human microRNAs to modulate the degree of live vaccine attenuation and reduce the likelihood of escape mutants.

CONCLUSIONS

The computational design of MREs helps reduce time and cost for experimental trials. While the flow was demonstrated using human microRNAs and Influenza A H1N1 virus, it could be flexibly applied to other hosts (e.g., animals) and viruses of interest for constructing host-specific live attenuated vaccines. Also, it could be deployed for engineering tissue-specific oncolytic viruses in cancer virotherapeutics. The MicroLive web server is freely accessible at http://www.biotec.or.th/isl/microlive.

C-mii: a tool for plant miRNA and target identification

BACKGROUND

MicroRNAs (miRNAs) have been known to play an important role in several biological processes in both animals and plants. Although several tools for miRNA and target identification are available, the number of tools tailored towards plants is limited, and those that are available have specific functionality, lack graphical user interfaces, and restrict the number of input sequences. Large-scale computational identifications of miRNAs and/or targets of several plants have been also reported. Their methods, however, are only described as flow diagrams, which require programming skills and the understanding of input and output of the connected programs to reproduce.

RESULTS

To overcome these limitations and programming complexities, we proposed C-mii as a ready-made software package for both plant miRNA and target identification. C-mii was designed and implemented based on established computational steps and criteria derived from previous literature with the following distinguishing features. First, software is easy to install with all-in-one programs and packaged databases. Second, it comes with graphical user interfaces (GUIs) for ease of use. Users can identify plant miRNAs and targets via step-by-step execution, explore the detailed results from each step, filter the results according to proposed constraints in plant miRNA and target biogenesis, and export sequences and structures of interest. Third, it supplies bird's eye views of the identification results with infographics and grouping information. Fourth, in terms of functionality, it extends the standard computational steps of miRNA target identification with miRNA-target folding and GO annotation. Fifth, it provides helper functions for the update of pre-installed databases and automatic recovery. Finally, it supports multi-project and multi-thread management.

CONCLUSIONS

C-mii constitutes the first complete software package with graphical user interfaces enabling computational identification of both plant miRNA genes and miRNA targets. With the provided functionalities, it can help accelerate the study of plant miRNAs and targets, especially for small and medium plant molecular labs without bioinformaticians. C-mii is freely available at http://www.biotec.or.th/isl/c-mii for both Windows and Ubuntu Linux platforms.

LinkinPath: from sequence to interconnected pathway

SUMMARY

LinkinPath is a pathway mapping and analysis tool that enables users to explore and visualize the list of gene/protein sequences through various Flash-driven interactive web interfaces including KEGG pathway maps, functional composition maps (TreeMaps), molecular interaction/reaction networks and pathway-to-pathway networks. Users can submit single or multiple datasets of gene/protein sequences to LinkinPath to (i) determine the co-occurrence and co-absence of genes/proteins on animated KEGG pathway maps; (ii) compare functional compositions within and among the datasets using TreeMaps; (iii) analyze the statistically enriched pathways across the datasets; (iv) build the pathway-to-pathway networks for each dataset; (v) explore potential interaction/reaction paths between pathways; and (vi) identify common pathway-to-pathway networks across the datasets.

AVAILABILITY

LinkinPath is freely available to all interested users at http://www.biotec.or.th/isl/linkinpath/.

MicroPC (microPC): A comprehensive resource for predicting and comparing plant microRNAs

Background

Plant microRNA (miRNA) has an important role in controlling gene regulation in various biological processes such as cell development, signal transduction, and environmental responses. While information on plant miRNAs and their targets is widely available, accessible online plant miRNA resources are limited; most of them are intended for economically important crops or plant model organisms. With abundant sequence data of numerous plants in public databases such as NCBI and PlantGDB, the identification of their miRNAs and targets would benefit researchers as a central resource for the comparative studies of plant miRNAs.

Results

MicroPC (μPC) is an online plant miRNA resource resulted from large-scale Expressed Sequence Tag (EST) analysis. It consists of 4,006 potential miRNA candidates in 128 families of 125 plant species and 2,995 proteins (4,953 EST sequences) potentially targeted by 78 families of miRNA candidates. In addition, it is incorporated with 1,727 previously reported plant mature miRNA sequences from miRBase. The μPC enables users to compare stored mature or precursor miRNAs and user-supplied sequences among plant species. The search utility allows users to investigate the predicted miRNAs and miRNA targets in detail via various search options such as miRNA family and plant species. To enhance the database usage, the prediction utility provides interactive steps for determining a miRNA or miRNA targets from an input nucleotide sequence and links the prediction results to their homologs in the μPC.

Conclusion

The μPC constitutes the first online resource that enables users to comprehensively compare and predict plant miRNAs and their targets. It imparts a basis for further research on revealing miRNA conservation, function, and evolution across plant species and classification. The μPC is available at http://www.biotec.or.th/isl/micropc.

d-Omix: a mixer of generic protein domain analysis tools

Domain combination provides important clues to the roles of protein domains in protein function, interaction and evolution. We have developed a web server d-Omix (a Mixer of Protein Domain Analysis Tools) aiming as a unified platform to analyze, compare and visualize protein data sets in various aspects of protein domain combinations. With InterProScan files for protein sets of interest provided by users, the server incorporates four services for domain analyses. First, it constructs protein phylogenetic tree based on a distance matrix calculated from protein domain architectures (DAs), allowing the comparison with a sequence-based tree. Second, it calculates and visualizes the versatility, abundance and co-presence of protein domains via a domain graph. Third, it compares the similarity of proteins based on DA alignment. Fourth, it builds a putative protein network derived from domain–domain interactions from DOMINE. Users may select a variety of input data files and flexibly choose domain search tools (e.g. hmmpfam, superfamily) for a specific analysis. Results from the d-Omix could be interactively explored and exported into various formats such as SVG, JPG, BMP and CSV. Users with only protein sequences could prepare an InterProScan file using a service provided by the server as well. The d-Omix web server is freely available at http://www.biotec.or.th/isl/Domix.

Prediction and integration of regulatory and protein-protein interactions

Knowledge of transcriptional regulatory interactions (TRIs) is essential for exploring functional genomics and systems biology in any organism. While several results from genome-wide analysis of transcriptional regulatory networks are available, they are limited to model organisms such as yeast ( 1 ) and worm ( 2 ). Beyond these networks, experiments on TRIs study only individual genes and proteins of specific interest. In this chapter, we present a method for the integration of various data sets to predict TRIs for 54 organisms in the Bioverse ( 3 ). We describe how to compile and handle various formats and identifiers of data sets from different sources and how to predict TRIs using a homology-based approach, utilizing the compiled data sets. Integrated data sets include experimentally verified TRIs, binding sites of transcription factors, promoter sequences, protein subcellular localization, and protein families. Predicted TRIs expand the networks of gene regulation for a large number of organisms. The integration of experimentally verified and predicted TRIs with other known protein-protein interactions (PPIs) gives insight into specific pathways, network motifs, and the topological dynamics of an integrated network with gene expression under different conditions, essential for exploring functional genomics and systems biology.