ILDgenDB: integrated genetic knowledge resource for interstitial lung diseases (ILDs).

Interstitial lung diseases (ILDs) are a diverse group of ∼200 acute and chronic pulmonary disorders that are characterized by variable amounts of inflammation, fibrosis and architectural distortion with substantial morbidity and mortality. Inaccurate and delayed diagnoses increase the risk, especially in developing countries. Studies have indicated the significant roles of genetic elements in ILDs pathogenesis. Therefore, the first genetic knowledge resource, ILDgenDB, has been developed with an objective to provide ILDs genetic data and their integrated analyses for the better understanding of disease pathogenesis and identification of diagnostics-based biomarkers. This resource contains literature-curated disease candidate genes (DCGs) enriched with various regulatory elements that have been generated using an integrated bioinformatics workflow of databases searches, literature-mining and DCGs-microRNA (miRNAs)-single nucleotide polymorphisms (SNPs) association analyses. To provide statistical significance to disease-gene association, ILD-specificity index and hypergeomatric test scores were also incorporated. Association analyses of miRNAs, SNPs and pathways responsible for the pathogenesis of different sub-classes of ILDs were also incorporated. Manually verified 299 DCGs and their significant associations with 1932 SNPs, 2966 miRNAs and 9170 miR-polymorphisms were also provided. Furthermore, 216 literature-mined and proposed biomarkers were identified. The ILDgenDB resource provides user-friendly browsing and extensive query-based information retrieval systems. Additionally, this resource also facilitates graphical view of predicted DCGs-SNPs/miRNAs and literature associated DCGs-ILDs interactions for each ILD to facilitate efficient data interpretation. Outcomes of analyses suggested the significant involvement of immune system and defense mechanisms in ILDs pathogenesis. This resource may potentially facilitate genetic-based disease monitoring and diagnosis.Database URL: http://14.139.240.55/ildgendb/index.php.

Establishment of hybridized focus measure functions as a universal method for autofocusing.

Exact focusing is essential for any automatic image capturing system. Performances of focus measure functions (FMFs) used for autofocusing are sensitive to image contents and imaging systems. Therefore, identification of universal FMF assumes a lot of significance. Eight FMFs were hybridized in pairs of two and implemented simultaneously on a single stack to calculate the hybrid focus measure. In total, 28 hybrid FMFs (HFMFs) and eight FMFs were implemented on stacks of images from three different imaging modalities. Performance of FMFs was found to the best at 50% region sampling. Accuracy, focus error, and false maxima were calculated to evaluate the performance of each FMF. Nineteen HFMFs provided >90% accuracy. Image distortion (noise, contrast, saturation, illumination, etc.) was performed to evaluate robustness of HFMFs. Hybrid of tenengrad variance and steerable filter-based (VGRnSFB) FMFs was identified as the most robust and accurate function with an accuracy of ≥90% and a relatively lower focus error and false maxima rate. Sharpness of focus curve of VGRnSFB along with eight individual FMFs was also computed to determine the efficacy of HFMF for the optimization process. VGRnSFB HFMF may be implemented for automated capturing of an image for any imaging system.

Ziehl-Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis.

Ziehl-Neelsen stained microscopy is a crucial bacteriological test for tuberculosis detection, but its sensitivity is poor. According to the World Health Organization (WHO) recommendation, 300 viewfields should be analyzed to augment sensitivity, but only a few viewfields are examined due to patient load. Therefore, tuberculosis diagnosis through automated capture of the focused image (autofocusing), stitching of viewfields to form mosaics (autostitching), and automatic bacilli segmentation (grading) can significantly improve the sensitivity. However, the lack of unified datasets impedes the development of robust algorithms in these three domains. Therefore, the Ziehl-Neelsen sputum smear microscopy image database (ZNSM iDB) has been developed, and is freely available. This database contains seven categories of diverse datasets acquired from three different bright-field microscopes. Datasets related to autofocusing, autostitching, and manually segmenting bacilli can be used for developing algorithms, whereas the other four datasets are provided to streamline the sensitivity and specificity. All three categories of datasets were validated using different automated algorithms. As images available in this database have distinctive presentations with high noise and artifacts, this referral resource can also be used for the validation of robust detection algorithms. The ZNSM-iDB also assists for the development of methods in automated microscopy.

Role of Gist and PHOG features in computer-aided diagnosis of tuberculosis without segmentation.

PURPOSE: Effective diagnosis of tuberculosis (TB) relies on accurate interpretation of radiological patterns found in a chest radiograph (CXR). Lack of skilled radiologists and other resources, especially in developing countries, hinders its efficient diagnosis. Computer-aided diagnosis (CAD) methods provide second opinion to the radiologists for their findings and thereby assist in better diagnosis of cancer and other diseases including TB. However, existing CAD methods for TB are based on the extraction of textural features from manually or semi-automatically segmented CXRs. These methods are prone to errors and cannot be implemented in X-ray machines for automated classification. METHODS: Gabor, Gist, histogram of oriented gradients (HOG), and pyramid histogram of oriented gradients (PHOG) features extracted from the whole image can be implemented into existing X-ray machines to discriminate between TB and non-TB CXRs in an automated manner. Localized features were extracted for the above methods using various parameters, such as frequency range, blocks and region of interest. The performance of these features was evaluated against textural features. Two digital CXR image datasets (8-bit DA and 14-bit DB) were used for evaluating the performance of these features. RESULTS: Gist (accuracy 94.2% for DA, 86.0% for DB) and PHOG (accuracy 92.3% for DA, 92.0% for DB) features provided better results for both the datasets. These features were implemented to develop a MATLAB toolbox, TB-Xpredict, which is freely available for academic use at http://sourceforge.net/projects/tbxpredict/. This toolbox provides both automated training and prediction modules and does not require expertise in image processing for operation. CONCLUSION: Since the features used in TB-Xpredict do not require segmentation, the toolbox can easily be implemented in X-ray machines. This toolbox can effectively be used for the mass screening of TB in high-burden areas with improved efficiency.

Can mycobacterial genomics generate novel targets as speed-breakers against the race for drug resistance.

The rise of multi-drug resistant and extensively drug resistant M. tuberculosis around the world poses a great threat to human health, and necessitates development of new, effective and inexpensive anti-tubercular agents. The availability of knowledge on molecular biology of M. tuberculosis infection coupled with whole genome sequences, transcriptomic, proteomic and metabolomic data sets have provided insights on the genes/proteins indispensable for initiation and maintenance of persistence, cross-talk with and/or sensing the host immune response, and finally the reactivation of persistent M. tuberculosis to a growing state. The review will focus on analysis of current state of M. tuberculosis genomic resources, host-pathogen interaction studies in the context of pathogen persistence, and the efforts made or required in the development and utilization of computational tools, models and metabolic network analyses to speed up the process of drug target discovery, particularly eradicating the dormant infections.

Common antigens prediction in bacterial bioweapons: a perspective for vaccine design.

Bioweapons (BWs) are a serious threat to mankind and the lack of efficient vaccines against bacterial bioweapons (BBWs) further worsens the situation in face of BW attack. Experts believe that difficulties in detection and ease in dissemination of deadly pathogens make BW a better option for attack compared to nuclear weapons. Molecular biology techniques facilitate the use of genetically modified BBWs thus creating uncertainty on which bacteria will be used for BW attack. In the present work, available resources such as proteomic sequences of BBWs, protective antigenic proteins (PAPs) reported in Protegen database and VaxiJen dataset, and immunogenic epitopes in immune epitope database (IEDB) were used to predict potential broad-specific vaccine candidates against BBWs. Comparison of proteomes sequences of BBWs and their analyses using in-house PERL scripts identified 44 conserved proteins and many of them were known to be immunogenic. Comparison of conserved proteins against PAPs identified six either as PAPs or their homologues with a potential of providing protection against multiple pathogens. Similarly, mapping of conserved proteins against experimentally known IEDB epitopes identified six epitopes which had exact epitope match in four proteins including three from earlier predicted six PAPs. These epitopes were also reported to provide protection against several pathogens. In the backdrop of conserved heat shock GroEL protein from Salmonella enterica providing protection against five diverse bacterial pathogens involved in different diseases, and synthetic proteins produced by combination of epitopes from Mycobacterium tuberculosis and 4 viruses providing protection against both bacterium and viruses, the identified putative immunogenic conserved proteins and immune-protective epitopes can further be explored for their potential as broad-specific vaccine candidates against BBWs.

Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions.

BACKGROUND: Subunit vaccines based on recombinant proteins have been effective in preventing infectious diseases and are expected to meet the demands of future vaccine development. Computational approach, especially reverse vaccinology (RV) method has enormous potential for identification of protein vaccine candidates (PVCs) from a proteome. The existing protective antigen prediction software and web servers have low prediction accuracy leading to limited applications for vaccine development. Besides machine learning techniques, those software and web servers have considered only protein's adhesin-likeliness as criterion for identification of PVCs. Several non-adhesin functional classes of proteins involved in host-pathogen interactions and pathogenesis are known to provide protection against bacterial infections. Therefore, knowledge of bacterial pathogenesis has potential to identify PVCs. RESULTS: A web server, Jenner-Predict, has been developed for prediction of PVCs from proteomes of bacterial pathogens. The web server targets host-pathogen interactions and pathogenesis by considering known functional domains from protein classes such as adhesin, virulence, invasin, porin, flagellin, colonization, toxin, choline-binding, penicillin-binding, transferring-binding, fibronectin-binding and solute-binding. It predicts non-cytosolic proteins containing above domains as PVCs. It also provides vaccine potential of PVCs in terms of their possible immunogenicity by comparing with experimentally known IEDB epitopes, absence of autoimmunity and conservation in different strains. Predicted PVCs are prioritized so that only few prospective PVCs could be validated experimentally. The performance of web server was evaluated against known protective antigens from diverse classes of bacteria reported in Protegen database and datasets used for VaxiJen server development. The web server efficiently predicted known vaccine candidates reported from Streptococcus pneumoniae and Escherichia coli proteomes. The Jenner-Predict server outperformed NERVE, Vaxign and VaxiJen methods. It has sensitivity of 0.774 and 0.711 for Protegen and VaxiJen dataset, respectively while specificity of 0.940 has been obtained for the latter dataset. CONCLUSIONS: Better prediction accuracy of Jenner-Predict web server signifies that domains involved in host-pathogen interactions and pathogenesis are better criteria for prediction of PVCs. The web server has successfully predicted maximum known PVCs belonging to different functional classes. Jenner-Predict server is freely accessible at http://117.211.115.67/vaccine/home.html.

EpiCombFlu: exploring known influenza epitopes and their combination to design a universal influenza vaccine.

MOTIVATION: Influenza is responsible for half a million deaths annually, and vaccination is the best preventive measure against this pervasive health problem. Influenza vaccines developed from surveillance data of each season are strain-specific, and therefore, are unable to provide protection against pandemic strains arising from antigenic shift and drift. Seasonal epidemics and occasional pandemics of influenza have created a need for a universal influenza vaccine (UIV). Researchers have shown that a combination of conserved epitopes has the potential to be used as a UIV. RESULT: In the present work, available data on strains, proteins, epitopes and their associated information were used to develop a Web resource, 'EpiCombFlu', which can explore different influenza epitopes and their combinations for conservation among different strains, population coverage and immune response for vaccine design. Forward selection algorithm was implemented in EpiCombFlu to select optimum combination of epitopes that may be expressed and evaluated as potential UIV. AVAILABILITY: The Web resource is freely available at http://117.211.115.67/influenza/home.html. CONTACT: chittaranjan.rout@juit.ac.in SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria.

BACKGROUND: Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed. METHODS: A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed. RESULTS: The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets. CONCLUSIONS/SIGNIFICANCE: UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.