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The iSwathX web application processes and normalizes mass spectrometry-based proteomics spectral libraries generated in the data-dependent acquisition (DDA) approach. These libraries are stored in various proteomics repositories such as PeptideAtlas and NIST, or are user-generated and provide reference data for data-independent acquisition (DIA) targeted data extraction and analysis. iSwathX 2.0 can efficiently normalize DDA data from different instruments, gathered at different instances, and make it compatible with specific DIA experiments. Novel functions for parallel processing of DDA libraries and DIA report files, along with various data visualizations, are available in iSwathX 2.0. The step-by-step protocols provided here describe how the libraries are uploaded, processed, visualized, and downloaded using various modules of the application. They also provide detailed guidelines on the use of DIA report files for data analysis and visualization. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Processing, combining, and visualizing two DDA libraries Basic Protocol 2: Parallel processing and combination of multiple DDA libraries Basic Protocol 3: Downstream processing, comparison, and visualization of DIA report files., (© 2020 Wiley Periodicals LLC.)

Reproduction efficiency of male animals is one of the key factors influencing the sustainability of livestock. Mass spectrometry (MS) based proteomics has become an important tool for studying seminal plasma proteomes. In this review, we summarize bioinformatics analysis strategies for current proteomics approaches, for identifying novel biomarkers of reproductive robustness., (Copyright © 2019 Elsevier Inc. All rights reserved.)

InCoB, one of the largest annual bioinformatics conferences in the Asia-Pacific region since its launch in 2002, returned to New Delhi, India after 12 years, with a conference attendance of 314 delegates. The 2018 conference had sessions on Big Data and Algorithms, Next Generation Sequencing and Omics Science, Structure, Function and Interactions, Disease and Drug Discovery and Plant and Agricultural Bioinformatics. The conference also featured an industry track as well as panel discussions on Women in Bioinformatics and Democratization vs. Quality control in academic publishing. Asia Pacific Bioinformatics Interaction & Networking Society (APbians) was launched as an APBionet Special Interest Group. Of the 52 oral presentations made, 22 were accepted in supplemental issues of BMC Bioinformatics, BMC Genomics or BMC Medical Genomics and are briefly reviewed here. Next year's InCoB will be held in Jakarta, Indonesia from September 10-12, 2019.

Post-translational modifications (PTMs) can occur soon after translation or at any stage in the lifecycle of a given protein, and they may help regulate protein folding, stability, cellular localisation, activity, or the interactions proteins have with other proteins or biomolecular species. PTMs are crucial to our functional understanding of biology, and new quantitative mass spectrometry (MS) and bioinformatics workflows are maturing both in labelled multiplexed and label-free techniques, offering increasing coverage and new opportunities to study human health and disease. Techniques such as Data Independent Acquisition (DIA) are emerging as promising approaches due to their re-mining capability. Many bioinformatics tools have been developed to support the analysis of PTMs by mass spectrometry, from prediction and identifying PTM site assignment, open searches enabling better mining of unassigned mass spectra-many of which likely harbour PTMs-through to understanding PTM associations and interactions. The remaining challenge lies in extracting functional information from clinically relevant PTM studies. This review focuses on canvassing the options and progress of PTM analysis for large quantitative studies, from choosing the platform, through to data analysis, with an emphasis on clinically relevant samples such as plasma and other body fluids, and well-established tools and options for data interpretation., Competing Interests: The authors declare no conflict of interest.

The biological process known as post-translational modification (PTM) contributes to diversifying the proteome hence affecting many aspects of normal cell biology and pathogenesis. There have been many recently reported PTMs, but lysine phosphoglycerylation has emerged as the most recent subject of interest. Despite a large number of proteins being sequenced, the experimental method for detection of phosphoglycerylated residues remains an expensive, time-consuming and inefficient endeavor in the post-genomic era. Instead, the computational methods are being proposed for accurately predicting phosphoglycerylated lysines. Though a number of predictors are available, performance in detecting phosphoglycerylated lysine residues is still limited. In this paper, we propose a new predictor called PhoglyStruct that utilizes structural information of amino acids alongside a multilayer perceptron classifier for predicting phosphoglycerylated and non-phosphoglycerylated lysine residues. For the experiment, we located phosphoglycerylated and non-phosphoglycerylated lysines in our employed benchmark. We then derived and integrated properties such as accessible surface area, backbone torsion angles, and local structure conformations. PhoglyStruct showed significant improvement in the ability to detect phosphoglycerylated residues from non-phosphoglycerylated ones when compared to previous predictors. The sensitivity, specificity, accuracy, Mathews correlation coefficient and AUC were 0.8542, 0.7597, 0.7834, 0.5468 and 0.8077, respectively. The data and Matlab/Octave software packages are available at https://github.com/abelavit/PhoglyStruct .

Background: Influenza viruses are undergoing continuous and rapid evolution. The fatal influenza A/H7N9 has drawn attention since the first wave of infections in March 2013, and raised more grave concerns with its increased potential to spread among humans. Experimental studies have revealed several host and virulence markers, indicating differential host binding preferences which can help estimate the potential of causing a pandemic. Here we systematically investigate the sequence pattern and structural characteristics of novel influenza A/H7N9 using computational approaches., Results: The sequence analysis highlighted mutations in protein functional domains of influenza viruses. Molecular docking and molecular dynamics simulation revealed that the hemagglutinin (HA) of A/Taiwan/1/2017(H7N9) strain enhanced the binding with both avian and human receptor analogs, compared with the previous A/Shanghai/02/2013(H7N9) strain. The Molecular Mechanics - Poisson Boltzmann Surface Area (MM-PBSA) calculation revealed the change of residue-ligand interaction energy and detected the residues with conspicuous binding preference., Conclusion: The results are novel and specific to the emerging influenza A/Taiwan/1/2017(H7N9) strain compared with A/Shanghai/02/2013(H7N9). Its enhanced ability to bind human receptor analogs, which are abundant in the human upper respiratory tract, may be responsible for the recent outbreak. Residues showing binding preference were detected, which could facilitate monitoring the circulating influenza viruses.

In the past decade, proteomics and mass spectrometry have taken tremendous strides forward, particularly in the life sciences, spurred on by rapid advances in technology resulting in generation and conglomeration of vast amounts of data. Though this has led to tremendous advancements in biology, the interpretation of the data poses serious challenges for many practitioners due to the immense size and complexity of the data. Furthermore, the lack of annotation means that a potential gold mine of relevant biological information may be hiding within this data. We present here a simple and intuitive workflow for the research community to investigate and mine this data, not only to extract relevant data but also to segregate usable, quality data to develop hypotheses for investigation and validation. We apply an MS evidence workflow for verifying peptides of proteins from one's own data as well as publicly available databases. We then integrate a suite of freely available bioinformatics analysis and annotation software tools to identify homologues and map putative functional signatures, gene ontology and biochemical pathways. We also provide an example of the functional annotation of missing proteins in human chromosome 7 data from the NeXtProt database, where no evidence is available at the proteomic, antibody, or structural levels. We give examples of protocols, tools and detailed flowcharts that can be extended or tailored to interpret and annotate the proteome of any novel organism.

The International Conference on Bioinformatics (InCoB) has been publishing peer-reviewed conference papers in BMC Bioinformatics since 2006. Of the 44 articles accepted for publication in supplement issues of BMC Bioinformatics, BMC Genomics, BMC Medical Genomics and BMC Systems Biology, 24 articles with a bioinformatics or systems biology focus are reviewed in this editorial. InCoB2017 is scheduled to be held in Shenzen, China, September 20-22, 2017.

GIW/InCoB2015 the joint 26th International Conference on Genome Informatics (GIW) and 14th International Conference on Bioinformatics (InCoB) held in Tokyo, September 9-11, 2015 was attended by over 200 delegates. Fifty-one out of 89 oral presentations were based on research articles accepted for publication in four BMC journal supplements and three other journals. Sixteen articles in this supplement and six articles in the BMC Systems Biology GIW/InCoB2015 Supplement are covered by this introduction. The topics range from genome informatics, protein structure informatics, image analysis to biological networks and biomarker discovery.

Structure-based clustering technique is useful for identifying superfamilies of major histocompatibility complex (MHC) proteins with similar binding specificities. The resolved MHC superfamilies play an important role in vaccine development, from discovering new targets for broad-based vaccines and therapeutics to optimizing the affinity and selectivity of hits. Here, we describe a protocol and provide a summary for grouping MHC proteins according to their structural interaction characteristics.

Molecular function in cellular processes is governed by protein-protein interactions (PPIs) within biological networks. Selective yet specific association of these protein partners contributes to diverse functionality such as catalysis, regulation, assembly, immunity, and inhibition in a cell. Therefore, understanding the principles of protein-protein association has been of immense interest for several decades. We provide an overview of the experimental methods used to determine PPIs and the key databases archiving this information. Structural and functional information of existing protein complexes confers knowledge on the principles of PPI, based on which a classification scheme for PPIs is then introduced. Obtaining high-quality non-redundant datasets of protein complexes for interaction characterisation is an essential step towards deciphering their underlying binding principles. Analysis of physicochemical features and their documentation has enhanced our understanding of the molecular basis of protein-protein association. We describe the diverse datasets created/collected by various groups and their key findings inferring distinguishing features. The currently available interface databases and prediction servers have also been compiled.

Since 2006, the International Conference on Bioinformatics (InCoB) has been publishing selected papers in BMC Bioinformatics. Papers within the scope of the journal from the 13th InCoB July 31-2 August, 2014 in Sydney, Australia have been compiled in this supplement. These span protein and proteome informatics, structural bioinformatics, software development and bioimaging to pharmacoinformatics and disease informatics, representing the breadth of bioinformatics research in the Asia-Pacific.

Background: Small bioinformatics databases, unlike institutionally funded large databases, are vulnerable to discontinuation and many reported in publications are no longer accessible. This leads to irreproducible scientific work and redundant effort, impeding the pace of scientific progress., Results: We describe a Web-accessible system, available online at http://biodb100.apbionet.org, for archival and future on demand re-instantiation of small databases within minutes. Depositors can rebuild their databases by downloading a Linux live operating system (http://www.bioslax.com), preinstalled with bioinformatics and UNIX tools. The database and its dependencies can be compressed into an ".lzm" file for deposition. End-users can search for archived databases and activate them on dynamically re-instantiated BioSlax instances, run as virtual machines over the two popular full virtualization standard cloud-computing platforms, Xen Hypervisor or vSphere. The system is adaptable to increasing demand for disk storage or computational load and allows database developers to use the re-instantiated databases for integration and development of new databases., Conclusions: Herein, we demonstrate that a relatively inexpensive solution can be implemented for archival of bioinformatics databases and their rapid re-instantiation should the live databases disappear.

The theme of the 2012 International Conference on Bioinformatics (InCoB) in Bangkok, Thailand was "From Biological Data to Knowledge to Technological Breakthroughs." Besides providing a forum for life scientists and bioinformatics researchers in the Asia-Pacific region to meet and interact, the conference also hosted thematic sessions on the Pan-Asian Pacific Genome Initiative and immunoinformatics. Over the seven years of conference papers published in BMC Bioinformatics and four years in BMC Genomics, we note that there is increasing interest in the applications of -omics technologies to the understanding of diseases, as a forerunner to personalized genomic medicine.

Ten years ago when Asia-Pacific Bioinformatics Network held the first International Conference on Bioinformatics (InCoB) in Bangkok its theme was North-South Networking. At that time InCoB aimed to provide biologists and bioinformatics researchers in the Asia-Pacific region a forum to meet, interact with, and disseminate knowledge about the burgeoning field of bioinformatics. Meanwhile InCoB has evolved into a major regional bioinformatics conference that attracts not only talented and established scientists from the region but increasingly also from East Asia, North America and Europe. Since 2006 InCoB yielded 114 articles in BMC Bioinformatics supplement issues that have been cited nearly 1,000 times to date. In part, these developments reflect the success of bioinformatics education and continuous efforts to integrate and utilize bioinformatics in biotechnology and biosciences in the Asia-Pacific region. A cross-section of research leading from biological data to knowledge and to technological applications, the InCoB2012 theme, is introduced in this editorial. Other highlights included sessions organized by the Pan-Asian Pacific Genome Initiative and a Machine Learning in Immunology competition. InCoB2013 is scheduled for September 18-21, 2013 at Suzhou, China.

Background: Lysosomal β-D-mannosidase is a glycosyl hydrolase that breaks down the glycosidic bonds at the non-reducing end of N-linked glycoproteins. Hence, it is a crucial enzyme in polysaccharide degradation pathway. Mutations in the MANBA gene that codes for lysosomal β-mannosidase, result in improper coding and malfunctioning of protein, leading to β-mannosidosis. Studying the location of mutations on the enzyme structure is a rational approach in order to understand the functional consequences of these mutations. Accordingly, the pathology and clinical manifestations of the disease could be correlated to the genotypic modifications., Results: The wild-type and inherited mutations of β-mannosidase were studied across four different species, human, cow, goat and mouse employing a previously demonstrated comprehensive homology modeling and mutational mapping technique, which reveals a correlation between the variation of genotype and the severity of phenotype in β-mannosidosis. X-ray crystallographic structure of β-mannosidase from Bacteroides thetaiotaomicron was used as template for 3D structural modeling of the wild-type enzymes containing all the associated ligands. These wild-type models subsequently served as templates for building mutational structures. Truncations account for approximately 70% of the mutational cases. In general, the proximity of mutations to the active site determines the severity of phenotypic expressions. Mapping mutations to the MANBA gene sequence has identified five mutational hot-spots., Conclusion: Although restrained by a limited dataset, our comprehensive study suggests a genotype-phenotype correlation in β-mannosidosis. A predictive approach for detecting likely β-mannosidosis is also demonstrated where we have extrapolated observed mutations from one species to homologous positions in other organisms based on the proximity of the mutations to the enzyme active site and their co-location from different organisms. Apart from aiding the detection of mutational hotspots in the gene, where novel mutations could be disease-implicated, this approach also provides a way to predict new disease mutations. Higher expression of the exoglycosidase chitobiase is said to play a vital role in determining disease phenotypes in human and mouse. A bigger dataset of inherited mutations as well as a parallel study of β-mannosidase and chitobiase activities in prospective patients would be interesting to better understand the underlying reasons for β-mannosidosis.

The 2011 International Conference on Bioinformatics (InCoB) conference, which is the annual scientific conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted by Kuala Lumpur, Malaysia, is co-organized with the first ISCB-Asia conference of the International Society for Computational Biology (ISCB). InCoB and the sequencing of the human genome are both celebrating their tenth anniversaries and InCoB's goalposts for the next decade, implementing standards in bioinformatics and globally distributed computational networks, will be discussed and adopted at this conference. Of the 49 manuscripts (selected from 104 submissions) accepted to BMC Genomics and BMC Bioinformatics conference supplements, 24 are featured in this issue, covering software tools, genome/proteome analysis, systems biology (networks, pathways, bioimaging) and drug discovery and design.

The 2010 International Conference on Bioinformatics, InCoB2010, which is the annual conference of the Asia-Pacific Bioinformatics Network (APBioNet) has agreed to publish conference papers in compliance with the proposed Minimum Information about a Bioinformatics investigation (MIABi), proposed in June 2009. Authors of the conference supplements in BMC Bioinformatics, BMC Genomics and Immunome Research have consented to cooperate in this process, which will include the procedures described herein, where appropriate, to ensure data and software persistence and perpetuity, database and resource re-instantiability and reproducibility of results, author and contributor identity disambiguation and MIABi-compliance. Wherever possible, datasets and databases will be submitted to depositories with standardized terminologies. As standards are evolving, this process is intended as a prelude to the 100 BioDatabases (BioDB100) initiative whereby APBioNet collaborators will contribute exemplar databases to demonstrate the feasibility of standards-compliance and participate in refining the process for peer-review of such publications and validation of scientific claims and standards compliance. This testbed represents another step in advancing standards-based processes in the bioinformatics community which is essential to the growing interoperability of biological data, information, knowledge and computational resources.

Background: Understanding cellular systems requires the knowledge of a protein's subcellular localization (SCL). Although experimental and predicted data for protein SCL are archived in various databases, SCL prediction remains a non-trivial problem in genome annotation. Current SCL prediction tools use amino-acid sequence features and text mining approaches. A comprehensive analysis of protein SCL in human PPI and metabolic networks for various subcellular compartments is necessary for developing a robust SCL prediction methodology., Results: Based on protein-protein interaction (PPI) and metabolite-linked protein interaction (MLPI) networks of proteins, we have compared, contrasted and analysed the statistical properties across different subcellular compartments. We integrated PPI and metabolic datasets with SCL information of human proteins from LOCATE and GOA (Gene Ontology Annotation) and estimated three statistical properties: Chi-square (χ2) test, Paired Localisation Correlation Profile (PLCP) and network topological measures. For the PPI network, Pearson's chi-square test shows that for the same SCL category, twice as many interacting protein pairs are observed than estimated when compared to non-interacting protein pairs (χ2 = 1270.19, P-value < 2.2 × 10(-16)), whereas for MLPI, metabolite-linked protein pairs having the same SCL are observed 20% more than expected, compared to non-metabolite linked proteins (χ2 = 110.02, P-value < 2.2 x10(-16)). To address the issue of proteins with multiple SCLs, we have specifically used the PLCP (Pair Localization Correlation Profile) measure. PLCP analysis revealed that protein interactions are majorly restricted to the same SCL, though significant cross-compartment interactions are seen for nuclear proteins. Metabolite-linked protein pairs are restricted to specific compartments such as the mitochondrion (P-value < 6.0e-07), the lysosome (P-value < 4.7e-05) and the Golgi apparatus (P-value < 1.0e-15). These findings indicate that the metabolic network adds value to the information in the PPI network for the localisation process of proteins in human subcellular compartments., Conclusions: The MLPI network differs significantly from the PPI network in its SCL distribution. The PPI network shows passive protein interaction, possibly due to its high false positive rate, across different subcellular compartments, which seem to be absent in the MLPI network, as the MLPI network has evolved to maintain high substrate specificity for proteins.

The International Conference on Bioinformatics (InCoB), the annual conference of the Asia-Pacific Bioinformatics Network (APBioNet), is hosted in one of countries of the Asia-Pacific region. The 2010 conference was awarded to Japan and has attracted more than one hundred high-quality research paper submissions. Thorough peer reviewing resulted in 47 (43.5%) accepted papers out of 108 submissions. Submissions from Japan, R.O. Korea, P.R. China, Australia, Singapore and U.S.A totaled 43.8% and contributed to 57.4% of accepted papers. Manuscripts originating from Taiwan and India added up to 42.8% of submissions and 28.3% of acceptances. The fifteen articles published in this BMC Bioinformatics supplement cover disease informatics, structural bioinformatics and drug design, biological databases and software tools, signaling pathways, gene regulatory and biochemical networks, evolution and sequence analysis.

Transcriptomics (at the level of single cells, tissues and/or whole organisms) underpins many fields of biomedical science, from understanding the basic cellular function in model organisms, to the elucidation of the biological events that govern the development and progression of human diseases, and the exploration of the mechanisms of survival, drug-resistance and virulence of pathogens. Next-generation sequencing (NGS) technologies are contributing to a massive expansion of transcriptomics in all fields and are reducing the cost, time and performance barriers presented by conventional approaches. However, bioinformatic tools for the analysis of the sequence data sets produced by these technologies can be daunting to researchers with limited or no expertise in bioinformatics. Here, we constructed a semi-automated, bioinformatic workflow system, and critically evaluated it for the analysis and annotation of large-scale sequence data sets generated by NGS. We demonstrated its utility for the exploration of differences in the transcriptomes among various stages and both sexes of an economically important parasitic worm (Oesophagostomum dentatum) as well as the prediction and prioritization of essential molecules (including GTPases, protein kinases and phosphatases) as novel drug target candidates. This workflow system provides a practical tool for the assembly, annotation and analysis of NGS data sets, also to researchers with a limited bioinformatic expertise. The custom-written Perl, Python and Unix shell computer scripts used can be readily modified or adapted to suit many different applications. This system is now utilized routinely for the analysis of data sets from pathogens of major socio-economic importance and can, in principle, be applied to transcriptomics data sets from any organism.

Background: The current approach to screen for drug-like molecules is to sieve for molecules with biochemical properties suitable for desirable pharmacokinetics and reduced toxicity, using predominantly biophysical properties of chemical compounds, based on empirical rules such as Lipinski's "rule of five" (Ro5). For over a decade, Ro5 has been applied to combinatorial compounds, drugs and ligands, in the search for suitable lead compounds. Unfortunately, till date, a clear distinction between drugs and non-drugs has not been achieved. The current trend is to seek out drugs which show metabolite-likeness. In identifying similar physicochemical characteristics, compounds have usually been clustered based on some characteristic, to reduce the search space presented by large molecular datasets. This paper examines the similarity of current drug molecules with human metabolites and toxins, using a range of computed molecular descriptors as well as the effect of comparison to clustered data compared to searches against complete datasets., Results: We have carried out statistical and substructure functional group analyses of three datasets, namely human metabolites, drugs and toxin molecules. The distributions of various molecular descriptors were investigated. Our analyses show that, although the three groups are distinct, present-day drugs are closer to toxin molecules than to metabolites. Furthermore, these distributions are quite similar for both clustered data as well as complete or unclustered datasets., Conclusion: The property space occupied by metabolites is dissimilar to that of drugs or toxin molecules, with current drugs showing greater similarity to toxins than to metabolites. Additionally, empirical rules like Ro5 can be refined to identify drugs or drug-like molecules that are clearly distinct from toxic compounds and more metabolite-like. The inclusion of human metabolites in this study provides a deeper insight into metabolite/drug/toxin-like properties and will also prove to be valuable in the prediction or optimization of small molecules as ligands for therapeutic applications.

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation from 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 9-11, 2009 at Biopolis, Singapore. InCoB has actively engaged researchers from the area of life sciences, systems biology and clinicians, to facilitate greater synergy between these groups. To encourage bioinformatics students and new researchers, tutorials and student symposium, the Singapore Symposium on Computational Biology (SYMBIO) were organized, along with the Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and the Clinical Bioinformatics (CBAS) Symposium. However, to many students and young researchers, pursuing a career in a multi-disciplinary area such as bioinformatics poses a Himalayan challenge. A collection to tips is presented here to provide signposts on the road to a career in bioinformatics. An overview of the application of bioinformatics to traditional and emerging areas, published in this supplement, is also presented to provide possible future avenues of bioinformatics investigation. A case study on the application of e-learning tools in undergraduate bioinformatics curriculum provides information on how to go impart targeted education, to sustain bioinformatics in the Asia-Pacific region. The next InCoB is scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010.

Background: The development of high throughput experimental technologies have given rise to the "-omics" era where terabyte-scale datasets for systems-level measurements of various cellular and molecular phenomena pose considerable challenges in data processing and extraction of biological meaning. Moreover, it has created an unmet need for the effective integration of these datasets to achieve insights into biological systems. While it has increased the demand for bioinformatics experts who can interface with biologists, it has also raised the requirement for biologists to possess a basic capability in bioinformatics and to communicate seamlessly with these experts. This may be achieved by embedding in their undergraduate and graduate life science education, basic training in bioinformatics geared towards acquiring a minimum skill set in computation and informatics., Results: Based on previous attempts to define curricula suitable for addressing the bioinformatics capability gap, an initiative was taken during the Workshops on Education in Bioinformatics and Computational Biology (WEBCB) in 2008 and 2009 to identify a minimum skill set for the training of future bioinformaticians and molecular biologists with informatics capabilities. The minimum skill set proposed is cross-disciplinary in nature, involving a combination of knowledge and proficiency from the fields of biology, computer science, mathematics and statistics, and can be tailored to the needs of the "-omics"., Conclusion: The proposed bioinformatics minimum skill set serves as a guideline for biology curriculum design and development in universities at both the undergraduate and graduate levels.

Background: Lysosomal alpha-mannosidase is an enzyme that acts to degrade N-linked oligosaccharides and hence plays an important role in mannose metabolism in humans and other mammalian species, especially livestock. Mutations in the gene (MAN2B1) encoding lysosomal alpha-D-mannosidase cause improper coding, resulting in dysfunctional or non-functional protein, causing the disease alpha-mannosidosis. Mapping disease mutations to the structure of the protein can help in understanding the functional consequences of these mutations and thus indirectly, the finer aspects of the pathology and clinical manifestations of the disease, including phenotypic severity as a function of the genotype., Results: A comprehensive homology modeling study of all the wild-type and inherited mutations of lysosomal alpha-mannosidase in four different species, human, cow, cat and guinea pig, reveals a significant correlation between the severity of the genotype and the phenotype in alpha-mannosidosis. We used the X-ray crystallographic structure of bovine lysosomal alpha-mannosidase as template, containing only two disulphide bonds and some ligands, to build structural models of wild-type structures with four disulfide linkages and all bound ligands. These wild-type models were then used as templates for disease mutations. All the truncations and substitutions involving the residues in and around the active site and those that destabilize the fold led to severe genotypes resulting in lethal phenotypes, whereas the mutations lying away from the active site were milder in both their genotypic and phenotypic expression., Conclusion: Based on the co-location of mutations from different organisms and their proximity to the enzyme active site, we have extrapolated observed mutations from one species to homologous positions in other organisms, as a predictive approach for detecting likely alpha-mannosidosis. Besides predicting new disease mutations, this approach also provides a way for detecting mutation hotspots in the gene, where novel mutations could be implicated in disease. The current study has identified five mutational hot-spot regions along the MAN2B1 gene. Structural mapping can thus provide a rational approach for predicting the phenotype of a disease, based on observed genotypic variations.

Background: Amino-terminal signal peptides (SPs) are short regions that guide the targeting of secretory proteins to the correct subcellular compartments in the cell. They are cleaved off upon the passenger protein reaching its destination. The explosive growth in sequencing technologies has led to the deposition of vast numbers of protein sequences necessitating rapid functional annotation techniques, with subcellular localization being a key feature. Of the myriad software prediction tools developed to automate the task of assigning the SP cleavage site of these new sequences, we review here, the performance and reliability of commonly used SP prediction tools., Results: The available signal peptide data has been manually curated and organized into three datasets representing eukaryotes, Gram-positive and Gram-negative bacteria. These datasets are used to evaluate thirteen prediction tools that are publicly available. SignalP (both the HMM and ANN versions) maintains consistency and achieves the best overall accuracy in all three benchmarking experiments, ranging from 0.872 to 0.914 although other prediction tools are narrowing the performance gap., Conclusion: The majority of the tools evaluated in this study encounter no difficulty in discriminating between secretory and non-secretory proteins. The challenge clearly remains with pinpointing the correct SP cleavage site. The composite scoring schemes employed by SignalP may help to explain its accuracy. Prediction task is divided into a number of separate steps, thus allowing each score to tackle a particular aspect of the prediction.

The 2009 annual conference of the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation dating back to 1998, was organized as the 8th International Conference on Bioinformatics (InCoB), Sept. 7-11, 2009 at Biopolis, Singapore. Besides bringing together scientists from the field of bioinformatics in this region, InCoB has actively engaged clinicians and researchers from the area of systems biology, to facilitate greater synergy between these two groups. InCoB2009 followed on from a series of successful annual events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea), New Delhi (India), Hong Kong and Taipei (Taiwan), with InCoB2010 scheduled to be held in Tokyo, Japan, Sept. 26-28, 2010. The Workshop on Education in Bioinformatics and Computational Biology (WEBCB) and symposia on Clinical Bioinformatics (CBAS), the Singapore Symposium on Computational Biology (SYMBIO) and training tutorials were scheduled prior to the scientific meeting, and provided ample opportunity for in-depth learning and special interest meetings for educators, clinicians and students. We provide a brief overview of the peer-reviewed bioinformatics manuscripts accepted for publication in this supplement, grouped into thematic areas. In order to facilitate scientific reproducibility and accountability, we have, for the first time, introduced minimum information criteria for our pubilcations, including compliance to a Minimum Information about a Bioinformatics Investigation (MIABi). As the regional research expertise in bioinformatics matures, we have delineated a minimum set of bioinformatics skills required for addressing the computational challenges of the "-omics" era.

Parasitic nematodes infect humans, other animals and plants, and impose a significant public health and economic burden worldwide due to the diseases that they cause. A better understanding of parasite genomes, host-parasite relationships and the molecular biology of parasites themselves will enable the rational development of diagnostic tests and/or safe anti-parasitic compounds, following the functional annotation of parasite genomic sequences. With only a few completely sequenced nematode genomes, expressed sequence tag (EST) datasets provide a low-cost alternative ("poor man's genome") to whole genome sequences and a glimpse of the transcriptome of an organism. EST data require a number of computational methods for their pre-processing, clustering, assembly and annotation to yield biologically relevant information. In this article, we review the steps involved in EST data analysis, the development of new semi-automated bioinformatic pipelines and their application to parasitic nematodes of major socio-economic significance, focused on identifying molecules involved in key biological processes or pathways that might serve as targets for new drugs or vaccines.

Hookworms of humans are blood-feeding parasitic nematodes of major socio-economic significance in a wide range of countries. They cause a neglected tropical disease (NTD) called "hookworm disease" (=necatoriasis and/or ancylostomiasis). Necator americanus is the most widely distributed hookworm of humans and is a leading cause of iron deficiency anaemia, which can cause physical and mental retardation and deaths in children as well as adverse maternal-foetal outcomes. Currently, there is a significant focus on the development of new approaches for the prevention and control of hookworms in humans. Technological advances are underpinning the discovery of drug and vaccine targets through insights into the molecular biology and genomics of these parasites and their relationship with the human host. In spite of the widespread socio-economic impacts of human necatoriasis, molecular datasets for N. americanus are scant, limiting progress in molecular research. The present article explores all currently available EST datasets for adult and larval stages of N. americanus using a semi-automated bioinformatic pipeline. In the current repertoire of molecules now available, some have been or are being considered as candidate vaccines against N. americanus. Among others, the most abundant sets of molecules relate to the pathogenesis-related protein (PRP) superfamily, comprising various members, such as the Ancylostoma-secreted or activation-associated proteins (ASPs) and the kunitz-type proteins, both of which are inferred to play key roles in the interplay between N. americanus and the human host. Understanding the molecular biology of these and other novel molecules discovered could have important implications for finding new ways of disrupting the pathways that they are involved in, and should facilitate the identification of new drug and vaccine targets. Also, the bioinformatic prediction of the essentiality of genes and gene products as well as molecular network connectivity of nematode-specific genes, together with sequencing by 454 technology, are likely to assist in the genomic discovery efforts in the very near future, to also underpin fundamental, molecular research of hookworms.

Background: The customary medicinal plant knowledge possessed by the Australian Aboriginal people is a significant resource. Published information on it is scattered throughout the literature, in heterogeneous data formats, and is scattered among various Aboriginal communities across Australia, due to a multiplicity of languages. This ancient knowledge is at risk due to loss of biodiversity, cultural impact and the demise of many of its custodians. We have developed the Customary Medicinal Knowledgebase (CMKb), an integrated multidisciplinary resource, to document, conserve and disseminate this knowledge., Description: CMKb is an online relational database for collating, disseminating, visualising and analysing initially public domain data on customary medicinal plants. The database stores information related to taxonomy, phytochemistry, biogeography, biological activities of customary medicinal plant species as well as images of individual species. The database can be accessed at http://biolinfo.org/cmkb. Known bioactive molecules are characterized within the chemoinformatics module of CMKb, with functions available for molecular editing and visualization., Conclusion: CMKb has been developed as a prototype data resource for documenting, integrating, disseminating, analysing multidisciplinary customary medicinal plant data from Australia and to facilitate user-defined complex querying. Each species in CMKb is linked to online resources such as the Integrated Taxonomic Information System (ITIS), NCBI Taxonomy, Australia's SpeciesLinks-Integrated Botanical Information System (IBIS) and Google images. The bioactive compounds are linked to the PubChem database. Overall, CMKb serves as a single knowledgebase for holistic plant-derived therapeutics and can be used as an information resource for biodiversity conservation, to lead discovery and conservation of customary medicinal knowledge.

Background: Signal peptides (SPs) mediate the targeting of secretory precursor proteins to the correct subcellular compartments in prokaryotes and eukaryotes. Identifying these transient peptides is crucial to the medical, food and beverage and biotechnology industries yet our understanding of these peptides remains limited. This paper examines the most common type of signal peptides cleavable by the endoprotease signal peptidase I (SPase I), and the residues flanking the cleavage sites of three groups of signal peptide sequences, namely (i) eukaryotes (Euk) (ii) Gram-positive (Gram+) bacteria, and (iii) Gram-negative (Gram-) bacteria., Results: In this study, 2352 secretory peptide sequences from a variety of organisms with amino-terminal SPs are extracted from the manually curated SPdb database for analysis based on physicochemical properties such as pI, aliphatic index, GRAVY score, hydrophobicity, net charge and position-specific residue preferences. Our findings show that the three groups share several similarities in general, but they display distinctive features upon examination in terms of their amino acid compositions and frequencies, and various physico-chemical properties. Thus, analysis or prediction of their sequences should be separated and treated as distinct groups., Conclusion: We conclude that the peptide segment recognized by SPase I extends to the start of the mature protein to a limited extent, upon our survey of the amino acid residues surrounding the cleavage processing site. These flanking residues possibly influence the cleavage processing and contribute to non-canonical cleavage sites. Our findings are applicable in defining more accurate prediction tools for recognition and identification of cleavage site of SPs.

We provide a 2007 update on the bioinformatics research in the Asia-Pacific from the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998. From 2002, APBioNet has organized the first International Conference on Bioinformatics (InCoB) bringing together scientists working in the field of bioinformatics in the region. This year, the InCoB2007 Conference was organized as the 6th annual conference of the Asia-Pacific Bioinformatics Network, on Aug. 27-30, 2007 at Hong Kong, following a series of successful events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea) and New Delhi (India). Besides a scientific meeting at Hong Kong, satellite events organized are a pre-conference training workshop at Hanoi, Vietnam and a post-conference workshop at Nansha, China. This Introduction provides a brief overview of the peer-reviewed manuscripts accepted for publication in this Supplement. We have organized the papers into thematic areas, highlighting the growing contribution of research excellence from this region, to global bioinformatics endeavours.

Rich information on point mutation studies is scattered across heterogeneous data sources. This paper presents an automated workflow for mining mutation annotations from full-text biomedical literature using natural language processing (NLP) techniques as well as for their subsequent reuse in protein structure annotation and visualization. This system, called mSTRAP (Mutation extraction and STRucture Annotation Pipeline), is designed for both information aggregation and subsequent brokerage of the mutation annotations. It facilitates the coordination of semantically related information from a series of text mining and sequence analysis steps into a formal OWL-DL ontology. The ontology is designed to support application-specific data management of sequence, structure, and literature annotations that are populated as instances of object and data type properties. mSTRAPviz is a subsystem that facilitates the brokerage of structure information and the associated mutations for visualization. For mutated sequences without any corresponding structure available in the Protein Data Bank (PDB), an automated pipeline for homology modeling is developed to generate the theoretical model. With mSTRAP, we demonstrate a workable system that can facilitate automation of the workflow for the retrieval, extraction, processing, and visualization of mutation annotations -- tasks which are well known to be tedious, time-consuming, complex, and error-prone. The ontology and visualization tool are available at (http://datam.i2r.a-star.edu.sg/mstrap).

The analysis of expressed sequence tag (EST) datasets offers a rapid and cost-effective approach to elucidate the transcriptome of an organism, but requiring several computational methods for assembly and annotation. ESTExplorer is a comprehensive workflow system for EST data management and analysis. The pipeline uses a 'distributed control approach' in which the most appropriate bioinformatics tools are implemented over different dedicated processors. Species-specific repeat masking and conceptual translation are in-built. ESTExplorer accepts a set of ESTs in FASTA format which can be analysed using programs selected by the user. After pre-processing and assembly, the dataset is annotated at the nucleotide and protein levels, following conceptual translation. Users may optionally provide ESTExplorer with assembled contigs for annotation purposes. Functionally annotated contigs/ESTs can be analysed individually. The overall outputs are gene ontologies, protein functional identifications in terms of mapping to protein domains and metabolic pathways. ESTExplorer has been applied successfully to annotate large EST datasets from parasitic nematodes and to identify novel genes as potential targets for parasite intervention. ESTExplorer runs on a Linux cluster and is freely available for the academic community at http://estexplorer.biolinfo.org.

Background: Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain., Results: A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P1' and P2' amino acids and (iii) the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants., Conclusion: This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.

Unlabelled: Normal adaptive immune responses operate under major histocompatibility complex (MHC) restriction by binding to specific, short antigenic peptides and presenting them to appropriate T-cell receptors (TcRs). Sequence-structure-function information is critical in understanding the principles governing peptide/MHC (pMHC) and TcR/pMHC recognition and binding. A new database for sequence-structure-function information on TcR/pMHC interactions, MHC-Peptide Interaction Database version T (MPID-T), is now available with the latest available Protein Data Bank (PDB) data and interaction parameters on TcR/pMHC complexes. MPID-T is a manually curated MySQL database containing experimentally determined structures of 187 pMHC complexes and 16 TcR/pMHC complexes available in the PDB. Each structure is manually verified, classified, and analysed for intermolecular interactions (i) between the MHC and its corresponding bound peptide and (ii) between TcR and its bound pMHC complex where TcR structural information is available. The MPID-T database retrieval system has precomputed interaction parameters that include solvent accessibility, hydrogen bonds, gap volume and gap index. Structural visualisation of the TcR/pMHC complex, pMHC complex, MHC or the bound peptide can be performed using freely available graphics applications such as MDL Chime or RasMol, while structural alignment (based on MHC class and peptide length) can be viewed using the Jmol molecular viewer or an MDL Chime-compatible web browser client. MPID-T contains structural descriptors for in-depth characterisation of TcR/pMHC and pMHC interactions. The ultimate purpose of MPID-T is to enhance the understanding of the binding mechanism underlying TcR/pMHC and pMHC interactions by mapping the TcR footprint on the MHC and its bound peptide, as this eventually determines T-cell recognition and binding., Availability: The MPID-T database retrieval system is available at http://surya.bic.nus.edu.sg/mpidt, Contact: Joo Chuan Tong (jctong@i2r.a-star.edu.sg).

Bioinformatics and computational biology, along with the related fields of genomics, proteomics, functional genomics and systems biology are new wave scientific disciplines that harness composite computational power across networks to advance biological knowledge at the most basic level and to direct traditional laboratory-based research efforts in the biomedical sciences. 'Fostering the growth of bioinformatics and allied disciplines in the Asia-Pacific region' is the motto of the first regional bioinformatics society, the Asia-Pacific Bioinformatics Network (APBioNet). APBioNet addresses the issues of hardware, software, databases and networks pertaining to bioinformatics, with the additional layer of pertinent education, training and research. Recent milestones achieved include hosting an international bioinformatics symposium in Asia and setting up large-scale regional grid-computing projects.

Knowledge discovery (data mining), biological data analysis biological databases data integration genome informatics genotype-phenotype relationships integrative data analysis machine learning multi-omics network analysis omics statistical methods systems biology, big data and artificial intelligence era, machine learning, bioinformatics & computational biology Bioinformatics and computational biology approaches are key for analyses of large-scale datasets, invaluable for basic biological research and the translation of data-driven discoveries. Keywords: bioinformatics & computational biology; knowledge discovery (data mining); biological data analysis biological databases data integration genome informatics genotype-phenotype relationships integrative data analysis machine learning multi-omics network analysis omics statistical methods systems biology; big data and artificial intelligence era; machine learning EN bioinformatics & computational biology knowledge discovery (data mining) biological data analysis biological databases data integration genome informatics genotype-phenotype relationships integrative data analysis machine learning multi-omics network analysis omics statistical methods systems biology big data and artificial intelligence era machine learning 1 3 3 05/12/22 20220510 NES 220510 Recent technological developments have given rise to multiple high-throughput biological data types, such as omics and other micro and macro-scale activities data, including those empowered by imaging technologies. [Extracted from the article]