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Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot. [ABSTRACT FROM AUTHOR]

Simple Summary: This review provides a roadmap to develop safe and effective OV-based future therapeutics by using several novel synthetic and system biology strategies. Integration of system and synthetic biology can improve the genetic design of OVs backbone, maintaining enough virulence-associated genes. Furthermore, specific computation pipelines can refine target discovery in several types of cancer, identifying the MHC-I and II-restricted peptide repertoire recognized by T-cells and reinforcing anticancer immune responses. Using these versatile approaches could encourage the development of a next-generation of OVs-based therapies, overcoming current challenges such as on-target, off-tumor effects, and variable clinical responses. The efficacy and safety profile of the current OVs-based regimens could be further enhanced with additional long-lasting clinical effects. Oncolytic virus (OV)-based immunotherapy is mainly dependent on establishing an efficient cell-mediated antitumor immunity. OV-mediated antitumor immunity elicits a renewed antitumor reactivity, stimulating a T-cell response against tumor-associated antigens (TAAs) and recruiting natural killer cells within the tumor microenvironment (TME). Despite the fact that OVs are unspecific cancer vaccine platforms, to further enhance antitumor immunity, it is crucial to identify the potentially immunogenic T-cell restricted TAAs, the main key orchestrators in evoking a specific and durable cytotoxic T-cell response. Today, innovative approaches derived from systems biology are exploited to improve target discovery in several types of cancer and to identify the MHC-I and II restricted peptide repertoire recognized by T-cells. Using specific computation pipelines, it is possible to select the best tumor peptide candidates that can be efficiently vectorized and delivered by numerous OV-based platforms, in order to reinforce anticancer immune responses. Beyond the identification of TAAs, system biology can also support the engineering of OVs with improved oncotropism to reduce toxicity and maintain a sufficient portion of the wild-type virus virulence. Finally, these technologies can also pave the way towards a more rational design of armed OVs where a transgene of interest can be delivered to TME to develop an intratumoral gene therapy to enhance specific immune stimuli. [ABSTRACT FROM AUTHOR]

Background: Transposable elements (TEs) are powerful creators of genotypic and phenotypic diversity due to their inherent mutagenic capabilities and in this way they serve as a deep reservoir of sequences for genomic variation. As agents of genetic disruption, a TE's potential to impact phenotype is partially a factor of its location in the genome. Previous research has shown TEs' ability to impact the expression of neighboring genes, however our understanding of this trend is hampered by the exceptional amount of diversity in the TE world, and a lack of publicly available computational methods that quantify the presence of TEs relative to genes. Results: Here, we have developed a tool to more easily quantify TE presence relative to genes through the use of only a gene and TE annotation, yielding a new metric we call TE Density. Briefly defined as the proportion of TE-occupied base-pairs relative to a window-size of the genome. This new pipeline reports TE density for each gene in the genome, for each type descriptor of TE (order and superfamily), and for multiple positions and distances relative to the gene (upstream, intragenic, and downstream) over sliding, user-defined windows. In this way, we overcome previous limitations to the study of TE-gene relationships by focusing on all TE types present in the genome, utilizing flexible genomic distances for measurement, and reporting a TE presence metric for every gene in the genome. Conclusions: Together, this new tool opens up new avenues for studying TE-gene relationships, genome architecture, comparative genomics, and the tremendous diversity present of the TE world. TE Density is open-source and freely available at: https://github.com/sjteresi/TE_Density. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) are a diverse class of RNAs whose functions are widespread in all branches of life and have been the focus of attention in the last decade. While a huge number of lncRNAs have been identified, there is still much work to be done and plenty to be learned. In the current review, we begin with the biogenesis and function of lncRNAs as they are involved in the different cellular processes from regulating the architecture of chromosomes to controlling translation and post‐translation modifications. Questions on how overexpression, mutations, or deficiency of lncRNAs can affect the cellular status and result in the pathogenesis of various human diseases are responded to. Besides, we allocate an overview of several studies, concerning the application of lncRNAs either as diagnostic and prognostic biomarkers or novel therapeutics. We also introduce the currently available techniques to explore details of lncRNAs such as their function, cellular localization, and structure. In the last section, as exponentially growing data in this area need to be gathered and organized in comprehensive databases, we have a particular focus on presenting general and specialized databases. Taken together, with this review, we aim to provide the latest information on different aspects of lncRNAs to highlight their importance in physiopathologic states and take a step towards helping future studies. Significance statement: This study aims to review the roles of long noncoding RNAs in the pathogenesis of diseases and introduce them as potential candidates for diagnostic biomarkers and therapeutic targets. However, there are a significant number of lncRNAs whose molecular mechanisms and functions are poorly understood; illustrating the importance of bioinformatics tools and databases to predict and analyze the structure, function, and other features of these molecules. In addition, developing high‐throughput techniques has accelerated the process of lncRNA research. Thus, we reviewed the bioinformatic tools and different techniques to explore diverse aspects of lncRNAs, hoping this information will be a beacon of light for future studies. [ABSTRACT FROM AUTHOR]

Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses. [ABSTRACT FROM AUTHOR]

Bioinformatics is the use of computers in biology, particularly to analyse DNA and protein sequences and associated data. Bioinformatics has become crucial to most areas of life sciences research. However, bioinformatics education has not kept up with the pace of these advances. To help address this problem, we have designed an open-access bioinformatics workshop for secondary school biology pupils. The workshop is linked to the curriculum in Scotland, addressing learning objectives for Scottish Qualifications Authority Higher Biology and Human Biology. Furthermore, it aims to inspire pupils more generally and includes critical evaluation of evidence as a more generic skill. We delivered this workshop to biology pupils of seven schools in Scotland and conducted evaluations of pupil and teacher feedback. Quantitative pupil and teacher feedback suggests the workshop is useful and enjoyable, with no statistically significant difference between pupils identifying as female and pupils identifying as male. Qualitative responses suggest the workshop gives an increased knowledge of the field of bioinformatics and its importance in everyday life, and that pupils enjoy working in groups. Teachers also highlight the importance of hands-on experience in the classroom. We conclude the workshop is successful in its aims and is suitable for wider deployment. [ABSTRACT FROM AUTHOR]

The past decades have witnessed an astounding rise of the nascent field of systems biology. By and large unknown or ignored for a long time, the field rapidly moved into the limelight and is now in the process of becoming a widely recognized and respected component of mainstream biology. Of course, much remains to be explored and accomplished in systems biology within its parent domain of biology, but the time seems ripe for expansions beyond this domain. The goal of such an expansion should not be the creation of new strongholds or academic silos outside biology, but the true integration of biological systems thinking into educational programs of other disciplines. The expansion should naturally start with closely related fields like biophysics, biochemistry, bioinformatics, and bioengineering, but should continue further into other areas invested in the study of life, such as medicine, epidemiology, and public health, as well as applied mathematics and computer science. This perspective sketches out how systems biological thinking might enrich the training of a new generation of scientists in different fields of scientific endeavor. [ABSTRACT FROM AUTHOR]

Alternative splicing (AS) can generate distinct transcripts and subsequent isoforms that play differential functions from the same pre-mRNA. Recently, increasing numbers of studies have emerged, unmasking the association between AS and cancer. In this review, we arranged AS events that are closely related to cancer progression and presented promising treatments based on AS for cancer therapy. Obtaining proliferative capacity, acquiring invasive properties, gaining angiogenic features, shifting metabolic ability, and getting immune escape inclination are all splicing events involved in biological processes. Spliceosome-targeted and antisense oligonucleotide technologies are two novel strategies that are hopeful in tumor therapy. In addition, bioinformatics applications based on AS were summarized for better prediction and elucidation of regulatory routines mingled in. Together, we aimed to provide a better understanding of complicated AS events associated with cancer biology and reveal AS a promising target of cancer treatment in the future. [ABSTRACT FROM AUTHOR]

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches. [ABSTRACT FROM AUTHOR]

Background: Transposable elements (TEs) are powerful creators of genotypic and phenotypic diversity due to their inherent mutagenic capabilities and in this way they serve as a deep reservoir of sequences for genomic variation. As agents of genetic disruption, a TE's potential to impact phenotype is partially a factor of its location in the genome. Previous research has shown TEs' ability to impact the expression of neighboring genes, however our understanding of this trend is hampered by the exceptional amount of diversity in the TE world, and a lack of publicly available computational methods that quantify the presence of TEs relative to genes. Results: Here, we have developed a tool to more easily quantify TE presence relative to genes through the use of only a gene and TE annotation, yielding a new metric we call TE Density. Briefly defined as the proportion of TE-occupied base-pairs relative to a window-size of the genome. This new pipeline reports TE density for each gene in the genome, for each type descriptor of TE (order and superfamily), and for multiple positions and distances relative to the gene (upstream, intragenic, and downstream) over sliding, user-defined windows. In this way, we overcome previous limitations to the study of TE-gene relationships by focusing on all TE types present in the genome, utilizing flexible genomic distances for measurement, and reporting a TE presence metric for every gene in the genome. Conclusions: Together, this new tool opens up new avenues for studying TE-gene relationships, genome architecture, comparative genomics, and the tremendous diversity present of the TE world. TE Density is open-source and freely available at: https://github.com/sjteresi/TE_Density. [ABSTRACT FROM AUTHOR]

This article records the author's experience in participating in the early human genome and bioinformatics research in China, especially the non-coding sequence of the genome. It also introduced the beginning of human genome research in china, including the experts and teams involved in the International Human Genome Project. All the progress of bioinformatics originates from the inheritance of theoretical biology and the layout of philosophers in china. [ABSTRACT FROM AUTHOR]

The way in which computer code is perceived and used in biological research has been a source of some controversy and confusion, and has resulted in sub-optimal outcomes related to reproducibility, scalability and productivity. We suggest that the confusion is due in part to a misunderstanding of the function of code when applied to the life sciences. Code has many roles, and in this paper we present a three-dimensional taxonomy to classify those roles and map them specifically to the life sciences. We identify a "sweet spot" in the taxonomy—a convergence where bioinformaticians should concentrate their efforts in order to derive the most value from the time they spend using code. We suggest the use of the "inverse Conway maneuver" to shape a research team so as to allow dedicated software engineers to interface with researchers working in this "sweet spot." We conclude that in order to address current issues in the use of software in life science research such as reproducibility and scalability, the field must reevaluate its relationship with software engineering, and adapt its research structures to overcome current issues in bioinformatics such as reproducibility, scalability and productivity. [ABSTRACT FROM AUTHOR]

The transcriptome represents the entire set of RNA transcripts expressed in a cell, reflecting both the underlying genetic and epigenetic landscape and environmental influences, providing a comprehensive view of functional cellular states at any given time. Recent technological advances now enable the study of the transcriptome at the resolution of individual cells, providing exciting opportunities to characterise cellular and molecular events that underpin immune-medicated diseases. Here, we draw on recent examples from the literature to highlight the application of advanced bioinformatics tools to extract mechanistic insight and disease biology from bulk and single-cell transcriptomic profiles. Key considerations for the use of available analysis techniques are presented throughout. [ABSTRACT FROM AUTHOR]

The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was initially established in 2012. Due to the coronavirus (COVID-19) pandemic, the ICIBM 2020 was held for the first time as a virtual online conference on August 9 to 10. The virtual conference had ~ 300 registered participants and featured 41 online real-time presentations. ICIBM 2020 received a total of 75 manuscript submissions, and 12 were selected to be published in this special issue of BMC Bioinformatics. These 12 manuscripts cover a wide range of bioinformatics topics including network analysis, imaging analysis, machine learning, gene expression analysis, and sequence analysis. [ABSTRACT FROM AUTHOR]

Between June 9–11, 2019, the International Conference on Intelligent Biology and Medicine (ICIBM 2019) was held in Columbus, Ohio, USA. The conference included 12 scientific sessions, five tutorials or workshops, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in bioinformatics, medical informatics, systems biology and intelligent computing. Here, we describe 13 high quality research articles selected for publishing in BMC Bioinformatics. [ABSTRACT FROM AUTHOR]

Group A rotaviruses (RVA) are one of the major causes of acute gastroenteritis (AGE) in young children worldwide. Owing to lack of proper surveillance programs and health facilities, developing countries of Asia and Africa carry a disproportionately heavy share of the RVA disease burden. The aim of this hospital-based study was to investigate the circulation of RVA genotypes in Rawalpindi and Islamabad, Pakistan in 2015 and 2016, prior to the implementation of RVA vaccine. 639 faecal samples collected from children under 10 years of age hospitalized with AGE were tested for RVA antigen by ELISA. Among 171 ELISA positive samples, 143 were successfully screened for RT-PCR and sequencing. The prevalence of RVA was found to be 26.8% with the highest frequency (34.9%) found among children of age group 6–11 months. The most predominant circulating genotypes were G3P[8] (22.4%) followed by G12P[6] (20.3%), G2P[4] (12.6%), G1P[8] (11.9%), G9P[6] (11.9%), G3P[4] (9.1%), G1P[6] (4.2%), G9P[8] (4.2%), and G3P[6] (0.7%). A single mixed genotype G1G3P[8] was also detected. The findings of this study provide baseline data, that will help to assess if future vaccination campaigns using currently available RVA vaccine will reduce RVA disease burden and instigate evolutionary changes in the overall RVA biology. The high prevalence of RVA infections in Pakistan require to improve and strengthen the surveillance and monitoring system for RVA. This will provide useful information for health authorities in planning public health care strategies to mitigate the disease burden caused by RVA. [ABSTRACT FROM AUTHOR]

Tauopathies are neurodegenerative diseases that affect millions of people worldwide including those with Alzheimer’s disease. While many efforts have focused on understanding the role of tau protein in neurodegeneration, there has been little done to systematically analyze and study the structures within tau’s encoding RNA and their connection to disease pathology. Knowledge of RNA structure can provide insights into disease mechanisms and how to affect protein production for therapeutic benefit. Using computational methods based on thermodynamic stability and evolutionary conservation, we identified structures throughout the tau pre-mRNA, especially at exon-intron junctions and within the 5′ and 3′ untranslated regions (UTRs). In particular, structures were identified at twenty exon-intron junctions. The 5′ UTR contains one structured region, which lies within a known internal ribosome entry site. The 3′ UTR contains eight structured regions, including one that contains a polyadenylation signal. A series of functional experiments were carried out to assess the effects of mutations associated with mis-regulation of alternative splicing of exon 10 and to identify regions of the 3′ UTR that contain cis-regulatory elements. These studies defined novel structural regions within the mRNA that affect stability and pre-mRNA splicing and may lead to new therapeutic targets for treating tau-associated diseases. [ABSTRACT FROM AUTHOR]

As an important part of the comprehensive treatment methods, the urate-lowering Chinese herbs could provide favorable clinical effects on hyperuricemia in its ability to invigorate spleen and remove dampness. Owing to the long-term duration, it brought up the potential adverse reactions (ADRs) and concerns about the drug-induced liver injury from these herbs. To address this problem, the bioinformatics approaches which combined the network pharmacology, computer simulation and molecular biology experiments were undertaken to elucidate the underlying drug-induced liver injury molecular mechanisms of urate-lowering Chinese herbs. Several electronic databases were searched to identify the potential liver injury compounds in published research. Then, the putative target profile of liver injury was predicted, and the interaction network was constructed based on the links between the compounds, corresponding targets and core pathways. Accordingly, the molecular docking simulation was performed to recognize the representative compounds with hepatotoxicity. Finally, the cell experiments were conducted to investigate the biochemical indicators and expression of the crucial protein that were closely associated with liver injury. In conclusion, the current research revealed that the compounds with potential liver injury including diosgenin, baicalin, saikosaponin D, tetrandrine, rutaecarpine and evodiamine from urate-lowering Chinese herbs, could lead to decline the survival rate of L-02 cell, increase the activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH) and alkaline phosphatase (ALP) in cell-culture medium, enhance the expression of p-p38/p38, while the p38 inhibitor could achieve the trend of regulating and controlling liver injury. These research findings bring further support to the growing evidence that the mechanism of the liver injury induced by the compounds from urate-lowering Chinese herbs may be associated with the activation of p38α. [ABSTRACT FROM AUTHOR]

Ideal decision-makers should constantly assess all sources of information about opportunities and threats, and be able to redetermine their choices promptly in the face of change. However, perpetual monitoring and reassessment impose inordinate sensing and computational costs, making them impractical for animals and machines alike. The obvious alternative of committing for extended periods of time to limited sensory strategies associated with particular courses of action can be dangerous and wasteful. Here, we explore the intermediate possibility of making provisional temporal commitments whilst admitting interruption based on limited broader observation. We simulate foraging under threat of predation to elucidate the benefits of such a scheme. We relate our results to diseases of distractibility and roving attention, and consider mechanistic substrates such as noradrenergic neuromodulation. [ABSTRACT FROM AUTHOR]

The emergence of novel pathogenic strains with increased antibacterial resistance patterns poses a significant threat to the management of infectious diseases. In this study, we aimed at utilizing the subtractive genomic approach to identify novel drug targets against Salmonella enterica subsp. enterica serovar Poona strain ATCC BAA-1673. We employed in silico bioinformatics tools to subtract the strain-specific paralogous and host-specific homologous sequences from the bacterial proteome. The sorted proteome was further refined to identify the essential genes in the pathogenic bacterium using the database of essential genes (DEG). We carried out metabolic pathway and subcellular location analysis of the essential proteins of the pathogen to elucidate the involvement of these proteins in important cellular processes. We found 52 unique essential proteins in the target proteome that could be utilized as novel targets to design newer drugs. Further, we investigated these proteins in the DrugBank databases and 11 of the unique essential proteins showed druggability according to the FDA approved drug bank databases with diverse broad-spectrum property. Molecular docking analyses of the novel druggable targets with the drugs were carried out by AutoDock Vina option based on scoring functions. The results showed promising candidates for novel drugs against Salmonella infections. [ABSTRACT FROM AUTHOR]

Neuro GUT is a EU-funded initial training network ( ITN) of 14 research projects in neurogastroenterology that have employed an equal number of early-stage researchers. Neurogut trainees have-among other activities-attended an international conference on irritable bowel syndrome ( IBS) in Bologna in 2016 and were asked to critically review and evaluate the current knowledge on IBS for their respective research activities, and to state what they were missing. Most appreciated were the topics brain imaging of gut activity, the role of the gut microbiota, the pharmacology of gut functions, the IBS- IBD interrelation, the new Rome IV criteria, the role of gas, and the placebo response in functional disorders. Missed were more detailed coverage of high-resolution manometry, functional brain imaging, advanced 'systems medicine' approaches and bioinformatics technology, better sub-classification of IBS patients, and the development of disease biomarkers, extended at the molecular (genetic/epigenetic, proteonomic) level. They summarize that despite excellent specialized research, there is a gap open that should be filled with systems medicine. For this, it would be necessary that medical research learns even more from the data sciences and other basic disciplines, for example, information technology and system biology, and also welcomes a change in paradigm that enhances open sharing of data, information, and resources. [ABSTRACT FROM AUTHOR]

The University of Pittsburgh's Department of Biomedical Informatics and Division of Pathology Informatics created a Science, Technology, Engineering, and Mathematics (STEM) pipeline in 2011 dedicated to providing cutting-edge informatics research and career preparatory experiences to a diverse group of highly motivated high-school students. In this third editorial installment describing the program, we provide a brief overview of the pipeline, report on achievements of the past scholars, and present results from self-reported assessments by the 2015 cohort of scholars. The pipeline continues to expand with the 2015 addition of the innovation internship, and the introduction of a program in 2016 aimed at offering first-time research experiences to undergraduates who are underrepresented in pathology and biomedical informatics. Achievements of program scholars include authorship of journal articles, symposium and summit presentations, and attendance at top 25 universities. All of our alumni matriculated into higher education and 90% remain in STEM majors. The 2015 high-school program had ten participating scholars who self-reported gains in confidence in their research abilities and understanding of what it means to be a scientist. [ABSTRACT FROM AUTHOR]

Multi-hierarchical profilingmay offer valuable insights into the structural stability and functional direction of biological networks in cellular development, pathological process and disease variation. Owing to the emergence of several new techniques, such as bioinformatics for omics data, structural biology and structural bioinformatics, the pace of network hierarchical research has accelerated a lot in recent years. Here, we discuss and compare the techniques available for quantifyingmultilevel hierarchies, with a focus on their features, capabilities and drawbacks when used for different applications. Then, we classify thesemethods into three types: topological spatial-scales, multilevel hierarchical control and feature ordering. We observe that challenges and limitations do exist in functional hierarchical identification. And, we also provide useful suggestions on how to analyze the dynamic data of complex network studies. [ABSTRACT FROM AUTHOR]

The University of Pittsburgh's Department of Biomedical Informatics and Division of Pathology Informatics created a Science, Technology, Engineering, and Mathematics (STEM) pipeline in 2011 dedicated to providing cutting-edge informatics research and career preparatory experiences to a diverse group of highly motivated high-school students. In this third editorial installment describing the program, we provide a brief overview of the pipeline, report on achievements of the past scholars, and present results from self-reported assessments by the 2015 cohort of scholars. The pipeline continues to expand with the 2015 addition of the innovation internship, and the introduction of a program in 2016 aimed at offering first-time research experiences to undergraduates who are underrepresented in pathology and biomedical informatics. Achievements of program scholars include authorship of journal articles, symposium and summit presentations, and attendance at top 25 universities. All of our alumni matriculated into higher education and 90% remain in STEM majors. The 2015 high-school program had ten participating scholars who self-reported gains in confidence in their research abilities and understanding of what it means to be a scientist. [ABSTRACT FROM AUTHOR]

Background: The process of discovering new drugs is a lengthy, time-consuming and expensive process. Modern day drug discovery relies heavily on the rapid identification of novel 'targets', usually proteins that can be modulated by small molecule drugs to cure or minimise the effects of a disease. Of the 20,000 proteins currently reported as comprising the human proteome, just under a quarter of these can potentially be modulated by known small molecules Storing information in curated, actively maintained drug discovery databases can help researchers access current drug discovery information quickly. However with the increase in the amount of data generated from both experimental and in silico efforts, databases can become very large very quickly and information retrieval from them can become a challenge. The development of database tools that facilitate rapid information retrieval is important to keep up with the growth of databases. Description: We have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99% of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the 'ChEMBL protein target slim' allows broad categorisation of the biology of 90% of the protein targets using just 300 high level, informative GO terms. Description: We have developed a Gene Ontology-based navigation tool (Gene Ontology Tree) to help users retrieve biological information to single protein targets in the ChEMBL drug discovery database. 99% of single protein targets in ChEMBL have at least one GO annotation associated with them. There are 12,500 GO terms associated to 6200 protein targets in the ChEMBL database resulting in a total of 140,000 annotations. The slim we have created, the 'ChEMBL protein target slim' allows broad categorisation of the biology of 90% of the protein targets using just 300 high level, informative GO terms. The 'ChEMBL protein target slim' is available as a browsable 'Gene Ontology Tree' on the ChEMBL site under the browse targets tab (https://www.ebi.ac.uk/chembl/target/browser). A ChEMBL protein target slim OBO file containing the GO slim terms pertinent to ChEMBL is available from the GOC website (http://geneontology. org/page/go-slim-and-subset-guide). Conclusions: We have created a protein target navigation tool based on the 'ChEMBL protein target slim'. The 'ChEMBL protein target slim' provides a way of browsing protein targets in ChEMBL using high level GO terms that describe the molecular functions, processes and subcellular localisations of protein drug targets in drug discovery. The tool also allows user to establish a link between ontological groupings representing protein target biology to relevant compound information in ChEMBL. We have demonstrated by the use of a simple example how the 'ChEMBL protein target slim' can be used to link biological processes with drug information based on the information in the ChEMBL database. The tool has potential to aid in areas of drug discovery such as drug repurposing studies or drug-disease-protein pathways. [ABSTRACT FROM AUTHOR]

Although it is regarded as self-evident that parasites interact with their hosts, with the primary aim of enhancing their own survival and transmission, the extent to which unicellular parasites communicate with each has been severely underestimated. Recent publications show that information is commonly exchanged between parasites of the same species and that this can govern their decisions to divide, to differentiate or to migrate as a group. Communication can take the form of soluble secreted factors, extracellular vesicles or contact between cells. Extracellular parasites can do this directly, while intracellular parasites use the infected host cell – or components derived from it – as an intermediary. By emitting signals that can be dispersed within the host, parasites can also have long-distance effects on the course of an infection and its pathology. This article presents an overview of recent developments in this field and draws attention to some older work that merits re-examination. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased worldwide. The resultant symptoms may be debilitating and can substantially reduce the of patients. Computational biology, which involves the use of computational tools to answer biomedical questions, may provide the basis for novel healthcare approaches in the context of MS. The rapid accumulation of health data, and the ever-increasing computational power and evolving technology have helped to modernize and refine MS research. From the discovery of novel biomarkers to the optimization of treatment and a number of quality-of-life enhancements for patients, computational biology methods and tools are shaping the field of MS diagnosis, management and treatment. The final goal in such a complex disease would be personalized medicine, i.e., providing healthcare services that are tailored to the individual patient, in accordance to the particular biology of their disease and the environmental factors to which they are subjected. The present review article summarizes the current knowledge on MS, modern computational biology and the impact of modern computational approaches of MS. [ABSTRACT FROM AUTHOR]

Bioinformatics is now a critical skill in many research and commercial environments as biological data are increasing in both size and complexity. South African researchers recognized this need in the mid-1990s and responded by working with the government as well as international bodies to develop initiatives to build bioinformatics capacity in the country. Significant injections of support from these bodies provided a springboard for the establishment of computational biology units at multiple universities throughout the country, which took on teaching, basic research and support roles. Several challenges were encountered, for example with unreliability of funding, lack of skills, and lack of infrastructure. However, the bioinformatics community worked together to overcome these, and South Africa is now arguably the leading country in bioinformatics on the African continent. Here we discuss how the discipline developed in the country, highlighting the challenges, successes, and lessons learnt. [ABSTRACT FROM AUTHOR]

Penaeus monodon nudivirus (PmNV) is one of the most important and most commonly reported shrimp viruses. In the present study, codon usage of PmNV was studied in detail. Based on effective number of codons (ENC) values, strong to low codon usage bias was observed in PmNV genes. Nucleotide composition-ENC correlation analysis and the GC3 versus ENC relationship indicated that compositional constraint has a major effect on codon usage of PmNV. At the whole-genome level, relative synonymous codon usage (RSCU) analysis showed almost complete antagonism between the codon usage pattern of PmNV and its host P. monodon. However, codon adaptive index (CAI) values indicated that forces of selective/translational constraints have been able to overcome this antagonism in some genes. [ABSTRACT FROM AUTHOR]

The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions—drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors—pyrimethamine and cycloguanil—across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary “forks in the road” that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their basic contribution to the study of empirical adaptive landscapes, and in terms of how they inform new models for the evolution of drug resistance. [ABSTRACT FROM AUTHOR]

The Computer Science, Biology, and Biomedical Informatics (CoSBBI) program was initiated in 2011 to expose the critical role of informatics in biomedicine to talented high school students.[1] By involving them in Science, Technology, Engineering, and Math (STEM) training at the high school level and providing mentorship and research opportunities throughout the formative years of their education, CoSBBI creates a research infrastructure designed to develop young informaticians. Our central premise is that the trajectory necessary to be an expert in the emerging fields of biomedical informatics and pathology informatics requires accelerated learning at an early age.In our 4th year of CoSBBI as a part of the University of Pittsburgh Cancer Institute (UPCI) Academy (http://www.upci.upmc.edu/summeracademy/), and our 2nd year of CoSBBI as an independent informatics-based academy, we enhanced our classroom curriculum, added hands-on computer science instruction, and expanded research projects to include clinical informatics. We also conducted a qualitative evaluation of the program to identify areas that need improvement in order to achieve our goal of creating a pipeline of exceptionally well-trained applicants for both the disciplines of pathology informatics and biomedical informatics in the era of big data and personalized medicine. [ABSTRACT FROM AUTHOR]

Approximate pattern matching is a fundamental problem in the bioinformatics and information retrieval applications. The problem involves different matching relations such as Hamming distance, edit distances, and the wildcards matching problem. The input is usually a text of length n over a fixed alphabet of length Σ, a pattern of length m, and an integer k. The output is to find all positions that have ≤ k Hamming distance, edit distance, or wildcards matching with P. Many algorithms and indexes have been proposed to solve the problems more efficiently, but due to the space and time complexities of the problems, most tools adopted heuristics approaches based on, for instance, suffix tree, suffix array, or Burrows Wheeler Transform to reach practical implementations. Error Tree is a novel tree structure that is mainly oriented to solve the approximate pattern matching problems, using less space and faster computation time. The algorithm proposes for Hamming distance and wildcards matching a tree structure that needs words and takes in the average case) of query time for any online/offline pattern, where occ is the number of outputs. In addition, a tree structure of words and in the average case) query time for edit distance for any online/offline pattern. [ABSTRACT FROM AUTHOR]

Physiological signals such as electrodermal activity and heart rate can help computing systems detect a user's stress level. Integrating additional measures not yet exploited by such systems could significantly increase stress-detection accuracy, enabling many new applications. [ABSTRACT FROM AUTHOR]

The use of fold-change (FC) to prioritize differentially expressed genes (DEGs) for post-hoc characterization is a common technique in the analysis of RNA sequencing datasets. However, the use of FC can overlook certain population of DEGs, such as high copy number transcripts which undergo metabolically expensive changes in expression yet fail to exceed the ratiometric FC cut-off, thereby missing potential important biological information. Here we evaluate an alternative approach to prioritizing RNAseq data based on absolute changes in normalized transcript counts (ΔT) between control and treatment conditions. In five pairwise comparisons with a wide range of effect sizes, rank-ordering of DEGs based on the magnitude of ΔT produced a power curve-like distribution, in which 4.7-5.0% of transcripts were responsible for 36-50% of the cumulative change. Thus, differential gene expression is characterized by the high production-cost expression of a small number of genes (large ΔT genes), while the differential expression of the majority of genes involves a much smaller metabolic investment by the cell. To determine whether the large ΔT datasets are representative of coordinated changes in the transcriptional program, we evaluated large ΔT genes for enrichment of gene ontologies (GOs) and predicted protein interactions. In comparison to randomly selected DEGs, the large ΔT transcripts were significantly enriched for both GOs and predicted protein interactions. Furthermore, enrichments were were consistent with the biological context of each comparison yet distinct from those produced using equal-sized populations of large FC genes, indicating that the large ΔT genes represent an orthagonal transcriptional response. Finally, the composition of the large ΔT gene sets were unique to each pairwise comparison, indicating that they represent coherent and context-specific responses to biological conditions rather than the non-specific upregulation of a family of genes. These findings suggest that the large ΔT genes are not a product of random or stochastic phenomenon, but rather represent biologically meaningful changes in the transcriptional program. They furthermore imply that high abundance transcripts are associated with particularly cellular states, and as cells change in response to internal or external conditions, the relative distribution of the abundant transcripts changes accordingly. Thus, prioritization of DEGs based on the concept of metabolic cost is a simple yet powerful method to identify biologically important transcriptional changes and provide novel insights into cellular behaviors. [ABSTRACT FROM AUTHOR]

The Research Collaboratory for Structural Bioinformatics (RCSB) Molecule of the Month series provides a curated introduction to the 3-D biomolecular structures available in the Protein Data Bank archive and the tools that are available at the RCSB website for accessing and exploring them. A variety of educational materials, such as articles, videos, posters, hands-on activities, lesson plans, and curricula, build on this series for use in a variety of educational settings as a general introduction to key topics, such as enzyme action, protein synthesis, and viruses. The series and associated educational materials are freely available at [ABSTRACT FROM AUTHOR]

The stochastic modelling of biological systems, coupled with Monte Carlo simulation of models, is an increasingly popular technique in bioinformatics. The simulation-analysis workflow may result computationally expensive reducing the interactivity required in the model tuning. In this work, we advocate the high-level software design as a vehicle for building efficient and portable parallel simulators for the cloud. In particular, the Calculus of Wrapped Components (CWC) simulator for systems biology, which is designed according to the FastFlow pattern-based approach, is presented and discussed. Thanks to the FastFlow framework, the CWC simulator is designed as a high-level workflow that can simulate CWC models, merge simulation results and statistically analyse them in a single parallel workflow in the cloud. To improve interactivity, successive phases are pipelined in such a way that the workflow begins to output a stream of analysis results immediately after simulation is started. Performance and effectiveness of the CWC simulator are validated on the Amazon Elastic Compute Cloud. [ABSTRACT FROM PUBLISHER]

Background Over the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks. Results We present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape. Conclusion With KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape's app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpiinf. mpg.de. [ABSTRACT FROM AUTHOR]

If college science instructors are to use inquiry practices more in the classroom, they need both professional support to foster comfort with the pedagogy and practical ways to engage students in inquiry. Over a semester, we studied 13 community college biology instructors as they adopted bioinformatics problem- based learning (PBL) modules in their classrooms. The study used a diffusion of an innovation theoretical framework and a systematic model for examining faculty decision making around instructional materials adoption. To study faculty decision making, researchers created an online professional development community using an action research reflection-on-practice approach. Following an inquiry continuum model, faculty could choose from modules that varied by activity structure and problem complexity. Most instructors chose easier PBL modules with high structure and low complexity to avoid alienating students and conserve class time, and then observed that students could transfer knowledge to other tasks. As a result, they intended to expand use of inquiry in their classes. [ABSTRACT FROM AUTHOR]

Despite a recent burst of research, knowledge on c-di-GMP signaling pathways remains largely fragmentary and molecular mechanisms of regulation and even c-di-GMP targets are yet unknown for most bacteria. Besides genomics or bioinformatics, accompanying alternative approaches are necessary to reveal c-di-GMP regulation in bacteria with complex lifestyles. We have approached this study by artificially altering the c-di-GMP economy of diverse pathogenic and mutualistic plant-interacting bacteria and examining the effects on the interaction with their respective host plants. Phytopathogenic Pseudomonas and symbiotic Rhizobium strains with enhanced levels of intracellular c-di-GMP displayed common free-living responses: reduction of motility, increased production of extracellular polysaccharides and enhanced biofilm formation. Regarding the interaction with the host plants, P. savastanoi pv. savastanoi cells containing high c-di-GMP levels formed larger knots on olive plants which, however, displayed reduced necrosis. In contrast, development of disease symptoms in P. syringae-tomato or P. syringae-bean interactions did not seem significantly affected by high c-di-GMP. On the other hand, increasing c-di-GMP levels in symbiotic R. etli and R. leguminosarum strains favoured the early stages of the interaction since enhanced adhesion to plant roots, but decreased symbiotic efficiency as plant growth and nitrogen contents were reduced. Our results remark the importance of c-di-GMP economy for plant-interacting bacteria and show the usefulness of our approach to reveal particular stages during plant-bacteria associations which are sensitive to changes in c-di-GMP levels. [ABSTRACT FROM AUTHOR]

Background: Genetic targeting methods have greatly advanced our understanding of many of the 20 Retinal Ganglion Cell (RGC) types conveying visual information from the eyes to the brain. However, the complexity and partial overlap of gene expression patterns in RGCs call for genetic intersectional or sparse labeling strategies. Loci carrying the Cre recombinase in conjunction with conditional knock-out, reporter or other genetic tools can be used for targeted cell type ablation and functional manipulation of specific cell populations. The three members of the Pou4f family of transcription factors, Brn3a, Brn3b and Brn3c, expressed early during RGC development and in combinatorial pattern amongst RGC types are excellent candidates for such gene manipulations. Methods and Findings: We generated conditional Cre knock-in alleles at the Brn3a and Brn3b loci, Brn3aCKOCre and Brn3bCKOCre. When crossed to mice expressing the Dre recombinase, the endogenous Brn3 gene expressed by Brn3aCKOCre or Brn3bCKOCre is removed and replaced with a Cre recombinase, generating Brn3aCre and Brn3bCre knock-in alleles. Surprisingly both Brn3aCre and Brn3bCre knock-in alleles induce early ubiquitous recombination, consistent with germline expression. However in later stages of development, their expression is limited to the expected endogenous pattern of the Brn3a and Brn3b genes. We use the Brn3aCre and Brn3bCre alleles to target a Cre dependent Adeno Associated Virus (AAV) reporter to RGCs and demonstrate its use in morphological characterization, early postnatal gene delivery and tracing the expression of Brn3 genes in RGCs. Conclusions: Dre recombinase effectively recombines the Brn3aCKOCre and Brn3bCKOCre alleles containing its roxP target sites. Sequential Dre to Cre recombination reveals Brn3a and Brn3b expression in early mouse development. The generated Brn3aCre and Brn3bCre alleles are useful tools that can target exogenously delivered Cre dependent reagents to RGCs in early postnatal development, opening up a large range of potential applications. [ABSTRACT FROM AUTHOR]

Abstract: Rice false smut, a fungal disease caused by Ustilaginoidea virens is becoming a severe detriment to rice production worldwide. However, little is known about the molecular response of rice to attacks by the smut pathogen. In this article, we define the initial infection process as having three stages: initial colonization on the pistil (stage 1, S1), amplification on the anther (stage 2, S2) and sporulation in the anther chambers (stage 3, S3). Based on the transcriptome of rice hosts in response to U. virens in two separate years, we identified 126, 204, and 580 specific regulated genes in their respective stages S1, S2, and S3, respectively, by excluding common expression patterns in other openly biotic/abiotic databases using bioinformatics. As the disease progresses, several stage-specific biological processes (BP) terms were distinctively enriched: “Phosphorylation” in stage S1, “PCD” in S2, and “Cell wall biogenesis” in S3, implying a concise signal cascade indicative of the tactics that smut pathogens use to control host rice cells during infection. 113 regulated genes were coexpressed among the three stages. They shared highly conserved promoter cis-element in the promoters in response to the regulation of WRKY and Myb for up-regulation, and ABA and Ca2+ for down regulation, indicating their potentially critical roles in signal transduction during rice-U. virens interaction. We further analyzed seven highly regulated unique genes; four were specific to pollen development, implying that pollen-related genes play critical roles in the establishment of rice susceptibility to U. virens. To my knowledge, this is the first report about probing of molecular response of rice to smut pathogen infection, which will greatly expand our understanding of the molecular events surrounding infection by rice false smut. [ABSTRACT FROM AUTHOR]

The elastic network model (ENM) is a widely used method to study native protein dynamics by normal mode analysis (NMA). In ENM we need information about all pairwise distances, and the distance between contacting atoms is restrained to the native value. Therefore ENM requires O(N2) information to realize its dynamics for a protein consisting of N amino acid residues. To see if (or to what extent) such a large amount of specific structural information is required to realize native protein dynamics, here we introduce a novel model based on only O(N) restraints. This model, named the ‘contact number diffusion’ model (CND), includes specific distance restraints for only local (along the amino acid sequence) atom pairs, and semi-specific non-local restraints imposed on each atom, rather than atom pairs. The semi-specific non-local restraints are defined in terms of the non-local contact numbers of atoms. The CND model exhibits the dynamic characteristics comparable to ENM and more correlated with the explicit-solvent molecular dynamics simulation than ENM. Moreover, unrealistic surface fluctuations often observed in ENM were suppressed in CND. On the other hand, in some ligand-bound structures CND showed larger fluctuations of buried protein atoms interacting with the ligand compared to ENM. In addition, fluctuations from CND and ENM show comparable correlations with the experimental B-factor. Although there are some indications of the importance of some specific non-local interactions, the semi-specific non-local interactions are mostly sufficient for reproducing the native protein dynamics. [ABSTRACT FROM AUTHOR]

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta. [ABSTRACT FROM AUTHOR]

The sterol regulatory element binding transcription factor 2 (SREBF2) gene encodes a transcription factor that activates the expression of many genes involved in the synthesis and uptake of cholesterol, fatty acids, triglycerides, and phospholipids. Through bioinformatics, we found that intron 16 of the chicken SREBF2 gene might encode the chicken miR-33. Using quantitative RT-PCR, we detected the expression of miR-33 in a variety of chicken tissues including skeletal muscle, adipose tissue, and liver. Three hundred and seventy eight genes were predicted to be potential targets of miR-33 in chickens via miRNA target prediction programs “miRanda” and “TargetScan”. Among these targets, the gene FTO (fat mass and obesity associated) encodes a Fe(II)- and 2-oxoglutarate-dependent nucleic acid demethylase that regulates lipid metabolism, and the possibility that its expression is negatively regulated by miR-33 in the chicken liver was therefore further studied. Co-transfection and dual-luciferase reporter assays showed that the expression of luciferase reporter gene linked to the 3′-untranslated region (3′UTR) of the chicken FTO mRNA was down-regulated by overexpression of the chicken miR-33 in the C2C12 cells (P<0.05). Furthermore, this down-regulation was completely abolished when the predicted miR-33 target site in the FTO 3′UTR was mutated. In contrast, the expression of FTO mRNA in the primary chicken hepatocytes was up-regulated after transfection with the miR-33 inhibitor LNA-anti-miR-33. Using quantitative RT-PCR, we also found that the expression of miR-33 was increased in the chicken liver from day 0 to day 49 of age, whereas that of the FTO mRNA was decreased during the same age period. These data together suggest that miR-33 might play an important role in lipid metabolism in the chicken liver by negatively regulating the expression of the FTO gene. [ABSTRACT FROM AUTHOR]

The alterations of MicroRNAs(miRNAs) in host cell after foot-and-mouth disease virus (FMDV) infection is still obscure. To increase our understanding of the pathogenesis of FMDV at the post-transcriptional regulation level, Solexa high-throu MicroRNAs (miRNAs) play an important role both in the post-transcriptional regulation of gene expression and host-virus interactions. Despite investigations of miRNA expression ghput sequencing and bioinformatic tools were used to identify differentially expressed miRNAs and analyze their functions during FMDV infection of PK-15cells. Results indicated that 9,165,674 and 9,230,378 clean reads were obtained, with 172 known and 72 novel miRNAs differently expressed in infected and uninfected groups respectively. Some of differently expressed miRNAs were validated using stem-loop real-time quantitative RT-PCR. The GO annotation and KEGG pathway analysis for target genes revealed that differently expressed miRNAs were involved in immune response and cell death pathways. [ABSTRACT FROM AUTHOR]