Output ordering and prioritisation system (OOPS): ranking biosynthetic gene clusters to enhance bioactive metabolite discovery

The rapid increase of publicly available microbial genome sequences has highlighted the presence of hundreds of thousands of biosynthetic gene clusters (BGCs) encoding valuable secondary metabolites. The experimental characterization of new BGCs is extremely laborious and struggles to keep pace with the in silico identification of potential BGCs. Therefore, the prioritisation of promising candidates among computationally predicted BGCs represents a pressing need. Here, we propose an output ordering and prioritisation system (OOPS) which helps sorting identified BGCs by a wide variety of custom-weighted biological and biochemical criteria in a flexible and user-friendly interface. OOPS facilitates a judicious prioritisation of BGCs using G+C content, coding sequence length, gene number, cluster self-similarity and codon bias parameters, as well as enabling the user to rank BGCs based upon BGC type, novelty, and taxonomic distribution. Effective prioritisation of BGCs will help to reduce experimental attrition rates and improve the breadth of bioactive metabolites characterized.     

Metabolite induction via microorganism co-culture: A potential way to enhance chemical diversity for drug discovery

Microorganisms have a long track record as important sources of novel bioactive natural products, particularly in the field of drug discovery. While microbes have been shown to biosynthesize a wide array of molecules, recent advances in genome sequencing have revealed that such organisms have the potential to yield even more structurally diverse secondary metabolites. Thus, many microbial gene clusters may be silent under standard laboratory growth conditions. In the last ten years, several methods have been developed to aid in the activation of these cryptic biosynthetic pathways. In addition to the techniques that demand prior knowledge of the genome sequences of the studied microorganisms, several genome sequence-independent tools have been developed. One of these approaches is microorganism co-culture, involving the cultivation of two or more microorganisms in the same confined environment. Microorganism co-culture is inspired by the natural microbe communities that are omnipresent in nature. Within these communities, microbes interact through signaling or defense molecules. Such compounds, produced dynamically, are of potential interest as new leads for drug discovery. Microorganism co-culture can be achieved in either solid or liquid media and has recently been used increasingly extensively to study natural interactions and discover new bioactive metabolites. Because of the complexity of microbial extracts, advanced analytical methods (e.g., mass spectrometry methods and metabolomics) are key for the successful detection and identification of co-culture-induced metabolites.     

CaGE: cardiac gene expression knowledgebase

CaGE is a Cardiac Gene Expression knowledgebase we have developed to facilitate the analysis of genes important to human cardiac function. CaGE integrates the functionality of the LocusLink database with data from several human cardiac expression libraries, phenotypic data from OMIM and data from large-scale microarray gene expression studies to create a knowledgebase of gene expression in human cardiac tissue. The knowledgebase is fully searchable via the web using several intuitive query interfaces. Results can be displayed in several concise easy to navigate formats. AVAILABILITY: CaGE is located at http://www.cage.wbmei.jhu.edu     

Application of multiple statistical tests to enhance mass spectrometry-based biomarker discovery

Background  

Mass spectrometry-based biomarker discovery has long been hampered by the difficulty in reconciling lists of discriminatory peaks identified by different laboratories for the same diseases studied. We describe a multi-statistical analysis procedure that combines several independent computational methods. This approach capitalizes on the strengths of each to analyze the same high-resolution mass spectral data set to discover consensus differential mass peaks that should be robust biomarkers for distinguishing between disease states.     

A case study in pathway knowledgebase verification

Background  

Biological databases and pathway knowledgebases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledgebase as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference.     

AmpliBASE MT: a Mycobacterium tuberculosis diversity knowledgebase

AmpliBASE MT is an online databank of high-resolution DNA fingerprints representing fluorescent amplified fragment length polymorphism (FAFLP) profiles or amplitypes developed for the Mycobacterium tuberculosis complex strains from 48 different countries. AmpliBASE MT is based on a relational database management system that is hyperlinked to visualize genotyping results in the form of DNA fingerprint images for individual strains. A flexible search system based on systematic comparisons of fragment sizes in base pairs allows inter-laboratory comparison of FAFLP profiles. Besides this, the database also displays previously published data on IS6110 profiles, spoligotypes, MIRU-VNTRs and large sequence polymorphisms along with the FAFLP records that will give the overall comparisons. Being the first of its kind, AmpliBASE MT is expected to be a very helpful tool in strengthening the concept of 'geographic genomics' and will be very helpful to molecular epidemiologists and those interested in diagnostic development for tuberculosis.     

A knowledgebase resource for interleukin-17 family mediated signaling

Interleukin-17 (IL-17) belongs to a relatively new family of cytokines that has garnered attention as the signature cytokine of Th17 cells. This cytokine family consists of 6 ligands, which bind to 5 receptor subtypes and induce downstream signaling. Although the receptors are ubiquitously expressed, cellular responses to ligands vary across tissues. The cytokine family is associated with various autoimmune disorders including rheumatoid arthritis, multiple sclerosis, inflammatory bowel disease, asthma and psoriasis in addition to being implicated in the pathogenesis of cancer. In addition, this family plays a role in host defense against bacterial and fungal infections. The signaling mechanisms of the IL-17 family of proinflammatory cytokines are not well explored. In this study, we present a resource of literature-annotated reactions induced by IL-17. The reactions are catalogued under 5 categories, namely; molecular association, catalysis, transport, activation/inhibition and gene regulation. A total of 93 molecules and 122 reactions have been annotated. The IL-17 pathway is freely available through NetPath, a resource of signal transduction pathways previously developed by our group.     

Desert Survivors: the design and implementation of a television program to enhance local scientific literacy

Outreach efforts by faculty members are oftentimes limited in scope due to hectic schedules. We developed a program to enhance science literacy in elementary school children that allows experts to reach a tremendous audience while minimizing their time commitment. The foundation of the program is a television series entitled "Desert Survivors." The episodes air on local cable access television and are available to teachers on DVD. Each episode features a guest expert who spotlights a particular organism and how that organism overcomes the myriad of hardships inherent to desert survival. Local classrooms are visited to solicit questions from students regarding the organism of interest. These videotaped questions are integrated into Desert Survivors television production and provide the guest expert with the basis to discuss the ecology, physiology, and evolutionary biology of the organism. The program is bolstered through the use of an interactive website. Assessment strategies are in place to ensure program efficacy. Herein, we describe the development of the program as a model for innovative outreach opportunities.     

Stem cells and cell therapy: On the eve of scientific discovery

Cell therapies and stem cells (SCs) have recently become a popular topic. This field of research is cluttered with numerous publications of questionable reliability. Not all methods applied in practice are founded on evidence from research. Although the literature is abundant, there is a gap between the supposed healing properties of SCs associated with their ability to migrate to and engraft on injured tissue, as well as a lack of clear morphological proof. Accordingly, there is a gap between advertising and a part of the professional literature; whereas the former speaks about the rejuvenation of tissues, the latter attributes sometimes questionable therapeutic effects to paracrine or immunomodulating mechanisms and the secretion of cytokines and growth factors. However, SCs are undifferentiated cells; therefore, specific and efficient paracrine function compared to other, more differentiated cells can hardly be expected. In conclusion, it should be noted that the main problem with SCs and cell therapy is commercial influence. The experiences of some foreign countries where attempts have been made to stop the use of unproven treatments, including some stem-cell therapies, should most likely be studied.     

NetiNeti: discovery of scientific names from text using machine learning methods

ABSTRACT: BACKGROUND: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. RESULTS: We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9 % and recall = 70.5 %) compared to a popular dictionary based approach (precision = 97.5 % and recall = 54.3 %) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central's full text articles annotated with scientific names, the precision and recall values are 98.5 % and 96.2 % respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Additionally, we present the comparison results of various machine learning algorithms on our annotated corpus. Naive Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms. CONCLUSIONS: We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.     

Observation and cogitation: how serendipity provides the building blocks of scientific discovery

The identification of serendipitous findings in field-based animal research is challenging in part because investigators are reluctant to declare a discovery accidental. Investigators recognize that many factors must be considered. For example, the impact of using carefully ordered observational search patterns in ecologic, pathologic, and epidemiologic investigations could result in findings being categorized as "sought" versus "unsought." Team collaborations are common in these types of investigations and have advantages related to the application of multiple paradigms, paradigm mixing, and paradigm shifting. This approach reduces the perception of serendipity. Issues of search image refinement and the co-discovery of sought and unsought discoveries additionally cloud the identification of a truly serendipitous finding. Nevertheless, basic curiosity and observation are necessary precursors to scientific discovery. It should be recognized that serendipitous discoveries are of significant value in the advancement of science and often present the foundation for important intellectual leaps of understanding.