Phylogenomic analysis of natural products biosynthetic gene clusters allows discovery of arseno-organic metabolites in model streptomycetes

We are indebted with Marnix Medema, Paul Straight and Sean Rovito, for useful discussions and critical reading of the manuscript, as well as with Alicia Chagolla and Yolanda Rodriguez of the MS Service of Unidad Irapuato, Cinvestav, and Araceli Fernandez for technical support in high-performance computing. This work was funded by Conacyt Mexico (grants No. 179290 and 177568) and FINNOVA Mexico (grant No. 214716) to FBG. PCM was funded by Conacyt scholarship (No. 28830) and a Cinvestav posdoctoral fellowship. JF and JFK acknowledge funding from the College of Physical Sciences, University of Aberdeen, UK.Peer reviewedPublisher PD

BacillOndex: An Integrated Data Resource for Systems and Synthetic Biology

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism

Identification of functionally related enzymes by learning-to-rank methods

Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually departs from some notion of similarity, comparing two enzymes by looking for correspondences in their sequences, structures or surfaces. For a given query, the search operation results in a ranking of the enzymes in the database, from very similar to dissimilar enzymes, while information about the biological function of annotated database enzymes is ignored. In this work we show that rankings of that kind can be substantially improved by applying kernel-based learning algorithms. This approach enables the detection of statistical dependencies between similarities of the active cleft and the biological function of annotated enzymes. This is in contrast to search-based approaches, which do not take annotated training data into account. Similarity measures based on the active cleft are known to outperform sequence-based or structure-based measures under certain conditions. We consider the Enzyme Commission (EC) classification hierarchy for obtaining annotated enzymes during the training phase. The results of a set of sizeable experiments indicate a consistent and significant improvement for a set of similarity measures that exploit information about small cavities in the surface of enzymes

No wisdom in the crowd: genome annotation at the time of big data - current status and future prospects

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader

EFICAz²: enzyme function inference by a combined approach enhanced by machine learning

©2009 Arakaki et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1471-2105/10/107doi:10.1186/1471-2105-10-107Background: We previously developed EFICAz, an enzyme function inference approach that combines predictions from non-completely overlapping component methods. Two of the four components in the original EFICAz are based on the detection of functionally discriminating residues (FDRs). FDRs distinguish between member of an enzyme family that are homofunctional (classified under the EC number of interest) or heterofunctional (annotated with another EC number or lacking enzymatic activity). Each of the two FDR-based components is associated to one of two specific kinds of enzyme families. EFICAz exhibits high precision performance, except when the maximal test to training sequence identity (MTTSI) is lower than 30%. To improve EFICAz's performance in this regime, we: i) increased the number of predictive components and ii) took advantage of consensual information from the different components to make the final EC number assignment. Results: We have developed two new EFICAz components, analogs to the two FDR-based components, where the discrimination between homo and heterofunctional members is based on the evaluation, via Support Vector Machine models, of all the aligned positions between the query sequence and the multiple sequence alignments associated to the enzyme families. Benchmark results indicate that: i) the new SVM-based components outperform their FDR-based counterparts, and ii) both SVM-based and FDR-based components generate unique predictions. We developed classification tree models to optimally combine the results from the six EFICAz components into a final EC number prediction. The new implementation of our approach, EFICAz², exhibits a highly improved prediction precision at MTTSI < 30% compared to the original EFICAz, with only a slight decrease in prediction recall. A comparative analysis of enzyme function annotation of the human proteome by EFICAz² and KEGG shows that: i) when both sources make EC number assignments for the same protein sequence, the assignments tend to be consistent and ii) EFICAz² generates considerably more unique assignments than KEGG. Conclusion: Performance benchmarks and the comparison with KEGG demonstrate that EFICAz² is a powerful and precise tool for enzyme function annotation, with multiple applications in genome analysis and metabolic pathway reconstruction. The EFICAz² web service is available at: http://cssb.biology.gatech.edu/skolnick/webservice/EFICAz2/index.htm

A mass spectrometry-guided genome mining approach for natural product peptidogenomics.

Peptide natural products show broad biological properties and are commonly produced by orthogonal ribosomal and nonribosomal pathways in prokaryotes and eukaryotes. To harvest this large and diverse resource of bioactive molecules, we introduce here natural product peptidogenomics (NPP), a new MS-guided genome-mining method that connects the chemotypes of peptide natural products to their biosynthetic gene clusters by iteratively matching de novo tandem MS (MS(n)) structures to genomics-based structures following biosynthetic logic. In this study, we show that NPP enabled the rapid characterization of over ten chemically diverse ribosomal and nonribosomal peptide natural products of previously unidentified composition from Streptomycete bacteria as a proof of concept to begin automating the genome-mining process. We show the identification of lantipeptides, lasso peptides, linardins, formylated peptides and lipopeptides, many of which are from well-characterized model Streptomycetes, highlighting the power of NPP in the discovery of new peptide natural products from even intensely studied organisms

Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text

We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations. Our experiments show that our approach yields interaction extraction systems that are more robust in environments where there is a significant mismatch between training and test conditions.Comment: Appearing in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included