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COVID19 Disease Map, a computational knowledge repository of virus–host interaction mechanisms
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
Chemical-damage MINE: A database of curated and predicted spontaneous metabolic reactions
Spontaneous reactions between metabolites are often neglected in favor of emphasizing enzyme-catalyzed chemistry because spontaneous reaction rates are assumed to be insignificant under physiological conditions. However, synthetic biology and engineering efforts can raise natural metabolites' levels or introduce unnatural ones, so that previously innocuous or nonexistent spontaneous reactions become an issue. Problems arise when spontaneous reaction rates exceed the capacity of a platform organism to dispose of toxic or chemically active reaction products. While various reliable sources list competing or toxic enzymatic pathways' side-reactions, no corresponding compilation of spontaneous side-reactions exists, nor is it possible to predict their occurrence. We addressed this deficiency by creating the Chemical Damage (CD)-MINE resource. First, we used literature data to construct a comprehensive database of metabolite reactions that occur spontaneously in physiological conditions. We then leveraged this data to construct 148 reaction rules describing the known spontaneous chemistry in a substrate-generic way. We applied these rules to all compounds in the ModelSEED database, predicting 180,891 spontaneous reactions. The resulting (CD)-MINE is available at https://minedatabase.mcs.anl. gov/cdmine/#/home and through developer tools. We also demonstrate how damage-prone intermediates and end products are widely distributed among metabolic pathways, and how predicting spontaneous chemical damage helps rationalize toxicity and carbon loss using examples from published pathways to commercial products. We explain how analyzing damage-prone areas in metabolism helps design effective engineering strategies. Finally, we use the CD-MINE toolset to predict the formation of the novel damage product N-carbamoyl proline, and present mass spectrometric evidence for its presence in Escherichia coli.
Knowledge Discovery in Databases 2-Hour Session
Knowledge Discovery in Databases, or Database Mining, is the process of information extraction from very large databases.
Pickaxe: a Python library for the prediction of novel metabolic reactions
Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.
(abstract) Modeling Protein Families and Human Genes: Hidden Markov Models and a Little Beyond
We will first give a brief overview of Hidden Markov Models (HMMs) and their use in Computational Molecular Biology. In particular, we will describe a detailed application of HMMs to the G-Protein-Coupled-Receptor Superfamily. We will also describe a number of analytical results on HMMs that can be used in discrimination tests and database mining. We will then discuss the limitations of HMMs and some new directions of research. We will conclude with some recent results on the application of HMMs to human gene modeling and parsing.
Computational Study of Mixing Solid Materials for CO2 Capture Technology
CO2 is one of the major combustion products that, once released into the air, can contribute to global climate change. Solid sorbents have been reported as promising candidates for CO2 sorbent applications due to their high CO2 absorption capacities at moderate working temperatures. By combining thermodynamic database mining with first-principles density functional theory and phonon lattice dynamics calculations, the National Energy Technology Laboratory has proposed and validated a theoretical screening methodology to identify the most promising CO2 sorbent candidates from a vast array of possible solid materials. The advantage of this method is that it identifies the thermodynamic properties of the CO2 capture reaction as a function of temperature and gas pressure without any experimental input beyond crystallographic structural information of the solid phases involved. The calculated thermodynamic properties of solid materials versus temperature and pressure changes were further used to evaluate the equilibrium properties for the CO2 adsorption/desorption cycles. The selected CO2 sorbent candidates were further considered for experimental validations. In this presentation, the results of M2O/Al2O3 (M=Li, Na, K) mixtures (MAlO2 and M5AlO4) capturing CO2 will be demonstrated in detail.
MaizeMine: A Data Mining Warehouse for the Maize Genetics and Genomics Database
MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.
DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases
A goal of multi-omics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes this involves studying changes in biomolecule relationships (e.g. interactions, metabolic relationships) of several types of biomolecules (e.g. proteins, lipids, metabolites). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementing complex machine learning tools. To make relationship extraction more accessible to the community, a python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multi-omics network. Here, in this study, an example dataset involving Caenorhabditis elegans is presented, where relationships are mined from 1443 publications using DancePartner. These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and visualized.
Mining a human transcriptome database for chemical modulators of NRF2
Nuclear factor erythroid-2 related factor 2 (NRF2) encoded by the NFE2L2 gene is a transcription factor critical for protecting cells from chemically-induced oxidative stress. We developed computational procedures to identify chemical modulators of NRF2 in a large database of human microarray data. A gene expression biomarker was built from statistically-filtered gene lists derived from microarray experiments in primary human hepatocytes and cancer cell lines exposed to NRF2-activating chemicals (oltipraz, sulforaphane, CDDOIm) or in which the NRF2 suppressor Keap1 was knocked down by siRNA. Directionally consistent biomarker genes were further filtered for those dependent on NRF2 using a microarray dataset from cells after NFE2L2 siRNA knockdown. The resulting 143-gene biomarker was evaluated as a predictive tool using the correlation-based Running Fisher algorithm. Using 59 gene expression comparisons from chemically-treated cells with known NRF2 activating potential, the biomarker gave a balanced accuracy of 93%. The biomarker was comprised of many well-known NRF2 target genes (AKR1B10, AKR1C1, NQO1, TXNRD1, SRXN1, GCLC, GCLM), 69% of which were found to be bound directly by NRF2 using ChIP-Seq. NRF2 activity was assessed across ~9840 microarray comparisons from ~1460 studies examining the effects of ~2260 chemicals in human cell lines. A total of 260 and 43 chemicals were found to activate or suppress NRF2, respectively, most of which have not been previously reported to modulate NRF2 activity. Using a NRF2-responsive reporter gene in HepG2 cells, we confirmed the activity of a set of chemicals predicted using the biomarker. The biomarker will be useful for future gene expression screening studies of environmentally-relevant chemicals.
Extracting Lessons of Resilience Using Machine Mining of the ASRS Database
NASA’s Aviation Safety Reporting System (ASRS) database is the world's largest repository of voluntary, confidential safety information provided by aviation's frontline personnel, including pilots, air traffic controllers, mechanics, flight attendants, dispatchers, and other members of the aviation community and the public. The database contains close to 2 million narratives, many of which describe everyday situations in which people saved the day. In these situations, people’s resilient behavior solved a problem, dealt with a malfunction, and maintained a safe operation despite a serious perturbation. To be able to extract lessons of such resilience from this large database, the use of machine learning algorithms is being explored. In this report, we describe a comparison between two such algorithms: Perilog and Word2Vec. An identical search using both programs was done on a database containing approximately 470,000 ASRS reports submitted between 1988 and 2022. The comparison reveals some of the strength and weaknesses of each algorithm as well as the challenges inherent in using such algorithms to extract lessons of resilience from the ASRS database.
Use of Business Intelligence Tools in the DSN
JPL has operated the Deep Space Network (DSN) on behalf of NASA since the 1960's. Over the last two decades, the DSN budget has generally declined in real-year dollars while the aging assets required more attention, and the missions became more complex. As a result, the DSN budget has been increasingly consumed by Operations and Maintenance (O&M), significantly reducing the funding wedge available for technology investment and for enhancing the DSN capability and capacity. Responding to this budget squeeze, the DSN launched an effort to improve the cost-efficiency of the O&M. In this paper we: elaborate on the methodology adopted to understand "where the time and money are used"-surprisingly, most of the data required for metrics development was readily available in existing databases-we have used commercial Business Intelligence (BI) tools to mine the databases and automatically extract the metrics (including trends) and distribute them weekly to interested parties; describe the DSN-specific effort to convert the intuitive understanding of "where the time is spent" into meaningful and actionable metrics that quantify use of resources, highlight candidate areas of improvement, and establish trends; and discuss the use of the BI-derived metrics-one of the most fascinating processes was the dramatic improvement in some areas of operations when the metrics were shared with the operators-the visibility of the metrics, and a self-induced competition, caused almost immediate improvement in some areas. While the near-term use of the metrics is to quantify the processes and track the improvement, these techniques will be just as useful in monitoring the process, e.g. as an input to a lean-six-sigma process.
Benchmark datasets for SARS-CoV-2 surveillance bioinformatics
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study.
Reference Material Kydex(registered trademark)-100 Test Data Message for Flammability Testing
The Marshall Space Flight Center (MSFC) Materials and Processes Technical Information System (MAPTIS) database contains, as an engineering resource, a large amount of material test data carefully obtained and recorded over a number of years. Flammability test data obtained using Test 1 of NASA-STD-6001 is a significant component of this database. NASA-STD-6001 recommends that Kydex 100 be used as a reference material for testing certification and for comparison between test facilities in the round-robin certification testing that occurs every 2 years. As a result of these regular activities, a large volume of test data is recorded within the MAPTIS database. The activity described in this technical report was undertaken to mine the database, recover flammability (Test 1) Kydex 100 data, and review the lessons learned from analysis of these data.
Spectral Data and Databases for Optical Sensing of Mining, Materials and Mineral Characterization including in situ and Standoff Sensing
This paper updates recent work to populate spectral databases for (near-) infrared sensing of solids in different modalities including passive and active sensing of minerals, ores and other mining chemicals and minerals.
Community Data Mining Approach for Surface Complexation Database Development
This paper presents a comprehensive data-to-model workflow, including a findable, accessible, interoperable, reusable (FAIR) community sorption database (newly developed LLNL Surface Complexation/Ion Exchange (L-SCIE) database) along with a data fitting workflow to efficiently optimize surface complexation reaction constants with multiple surface complexation model (SCM) constructs. This workflow serves as a universal framework to mine, compile, and analyze large numbers of published sorption data as well as to estimate reaction constants for parameterizing reactive transport models. Here the framework includes (1) data digitization from published papers, (2) data unification including unit conversions, and (3) data-model integration and reaction constant estimation using geochemical software PHREEQC coupled with the universal parameter estimation code PEST. We demonstrate our approach using an analysis of U(VI) sorption to quartz based on a first L-SCIE implementation, concluding that a multisite SCM construct with carbonate surface species yielded the best fit to community data. Surface complexation reaction constants extracted from this approach captured all available sorption data available in the literature and provided insight into previously published reaction constants and surface complexation model constructs. The L-SCIE sorption database presented herein allows for automating this approach across a wide range of metals and minerals and implementing novel machine learning approaches to reactive transport in the future.
MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics
Abstract Summary Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. Availability and implementation The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). Supplementary information Supplementary data are available at Bioinformatics online.
Kaona: Deep Searching and Curating Data from Aviation Safety Reporting Systems
Context: Several works in the literature have examined how safety narrative databases can be leveraged to share lessons learned. However, less attention has been given to augmenting existing processes for mining these safety reporting system databases. Aim: In this work, we introduce Kaona: An interface that weaves machine learning in existing aviation safety database mining activities. Method: We provide a use case of search, curation and newsletter writing to showcase how Kaona features build on existing processes and on its own to enhance information retrieval, curation and synthesis of narratives. Results: We created two instances of Kaona internally for evaluation, one using publicly available NASA’s ASRS narratives and another using publicly available C3RS narratives. Data ranged from 1998 to 2024. Conclusion: Our tool provides a new way to explore safety narratives, serving to re-imagine how text databases can benefit of novel information retrieval mechanisms in the era of large language models.