Examples of the <i>input</i> and <i>output</i> XML descriptions in the Pipeline, an integrated graphical workflow environment that mediates inter-resource communications.

<p>If resources described in <i>iTools</i> include such data I/O descriptions, external interoperability environments (like the Pipeline) will be able to automatically enable construction and validation of inter-resource computational workflows.</p

A schematic and dynamic integration of <i>iTools</i> resources demonstrating interoperability of multi-disciplinary tools via graphical workflow environments.

<p>The three nodes with dash-boundaries on the <i>left</i> demonstrate schematically the integration of some computational biology tools. The graphical workflow on the <i>right</i> depicts the practical means of using <i>iTools</i> meta-data to construct module descriptions and generate multidisciplinary and heterogeneous data analysis protocols.</p

Examples of Meta-Resources for Computational Biology.

<p>Summary comparing <i>iTools</i> to other similar meta-resources environments for archival and retrieval of software tools for computational biology.</p

This figure illustrates the utilization of <i>iTools</i> for search, comparison and integration of bioinformatics tools.

<p>In this example, we demonstrate the use of the Basic Local Alignment Search Tool (BLAST) for comparing gene and protein sequences against other nucleic sequences available in various public databases. The <i>top row</i> shows <i>iTools</i> traversal and search (keyword = blast) using the hyperbolic graphical interface, and tools comparison and investigation of interoperability using the tabular resource view panel. The <i>bottom row</i> shows the design of a simple BLAST analysis workflow using one specific graphical workflow environment (LONI Pipeline). This BLAST analysis protocol depicts the NCBI DB formatting, index generation and filtering using <i>miBLAST</i>, sequence alignment and result textual visualization.</p

The main two displays of <i>iTools</i> resources provide tabular (left) and graph-based (right) human interfaces to the resource database (http://<i>iTools</i>.ccb.ucla.edu/).

<p>Both of these facilitate comprehensive traversal, comparison and search of resources. There are several other human and machine interfaces to the <i>iTools</i> database which are discussed in the text.</p

Left <i>panel</i> shows the search, traversal and comparison of tools (in this case image alignment and visualization) based on their data input/output specifications.

<p>The <i>right</i> panel illustrates how streaming data through independent tools (via an external graphical workflow environment, e.g., LONI Pipeline) may be facilitated by the types of data I/O parameters stored as iTools resource-specific meta-data.</p

<i>iTools CompBiome </i>– the <i>iTools</i> Computational Biology Resourceome plug-in consists of a decentralized collection of BioSiteMaps (<i>sitemaps</i> of resources for biomedical computing) and a <i>Yahoo!Search</i>-based crawler for discovering new and updating existent BioSiteMaps anywhere on the web.

<p> These updates propagate automatically to <i>iTools</i>' SandBox and are later reviewed by expert users for inclusion in the <i>iTools</i> DB. The distributed nature of the NCBC CompBiome may be utilized by any tool developer, user or librarian to find, compare, integrate and expand the functionality of different resources for biomedical computing. The left and right panels illustrate the XML schema definition for the BioSiteMap.xml files and the results of a <i>manual</i> initiation of the <i>Yahoo!Search</i> using the <i>iTools</i> CompBiome plug-in, respectively. <i>iTools</i> has an automated weekly crawler initiation as well as manual triggering of the crawler.</p

DataSheet_1_A new framework for host-pathogen interaction research.zip

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.</p