The Coronavirus and the Risks to the Elderly in Long-Term Care.

The elderly in long-term care (LTC) and their caregiving staff are at elevated risk from COVID-19. Outbreaks in LTC facilities can threaten the health care system. COVID-19 suppression should focus on testing and infection control at LTC facilities. Policies should also be developed to ensure that LTC facilities remain adequately staffed and that infection control protocols are closely followed. Family will not be able to visit LTC facilities, increasing isolation and vulnerability to abuse and neglect. To protect residents and staff, supervision of LTC facilities should remain a priority during the pandemic.

Metab2MeSH: annotating compounds with medical subject headings.

SUMMARY: Progress in high-throughput genomic technologies has led to the development of a variety of resources that link genes to functional information contained in the biomedical literature. However, tools attempting to link small molecules to normal and diseased physiology and published data relevant to biologists and clinical investigators, are still lacking. With metabolomics rapidly emerging as a new omics field, the task of annotating small molecule metabolites becomes highly relevant. Our tool Metab2MeSH uses a statistical approach to reliably and automatically annotate compounds with concepts defined in Medical Subject Headings, and the National Library of Medicine's controlled vocabulary for biomedical concepts. These annotations provide links from compounds to biomedical literature and complement existing resources such as PubChem and the Human Metabolome Database.

GLay: community structure analysis of biological networks.

SUMMARY: GLay provides Cytoscape users an assorted collection of versatile community structure algorithms and graph layout functions for network clustering and structured visualization. High performance is achieved by dynamically linking highly optimized C functions to the Cytoscape JAVA program, which makes GLay especially suitable for decomposition, display and exploratory analysis of large biological networks. AVAILABILITY: http://brainarray.mbni.med.umich.edu/glay/.

Michigan molecular interactions r2: from interacting proteins to pathways.

Molecular interaction data exists in a number of repositories, each with its own data format, molecule identifier and information coverage. Michigan molecular interactions (MiMI) assists scientists searching through this profusion of molecular interaction data. The original release of MiMI gathered data from well-known protein interaction databases, and deep merged this information while keeping track of provenance. Based on the feedback received from users, MiMI has been completely redesigned. This article describes the resulting MiMI Release 2 (MiMIr2). New functionality includes extension from proteins to genes and to pathways; identification of highlighted sentences in source publications; seamless two-way linkage with Cytoscape; query facilities based on MeSH/GO terms and other concepts; approximate graph matching to find relevant pathways; support for querying in bulk; and a user focus-group driven interface design. MiMI is part of the NIH's; National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: http://mimi.ncibi.org.

SciMiner: web-based literature mining tool for target identification and functional enrichment analysis.

UNLABELLED: SciMiner is a web-based literature mining and functional analysis tool that identifies genes and proteins using a context specific analysis of MEDLINE abstracts and full texts. SciMiner accepts a free text query (PubMed Entrez search) or a list of PubMed identifiers as input. SciMiner uses both regular expression patterns and dictionaries of gene symbols and names compiled from multiple sources. Ambiguous acronyms are resolved by a scoring scheme based on the co-occurrence of acronyms and corresponding description terms, which incorporates optional user-defined filters. Functional enrichment analyses are used to identify highly relevant targets (genes and proteins), GO (Gene Ontology) terms, MeSH (Medical Subject Headings) terms, pathways and protein-protein interaction networks by comparing identified targets from one search result with those from other searches or to the full HGNC [HUGO (Human Genome Organization) Gene Nomenclature Committee] gene set. The performance of gene/protein name identification was evaluated using the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) version 2 (Year 2006) Gene Normalization Task as a gold standard. SciMiner achieved 87.1% recall, 71.3% precision and 75.8% F-measure. SciMiner's literature mining performance coupled with functional enrichment analyses provides an efficient platform for retrieval and summary of rich biological information from corpora of users' interests. AVAILABILITY: http://jdrf.neurology.med.umich.edu/SciMiner/. A server version of the SciMiner is also available for download and enables users to utilize their institution's journal subscriptions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MiSearch adaptive pubMed search tool.

UNLABELLED: MiSearch is an adaptive biomedical literature search tool that ranks citations based on a statistical model for the likelihood that a user will choose to view them. Citation selections are automatically acquired during browsing and used to dynamically update a likelihood model that includes authorship, journal and PubMed indexing information. The user can optionally elect to include or exclude specific features and vary the importance of timeliness in the ranking. AVAILABILITY: http://misearch.ncibi.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Integrating and annotating the interactome using the MiMI plugin for cytoscape.

UNLABELLED: The MiMI molecular interaction repository integrates data from multiple sources, resolves interactions to standard gene names and symbols, links to annotation data from GO, MeSH and PubMed and normalizes the descriptions of interaction type. Here, we describe a Cytoscape plugin that retrieves interaction and annotation data from MiMI and links out to multiple data sources and tools. Community annotation of the interactome is supported. AVAILABILITY: MiMI plugin v3.0.1 can be installed from within Cytoscape 2.6 using the Cytoscape plugin manager in 'Network and Attribute I/0' category. The plugin is also preloaded when Cytoscape is launched using Java WebStart at http://mimi.ncibi.org by querying a gene and clicking 'View in MiMI Plugin for Cytoscape' link.

A bioinformatics analysis of the cell line nomenclature.

MOTIVATION: Cell lines are used extensively in biomedical research, but the nomenclature describing cell lines has not been standardized. The problems are both linguistic and experimental. Many ambiguous cell line names appear in the published literature. Users of the same cell line may refer to it in different ways, and cell lines may mutate or become contaminated without the knowledge of the user. As a first step towards rationalizing this nomenclature, we created a cell line knowledgebase (CLKB) with a well-structured collection of names and descriptive data for cell lines cultured in vitro. The objectives of this work are: (i) to assist users in extracting useful information from biomedical text and (ii) to highlight the importance of standardizing cell line names in biomedical research. This CLKB contains a broad collection of cell line names compiled from ATCC, Hyper CLDB and MeSH. In addition to names, the knowledgebase specifies relationships between cell lines. We analyze the use of cell line names in biomedical text. Issues include ambiguous names, polymorphisms in the use of names and the fact that some cell line names are also common English words. Linguistic patterns associated with the occurrence of cell line names are analyzed. Applying these patterns to find additional cell line names in the literature identifies only a small number of additional names. Annotation of microarray gene expression studies is used as a test case. The CLKB facilitates data exploration and comparison of different cell lines in support of clinical and experimental research. AVAILABILITY: The web ontology file for this cell line collection can be downloaded at http://www.stateslab.org/data/celllineOntology/cellline.zip.

Cytoscape ESP: simple search of complex biological networks.

SUMMARY: Cytoscape enhanced search plugin (ESP) enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. Availabiity: http://chianti.ucsd.edu/cyto_web/plugins/ CONTACT: ashkenaz@agri.huji.ac.il.

Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study.

The Human Proteome Organization (HUPO) recently completed the first large-scale collaborative study to characterize the human serum and plasma proteomes. The study was carried out in different locations and used diverse methods and instruments to compare and integrate tandem mass spectrometry (MS/MS) data on aliquots of pooled serum and plasma from healthy subjects. Liquid chromatography (LC)-MS/MS data sets from 18 laboratories were matched to the International Protein Index database, and an initial integration exercise resulted in 9,504 proteins identified with one or more peptides, and 3,020 proteins identified with two or more peptides. This article uses a rigorous statistical approach to take into account the length of coding regions in genes, and multiple hypothesis-testing techniques. On this basis, we now present a reduced set of 889 proteins identified with a confidence level of at least 95%. We also discuss the importance of such an integrated analysis in providing an accurate representation of a proteome as well as the value such data sets contain for the high-confidence identification of protein matches to novel exons, some of which may be localized in alternatively spliced forms of known plasma proteins and some in previously nonannotated gene sequences.

Michigan Molecular Interactions (MiMI): putting the jigsaw puzzle together.

Protein interaction data exists in a number of repositories. Each repository has its own data format, molecule identifier and supplementary information. Michigan Molecular Interactions (MiMI) assists scientists searching through this overwhelming amount of protein interaction data. MiMI gathers data from well-known protein interaction databases and deep-merges the information. Utilizing an identity function, molecules that may have different identifiers but represent the same real-world object are merged. Thus, MiMI allows the users to retrieve information from many different databases at once, highlighting complementary and contradictory information. To help scientists judge the usefulness of a piece of data, MiMI tracks the provenance of all data. Finally, a simple yet powerful user interface aids users in their queries, and frees them from the onerous task of knowing the data format or learning a query language. MiMI allows scientists to query all data, whether corroborative or contradictory, and specify which sources to utilize. MiMI is part of the National Center for Integrative Biomedical Informatics (NCIBI) and is publicly available at: http://mimi.ncibi.org.

SAGA: a subgraph matching tool for biological graphs.

MOTIVATION: With the rapid increase in the availability of biological graph datasets, there is a growing need for effective and efficient graph querying methods. Due to the noisy and incomplete characteristics of these datasets, exact graph matching methods have limited use and approximate graph matching methods are required. Unfortunately, existing graph matching methods are too restrictive as they only allow exact or near exact graph matching. This paper presents a novel approximate graph matching technique called SAGA. This technique employs a flexible model for computing graph similarity, which allows for node gaps, node mismatches and graph structural differences. SAGA employs an indexing technique that allows it to efficiently evaluate queries even against large graph datasets. RESULTS: SAGA has been used to query biological pathways and literature datasets, which has revealed interesting similarities between distinct pathways that cannot be found by existing methods. These matches associate seemingly unrelated biological processes, connect studies in different sub-areas of biomedical research and thus pose hypotheses for new discoveries. SAGA is also orders of magnitude faster than existing methods. AVAILABILITY: SAGA can be accessed freely via the web at http://www.eecs.umich.edu/saga. Binaries are also freely available at this website.

Using directed information to build biologically relevant influence networks.

The systematic inference of biologically relevant influence networks remains a challenging problem in computational biology. Even though the availability of high-throughput data has enabled the use of probabilistic models to infer the plausible structure of such networks, their true interpretation of the biology of the process is questionable. In this work, we propose a network inference methodology, based on the directed information (DTI) criterion, that incorporates the biology of transcription within the framework so as to enable experimentally verifiable inference. We use publicly available embryonic kidney and T-cell microarray datasets to demonstrate our results. We present two variants of network inference via DTI--supervised and unsupervised--and the inferred networks relevant to mammalian nephrogenesis and T-cell activation. Conformity of the obtained interactions with the literature as well as comparison with the coefficient of determination (CoD) method are demonstrated. Apart from network inference, the proposed framework enables the exploration of specific interactions, not just those revealed by data. To illustrate the latter point, a DTI-based framework to resolve interactions between transcription factor modules and target coregulated genes is proposed. Additionally, we show that DTI can be used in conjunction with mutual information to infer higher-order influence networks involving cooperative gene interactions.

Integration of genome and chromatin structure with gene expression profiles to predict c-MYC recognition site binding and function.

The MYC genes encode nuclear sequence specific-binding DNA-binding proteins that are pleiotropic regulators of cellular function, and the c-MYC proto-oncogene is deregulated and/or mutated in most human cancers. Experimental studies of MYC binding to the genome are not fully consistent. While many c-MYC recognition sites can be identified in c-MYC responsive genes, other motif matches-even experimentally confirmed sites-are associated with genes showing no c-MYC response. We have developed a computational model that integrates multiple sources of evidence to predict which genes will bind and be regulated by MYC in vivo. First, a Bayesian network classifier is used to predict those c-MYC recognition sites that are most likely to exhibit high-occupancy binding in chromatin immunoprecipitation studies. This classifier incorporates genomic sequence, experimentally determined genomic chromatin acetylation islands, and predicted methylation status from a computational model estimating the likelihood of genomic DNA methylation. We find that the predictions from this classifier are also applicable to other transcription factors, such as cAMP-response element-binding protein, whose binding sites are sensitive to DNA methylation. Second, the MYC binding probability is combined with the gene expression profile data from nine independent microarray datasets in multiple tissues. Finally, we may consider gene function annotations in Gene Ontology to predict the c-MYC targets. We assess the performance of our prediction results by comparing them with the c-myc targets identified in the biomedical literature. In total, we predict 460 likely c-MYC target genes in the human genome, of which 67 have been reported to be both bound and regulated by MYC, 68 are bound by MYC, and another 80 are MYC-regulated. The approach thus successfully identifies many known c-MYC targets and suggests many novel sites. Our findings suggest that to identify c-MYC genomic targets, integration of different data sources helps to improve the accuracy.

Deconvoluting the intestine: molecular evidence for a major role of the mesenchyme in the modulation of signaling cross talk.

Reciprocal cross talk between the endodermally derived epithelium and the underlying mesenchyme is required for regional patterning and proper differentiation of the developing mammalian intestine. Though both epithelium and mesenchyme participate in patterning, the mesenchyme is thought to play a prominent role in the determination of the epithelial phenotype during development and in adult life. However, the molecular basis for this instructional dominance is unclear. In fact, surprisingly little is known about the cellular origins of many of the critical signaling molecules and the gene transcriptional events that they impact. Here, we profile genes that are expressed in the separate mesenchymal and epithelial compartments of the perinatal mouse intestine. The data indicate that the vast majority of soluble inhibitors and modulators of signaling pathways such as Hedgehog, Bmp, Wnt, Fgf, and Igf are expressed predominantly or exclusively by the mesenchyme, accounting for its ability to dominate instructional cross talk. We also catalog the most highly enriched transcription factors in both compartments. The results bolster previous evidence suggesting a major role for Hnf4gamma and Hnf4alpha in the regulation of epithelial genes. Finally, we find that while epithelially enriched genes tend to be highly tissue restricted in their expression, mesenchymally enriched genes tend to be broadly expressed in multiple tissues. Thus, the unique tissue-specific signature that characterizes the intestinal epithelium is instructed and supported by a mesenchyme that itself expresses genes that are largely nontissue specific.

Evolutionary-conserved gene expression response profiles across mammalian tissues.

Gene expression responses are complex and frequently involve the actions of many genes to effect coordinated patterns. We hypothesized these coordinated responses are evolutionarily conserved and used a comparison of human and mouse gene expression profiles to identify the most prominent conserved features across a set of normal mammalian tissues. Based on data from multiple studies across multiple tissues in human and mouse, 13 gene expression modes across multiple tissues were identified in each of these species using principal component analysis. Strikingly, 1-to-1 pairing of human and mouse modes was observed in 12 out of 13 modes obtained from the two species independently. These paired modes define evolutionarily conserved gene expression response modes (CGEMs). Notably, in this study we were able to extract biological responses that are not overwhelmed by laboratory-to-laboratory or species-to-species variation. Of the variation in our gene expression dataset, 84% can be explained using these CGEMs. Functional annotation was performed using Gene Ontology, pathway, and transcription factor binding site over representation. Our conclusion is that we found an unbiased way of obtaining conserved gene response modes that accounts for a considerable portion of gene expression variation in a given dataset, as well as validates the conservation of major gene expression response modes across the mammals.

Comparison of whole genome assemblies of the human genome.

A fundamental problem in the human genome project is uncovering the correct assembly of the human genome. Many studies, including transcriptional analysis, SNP detection and characterization, gene finding and EST clustering, use genome assemblies as templates so it is important to determine the consistency among the various whole genome assemblies. A comparison of the order and orientation of the GenBank entries used to construct the NCBI and UCSC Goldenpath assemblies was made. In addition, a sequence level comparison was performed using MULTI, an efficient database search tool developed to make whole genome comparisons possible. The resulting comparisons show significant discrepancies in the sequence as well as in the order and orientation of GenBank entries used in constructing the NCBI and UCSC assemblies.

CLO: The cell line ontology.

BACKGROUND: Cell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions. CONSTRUCTION AND CONTENT: Collaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as 'cell line', 'cell line cell', 'cell line culturing', and 'mortal' vs. 'immortal cell line cell'. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains ~38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms. UTILITY AND DISCUSSION: The CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLO's utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.

Understanding distal transcriptional regulation from sequence, expression and interactome perspectives.

Gene regulation in eukaryotes involves a complex interplay between the proximal promoter and distal genomic elements (such as enhancers) which work in concert to drive precise spatio-temporal gene expression. The experimental localization and characterization of gene regulatory elements is a very complex and resource-intensive process. The computational identification of regulatory regions that confer spatiotemporally specific tissue-restricted expression of a gene is thus an important challenge for computational biology. One of the most popular strategies for enhancer localization from DNA sequence is the use of conservation-based prefiltering and more recently, the use of canonical (transcription factor motifs) or de novo tissue-specific sequence motifs. However, there is an ongoing effort in the computational biology community to further improve the fidelity of enhancer predictions from sequence data by integrating other, complementary genomic modalities. In this work, we propose a framework that complements existing methodologies for prospective enhancer identification. The methods in this work are derived from two key insights: (i) that chromatin modification signatures can discriminate proximal and distally located regulatory regions and (ii) the notion of promoter-enhancer cross-talk (as assayed in 3C/5C experiments) might have implications in the search for regulatory sequences that co-operate with the promoter to yield tissue-restricted, gene-specific expression.