Author Name Disambiguation (AND) is the task
of clustering unique author names from publication records
in scholarly or related databases. Although AND has been
extensively studied and has served as an important preprocessing
step for several tasks (e.g. calculating bibliometrics and scientometrics
for authors), there are few publicly available tools for
disambiguation in large-scale scholarly databases. Furthermore,
most of the disambiguated data is embedded within the search
engines of the scholarly databases, and existing application
programming interfaces (APIs) have limited features and are
often unavailable for users for various reasons. This makes it
difficult for researchers and developers to use the data for various
applications (e.g. author search) or research. Here, we design
a novel, web-based, RESTful API for searching disambiguated
authors, using the PubMed database as a sample application. We
offer two type of queries, attribute-based queries and record-based
queries which serve different purposes. Attribute-based
queries retrieve authors with the attributes available in the
database. We study different search engines to find the most
appropriate one for processing attribute-based queries. Record-based
queries retrieve authors that are most likely to have
written a query publication provided by a user. To accelerate
record-based queries, we develop a novel algorithm that has a
fast record-to-cluster match. We show that our algorithm can
accelerate the query by a factor of 4.01 compared to a baseline
naive approach.
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signatureSearch : environment for gene expression signature searching and functional interpretation
Abstract signatureSearch is an R/Bioconductor package that integrates a suite of existing and novel algorithms into an analysis environment for gene expression signature (GES) searching combined with functional enrichment analysis (FEA) and visualization methods to facilitate the interpretation of the search results. In a typical GES search (GESS), a query GES is searched against a database of GESs obtained from large numbers of measurements, such as different genetic backgrounds, disease states and drug perturbations. Database matches sharing correlated signatures with the query indicate related cellular responses frequently governed by connected mechanisms, such as drugs mimicking the expression responses of a disease. To identify which processes are predominantly modulated in the GESS results, we developed specialized FEA methods combined with drug-target network visualization tools. The provided analysis tools are useful for studying the effects of genetic, chemical and environmental perturbations on biological systems, as well as searching single cell GES databases to identify novel network connections or cell types. The signatureSearch software is unique in that it provides access to an integrated environment for GESS/FEA routines that includes several novel search and enrichment methods, efficient data structures, and access to pre-built GES databases, and allowing users to work with custom databases.
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- Award ID(s):
- 1661152
- NSF-PAR ID:
- 10223460
- Date Published:
- Journal Name:
- Nucleic Acids Research
- Volume:
- 48
- Issue:
- 21
- ISSN:
- 0305-1048
- Page Range / eLocation ID:
- e124 to e124
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Protein identification by tandem mass spectrometry sequence database searching is a standard practice in many proteomics laboratories. The de facto standard for the representation of sequence databases used as input to sequence database search tools is the FASTA format. The Human Proteome Organization's Proteomics Standards Initiative has developed an extension to the FASTA format termed the proteomics standards initiative extended FASTA format or PSI extended FASTA format (PEFF) where additional information such as structural annotations are encoded in the protein description lines. Comet has been extended to automatically analyze the post translational modifications and amino acid substitutions encoded in PEFF databases. Comet's PEFF implementation and example analysis results searching a HEK293 dataset against the neXtProt PEFF database are presented.
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Abstract Motivation Gene lists are routinely produced from various omic studies. Enrichment analysis can link these gene lists with underlying molecular pathways and functional categories such as gene ontology (GO) and other databases.
Results To complement existing tools, we developed ShinyGO based on a large annotation database derived from Ensembl and STRING-db for 59 plant, 256 animal, 115 archeal and 1678 bacterial species. ShinyGO’s novel features include graphical visualization of enrichment results and gene characteristics, and application program interface access to KEGG and STRING for the retrieval of pathway diagrams and protein–protein interaction networks. ShinyGO is an intuitive, graphical web application that can help researchers gain actionable insights from gene-sets.
Availability and implementation http://ge-lab.org/go/.
Supplementary information Supplementary data are available at Bioinformatics online.
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null (Ed.)Abstract FlyBase (flybase.org) is an essential online database for researchers using Drosophila melanogaster as a model organism, facilitating access to a diverse array of information that includes genetic, molecular, genomic and reagent resources. Here, we describe the introduction of several new features at FlyBase, including Pathway Reports, paralog information, disease models based on orthology, customizable tables within reports and overview displays (‘ribbons’) of expression and disease data. We also describe a variety of recent important updates, including incorporation of a developmental proteome, upgrades to the GAL4 search tab, additional Experimental Tool Reports, migration to JBrowse for genome browsing and improvements to batch queries/downloads and the Fast-Track Your Paper tool.more » « less
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null (Ed.)With the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in stud-ies involving scRNA-seq of several tissues across diverse species includingDrosophila. Although a fewdatabases exist for users to query genes of interest within the scRNA-seq studies, search tools that enableusers to find orthologous genes and their cell type-specific expression patterns across species are limited.Here, we built a new search database, DRscDB (https://www.flyrnai.org/tools/single_cell/web/), toaddress this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets forDrosophilaand relevant datasets from human and other model organisms. DRscDB is based on manualcuration ofDrosophilascRNA-seq studies of various tissue types and their corresponding analogoustissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides mostof the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seqdatasets. Finally, DRscDB serves as a web-based user interface that allows users to mine gene expressiondata from scRNA-seq studies and perform cell cluster enrichment analyses pertaining to variousscRNA-seq studies, both within and across species.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/nar/gkaa878
