Detecting fake-review buyers using network structure: Direct evidence from Amazon.

Online reviews significantly impact consumers' decision-making process and firms' economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve for over two decades and on which platforms expend a large amount of resources. Nevertheless, the prevalence of fake reviews is arguably higher than ever. To combat this, we collect a dataset of reviews for thousands of Amazon products and develop a general and highly accurate method for detecting fake reviews. A unique difference between previous datasets and ours is that we directly observe which sellers buy fake reviews. Thus, while prior research has trained models using laboratory-generated reviews or proxies for fake reviews, we are able to train a model using actual fake reviews. We show that products that buy fake reviews are highly clustered in the product reviewer network. Therefore, features constructed from this network are highly predictive of which products buy fake reviews. We show that our network-based approach is also successful at detecting fake review buyers even without ground truth data, as unsupervised clustering methods can accurately identify fake review buyers by identifying clusters of products that are closely connected in the network. While text or metadata can be manipulated to evade detection, network-based features are more costly to manipulate because these features result directly from the inherent limitations of buying reviews from online review marketplaces, making our detection approach more robust to manipulation.

Mat_peptide: comprehensive annotation of mature peptides from polyproteins in five virus families.

MOTIVATION: Sequence repositories have few well-annotated virus mature peptide sequences. Therefore post-translational proteolytic processing of polyproteins into mature peptides (MPs) has been performed in silico, with a new computational method, for over 200 species in 5 pathogenic virus families (Caliciviridae, Coronaviridae, Flaviviridae, Picornaviridae and Togaviridae). RESULTS: Using pairwise alignment with reference sequences, MPs have been annotated and their sequences made available for search, analysis and download. At publication the method had produced 156 216 sequences, a large portion of the protein sequences now available in https://www.viprbrc.org. It represents a new and comprehensive mature peptide collection. AVAILABILITY AND IMPLEMENTATION: The data are available at the Virus Pathogen Resource https://www.viprbrc.org, and the software at https://github.com/VirusBRC/vipr_mat_peptide.

Influenza Research Database: An integrated bioinformatics resource for influenza virus research.

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

Genetic changes found in a distinct clade of Enterovirus D68 associated with paralysis during the 2014 outbreak.

Enterovirus D68 (EV-D68) caused a severe respiratory illness outbreak in the United States in 2014. Reports of acute flaccid myelitis (AFM)/paralysis (AFP) in several independent epidemiological clusters of children with detectable EV-D68 have raised concerns that genetic changes in EV-D68 could be causing increased disease severity and neurological symptoms. To explore the potential link between EV-D68 genetic variations and symptom changes, we performed a series of comparative genomic analyses of EV-D68 2014 outbreak isolate sequences using data and analytical tools in the Virus Pathogen Resource (ViPR; www.viprbrc.org). Our results suggest that (1) three distinct lineages of EV-D68 were co-circulating in 2013 and 2014; (2) isolates associated with AFM/AFP belong to a single phylogenetic subclade - B1; (3) the majority of isolates from the B1 subclade have 21 unique substitutions that distinguish them from other isolates, including amino acid substitutions in the VP1, VP2, and VP3 capsid proteins and the 3D RNA-dependent RNA polymerase, and nucleotide substitutions in the internal ribosome entry sequence (IRES); (4) at 12 of these positions, B1 isolates carry the same residues observed at equivalent positions in paralysis-causing enteroviruses, including poliovirus, EV-D70 and EV-A71. Based on these results, we hypothesize that unique B1 substitutions may be responsible for the apparent increased incidence of neuropathology associated with the 2014 outbreak.

A RESTful API for Access to Phylogenetic Tools via the CIPRES Science Gateway.

The CIPRES Science Gateway is a community web application that provides public access to a set of parallel tree inference and multiple sequence alignment codes run on large computational resources. These resources are made available at no charge to users by the NSF Extreme Science and Engineering Discovery Environment (XSEDE) project. Here we describe the CIPRES RESTful application programmer interface (CRA), a web service that provides programmatic access to all resources and services currently offered by the CIPRES Science Gateway. Software developers can use the CRA to extend their web or desktop applications to include the ability to run MrBayes, BEAST, RAxML, MAFFT, and other computationally intensive algorithms on XSEDE. The CRA also makes it possible for individuals with modest scripting skills to access the same tools from the command line using curl, or through any scripting language. This report describes the CRA and its use in three web applications (Influenza Research Database - www.fludb.org, Virus Pathogen Resource - www.viprbrc.org, and MorphoBank - www.morphobank.org). The CRA is freely accessible to registered users at https://cipresrest.sdsc.edu/cipresrest/v1; supporting documentation and registration tools are available at https://www.phylo.org/restusers.