Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.

g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update).

Genetic robustness, or the ability of an organism to maintain fitness in the presence of harmful mutations, can be achieved via protein feedback loops. Previous work has suggested that organisms may also respond to mutations by transcriptional adaptation, a process by which related gene(s) are upregulated independently of protein feedback loops. However, the prevalence of transcriptional adaptation and its underlying molecular mechanisms are unknown. Here, by analysing several models of transcriptional adaptation in zebrafish and mouse, we uncover a requirement for mutant mRNA degradation. Alleles that fail to transcribe the mutated gene do not exhibit transcriptional adaptation, and these alleles give rise to more severe phenotypes than alleles displaying mutant mRNA decay. Transcriptome analysis in alleles displaying mutant mRNA decay reveals the upregulation of a substantial proportion of the genes that exhibit sequence similarity with the mutated gene's mRNA, suggesting a sequence-dependent mechanism. These findings have implications for our understanding of disease-causing mutations, and will help in the design of mutant alleles with minimal transcriptional adaptation-derived compensation.

Genetic compensation triggered by mutant mRNA degradation

Gene set enrichment (GSE) analysis plays an essential role in extracting biological insight from genome-scale experiments. ORA (overrepresentation analysis), FCS (functional class scoring), and PT (pathway topology) approaches are three generations of GSE methods along the timeline of development. Previous versions of KOBAS provided services based on just the ORA method. Here we presented version 3.0 of KOBAS, which is named KOBAS-i (short for KOBAS intelligent version). It introduced a novel machine learning-based method we published earlier, CGPS, which incorporates seven FCS tools and two PT tools into a single ensemble score and intelligently prioritizes the relevant biological pathways. In addition, KOBAS has expanded the downstream exploratory visualization for selecting and understanding the enriched results. The tool constructs a novel view of cirFunMap, which presents different enriched terms and their correlations in a landscape. Finally, based on the previous version's framework, KOBAS increased the number of supported species from 1327 to 5944. For an easier local run, it also provides a prebuilt Docker image that requires no installation, as a supplementary to the source code version. KOBAS can be freely accessed at http://kobas.cbi.pku.edu.cn, and a mirror site is available at http://bioinfo.org/kobas.

/pdf/kobas-i-intelligent-prioritization-and-exploratory-40b7zxr2pw.pdf

KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis.

Blockade of angiogenesis can retard tumour growth, but may also paradoxically increase metastasis. This paradox may be resolved by vessel normalization, which involves increased pericyte coverage, improved tumour vessel perfusion, reduced vascular permeability, and consequently mitigated hypoxia. Although these processes alter tumour progression, their regulation is poorly understood. Here we show that type 1 T helper (TH1) cells play a crucial role in vessel normalization. Bioinformatic analyses revealed that gene expression features related to vessel normalization correlate with immunostimulatory pathways, especially T lymphocyte infiltration or activity. To delineate the causal relationship, we used various mouse models with vessel normalization or T lymphocyte deficiencies. Although disruption of vessel normalization reduced T lymphocyte infiltration as expected, reciprocal depletion or inactivation of CD4+ T lymphocytes decreased vessel normalization, indicating a mutually regulatory loop. In addition, activation of CD4+ T lymphocytes by immune checkpoint blockade increased vessel normalization. TH1 cells that secrete interferon-γ are a major population of cells associated with vessel normalization. Patient-derived xenograft tumours growing in immunodeficient mice exhibited enhanced hypoxia compared to the original tumours in immunocompetent humans, and hypoxia was reduced by adoptive TH1 transfer. Our findings elucidate an unexpected role of TH1 cells in vasculature and immune reprogramming. TH1 cells may be a marker and a determinant of both immune checkpoint blockade and anti-angiogenesis efficacy.

/pdf/mutual-regulation-of-tumour-vessel-normalization-and-3xgruvozq1.pdf

Mutual regulation of tumour vessel normalization and immunostimulatory reprogramming

Pan-Genome of Wild and Cultivated Soybeans.

High-throughput experimental technologies often identify dozens to hundreds of genes related to, or changed in, a biological or pathological process. From these genes one wants to identify biological pathways that may be involved and diseases that may be implicated. Here, we report a web server, KOBAS 2.0, which annotates an input set of genes with putative pathways and disease relationships based on mapping to genes with known annotations. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). KOBAS 2.0 can be accessed at http://kobas.cbi.pku.edu.cn.

/pdf/kobas-2-0-a-web-server-for-annotation-and-identification-of-krnh95v775.pdf

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

Keystroke authentication can help significantly improve computer security by hardening passwords or offering active, continuous authentication. Over the years, many keystroke authentication algorithms have been reported to produce promising results. However, these results are tested on proprietary datasets with varying numbers of subjects and amounts of text, making it difficult to compare and improve the state of art. We describe a new dataset that we have developed with the goal to serve as a shared common testbed to enable future improvements. The new dataset includes keystroke data for short pass-phrases, fixed text (transcription of long proses), and free text. It also includes video of a subject’s facial expression and hand movement during the data collection sessions, allowing for a deeper understanding of why an algorithm works the way it does, for example, by finding out whether a subject is a touchtypist or not. As a baseline for benchmarking, we also include the results of replicating two existing algorithms using the new dataset.

Shared research dataset to support development of keystroke authentication

Conventional one-stop authentication of a computer terminal takes place at a user's initial sign-on In contrast, continuous authentication protects against the case where an intruder takes over an authenticated terminal or simply has access to sign-on credentials Behavioral biometrics has had some success in providing continuous authentication without requiring additional hardware However, further advancement requires benchmarking existing algorithms against large, shared datasets To this end, we provide a novel large dataset that captures not only keystrokes, but also mouse events and active programs Our dataset is collected using passive logging software to monitor user interactions with the mouse, keyboard, and software programs Data was collected from 103 users in a completely uncontrolled, natural setting, over a time span of 25 years We apply Gunetti & Picardi's algorithm, a state-of-the-art algorithm in free text keystroke dynamics, as an initial benchmarkfor the new dataset

Shared dataset on natural human-computer interaction to support continuous authentication research

Free-text keystroke authentication has been demonstrated to be a promising behavioral biometric. But unlike physiological traits such as fingerprints, in free-text keystroke authentication, there is no natural way to identify what makes a sample. It remains an open problem as to how much keystroke data are necessary for achieving acceptable authentication performance. Using public datasets and two existing algorithms, we conduct two experiments to investigate the effect of the reference profile size and test sample size on False Alarm Rate (FAR) and Imposter Pass Rate (IPR). We find that (1) larger reference profiles will drive down both IPR and FAR values, provided that the test samples are large enough, and (2) larger test samples have no obvious effect on IPR, regardless of the reference profile size. We discuss the practical implication of our findings.

Effect of data size on performance of free-text keystroke authentication

Free-text keystroke dynamics is a behavioral biometric that has the strong potential to offer unobtrusive and continuous user authentication Such behavioral biometrics are important as they may serve as an additional layer of protection over other one-stop authentication methods such as the user ID and passwords Unfortunately, evaluation and comparison of keystroke dynamics algorithms are still lacking due to the absence of large, shared free-text datasets In this research, we present a novel keystroke dynamics algorithm, based on kernel density estimation (KDE), and contrast it with two other state-of-the-art algorithms, namely Gunetti & Picardi's and Buffalo's SVM algorithms, using three published datasets, as well as our own new, unconstrained dataset that is an order of magnitude larger than the previous ones We modify the algorithms when necessary such that they have comparable settings, including profile and test sample sizes Both Gunetti & Picardi's and our own KDE algorithms have performed much better than Buffalo's SVM algorithm Although much simpler, the newly developed KDE algorithm is shown to perform similarly as Gunetti & Picardi's algorithm on the three constrained datasets, but the best on our new unconstrained dataset All three algorithms perform significantly better on the three prior datasets, which are constrained in one way or another, than our new dataset, which is truly unconstrained This highlights the importance of our unconstrained dataset in representing the real-world scenarios for keystroke dynamics Lastly, the new KDE algorithm degrades the least in performance on our new dataset

Jiaju Huang

Papers

KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases

Shared research dataset to support development of keystroke authentication

Shared dataset on natural human-computer interaction to support continuous authentication research

Effect of data size on performance of free-text keystroke authentication

Benchmarking keystroke authentication algorithms