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Background: The Sustainable Development Goals have helped to focus attention on the importance of reducing inequality and 'leaving no one behind'. Monitoring health inequalities is essential for providing evidence to inform policies, programmes and practices that can close existing gaps and achieve health equity. The Health Equity Assessment Toolkit (HEAT and HEAT Plus) software was developed by the World Health Organization to facilitate the assessment of within-country health inequalities. Results: HEAT contains a built-in database of disaggregated health data, while HEAT Plus allows users to upload and analyze inequalities using their own datasets. Version 4.0 of the software incorporated enhancements to the toolkit's capacity for equity assessments. This includes a multilingual interface, interactive and downloadable visualizations, flexibility to analyze inequalities using any dataset of disaggregated data, and the built-in calculation of 19 summary measures of inequality. This paper outlines the improved features and functionalities of the HEAT and HEAT Plus software since their original release, highlighted through an example of how the toolkit can be used to assess inequalities in the COVID-19 pandemic era. Conclusions: The features of the HEAT and HEAT Plus software make it a valuable tool for analyzing and reporting inequalities related to the COVID-19 pandemic, as well as its indirect impacts on inequalities in other health and non-health areas, providing evidence to inform equity-oriented interventions and strategies. [ABSTRACT FROM AUTHOR]

Background: Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments. Results: In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction. Conclusions: In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods. [ABSTRACT FROM AUTHOR]

Background: Proteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets. Results: This paper describes the design, implementation and use of a P̱roteome Experimental Ḏata Ṟep_̱ository (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available. Conclusions: The PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation. [ABSTRACT FROM AUTHOR]

Background: Birth defects pose a major challenge to infant health. Thus far, however, the causes of most birth defects remain cryptic. Over the past few decades, considerable effort has been expended on disclosing the underlying mechanisms related to birth defects, yielding myriad treatises and data. To meet the increasing requirements for data resources, we developed a freely accessible birth defect multi-omics database (BDdb, http://t21omics.cngb.org) consisting of multi-omics data and potential disease biomarkers. Results: In total, omics datasets from 136 Gene Expression Omnibus (GEO) Series records, including 5245 samples, as well as 869 biomarkers of 22 birth defects in six different species, were integrated into the BDdb. The database provides a user-friendly interface for searching, browsing, and downloading data of interest. The BDdb also enables users to explore the correlations among different sequencing methods, such as chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq) from different studies, to obtain the information on gene expression patterns from diverse aspects. Conclusion: To the best of our knowledge, the BDdb is the first comprehensive database associated with birth defects, which should benefit the diagnosis and prevention of birth defects. [ABSTRACT FROM AUTHOR]

Background: Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. Main: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments. Conclusion: Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico. [ABSTRACT FROM AUTHOR]

Background: DNA methylation (DNAm) profiling has emerged as a powerful tool for characterizing the placental methylome. However, previous studies have focused primarily on whole placental tissue, which is a mixture of epigenetically distinct cell populations. Here, we present the first methylome-wide analysis of first trimester (n = 9) and term (n = 19) human placental samples of four cell populations: trophoblasts, Hofbauer cells, endothelial cells, and stromal cells, using the Illumina EPIC methylation array, which quantifies DNAm at > 850,000 CpGs. Results: The most distinct DNAm profiles were those of placental trophoblasts, which are central to many pregnancy-essential functions, and Hofbauer cells, which are a rare fetal-derived macrophage population. Cell-specific DNAm occurs at functionally-relevant genes, including genes associated with placental development and preeclampsia. Known placental-specific methylation marks, such as those associated with genomic imprinting, repetitive element hypomethylation, and placental partially methylated domains, were found to be more pronounced in trophoblasts and often absent in Hofbauer cells. Lastly, we characterize the cell composition and cell-specific DNAm dynamics across gestation. Conclusions: Our results provide a comprehensive analysis of DNAm in human placental cell types from first trimester and term pregnancies. This data will serve as a useful DNAm reference for future placental studies, and we provide access to this data via download from GEO (GSE159526), through interactive exploration from the web browser (https://robinsonlab.shinyapps.io/Placental%5fMethylome%5fBrowser/), and through the R package planet, which allows estimation of cell composition directly from placental DNAm data. [ABSTRACT FROM AUTHOR]

Background: Immune-related genes is closely related to the occurrence and prognosis of head and neck squamous cell carcinoma (HNSCC). At the same time, immune-related genes have great potential as prognostic markers in many types of cancer. The prognosis of HNSCC is still poor currently, and it may be effective to predict the clinical outcome of HNSCC by immunogenic analysis.Methods: RNASeq and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), the MINiML format GSE65858 chip expression data was downloaded from NCBI, and immune-related genes was downloaded from the InnateDB database. Immune-related genes in 519 HNSC patients were integrated from TCGA dataset. By using multivariate COX analysis and Lasso regression, robust immune-related gene pairs (IRGPs) that predict clinical outcomes of HNSCC were identified. Finally, a risk prognostic model related to immune gene pair was established and verified by clinical features, test sets and GEO external validation set.Results: A total of 699 IRGPs were significantly correlated with the prognosis of HNSCC patients. Fourteen robust IRGPs were finally obtained by Lasso regression and a prognostic risk prediction model was constructed. Risk score of each sample were calculated based on Risk models and divided into the high-risk group (Risk-H) and low Risk group (Risk-L). Risk models were able to stratify the risk in patients with TNM Stage, Age, gender, and smoking history, and the AUC > 0.65 in training set and test set, shows that 14-IRGPs signature in patients with HNSCC has excellent classification performance. In addition, 14-IRGPs had the highest average C index compared with the prognostic characteristics and T, N, and Age of the 3 previously reported HNSCC.Conclusion: This study constructed 14-IRGPs as a novel prognostic marker for predicting survival in HNSCC patients. [ABSTRACT FROM AUTHOR]

Background: Genomic variants are considered sensitive information, revealing potentially private facts about individuals. Therefore, it is important to control access to such data. A key aspect of controlled access is secure storage and efficient query of access logs, for potential misuse. However, there are challenges to securing logs, such as designing against the consequences of "single points of failure". A potential approach to circumvent these challenges is blockchain technology, which is currently popular in cryptocurrency due to its properties of security, immutability, and decentralization. One of the tasks of the iDASH (Integrating Data for Analysis, Anonymization, and Sharing) Secure Genome Analysis Competition in 2018 was to develop time- and space-efficient blockchain-based ledgering solutions to log and query user activity accessing genomic datasets across multiple sites, using MultiChain. Methods: MultiChain is a specific blockchain platform that offers "data streams" embedded in the chain for rapid and secure data storage. We devised a storage protocol taking advantage of the keys in the MultiChain data streams and created a data frame from the chain allowing efficient query. Our solution to the iDASH competition was selected as the winner at a workshop held in San Diego, CA in October 2018. Although our solution worked well in the challenge, it has the drawback that it requires downloading all the data from the chain and keeping it locally in memory for fast query. To address this, we provide an alternate "bigmem" solution that uses indices rather than local storage for rapid queries. Results: We profiled the performance of both of our solutions using logs with 100,000 to 600,000 entries, both for querying the chain and inserting data into it. The challenge solution requires 12 seconds time and 120 Mb of memory for querying from 100,000 entries. The memory requirement increases linearly and reaches 470 MB for a chain with 600,000 entries. Although our alternate bigmem solution is slower and requires more memory (408 seconds and 250 MB, respectively, for 100,000 entries), the memory requirement increases at a slower rate and reaches only 360 MB for 600,000 entries. Conclusion: Overall, we demonstrate that genomic access log files can be stored and queried efficiently with blockchain. Beyond this, our protocol potentially could be applied to other types of health data such as electronic health records. [ABSTRACT FROM AUTHOR]

Emerging evidence has revealed significant roles for small nucleolar RNAs (snoRNAs) in tumorigenesis. However, the genetic and pharmacogenomic landscape of snoRNAs has not been characterized. Using the genotype and snoRNA expression data from The Cancer Genome Atlas, we characterized the effects of genetic variants on snoRNAs across 29 cancer types and further linked related alleles with patient survival as well as genome-wide association study risk loci. Furthermore, we characterized the impact of snoRNA expression on drug response in patients to facilitate the clinical utility of snoRNAs in cancer. We also developed a user-friendly data resource, GPSno (http://hanlab.uth.edu/GPSno), with multiple modules for researchers to visualize, browse, and download multi-dimensional data. Our study provides a comprehensive genetic and pharmacogenomic landscape of snoRNAs, which will shed light on future clinical considerations for the development of snoRNA-based targeted therapies. [ABSTRACT FROM AUTHOR]

Background: The past decade has seen the rise of omics data for the understanding of biological systems in health and disease. This wealth of information includes protein-protein interaction (PPI) data derived from both low- and high-throughput assays, which are curated into multiple databases that capture the extent of available information from the peer-reviewed literature. Although these curation efforts are extremely useful, reliably downloading and integrating PPI data from the variety of available repositories is challenging and time consuming. Methods: We here present a novel user-friendly web-resource called PINOT (Protein Interaction Network Online Tool; available at http://www.reading.ac.uk/bioinf/PINOT/PINOT%5fform.html) to optimise the collection and processing of PPI data from IMEx consortium associated repositories (members and observers) and WormBase, for constructing, respectively, human and Caenorhabditis elegans PPI networks. Results: Users submit a query containing a list of proteins of interest for which PINOT extracts data describing PPIs. At every query submission PPI data are downloaded, merged and quality assessed. Then each PPI is confidence scored based on the number of distinct methods used for interaction detection and the number of publications that report the specific interaction. Examples of how PINOT can be applied are provided to highlight the performance, ease of use and potential utility of this tool. Conclusions: PINOT is a tool that allows users to survey the curated literature, extracting PPI data in relation to a list of proteins of interest. PINOT extracts a similar numbers of PPIs as other, analogous, tools and incorporates a set of innovative features. PINOT is able to process large queries, it downloads human PPIs live through PSICQUIC and it applies quality control filters on the downloaded PPI data (i.e. removing the need for manual inspection by the user). PINOT provides the user with information on detection methods and publication history for each downloaded interaction data entry and outputs the results in a table format that can be straightforwardly further customised and/or directly uploaded into network visualization software. Video abstract [ABSTRACT FROM AUTHOR]

Background: Stomach cancer (SC) is a type of cancer, which is derived from the stomach mucous membrane. As there are non-specific symptoms or no noticeable symptoms observed at the early stage, newly diagnosed SC cases usually reach an advanced stage and are thus difficult to cure. Therefore, in this study, we aimed to develop an integrated database of SC.Methods: SC-related genes were identified through literature mining and by analyzing the publicly available microarray datasets. Using the RNA-seq, miRNA-seq and clinical data downloaded from The Cancer Genome Atlas (TCGA), the Kaplan-Meier (KM) survival curves for all the SC-related genes were generated and analyzed. The miRNAs (miRanda, miRTarget2, PicTar, PITA and TargetScan databases), SC-related miRNAs (HMDD and miR2Disease databases), single nucleotide polymorphisms (SNPs, dbSNP database), and SC-related SNPs (ClinVar database) were also retrieved from the indicated databases. Moreover, gene_disease (OMIM and GAD databases), copy number variation (CNV, DGV database), methylation (PubMeth database), drug (WebGestalt database), and transcription factor (TF, TRANSFAC database) analyses were performed for the differentially expressed genes (DEGs).Results: In total, 9990 SC-related genes (including 8347 up-regulated genes and 1643 down-regulated genes) were identified, among which, 65 genes were further confirmed as SC-related genes by performing enrichment analysis. Besides this, 457 miRNAs, 20 SC-related miRNAs, 1570 SNPs, 108 SC-related SNPs, 419 TFs, 44,605 CNVs, 3404 drug-associated genes, 63 genes with methylation, and KM survival curves of 20,264 genes were obtained. By integrating these datasets, an integrated database of stomach cancer, designated as SCDb, (available at http://www.stomachcancerdb.org/) was established.Conclusions: As a comprehensive resource for human SC, SCDb database will be very useful for performing SC-related research in future, and will thus promote the understanding of the pathogenesis of SC. [ABSTRACT FROM AUTHOR]

Background: Colon cancer is a common and highly malignant cancer. Its morbidity is rapidly increasing, and its prognosis is poor. Currently, immunotherapy is a rapidly developing therapeutic modality of colon cancer. This study aimed to construct a prognostic risk model based on immune genes for the early diagnosis and accurate prognostic prediction of colon cancer.Methods: Transcriptomic data and clinical data were downloaded from The Cancer Genome Atlas database. Immune genes were obtained from the ImmPort database. Differentially expressed (DE) immune genes between 473 colon cancer and 41 adjacent normal tissues were identified. The entire cohort was randomly divided into the training and testing cohort. The training cohort was used to construct the prognostic model. The testing and entire cohorts were used to validate the model. The clinical utility of the model and its correlation with immune cell infiltration were analyzed.Results: A total of 333 DE immune genes (176 up-regulated and 157 down-regulated) were detected. We developed and validated a five-immune gene model of colon cancer, including LBP, TFR2, UCN, UTS2, and MC1R. This model was approved to be an independent prognostic variable, which was more accurate than age and the pathological stage for predicting overall survival at five years. Besides, as the risk score increased, the content of CD8+ T cells in colon cancer was decreased.Conclusions: We developed and validated a five-immune gene model of colon cancer, including LBP, TFR2, UCN, UTS2, and MC1R. This model could be used as an instrumental variable in the prognosis prediction of colon cancer. [ABSTRACT FROM AUTHOR]

Background: Low expression of NCALD(neurocalcin delta) in peripheral blood of ovarian cancer patients predicts poor prognosis. However, the molecular mechanism of NCALD in ovarian cancer and its relationship with chemotherapy outcomes is unclear. The aim of this study was to investigate the potential signaling pathways of NCALD and to evaluate its ability to predict chemotherapy outcomes and prognosis. Methods: High-throughput RNA sequencing data were downloaded from TCGA. GSEA explored the potential signaling pathways of NCALD. The expression of NCALD in chemotherapy sensitive and chemotherapy resistant ovarian cancer patients was detected by TCGA data and clinical samples. ROC analysis confirmed the ability of NCALD to predict chemotherapy outcomes. The association between NCALD expression and prognosis in ovarian cancer patients was assessed using Kaplan-Meier plotter. Results: In patients with NCALD overexpression, genes expression related to ERK1 / 2 signaling pathway, NF-kappaB signaling pathway, TGF-β signaling pathway and immune response pathway was increased, especially ERK1 / 2 signaling pathway. The expression of NCALD in chemoresistant patients was significantly lower than chemosensitive patients. In TCGA data and immunohistochemical samples, the AUC of NCALD expression predicting chemotherapy outcome was 0.59 and 0.64, respectively. In clinical samples, low expression of NCALD was associated with poor OS and PFS. Conclusions: NCALD may activate the ERK1 / 2 signaling pathway in ovarian cancer. As a new biomarker of chemotherapy sensitivity, NCALD was significantly down-regulated in chemotherapy resistance ovarian cancer patients. Low expression of NCALD in ovarian cancer is associated with poor OS and PFS. In the future, further research will be needed on the potential mechanism and clinical application value of NCALD in ovarian cancer. [ABSTRACT FROM AUTHOR]

NoncoRNA (http://www.ncdtcdb.cn:8080/NoncoRNA/) is a manually curated database of experimentally supported non-coding RNAs (ncRNAs) and drug target associations that aim to potentially provide a high-quality data resource for exploring drug sensitivity/resistance-related ncRNAs in various human cancers. ncRNA are RNA molecular that do not encode proteins, but are involved in gene regulation and cellular functions in variety of human diseases, including neurodegenerative diseases and cancers. Here, we developed NoncoRNA which contained 8233 entries between 5568 ncRNAs and 154 drugs in 134 cancers. Each entry in the NoncoRNA contains detailed information on the ncRNAs, drugs, and cancers, the ncRNA expression pattern and experimental detection techniques, drug response and other targets, literature references, and other information. NoncoRNA offers a user-friendly, open access web interface to easily browse, search, and download data. NoncoRNA also provides a submission page for researchers to submit newly validated ncRNA-drug-cancer associations. NoncoRNA might serve as an immeasurable resource for understanding the roles of ncRNAs in cancer therapy. [ABSTRACT FROM AUTHOR]

Background: Epigenetic regulation is essential in regulating gene expression across a variety of biological processes. Many high-throughput sequencing technologies have been widely used to generate epigenetic data, such as histone modification, transcription factor binding sites, DNA modifications, chromatin accessibility, and etc. A large scale of epigenetic data is stored in NCBI Gene Expression Omnibus (GEO). However, it is a great challenge to reanalyze these large scale and complex data, especially for researchers who do not specialize in bioinformatics skills or do not have access to expensive computational infrastructure. Results: GsmPlot can simply accept GSM IDs to automatically download NCBI data or can accept user's private bigwig files as input to plot the concerned data on promoters, exons or any other user-defined genome locations and generate UCSC visualization tracks. By linking public data repository and private data, GsmPlot can spark data-driven ideas and hence promote the epigenetic research. Conclusions: GsmPlot web server allows convenient visualization and efficient exploration of any NCBI epigenetic data in any genomic region without need of any bioinformatics skills or special computing resources. GsmPlot is freely available at https://gsmplot.deqiangsun.org/. [ABSTRACT FROM AUTHOR]

Background: Jatropha curcas is an oil-bearing plant, and has seeds with high oil content (~ 40%). Several advantages, such as easy genetic transformation and short generation duration, have led to the emergence of J. curcas as a model for woody energy plants. With the development of high-throughput sequencing, the genome of Jatropha curcas has been sequenced by different groups and a mass of transcriptome data was released. How to integrate and analyze these omics data is crucial for functional genomics research on J. curcas. Results: By establishing pipelines for processing novel gene identification, gene function annotation, and gene network construction, we systematically integrated and analyzed a series of J. curcas transcriptome data. Based on these data, we constructed a J. curcas database (JCDB), which not only includes general gene information, gene functional annotation, gene interaction networks, and gene expression matrices but also provides tools for browsing, searching, and downloading data, as well as online BLAST, the JBrowse genome browser, ID conversion, heatmaps, and gene network analysis tools. Conclusions: JCDB is the most comprehensive and well annotated knowledge base for J. curcas. We believe it will make a valuable contribution to the functional genomics study of J. curcas. The database is accessible at http://jcdb.xtbg.ac.cn. [ABSTRACT FROM AUTHOR]

Background: Attentive eating means eating devoid of distraction and increasing awareness and memory for food being consumed. Encouraging individuals to eat more attentively could help reduce calorie intake, as a strong evidence base suggests that memory and awareness of food being consumed substantially influence energy intake. Methods: The development and feasibility testing of a smartphone based attentive eating intervention is reported. Informed by models of behavioral change, a smartphone application was developed. Feasibility was tested in twelve overweight and obese volunteers, sampled from university staff. Participants used the application during a four week trial and semi-structured interviews were conducted to assess acceptability and to identify barriers to usage. We also recorded adherence by downloading application usage data from participants' phones at the end of the trial. Results: Adherence data indicated that participants used the application regularly. Participants also felt the application was easy to use and lost weight during the trial. Thematic analysis indicated that participants felt that the application raised their awareness of what they were eating. Analysis also indicated barriers to using a smartphone application to change dietary behavior. Conclusions: An attentive eating based intervention using smartphone technology is feasible and testing of its effectiveness for dietary change and weight loss is warranted. [ABSTRACT FROM AUTHOR]

Background: Short-read data from next-generation sequencing technologies are now being generated across a range of research projects. The fidelity of this data can be affected by several factors and it is important to have simple and reliable approaches for monitoring it at the level of individual experiments. Results: We developed a fast, scalable and accurate approach to estimating error rates in short reads, which has the added advantage of not requiring a reference genome. We build on the fundamental observation that there is a linear relationship between the copy number for a given read and the number of erroneous reads that differ from the read of interest by one or two bases. The slope of this relationship can be transformed to give an estimate of the error rate, both by read and by position. We present simulation studies as well as analyses of real data sets illustrating the precision and accuracy of this method, and we show that it is more accurate than alternatives that count the difference between the sample of interest and a reference genome. We show how this methodology led to the detection of mutations in the genome of the PhiX strain used for calibration of Illumina data. The proposedmethod is implemented in an R package, which can be downloaded from http://bcb.dfci.harvard.edu/ãvwang/shadowRegression.html. Conclusions: The proposed method can be used to monitor the quality of sequencing pipelines at the level of individual experiments without the use of reference genomes. Furthermore, having an estimate of the error rates gives one the opportunity to improve analyses and inferences in many applications of next-generation sequencing data. [ABSTRACT FROM AUTHOR]

The article presents information on SpiroESTdb, which refers to a transcriptome database and online tool for sparganum expressed sequences tags (ESTs). The database is a web-based information resource which has been created upon the annotation and curation of 5,655 ESTs data. It offers an integrated platform for expression dynamics, expressed sequence data, genetic markers like single nucleotide polymorphisms, functional genes, and tandem repeats. The background, findings, and conclusions of the analysis, are discussed in detail.

Background: SHV β-lactamases confer resistance to a broad range of antibiotics by accumulating mutations. The number of SHV variants is steadily increasing. 117 SHV variants have been assigned in the SHV mutation table (http://www.lahey.org/Studies/). Besides, information about SHV β-lactamases can be found in the rapidly growing NCBI protein database. The SHV β-Lactamase Engineering Database (SHVED) has been developed to collect the SHV β-lactamase sequences from the NCBI protein database and the SHV mutation table. It serves as a tool for the detection and reconciliation of inconsistencies, and for the identification of new SHV variants and amino acid substitutions. Description: The SHVED contains 200 protein entries with distinct sequences and 20 crystal structures. 83 protein sequences are included in the both the SHV mutation table and the NCBI protein database, while 35 and 82 protein sequences are only in the SHV mutation table and the NCBI protein database, respectively. Of these 82 sequences, 41 originate from microbial sources, and 22 of them are full-length sequences that harbour a mutation profile which has not been classified yet in the SHV mutation table. 27 protein entries from the NCBI protein database were found to have an inconsistency in SHV name identification. These inconsistencies were reconciled using information from the SHV mutation table and stored in the SHVED. The SHVED is accessible at http://www.LacED.uni-stuttgart.de/classA/SHVED/. It provides sequences, structures, and a multisequence alignment of SHV β-lactamases with the corrected annotation. Amino acid substitutions at each position are also provided. The SHVED is updated monthly and supplies all data for download. Conclusions: The SHV β-Lactamase Engineering Database (SHVED) contains information about SHV variants with reconciled annotation. It serves as a tool for detection of inconsistencies in the NCBI protein database, helps to identify new mutations resulting in new SHV variants, and thus supports the investigation of sequence-function relationships of SHV β-lactamases. [ABSTRACT FROM AUTHOR]

Background: Due to the complex and distributed nature of biological research, our current biological knowledge is spread over many redundant annotation databases maintained by many independent groups. Analysts usually need to visit many of these bioinformatics databases in order to integrate comprehensive annotation information for their genes, which becomes one of the bottlenecks, particularly for the analytic task associated with a large gene list. Thus, a highly centralized and ready-to-use gene-annotation knowledgebase is in demand for high throughput gene functional analysis. Description: The DAVID Knowledgebase is built around the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of gene/protein identifiers from a variety of public genomic resources into DAVID gene clusters. The grouping of such identifiers improves the cross-reference capability, particularly across NCBI and UniProt systems, enabling more than 40 publicly available functional annotation sources to be comprehensively integrated and centralized by the DAVID gene clusters. The simple, pair-wise, text format files which make up the DAVID Knowledgebase are freely downloadable for various data analysis uses. In addition, a well organized web interface allows users to query different types of heterogeneous annotations in a high-throughput manner. Conclusion: The DAVID Knowledgebase is designed to facilitate high throughput gene functional analysis. For a given gene list, it not only provides the quick accessibility to a wide range of heterogeneous annotation data in a centralized location, but also enriches the level of biological information for an individual gene. Moreover, the entire DAVID Knowledgebase is freely downloadable or searchable at http://david.abcc.ncifcrf.gov/knowledgebase/. [ABSTRACT FROM AUTHOR]

Background: Glioma is the most common malignant tumor of the brain. The existence of metastatic tumor cells is an important cause of recurrence even after radical glioma resection.Methods: Single-cell sequencing data and high-throughput data were downloaded from GEO database and TCGA/CGGA database. By means of PCA and tSNE clustering methods, metastasis-associated genes in glioma were identified. GSEA explored possible biological functions that these metastasis-associated genes may participate in. Univariate and multivariate Cox regression were used to construct a prognostic model.Results: Glioma metastatic cells and metastasis-associated genes were identified. The prognostic model based on metastasis-associated genes had good sensitivity and specificity for the prognosis of glioma. These genes may be involved in signal pathways such as cellular protein catabolic process, p53 signaling pathway, transcriptional misregulation in cancer and JAK-STAT signaling pathway.Conclusion: This study explored glioma metastasis-associated genes through single-cell sequencing data mining, and aimed to identify prognostic metastasis-associated signatures for glioma and may provide potential targets for further cancer research. [ABSTRACT FROM AUTHOR]

Background: Phylogenetic species trees are widely used in inferring evolutionary relationships. Existing software and algorithms mainly focus on phylogenetic inference. However, less attention has been paid to intermediate steps, such as processing extremely large sequences and preparing configure files to connect multiple software. When the species number is large, the intermediate steps become a bottleneck that may seriously affect the efficiency of tree building. Results: Here, we present an easy-to-use pipeline named PhySpeTree to facilitate the reconstruction of species trees across bacterial, archaeal, and eukaryotic organisms. Users need only to input the abbreviations of species names; PhySpeTree prepares complex configure files for different software, then automatically downloads genomic data, cleans sequences, and builds trees. PhySpeTree allows users to perform critical steps such as sequence alignment and tree construction by adjusting advanced options. PhySpeTree provides two parallel pipelines based on concatenated highly conserved proteins and small subunit ribosomal RNA sequences, respectively. Accessory modules, such as those for inserting new species, generating visualization configurations, and combining trees, are distributed along with PhySpeTree. Conclusions: Together with accessory modules, PhySpeTree significantly simplifies tree reconstruction. PhySpeTree is implemented in Python running on modern operating systems (Linux, macOS, and Windows). The source code is freely available with detailed documentation (https://github.com/yangfangs/physpetools). [ABSTRACT FROM AUTHOR]

Introduction: Head and neck squamous carcinoma (HNSCC) is one of the most invasive types of cancer with high mortality. A previous study has indicated that low levels of let-7d and miR-205 in HNSCC patients are correlated with poor survival. Let-7d and miR-205 are tumor suppressors and regulators of epithelial-to-mesenchymal transition (EMT). However, it is unclear if let-7d and miR-205 together influence cancer cells. Aim: To determine if let-7d and miR-205 expression levels influence HNSCC patient outcome. Methods: The TCGA expression data for let-7d, miR-205 and their targets as well as clinical data were downloaded from cBioPortal and starBase v2.0 for 307 patients. The expression levels of let-7d and miR-205 were verified according to clinicopathological parameters. The let-7d and miR-205 high- and low-expression groups as well as disease-free survival (DFS), overall survival (OS) and expression levels of genes related to EMT, cancer stem cells, metastasis, cell cycle, drug response and irradiation response were investigated. Results: Let-7d and miR-205 were frequently upregulated in HNSCC compared to normal samples, and ROC analysis showed high discrimination ability for let-7d and miR-205 (area 0.7369 and 0.7739, respectively; p < 0.0001). Differences between expression levels of let-7d or miR-205 and grade, angiolymphatic invasion, perineural invasion and alcohol consumption were indicated. No differences were observed in N-stage, tumor localization, gender or patient age. Patients with lower let-7d levels and higher miR-205 levels had significantly better OS (p = 0.0325) than patients with higher let-7d levels and lower miR-205 levels. In the low let-7d level and high miR-205 level group, a lower percentage of more advanced cancers was observed. The analysis of genes related to EMT, cancer stem cells, metastasis, cell cycle, drug response and irradiation response revealed a distinct phenotype of analyzed groups. Conclusions: The present findings indicated that let-7d down-regulation and miR-205 overexpression create a unique cell phenotype with different behavior compared to cells with upregulated let-7d and down-regulated miR-205. Thus, let-7d and miR-205 are good candidates for new HNSCC biomarkers. [ABSTRACT FROM AUTHOR]