Public Data Repositories Research Articles

Facilitating Focused Process Improvement Efforts in Home Health Agencies to Improve Utilization Outcomes Effectively and Efficiently

The aim of this study was to identify a smaller set of clinical practices most associated with the utilization outcomes of the home health agencies. A secondary data analysis approach was adopted using the publicly accessible Home Health Compare repository as the main data source; the control variables such as rurality, agency size, and median income levels were obtained from other public data repositories. Checking for fall risks and starting care in a timely manner were most associated with hospital readmissions. These two practices and treating patients for pain are most associated with the emergency room visit rates. The results provide additional guidance to home health agencies in their prioritized and focused performance improvement initiatives to improve their utilization outcomes.

Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle.

Gene networks have been broadly used to predict gene functions based on guilt by association (GBA) principle. Thus, in order to better understand the molecular mechanisms of esophageal squamous cell carcinoma (ESCC), our study was designed to use a network-based GBA method to identify the optimal gene functions for ESCC. To identify genomic bio-signatures for ESCC, microarray data of GSE20347 were first downloaded from a public functional genomics data repository of Gene Expression Omnibus database. Then, differentially expressed genes (DEGs) between ESCC patients and controls were identified using the LIMMA method. Afterwards, construction of differential co-expression network (DCN) was performed relying on DEGs, followed by gene ontology (GO) enrichment analysis based on a known confirmed database and DEGs. Eventually, the optimal gene functions were predicted using GBA algorithm based on the area under the curve (AUC) for each GO term. Overall, 43 DEGs and 67 GO terms were gained for subsequent analysis. GBA predictions demonstrated that 13 GO functions with AUC>0.7 had a good classification ability. Significantly, 6 out of 13 GO terms yielded AUC>0.8, which were determined as the optimal gene functions. Interestingly, there were two GO categories with AUC>0.9, which included cell cycle checkpoint (AUC=0.91648), and mitotic sister chromatid segregation (AUC=0.91597). Our findings highlight the clinical implications of cell cycle checkpoint and mitotic sister chromatid segregation in ESCC progression and provide the molecular foundation for developing therapeutic targets.

Genome Variation Map: a data repository of genome variations in BIG Data Center.

The Genome Variation Map (GVM; http://bigd.big.ac.cn/gvm/) is a public data repository of genome variations. As a core resource in the BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, GVM dedicates to collect, integrate and visualize genome variations for a wide range of species, accepts submissions of different types of genome variations from all over the world and provides free open access to all publicly available data in support of worldwide research activities. Unlike existing related databases, GVM features integration of a large number of genome variations for a broad diversity of species including human, cultivated plants and domesticated animals. Specifically, the current implementation of GVM not only houses a total of ∼4.9 billion variants for 19 species including chicken, dog, goat, human, poplar, rice and tomato, but also incorporates 8669 individual genotypes and 13 262 manually curated high-quality genotype-to-phenotype associations for non-human species. In addition, GVM provides friendly intuitive web interfaces for data submission, browse, search and visualization. Collectively, GVM serves as an important resource for archiving genomic variation data, helpful for better understanding population genetic diversity and deciphering complex mechanisms associated with different phenotypes.

Abstract A18: Predicting synergistic drug combinations from genomic features and single-agent response profiles

Abstract Drug combinations promise to improve clinical responses and/or forestall drug resistance. To capitalize on this promise, we need to know which drugs to combine, and whom to give them to based on the genetic or pathological features of their disease. However, accomplishing this goal has been precluded by the infeasibility of performing comprehensive drug-combination studies across thousands of cellular contexts. We hypothesized that the basal gene-transcription state of cancer cell lines, in concert with the response profiles of hundreds of single-agent small molecules, might be leveraged to nominate synergistic drug combinations, eliminating the need to test all possible drug/drug combinations across cellular models. Specifically, we predicted that inhibiting the protein product of transcripts associated with drug resistance to a given small molecule might induce drug synergy. To test this notion, we analyzed public cell-line drug-sensitivity data to identify candidate compound-gene pairs. We identified 7 examples in which outlier expression of a druggable candidate protein was associated with lack of single-agent response. Inhibition of 6/7 candidate co-targets resulted in cell-line-specific synergistic cell killing across multiple cell line models, validating the overall approach. For example, consistent with clinical findings, we found that high expression of the MGMT gene, encoding O-6-methylguanine-DNA methyltransferase, was uniquely associated with response to alkylating agents such as temozolomide, and that combination of the MGMT inhibitor O-6-benzylguanine with temozolomide resulted in synergistic killing. These initial studies highlight the potential of integrating basal gene expression features with small-molecule response to nominate rational candidates for drug combinations. As public repositories of single-agent response data from diverse cellular contexts continue to expand, so too will our repertoire of therapeutic combinations. Moreover, this approach permits the parallel identification of genomic features that indicate which patient populations are most likely to benefit from such combinations. Citation Format: Matthew G. Rees, Lisa Brenan, Amanda Walker, Cory M. Johannessen. Predicting synergistic drug combinations from genomic features and single-agent response profiles [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr A18.

Improving the Generation of Infoboxes from Data Silos through Machine Learning and the Use of Semantic Repositories

Nowadays, both public and private organizations own large private text-based data repositories with critical information. The information stored in these data silos is usually queried through information retrieval systems based on indexes, which yield hundreds or thousands of results when interrogated using keywords. In order to improve data accessibility when searching for specific information, the use of infoboxes can be very useful. The generation such infoboxes is by itself a complex problem, but in this type of isolated environments, it becomes even harder as the selection of the entities and their attributes can be conditioned by local and very specific parameters. In this work, we propose a methodology to tackle this special problem, combining classical approaches with machine learning, and leveraging the resources provided by the Semantic Web. The working methodology has been applied to two well-known datasets, and also it has been tested on a real environment scenario, showing the feasibility of our approach.

Functional gene networks based on the gene neighborhood in metagenomes

ABSTRACTThe gene neighborhood in prokaryotic genomes has been effectively utilized in inferring co-functional networks in various organisms. Previously, such genomic context information has been sought among completely assembled prokaryotic genomes. Here, we present a method to infer functional gene networks according to the gene neighborhood in metagenome contigs, which are incompletely assembled genomic fragments. Given that the amount of metagenome sequence data has now surpassed that of completely assembled prokaryotic genomes in the public domain, we expect benefits of inferring networks by the metagenome-based gene neighborhood. We generated co-functional networks for diverse taxonomical species using metagenomics contigs derived from the human microbiome and the ocean microbiome. We found that the networks based on the metagenome gene neighborhood outperformed those based on 1748 completely assembled prokaryotic genomes. We also demonstrated that the metagenome-based gene neighborhood could predict genes related to virulence-associated phenotypes in a bacterial pathogen, indicating that metagenome-based functional links could be sufficiently predictive for some phenotypes of medical importance. Owing to the exponential growth of metagenome sequence data in public repositories, metagenome-based inference of co-functional networks will facilitate understanding of gene functions and pathways in diverse species.

Identification of differentially expressed genes through a meta-analysis approach for oral cancer classification.

We propose a meta-analysis scheme for identifying differentially expressed genes in Oral Squamous Cell Carcinoma (OSCC) from different microarray studies. We detect a subset of relevant features and further classify samples under two experimental conditions (i.e healthy and cancer samples) for better patient stratification. A well-established meta-analysis method is adopted and gene expression data sets are derived from a public functional genomics data repository. Our primary aim is the accurate identification of up- and down-regulated genes in order to extract valuable biological information concerning the changes in expression between healthy and cancer samples. According to our results and the extracted informative gene list, a high classification accuracy of healthy and OSCC tumors is achieved with as few genes as possible. Furthermore, the proposed scheme implies that the combination of datasets from different origins may reduce the estimated percentage of false predictions, while the power of gene identification and disease classification is increased.

Using taxonomic consistency with semi‐automated data pre‐processing for high quality DNA barcodes

Abstract In recent years, large‐scale DNA barcoding campaigns have generated an enormous amount of COI barcodes, which are usually stored in NCBI's GenBank and the official Barcode of Life database (BOLD). BOLD data are generally associated with more detailed and better curated meta‐data, because a great proportion is based on expert‐verified and vouchered material, accessible in public collections. In the course of the initiative German Barcode of Life data were generated for the reference library of 2,846 species of Coleoptera from 13,516 individuals. Confronted with the high effort associated with the identification, verification and data validation, a bioinformatic pipeline, “TaxCI” was developed that (1) identifies taxonomic inconsistencies in a given tree topology (optionally including a reference dataset), (2) discriminates between different cases of incongruence in order to identify contamination or misidentified specimens, (3) graphically marks those cases in the tree, which finally can be checked again and, if needed, corrected or removed from the dataset. For this, “TaxCI” may use DNA‐based species delimitations from other approaches (e.g. mPTP) or may perform implemented threshold‐based clustering. The data‐processing pipeline was tested on a newly generated set of barcodes, using the available BOLD records as a reference. A data revision based on the first run of the TaxCI tool resulted in the second TaxCI analysis in a taxonomic match ratio very similar to the one recorded from the reference set (92% vs. 94%). The revised dataset improved by nearly 20% through this procedure compared to the original, uncorrected one. Overall, the new processing pipeline for DNA barcode data allows for the rapid and easy identification of inconsistencies in large datasets, which can be dealt with before submitting them to public data repositories like BOLD or GenBank. Ultimately, this will increase the quality of submitted data and the speed of data submission, while primarily avoiding the deterioration of the accuracy of the data repositories due to ambiguously identified or contaminated specimens.

Mutation of the DNA damage gene signature to predict for better outcome in malignant melanoma.

e21036 Background: DNA damage response is a fundamental cellular pathway with an important role in maintaining genomic integrity. As Malignant Melanoma (MM) is associated with high mutation rates, and we postulated that mm is likely to have abnormal DNA repair pathway aberrations that could have prognostic and/or therapeutic significance. Methods: We conducted an unbiased assessment of genes involved in the DNA damage response through a comprehensive survey of established public data repositories and published literature and abstracts in all cancers. This survey was used to generate a 6-gene DNA Damage Signature (DDS). The DDS was then applied to 287 mm samples in The Cancer Genome Atlas. Results: The DDS was altered in 25% (72/287) of patients with mutations being present in all 6 genes including ATM (6%), TP53BP1 (7%), MDC1 (7%), BRCA1 (5%), PRKDC (8%) and RAD50 (1.4%). The DDS was more commonly altered in mm harboring alterations in BRAF (78%, 112/143), NRAS (89%, 73/82) or NF1 (75%, 5/20) than in triple wild type tumors (60%,19/32). The correlation between alteration in BRAF/NRAS/NF1 and DDS (p = 0.02) reached significance, as did the tendency for DDS and NF1 altered subtype to co-occur (p < 0.05). Importantly, alteration in the DDS was associated with significantly longer disease free (88 vs. 44 months, p = 0.02) and overall survival (152 vs. 66 months, P = 0.02). The mutations in ATM and BRCA1, shown to be targets in other cancers for PARP inhibition were seen in these patients as well as other oncogenic or hot-spot mutations. Conclusions: The improved survival associated with alteration of the DDS implicate DNA damage response signaling as favorably modulating the immune system, and propose a possible role for PARP inhibition as a therapeutic strategy in melanoma.

Reconstructing context-specific gene regulatory network and identifying modules and network rewiring through data integration.

Reconstructing context-specific transcriptional regulatory network is crucial for deciphering principles of regulatory mechanisms underlying various conditions. Recently studies that reconstructed transcriptional networks have focused on individual organisms or cell types and relied on data repositories of context-free regulatory relationships. Here we present a comprehensive framework to systematically derive putative regulator-target pairs in any given context by integrating context-specific transcriptional profiling and public data repositories of gene regulatory networks. Moreover, our framework can identify core regulatory modules and signature genes underlying global regulatory circuitry, and detect network rewiring and core rewired modules in different contexts by considering gene modules and edge (gene interaction) modules collaboratively. We applied our methods to analyzing Autism RNA-seq experiment data and produced biologically meaningful results. In particular, all 11 hub genes in a predicted rewired autistic regulatory subnetwork have been linked to autism based on literature review. The predicted rewired autistic regulatory network may shed some new insight into disease mechanism.

Calculating the quality of public high-throughput sequencing data to obtain a suitable subset for reanalysis from the Sequence Read Archive.

It is important for public data repositories to promote the reuse of archived data. In the growing field of omics science, however, the increasing number of submissions of high-throughput sequencing (HTSeq) data to public repositories prevents users from choosing a suitable data set from among the large number of search results. Repository users need to be able to set a threshold to reduce the number of results to obtain a suitable subset of high-quality data for reanalysis. We calculated the quality of sequencing data archived in a public data repository, the Sequence Read Archive (SRA), by using the quality control software FastQC. We obtained quality values for 1 171 313 experiments, which can be used to evaluate the suitability of data for reuse. We also visualized the data distribution in SRA by integrating the quality information and metadata of experiments and samples. We provide quality information of all of the archived sequencing data, which enable users to obtain sufficient quality sequencing data for reanalyses. The calculated quality data are available to the public in various formats. Our data also provide an example of enhancing the reuse of public data by adding metadata to published research data by a third party.

MP87-05 UPREGULATION OF OPIORPHIN IS ASSOCIATED WITH PROSTATE CANCER; A POTENTIAL NOVEL THERAPEUTIC TARGET

You have accessJournal of UrologyProstate Cancer: Basic Research & Pathophysiology III1 Apr 2017MP87-05 UPREGULATION OF OPIORPHIN IS ASSOCIATED WITH PROSTATE CANCER; A POTENTIAL NOVEL THERAPEUTIC TARGET Amarnath Mukherjee, Li Wang, Mark Schoenberg, and Kelvin Davies Amarnath MukherjeeAmarnath Mukherjee More articles by this author , Li WangLi Wang More articles by this author , Mark SchoenbergMark Schoenberg More articles by this author , and Kelvin DaviesKelvin Davies More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2017.02.2709AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Patients who die from prostate cancer (PrCa) do so as a result of the development of castrate resistant disease. Identification of novel targets not related to androgen biosynthesis may provide useful therapeutic alternatives that could have a positive impact on the long term survival of these patients. Recently, it has been shown that upregulation of the opiorphin gene (encoding an endogenous neutral endopeptidase inhibitor involved in the hypoxic response) is an unfavorable risk factor for the survival of oropharyngeal squamous cell carcinoma patients after surgery. In the present studies we determined whether opiorphin is also upregulated in PrCa, and if it plays a role in the hypoxic response in PrCa cells. METHODS We searched the Gene Expression Omnibus (GEO), a public functional genomics data repository administered by the NCBI, for evidence of opiorphin gene (ProL1) expression. Quantitative-(qt)-RT-PCR was used to compare expression of ProL1 in 41 PrCa tissues and 7 control, non-cancerous prostate tissue present on a commercially available tissue array. Two PrCa cell lines (LnCaP, androgen dependent and PC-3, androgen independent) were exposed to hypoxia for various lengths of time (1-24 hours, RNA isolated and expression of opiorphin and other genes involved in the hypoxic response determined by qt-RT-PCR or microarray analysis. RESULTS A search of GEO data demonstrated that the gene encoding opiorphin (ProL1) was significantly upregulated in PrCa. We confirmed a significant upregulation, with a trend for greater upregulation with PrCa stage, on an RNA PrCa tissue array. We found hypoxia resulted in an early (2h) 2-fold upregulation of ProL1 in LnCaP cells. In contrast, in PC3 cells, there was only a significant (3-fold) upregulation of ProL1 after 24h of hypoxia, with other markers of the hypoxic response (Hif1a and VEGF) significantly activated after 5h. CONCLUSIONS For the first time we report up-regulation of the opiorphin gene (ProL1) in PrCa. Opiorphin is a mediator of the hypoxic response in certain cell types. We demonstrate hypoxic upregulation of ProL1 in two PrCa cell lines. Early upregulation in LnCaP cells, prior to other known markers of the hypoxic response (Hif1a and VEGF) suggests in certain PrCa cells (androgen dependent) ProL1 may play a role in regulating downstream modulators of the hypoxic response. Taken together, these results suggest opiorphin may play a role in overcoming the hypoxic environment in certain PrCa tumors and might therefore represent a novel therapeutic for PrCa. © 2017FiguresReferencesRelatedDetails Volume 197Issue 4SApril 2017Page: e1167-e1168 Advertisement Copyright & Permissions© 2017MetricsAuthor Information Amarnath Mukherjee More articles by this author Li Wang More articles by this author Mark Schoenberg More articles by this author Kelvin Davies More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

A reductionist approach to extract robust molecular markers from microarray data series – Isolating markers to track osseointegration

Complexities in the full genome expression studies hinder the extraction of tracker genes to analyze the course of biological events. In this study, we demonstrate the applications of supervised machine learning methods to reduce the irrelevance in microarray data series and thereby extract robust molecular markers to track biological processes. The methodology has been illustrated by analyzing whole genome expression studies on bone-implant integration (ossointegration). Being a biological process, osseointegration is known to leave a trail of genetic footprint during the course. In spite of existence of enormous amount of raw data in public repositories, researchers still do not have access to a panel of genes that can definitively track osseointegration. The results from our study revealed panels comprising of matrix metalloproteinases and collagen genes were able to track osseointegration on implant surfaces (MMP9 and COL1A2 on micro-textured; MMP12 and COL6A3 on superimposed nano-textured surfaces) with 100% classification accuracy, specificity and sensitivity. Further, our analysis showed the importance of the progression of the duration in establishment of the mechanical connection at bone-implant surface. The findings from this study are expected to be useful to researchers investigating osseointegration of novel implant materials especially at the early stage. The methodology demonstrated can be easily adapted by scientists in different fields to analyze large databases for other biological processes.

A novel RNA sequencing data analysis method for cell line authentication.

We have developed a novel analysis method that can interrogate the authenticity of biological samples used for generation of transcriptome profiles in public data repositories. The method uses RNA sequencing information to reveal mutations in expressed transcripts and subsequently confirms the identity of analysed cells by comparison with publicly available cell-specific mutational profiles. Cell lines constitute key model systems widely used within cancer research, but their identity needs to be confirmed in order to minimise the influence of cell contaminations and genetic drift on the analysis. Using both public and novel data, we demonstrate the use of RNA-sequencing data analysis for cell line authentication by examining the validity of COLO205, DLD1, HCT15, HCT116, HKE3, HT29 and RKO colorectal cancer cell lines. We successfully authenticate the studied cell lines and validate previous reports indicating that DLD1 and HCT15 are synonymous. We also show that the analysed HKE3 cells harbour an unexpected KRAS-G13D mutation and confirm that this cell line is a genuine KRAS dosage mutant, rather than a true isogenic derivative of HCT116 expressing only the wild type KRAS. This authentication method could be used to revisit the numerous cell line based RNA sequencing experiments available in public data repositories, analyse new experiments where whole genome sequencing is not available, as well as facilitate comparisons of data from different experiments, platforms and laboratories.

CellFinder's Molecular Database and its Application to Stem Cell Research

CellFinder is a freely available on-line resource for cell-based data in mammalian cells and in vitro cell lines. CellFinder was developed to provide easier access to different types of cell-based data, which include anatomical and microscopic images, protein expression and whole genome mRNA expression profiles. In this short communication, we describe the collection, processing and storage of gene expression data for the CellFinder resource. Studies involving the use of normal cells and/or production of pluripotent stem cells or their engineered cell types, were chosen from public data repositories of mRNA expression data. Samples in each study were manually curated with ontology terms, and the data was further processed to yield normalized expression data. Sample, experiment and molecular data were stored in a postgreSQL database. The resulting molecular database currently contains 1588 samples from 56 studies, covering 42 cell types in human and mouse. The molecular database is undergoing continuous development and will serve as a source of standardized, processed and expertly annotated data for CellFinder analysis tools such as MarkerTool and CompareTool.

Databases and tools for constructing signal transduction networks in cancer

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., “big data”), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called “systems biology”. One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.

An attempt to construct a public data repository for research data at Kanazawa University

An attempt to construct a public data repository for research data at Kanazawa University

Raw diffraction data preservation and reuse: overview, update on practicalities and metadata requirements.

A topical review is presented of the rapidly developing interest in and storage options for the preservation and reuse of raw data within the scientific domain of the IUCr and its Commissions, each of which operates within a great diversity of instrumentation. A résumé is included of the case for raw diffraction data deposition. An overall context is set by highlighting the initiatives of science policy makers towards an 'Open Science' model within which crystallographers will increasingly work in the future; this will bring new funding opportunities but also new codes of procedure within open science frameworks. Skills education and training for crystallographers will need to be expanded. Overall, there are now the means and the organization for the preservation of raw crystallographic diffraction data via different types of archive, such as at universities, discipline-specific repositories (Integrated Resource for Reproducibility in Macromol-ecular Crystallography, Structural Biology Data Grid), general public data repositories (Zenodo, ResearchGate) and centralized neutron and X-ray facilities. Formulation of improved metadata descriptors for the raw data types of each of the IUCr Commissions is in progress; some detailed examples are provided. A number of specific case studies are presented, including an example research thread that provides complete open access to raw data.

External Resource Annotation Framework and its applications in E-learning

Rich Internet Applications that enable new ways of interaction with digital objects within multimedia scenes become important for educational communities. Such new ways of advanced human-computer interaction, in its different forms match the needs of formal education as well as lifelong learning in medicine, architecture, construction or cultural heritage management. This paper presents innovative multimedia annotation methods based on advanced human-computer interaction and (a)synchronous interpersonal collaboration features, focusing on e-learning activities. They have been implemented within eRAF (External Resource Annotation Framework) with the scope of going beyond the existing solutions by introducing new annotation types using external resources. Thus, anyone can easily annotate the own resources with additional information such as real-time handwriting, text and animation, or even audio/video sequences stored into public data repositories, or recorded ad-hoc, in order to create value-added interactive content.

EasyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies.

The ever-growing availability of high-quality genotypes for a multitude of species has enabled researchers to explore the underlying genetic architecture of complex phenotypes at an unprecedented level of detail using genome-wide association studies (GWAS). The systematic comparison of results obtained from GWAS of different traits opens up new possibilities, including the analysis of pleiotropic effects. Other advantages that result from the integration of multiple GWAS are the ability to replicate GWAS signals and to increase statistical power to detect such signals through meta-analyses. In order to facilitate the simple comparison of GWAS results, we present easyGWAS, a powerful, species-independent online resource for computing, storing, sharing, annotating, and comparing GWAS. The easyGWAS tool supports multiple species, the uploading of private genotype data and summary statistics of existing GWAS, as well as advanced methods for comparing GWAS results across different experiments and data sets in an interactive and user-friendly interface. easyGWAS is also a public data repository for GWAS data and summary statistics and already includes published data and results from several major GWAS. We demonstrate the potential of easyGWAS with a case study of the model organism Arabidopsis thaliana, using flowering and growth-related traits.