Browsing by Subject "Database"

Now showing 1 - 10 of 22
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    Advanced Functions Embedded in the Second Version of Database, Global Evaluation of SARS-CoV-2/hCoV-19 Sequences 2
    (Frontiers Media, 2022-04-11) Li, Kailing; Wang, Audrey K.Y.; Liu, Sheng; Fang, Shuyi; Lu, Alex Z.; Shen, Jikui; Yang, Lei; Hu, Chang-Deng; Yang, Kai; Wan, Jun; BioHealth Informatics, School of Informatics and Computing
    The Global Evaluation of SARS-CoV-2/hCoV-19 Sequences 2 (GESS v2 https://shiny.ph.iu.edu/GESS_v2/) is an updated version of GESS, which has offered a handy query platform to analyze single-nucleotide variants (SNVs) on millions of high coverages and high-quality severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) complete genomes provided by the Global Initiative on Sharing Avian Influenza Data (GISAID). Including the tools in the first version, the GESS v2 is embedded with new functions, which allow users to search SNVs, given the viral nucleotide or amino acid sequence. The GESS v2 helps users to identify SNVs or SARS-CoV-2 lineages enriched in countries of user's interest and show the migration path of a selected lineage on a world map during specific time periods chosen by the users. In addition, the GESS v2 can recognize the dynamic variations of newly emerging SNVs in each month to help users monitor SNVs, which will potentially become dominant soon. More importantly, multiple sets of analyzed results about SNVs can be downloaded directly from the GESS v2 by which users can conduct their own independent research. With these significant updates, the GESS v2 will continue to serve as a public open platform for researchers to explore SARS-CoV-2 evolutionary patterns from the perspectives of the prevalence and impact of SNVs.
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    Celltyper: A Single-Cell Sequencing Marker Gene Tool Suite
    (2023-05) Paisley, Brianna Meadow; Liu, Yunlong; Yan, Jingwen; Cao, Sha; Wang, Juexin; Carfagna, Mark
    Single-cell RNA-sequencing (scRNA-seq) has enabled researchers to study interindividual cellular heterogeneity, to explore disease impact on cellular composition of tissue, and to identify novel cell subtypes. However, a major challenge in scRNA-seq analysis is to identify the cell type of individual cells. Accurate cell type identification is crucial for any scRNA-seq analysis to be valid as incorrect cell type assignment will reduce statistical robustness and may lead to incorrect biological conclusions. Therefore, accurate and comprehensive cell type assignment is necessary for reliable biological insights into scRNA-seq datasets. With over 200 distinct cell types in humans alone, the concept of cell identity is large. Even within the same cell type there exists heterogeneity due to cell cycle phase, cell state, cell subtypes, cell health and the tissue microenvironment. This makes cell type classification a complicated biological problem requiring bioinformatics. One approach to classify cell type identity is using marker genes. Marker genes are genes specific for one or a few cell types. When coupled with bioinformatic methods, marker genes show promise of improving cell type classification. However, current scRNA-seq classification methods and databases use marker genes that are non-specific across sources, samples, and/or species leading to bias and errors. Furthermore, many existing tools require manual intervention by the user to provide training datasets or the expected number and name of cell types, which can introduce selection bias. The selection bias negatively impacts the accuracy of cell type classification methods as the model cannot extrapolate outside of the user inputs even when it is biologically meaningful to do so. In this dissertation I developed CellTypeR, a suite of tools to explore the biology governing cell identity in a “normal” state for humans and mice. The work presented here accomplishes three aims: 1. Develop an ontology standardized database of published marker gene literature; 2. Develop and apply a marker gene classification algorithm; and 3. Create user interface and input data structure for scRNA-seq cell type prediction.
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    Construction of a Database for Socio-Demographic, Medico-Legal, Anatomic, and Genomic Research into Suicide
    (Office of the Vice Chancellor for Research, 2015-04-17) Engle, Kaitlyn; Cook, Shannon; Levey, Daniel; Ballew, Alfarena; Yard, Michael
    Suicide is a potentially preventable tragedy. Over 180 cases of suicide a year occur in Marion County. We have created a database that permits integration of socio-demographic data, medico-legal information, anatomic images, and genomic results. We have collected over 50 cases to date. We will show results of analyses looking at method of suicide, toxicology results, and genomic biomarker correlates. It is hoped that this resource would permit the study of risk factors and the creation of predictive algorithms that may better identify people at risk, and lead to early intervention and prevention efforts.
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    Continuous Kidney Replacement Therapy and Survival in Children and Young Adults: Findings From the Multinational WE-ROCK Collaborative
    (Elsevier, 2024) Starr, Michelle C.; Gist, Katja M.; Zang, Huaiyu; Ollberding, Nicholas J.; Balani, Shanthi; Cappoli, Andrea; Ciccia, Eileen; Joseph, Catherine; Kakajiwala, Aadil; Kessel, Aaron; Muff-Luett, Melissa; Santiago Lozano, María J.; Pinto, Matthew; Reynaud, Stephanie; Solomon, Sonia; Slagle, Cara; Srivastava, Rachana; Shih, Weiwen V.; Webb, Tennille; Menon, Shina; Worldwide Exploration of Renal Replacement Outcomes Collaborative in Kidney Disease (WE-ROCK) Collaborative; Pediatrics, School of Medicine
    Rationale & objective: There are limited studies describing the epidemiology and outcomes in children and young adults receiving continuous kidney replacement therapy (CKRT). We aimed to describe associations between patient characteristics, CKRT prescription, and survival. Study design: Retrospective multicenter cohort study. Setting & participants: 980 patients aged from birth to 25 years who received CKRT between 2015 and 2021 at 1 of 32 centers in 7 countries participating in WE-ROCK (Worldwide Exploration of Renal Replacement Outcomes Collaborative in Kidney Diseases). Exposure: CKRT for acute kidney injury or volume overload. Outcomes: Death before intensive care unit (ICU) discharge. Analytical approach: Descriptive statistics. Results: Median age was 8.8 years (IQR, 1.6-15.0), and median weight was 26.8 (IQR, 11.6-55.0) kg. CKRT was initiated a median of 2 (IQR, 1-6) days after ICU admission and lasted a median of 6 (IQR, 3-14) days. The most common CKRT modality was continuous venovenous hemodiafiltration. Citrate anticoagulation was used in 62%, and the internal jugular vein was the most common catheter placement location (66%). 629 participants (64.1%) survived at least until ICU discharge. CKRT dose, filter type, and anticoagulation were similar in those who did and did not survive to ICU discharge. There were apparent practice variations by institutional ICU size. Limitations: Retrospective design; limited representation from centers outside the United States. Conclusions: In this study of children and young adults receiving CKRT, approximately two thirds survived at least until ICU discharge. Although variations in dialysis mode and dose, catheter size and location, and anticoagulation were observed, survival was not detected to be associated with these parameters. Plain-language summary: In this large contemporary epidemiological study of children and young adults receiving continuous kidney replacement therapy in the intensive care unit, we observed that two thirds of patients survived at least until ICU discharge. However, patients with comorbidities appeared to have worse outcomes. Compared with previously published reports on continuous kidney replacement therapy practice, we observed greater use of continuous venovenous hemodiafiltration with regional citrate anticoagulation.
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    Design and Implementation of Energy Usage Monitoring and Control Systems Using Modular IIOT Framework
    (2021-05) Chheta, Monil Vallabhbhai; Chien, Stanley Yung-Ping; Chen, Jie; Li, Lingxi
    This project aims to develop a cloud-based platform that integrates sensors with business intelligence for real-time energy management at the plant level. It provides facility managers, an energy management platform that allows them to monitor equipment and plant-level energy consumption remotely, receive a warning, identify energy loss due to malfunction, present options with quantifiable effects for decision-making, and take actions, and assess the outcomes. The objectives consist of: 1. Developing a generic platform for the monitoring energy consumption of industrial equipment using sensors 2. Control the connected equipment using an actuator 3. Integrating hardware, cloud, and application algorithms into the platform 4. Validating the system using an Energy Consumption Forecast scenario A Demo station was created for testing the system. The demo station consists of equip- ment such as air compressor, motor and light bulb. The current usage of these equipment is measured using current sensors. Apart from current sensors, temperature sensor, pres- sure sensor and CO2 sensor were also used. Current consumption of these equipment was measured over a couple of days. The control system was tested randomly by turning on equipment at random times. Turning on the equipment resulted in current consumption which ensured that the system is running. Thus, the system worked as expected and user could monitor and control the connected equipment remotely.
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    Establishment of a clinically correlated human pericardial fluid bank: Evaluation of intrapericardial diagnostic potential
    (Wiley, 1999-01) Dickson, Tonya J.; Nguyen, A.Q.; Kumfer, K.; Maxted, W.; Gurudutt, Vivek; Brown, John; Mahomed, Yousuf; Sharp, Thomas; Aufiero, Thomas X.; Fineberg, Naomi; March, Keith L.; Medicine, School of Medicine
    The development of a clinically correlated human pericardial fluid bank and database is described. A unique feature of this registry is the availability of a large number of pericardial fluid samples for testing with respect to multiple factors and for correlation with angiographic findings and clinical syndromes expressed by the patients. The collection of data at the present time comprises frozen pericardial fluid samples obtained from patients who have undergone cardiac surgery; and historical, clinical, and laboratory data obtained from the patient records. Nearly 400 samples have been stored and analyzed thus far, with sample entry continuing. This registry is designed to evaluate the local factors that play a role in mediating or reflecting myocardial or coronary responses. Pathophysiologic processes of particular interest include restenosis, plaque ruptures, and angiogenesis. Study of the pericardial fluid bank should lead to enhanced understanding of molecular mechanisms, as well as to the explanation for the reasons underlying interpatient variability in these processes. It is further anticipated that this information might provide a foundation for the diagnostic use of pericardial fluid to individualize therapies targeting angiogenesis or plaque physiology.
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    GeneMarkeR: A Database and User Interface for scRNA-seq Marker Genes
    (Frontiers Media, 2021-10-26) Paisley, Brianna M.; Liu, Yunlong; BioHealth Informatics, School of Informatics and Computing
    Single-cell sequencing (scRNA-seq) has enabled researchers to study cellular heterogeneity. Accurate cell type identification is crucial for scRNA-seq analysis to be valid and robust. Marker genes, genes specific for one or a few cell types, can improve cell type classification; however, their specificity varies across species, samples, and cell subtypes. Current marker gene databases lack standardization, cell hierarchy consideration, sample diversity, and/or the flexibility for updates as new data become available. Most of these databases are derived from a single statistical analysis despite many such analyses scattered in the literature to identify marker genes from scRNA-seq data and pure cell populations. An R Shiny web tool called GeneMarkeR was developed for researchers to retrieve marker genes demonstrating cell type specificity across species, methodology and sample types based on a novel algorithm. The web tool facilitates online submission and interfaces with MySQL to ensure updatability. Furthermore, the tool incorporates reactive programming to enable researchers to retrieve standardized public data supporting the marker genes. GeneMarkeR currently hosts over 261,000 rows of standardized marker gene results from 25 studies across 21,012 unique genomic entities and 99 unique cell types mapped to hierarchical ontologies.
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    GESS: a database of global evaluation of SARS-CoV-2/hCoV-19 sequences
    (Oxford University Press, 2020-10-12) Fang, Shuyi; Li, Kailing; Shen, Jikui; Liu, Sheng; Liu, Juli; Yang, Lei; Hu, Chang-Deng; Wan, Jun; BioHealth Informatics, School of Informatics and Computing
    The COVID-19 outbreak has become a global emergency since December 2019. Analysis of SARS-CoV-2 sequences can uncover single nucleotide variants (SNVs) and corresponding evolution patterns. The Global Evaluation of SARS-CoV-2/hCoV-19 Sequences (GESS, https://wan-bioinfo.shinyapps.io/GESS/) is a resource to provide comprehensive analysis results based on tens of thousands of high-coverage and high-quality SARS-CoV-2 complete genomes. The database allows user to browse, search and download SNVs at any individual or multiple SARS-CoV-2 genomic positions, or within a chosen genomic region or protein, or in certain country/area of interest. GESS reveals geographical distributions of SNVs around the world and across the states of USA, while exhibiting time-dependent patterns for SNV occurrences which reflect development of SARS-CoV-2 genomes. For each month, the top 100 SNVs that were firstly identified world-widely can be retrieved. GESS also explores SNVs occurring simultaneously with specific SNVs of user's interests. Furthermore, the database can be of great help to calibrate mutation rates and identify conserved genome regions. Taken together, GESS is a powerful resource and tool to monitor SARS-CoV-2 migration and evolution according to featured genomic variations. It provides potential directive information for prevalence prediction, related public health policy making, and vaccine designs.
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    HAPPI: A Bioinformatics Database Platform Enabling Network Biology Studies
    (2006-06-29T19:05:24Z) Mamidipalli, SudhaRani; Chen, Jake Yue
    The publication of the draft human genome consisting of 30,000 genes is merely the beginning of genome biology. A new way to understand the complexity and richness of molecular and cellular function of proteins in biological processes is through understanding of biological networks. These networks include protein-protein interaction networks, gene regulatory networks, and metabolic networks. In this thesis, we focus on human protein-protein interaction networks using informatics techniques. First, we performed a thorough literature survey to document different experimental methods to detect and collect protein interactions, current public databases that store these interactions, computational software to predict, validate and interpret protein networks. Then, we developed the Human Annotated Protein-Protein Interaction (HAPPI) database to manage a wealth of integrated information related to protein functions, protein-protein functional links, and protein-protein interactions. Approximately 12900 proteins from Swissprot, 57900 proteins from Trembl, 52186 protein-domains from Swisspfam, 4084 gene-pathways from KEGG, 2403190 interactions from STRING and 51207 interactions from OPHID public databases were integrated into a single relational database platform using Oracle 10g on an IU Supercomputing grid. We further assigned a confidence score to each protein interaction pair to help assess the quality and reliability of protein-protein interaction. We hosted the database on the Discovery Informatics and Computing web site, which is now publicly accessible. HAPPI database differs from other protein interaction databases in these following aspects: 1) It focuses on human protein interactions and contains approximately 860000 high-confidence protein interaction records—one of the most complete and reliable sources of human protein interaction today; 2) It includes thorough protein domain, gene and pathway information of interacting proteins, therefore providing a whole view of protein functional information; 3) It contains a consistent ranking score that can be used to gauge the confidence of protein interactions. To show the benefits of HAPPI database, we performed a case study using Insulin Signaling pathway in collaboration with a biology team on campus. We began by taking two sets of proteins that were previously well studied as separate processes, set A and set B. We queried these proteins against the HAPPI database, and derived high-confidence protein interaction data sets annotated with known KEGG pathways. We then organized these protein interactions on a network diagram. The end result shows many novel hub proteins that connect set A or B proteins. Some hub proteins are even novel members outside of any annotated pathway, making them interesting targets to validate for subsequent biological studies.
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    Lithobates Pipiens (Northern Leopard Frog). Malformation
    (Ssar, 2013-06) Rabe, Allison; Lannoo, Michael; Beachy, Christopher K.; Anatomy and Cell Biology, School of Medicine