A data mining paradigm for identifying key factors in biological processes using gene expression data
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE100100 | GEO | 2018/05/31
REPOSITORIES: GEO
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Project description:A large volume of biological data is being generated for studying mechanisms of various biological processes. These precious data enable large-scale computational analyses to gain biological insights. However, it remains a challenge to mine the data efficiently for knowledge discovery. The heterogeneity of these data makes it difficult to consistently integrate them, slowing down the process of biological discovery. We introduce a data processing paradigm to identify key factors in biological processes via systematic collection of gene expression datasets, primary analysis of data, and evaluation of consistent signals. To demonstrate its effectiveness, our paradigm was applied to epidermal development and identified many genes that play a potential role in this process. Besides the known epidermal development genes, a substantial proportion of the identified genes are still not supported by gain- or loss-of-function studies, yielding many novel genes for future studies. Among them, we selected a top gene for loss-of-function experimental validation and confirmed its function in epidermal differentiation, proving the ability of this paradigm to identify new factors in biological processes. In addition, this paradigm revealed many key genes in cold-induced thermogenesis using data from cold-challenged tissues, demonstrating its generalizability. This paradigm can lead to fruitful results for studying molecular mechanisms in an era of explosive accumulation of publicly available biological data.
| S-EPMC5998123 | biostudies-literature
Project description:Novel coronavirus pneumonia (COVID-19) is a new type of viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has spread rapidly and become a global pandemic. Heart failure (HF) is the ultimate period of the development of various cardiovascular diseases. There are several research have found that SARS-CoV-2 infection may induce cardiac complications including enhanced cardiac stress biomarkers and heart failure. Our research aims at identifying underlying biological processes and key targets in COVID-19-associated heart failure via bioinformatics analysis. A total of three heart failure datasets and three COVID-19 datasets were obtained using the Gene Expression Omnibus (GEO) database. Batch effects cross each sample were eliminated with surrogate variable analysis algorithm. Then, we identified key modules of COVID-19 datasets and heart failure datasets through weighted gene co-expression network analysis. HF-associated as well as COVID-19-associated key modules were intersected for determining the shared genes of COVID-19-associated heart failure. The pivotal genes associated with COVID-19-related heart failure were determined by intersecting the shared genes with the HF-associated hub genes selected through WGCNA. Furthermore, we conducted GO as well as KEGG enrichment analysis on shared genes of COVID-19-associated heart failure. Two COVID-19-associated key modules as well as three HF-associated key modules were determined. In addition, eleven shared genes for COVID-19-associated heart failure were determined. In conclusion, our work screened two critical genes, namely PYGM and BLM, which may be possible intervention targets for COVID-19-associated heart failure. According to functional enrichment results, the shared genes of COVID-19-associated heart failure showed high enrichment in starch and sucrose metabolism, homologous recombination, Fanconi anemia pathway, and insulin resistance indicate the probably biological processes linked to COVID-19-associated heart failure. These results provided further insights in possible interventional and therapeutic targets of COVID-19-associated heart failure.
| S-EPMC10404953 | biostudies-literature
Project description:To elucidate ecosystem functioning, it is fundamental to recognize what processes occur in which environments (where) and which microorganisms carry them out (who). Here, we present PREGO, a one-stop-shop knowledge base providing such associations. PREGO combines text mining and data integration techniques to mine such what-where-who associations from data and metadata scattered in the scientific literature and in public omics repositories. Microorganisms, biological processes, and environment types are identified and mapped to ontology terms from established community resources. Analyses of comentions in text and co-occurrences in metagenomics data/metadata are performed to extract associations and a level of confidence is assigned to each of them thanks to a scoring scheme. The PREGO knowledge base contains associations for 364,508 microbial taxa, 1090 environmental types, 15,091 biological processes, and 7971 molecular functions with a total of almost 58 million associations. These associations are available through a web portal, an Application Programming Interface (API), and bulk download. By exploring environments and/or processes associated with each other or with microbes, PREGO aims to assist researchers in design and interpretation of experiments and their results. To demonstrate PREGO's capabilities, a thorough presentation of its web interface is given along with a meta-analysis of experimental results from a lagoon-sediment study of sulfur-cycle related microbes.
| S-EPMC8879827 | biostudies-literature
Project description:BACKGROUND:Exploratory analysis of multi-dimensional high-throughput datasets, such as microarray gene expression time series, may be instrumental in understanding the genetic programs underlying numerous biological processes. In such datasets, variations in the gene expression profiles are usually observed across replicates and time points. Thus mining the temporal expression patterns in such multi-dimensional datasets may not only provide insights into the key biological processes governing organs to grow and develop but also facilitate the understanding of the underlying complex gene regulatory circuits. RESULTS:In this work we have developed an evolutionary multi-objective optimization for our previously introduced triclustering algorithm ?-TRIMAX. Its aim is to make optimal use of ?-TRIMAX in extracting groups of co-expressed genes from time series gene expression data, or from any 3D gene expression dataset, by adding the powerful capabilities of an evolutionary algorithm to retrieve overlapping triclusters. We have compared the performance of our newly developed algorithm, EMOA- ?-TRIMAX, with that of other existing triclustering approaches using four artificial dataset and three real-life datasets. Moreover, we have analyzed the results of our algorithm on one of these real-life datasets monitoring the differentiation of human induced pluripotent stem cells (hiPSC) into mature cardiomyocytes. For each group of co-expressed genes belonging to one tricluster, we identified key genes by computing their membership values within the tricluster. It turned out that to a very high percentage, these key genes were significantly enriched in Gene Ontology categories or KEGG pathways that fitted very well to the biological context of cardiomyocytes differentiation. CONCLUSIONS:EMOA- ?-TRIMAX has proven instrumental in identifying groups of genes in transcriptomic data sets that represent the functional categories constituting the biological process under study. The executable file can be found at http://www.bioinf.med.uni-goettingen.de/fileadmin/download/EMOA-delta-TRIMAX.tar.gz .
| S-EPMC4480927 | biostudies-literature
Project description:BackgroundThis paper reports on an effort to identify a streamlined set of issues important for colorectal cancer communication and interventions with older African Americans.MethodsAfrican American (N = 1,021), 683 women and 338 men, 50 to 75 years completed a telephone survey addressing demographics, colorectal cancer screening, cancer attitudes, and cancer related cultural attitudes. Several data analytics methods were applied and evaluated. Among them, results from associative data mining identified key variables and logistic regression was used to confirm associations to screening adherence.ResultsSets of co-occurring variables identified by associative data mining methods are extracted to further study differences between adherent and non-adherent groups. Logistic regressions suggested four variables were significantly associated with adherence: healthcare provider colonoscopy recommendation, prevention services at the place health care is usually sought, a history of colitis, and a history of polyps.ConclusionsThe findings suggest a streamlined set of issues and concerns that may be used by providers advising patients or developing colorectal cancer intervention strategies for older African Americans. The data suggest the continued importance of healthcare provider recommendation to screen. It is important that providers give a clear recommendation to screen regardless of the test ultimately selected and should advise all patients that family history and the absence of symptoms or colitis do not eliminate the value of screening.
| S-EPMC4256804 | biostudies-literature
Project description:Asthma is the most common chronic condition among children; however, the underlying molecular mechanism remains unclear. Dysregulated immune response and different infiltration states of immune cells are critical for asthma pathogenesis. Here, three childhood asthma gene expression datasets were used to detect key genes, immune cells, and pathways involved in childhood asthma. From these datasets, 33 common differentially expressed genes (DEGs) were identified, which showed enrichment in the T helper 1 (Th1) and T helper 2 (Th2) cell differentiation pathway and the T helper 17 (Th17) cell differentiation pathway. Using the weighted gene co-expression network analysis (WGCNA), CD3D and CD3G were identified as key genes closely correlated with childhood asthma. Upregulation of CD3D and CD3G was further validated in bronchoalveolar lavage cells from childhood asthmatics with control individuals by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The immune cell infiltration analysis indicated that CD3D and CD3G were negatively correlated with increased resting mast cells and eosinophils, and highly correlated with several cell markers of Th1, Th2, and Th17 cells. In addition, we found that CD3D and CD3G were closely related to the Th1 and Th2 cell differentiation pathway and the Th17 cell differentiation pathway. Our results reveal the important roles of two key genes and immune infiltration in the pathogenesis of childhood asthma. Thus, this study provides a new perspective for exploring potential molecular targets for childhood asthma treatment.
| S-EPMC9479007 | biostudies-literature
Project description:In this article we discuss the requirements to use data mining of published proteomics datasets to assist proteomics-based biomarker discovery, the use of external data integration to solve the issue of inadequate small sample sizes and finally, we try to estimate the probability that new biomarkers will be identified through data mining alone.
| S-EPMC4833187 | biostudies-literature
Project description:BACKGROUND: Identification of transcription factors (TFs) involved in a biological process is the first step towards a better understanding of the underlying regulatory mechanisms. However, due to the involvement of a large number of genes and complicated interactions in a gene regulatory network (GRN), identification of the TFs involved in a biology process remains to be very challenging. In reality, the recognition of TFs for a given a biological process can be further complicated by the fact that most eukaryotic genomes encode thousands of TFs, which are organized in gene families of various sizes and in many cases with poor sequence conservation except for small conserved domains. This poses a significant challenge for identification of the exact TFs involved or ranking the importance of a set of TFs to a process of interest. Therefore, new methods for recognizing novel TFs are desperately needed. Although a plethora of methods have been developed to infer regulatory genes using microarray data, it is still rare to find the methods that use existing knowledge base in particular the validated genes known to be involved in a process to bait/guide discovery of novel TFs. Such methods can replace the sometimes-arbitrary process of selection of candidate genes for experimental validation and significantly advance our knowledge and understanding of the regulation of a process. RESULTS: We developed an automated software package called TF-finder for recognizing TFs involved in a biological process using microarray data and existing knowledge base. TF-finder contains two components, adaptive sparse canonical correlation analysis (ASCCA) and enrichment test, for TF recognition. ASCCA uses positive target genes to bait TFS from gene expression data while enrichment test examines the presence of positive TFs in the outcomes from ASCCA. Using microarray data from salt and water stress experiments, we showed TF-finder is very efficient in recognizing many important TFs involved in salt and drought tolerance as evidenced by the rediscovery of those TFs that have been experimentally validated. The efficiency of TF-finder in recognizing novel TFs was further confirmed by a thorough comparison with a method called Intersection of Coexpression (ICE). CONCLUSIONS: TF-finder can be successfully used to infer novel TFs involved a biological process of interest using publicly available gene expression data and known positive genes from existing knowledge bases. The package for TF-finder includes an R script for ASCCA, a Perl controller, and several Perl scripts for parsing intermediate outputs. The package is available upon request (hairong@mtu.edu). The R code for standalone ASCCA is also available.
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Project description:After tomato fruits harvesting huge amounts of biomass residues, including plant and immature fruit, remaining in the field can be utilized to produce bioenergy. Little is known about the molecular aspects underlying tomato plant biomass production and hydrolysis. To identify genes involved in the regulation of plant biomass accumulation and composition, two Solanum pennellii introgression lines (ILs) with contrasting phenotypes for plant architecture and biomass characteristics, were analyzed. A multiple approach aimed to characterize such near-isogenic lines was carried out for studying gene expression dynamics, microscopy cell traits and qualitative and quantitative cell wall chemical compounds variation. Transcriptomic analysis showed that the enhanced biomass production observed in IL2-6 line is due to a more effective coordination of chloroplast and mitochondria energy fluxes (photosynthesis, cell division, cell wall and hormone metabolism activation). In parallel, microscopy analysis revealed a higher number of cells and chloroplasts in leaf epidermis in the high biomass line whilst chemical measurements on the two lines pointed out striking differences in the cell wall composition and organization. Taken together, our findings shed light on the mechanisms underlying the tomato biomass production and open new routes for improving the tomato lignocellulosic processability.
2019-03-17 | GSE128409 | GEO
Project description:UnlabelledOne way to initiate the reconstruction of molecular circuits is by using automated text-mining techniques. Developing more efficient methods for such reconstruction is a topic of active research, and those methods are typically included by bioinformaticians in pipelines used to mine and curate large literature datasets. Nevertheless, experimental biologists have a limited number of available user-friendly tools that use text-mining for network reconstruction and require no programming skills to use. One of these tools is Biblio-MetReS. Originally, this tool permitted an on-the-fly analysis of documents contained in a number of web-based literature databases to identify co-occurrence of proteins/genes. This approach ensured results that were always up-to-date with the latest live version of the databases. However, this 'up-to-dateness' came at the cost of large execution times. Here we report an evolution of the application Biblio-MetReS that permits constructing co-occurrence networks for genes, GO processes, Pathways, or any combination of the three types of entities and graphically represent those entities. We show that the performance of Biblio-MetReS in identifying gene co-occurrence is as least as good as that of other comparable applications (STRING and iHOP). In addition, we also show that the identification of GO processes is on par to that reported in the latest BioCreAtIvE challenge. Finally, we also report the implementation of a new strategy that combines on-the-fly analysis of new documents with preprocessed information from documents that were encountered in previous analyses. This combination simultaneously decreases program run time and maintains 'up-to-dateness' of the results.Availabilityhttp://metres.udl.cat/index.php/downloads,Contactmetres.cmb@gmail.com.
| S-EPMC3940481 | biostudies-literature