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A data mining paradigm for identifying key factors in biological processes using gene expression data.

ABSTRACT: 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.

SUBMITTER: Li J

PROVIDER: S-EPMC5998123 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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A data mining paradigm for identifying key factors in biological processes using gene expression data.

Li Jin J Zheng Le L Uchiyama Akihiko A Bin Lianghua L Mauro Theodora M TM Elias Peter M PM Pawelczyk Tadeusz T Sakowicz-Burkiewicz Monika M Trzeciak Magdalena M Leung Donald Y M DYM Morasso Maria I MI Yu Peng P

Scientific reports 20180613 1

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 pr ...[more]

PMID: 29899432

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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.

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.

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 .

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A data mining paradigm for identifying key factors in biological processes using gene expression data.

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A data mining paradigm for identifying key factors in biological processes using gene expression data.

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