Mining approximate frequent dense modules from multiple gene expression datasets

Mapping Intimacies ◽

10.29007/d87q ◽

2020 ◽

Author(s):

San Ha Seo ◽

Saeed Salem

Keyword(s):

Gene Expression ◽

Gene Annotation ◽

Expression Data ◽

Frequent Subgraph Mining ◽

Multiple Gene ◽

Real Gene ◽

Frequent Subgraph ◽

Frequent Subgraphs ◽

Coexpression Networks ◽

Functional Gene Annotation

Large amount of gene expression data has been collected for various environmental and biological conditions. Extracting co-expression networks that are recurrent in multiple co-expression networks has been shown promising in functional gene annotation and biomarkers discovery. Frequent subgraph mining reports a large number of subnetworks. In this work, we propose to mine approximate dense frequent subgraphs. Our proposed approach reports representative frequent subgraphs that are also dense. Our experiments on real gene coexpression networks show that frequent subgraphs are biologically interesting as evidenced by the large percentage of biologically enriched frequent dense subgraphs.

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A qualitative survey on frequent subgraph mining

Open Computer Science ◽

10.1515/comp-2018-0018 ◽

2018 ◽

Vol 8 (1) ◽

pp. 194-209 ◽

Cited By ~ 1

Author(s):

Büsra Güvenoglu ◽

Belgin Ergenç Bostanoglu

Keyword(s):

Data Mining ◽

Graph Mining ◽

Research Area ◽

Heterogeneous Data ◽

Graph Representation ◽

Frequent Subgraph Mining ◽

Subgraph Mining ◽

Frequent Subgraph ◽

Input Type ◽

Frequent Subgraphs

AbstractData mining is a popular research area that has been studied by many researchers and focuses on finding unforeseen and important information in large databases. One of the popular data structures used to represent large heterogeneous data in the field of data mining is graphs. So, graph mining is one of the most popular subdivisions of data mining. Subgraphs that are more frequently encountered than the user-defined threshold in a database are called frequent subgraphs. Frequent subgraphs in a database can give important information about this database. Using this information, data can be classified, clustered and indexed. The purpose of this survey is to examine frequent subgraph mining algorithms (i) in terms of frequent subgraph discovery process phases such as candidate generation and frequency calculation, (ii) categorize the algorithms according to their general attributes such as input type, dynamicity of graphs, result type, algorithmic approach they are based on, algorithmic design and graph representation as well as (iii) to discuss the performance of algorithms in comparison to each other and the challenges faced by the algorithms recently.

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Visual Comparison of Multiple Gene Expression Datasets in a Genomic Context

Journal of Integrative Bioinformatics ◽

10.1515/jib-2008-97 ◽

2008 ◽

Vol 5 (2) ◽

Author(s):

Krzysztof Borowski ◽

Jung Soh ◽

Christoph W. Sensen

Keyword(s):

Gene Expression ◽

Microarray Data ◽

Gene Function ◽

Graphical Representation ◽

Expression Profiles ◽

Expression Patterns ◽

Expression Data ◽

Genomic Context ◽

Multiple Gene ◽

Microarray Datasets

SummaryThe need for novel methods of visualizing microarray data is growing. New perspectives are beneficial to finding patterns in expression data. The Bluejay genome browser provides an integrative way of visualizing gene expression datasets in a genomic context. We have now developed the functionality to display multiple microarray datasets simultaneously in Bluejay, in order to provide researchers with a comprehensive view of their datasets linked to a graphical representation of gene function. This will enable biologists to obtain valuable insights on expression patterns, by allowing them to analyze the expression values in relation to the gene locations as well as to compare expression profiles of related genomes or of di erent experiments for the same genome.

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Evaluating switching neural networks through artificial and real gene expression data

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2008.08.002 ◽

2009 ◽

Vol 45 (2-3) ◽

pp. 163-171 ◽

Cited By ~ 6

Author(s):

Marco Muselli ◽

Massimiliano Costacurta ◽

Francesca Ruffino

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Gene Expression Data ◽

Expression Data ◽

Real Gene

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Improvements to Bayesian Gene Activity State Estimation from Genome-Wide Transcriptomics Data

10.1101/241000 ◽

2017 ◽

Author(s):

Craig Disselkoen ◽

Nathan Hekman ◽

Brian Gilbert ◽

Sydney Benson ◽

Matthew Anderson ◽

...

Keyword(s):

Gene Expression ◽

Bayesian Method ◽

Gene Activity ◽

Expression Data ◽

Real Gene ◽

Genome Wide ◽

Transcriptomics Data ◽

Activity State ◽

Regulatory Models ◽

Better Than

AbstractAn important question in many biological applications, is to estimate or classify gene activity states (active or inactive) based on genome-wide transcriptomics data. Recently, we proposed a Bayesian method, titled MultiMM, which showed superior results compared to existing methods. In short, MultiMM performed better than existing methods on both simulated and real gene expression data, confirming well-known biological results and yielding better agreement with fluxomics data. Despite these promising results, MultiMM has numerous limitations. First, MultiMM leverages co-regulatory models to improve activity state estimates, but information about co-regulation is incorporated in a manner that assumes that networks are known with certainty. Second, MultiMM assumes that genes that change states in the dataset can be distinguished with certainty from those that remain in one state. Third, the model can be sensitive to extreme measures (outliers) of gene expression. In this manuscript, we propose a modified Bayesian approach, which addresses these three limitations by improving outlier handling and by explicitly modeling network and other uncertainty yielding improved gene activity state estimates when compared to MultiMM.

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Impact of Gene Annotation Choice on the Quantification of RNA-Seq Data

10.21203/rs.3.rs-421080/v1 ◽

2021 ◽

Author(s):

David Chisanga ◽

Yang Liao ◽

Wei Shi

Keyword(s):

Gene Expression ◽

Gene Annotation ◽

Expression Data ◽

Refseq Gene ◽

Rna Seq ◽

Sequencing Data ◽

Microarray Expression Data ◽

Sequencing Quality ◽

Gene Expression Quantification ◽

Expression Quantification

Abstract Background: RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis.Results: In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from >800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.Conclusion: In conclusion, our study found that the use of the conservative RefSeq gene annotation yields better RNA-seq quantification results than the more comprehensive Ensembl annotation. We also found that, surprisingly, the recent expansion of the RefSeq database, which was primarily driven by the incorporation of sequencing data into the gene annotation process, resulted in a reduction in the accuracy of RNA-seq quantification.

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Impact of gene annotation choice on the quantification of RNA-seq data

10.1101/2021.01.07.425794 ◽

2021 ◽

Author(s):

David Chisanga ◽

Yang Liao ◽

Wei Shi

Keyword(s):

Gene Expression ◽

Gene Annotation ◽

Expression Data ◽

Rna Seq ◽

Microarray Expression Data ◽

Refseq Annotation ◽

Sequencing Quality ◽

Gene Expression Quantification ◽

Microarray Expression ◽

Expression Quantification

RNA sequencing is currently the method of choice for genome-wide profiling of gene expression. A popular approach to quantify expression levels of genes from RNA-seq data is to map reads to a reference genome and then count mapped reads to each gene. Gene annotation data, which include chromosomal coordinates of exons for tens of thousands of genes, are required for this quantification process. There are several major sources of gene annotations that can be used for quantification, such as Ensembl and RefSeq databases. However, there is very little understanding of the effect that the choice of annotation has on the accuracy of gene expression quantification in an RNA-seq analysis. In this paper, we present results from our comparison of Ensembl and RefSeq human annotations on their impact on gene expression quantification using a benchmark RNA-seq dataset generated by the SEquencing Quality Control (SEQC) consortium. We show that the use of RefSeq gene annotation models led to better quantification accuracy, based on the correlation with ground truths including expression data from $>$800 real-time PCR validated genes, known titration ratios of gene expression and microarray expression data. We also found that the recent expansion of the RefSeq annotation has led to a decrease in its annotation accuracy. Finally, we demonstrated that the RNA-seq quantification differences observed between different annotations were not affected by the use of different normalization methods.

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Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

Genome Biology ◽

10.1186/s13059-021-02568-9 ◽

2022 ◽

Vol 23 (1) ◽

Author(s):

Kayla A. Johnson ◽

Arjun Krishnan

Keyword(s):

Gene Expression ◽

Expression Data ◽

Rna Seq ◽

Functional Relationships ◽

Gene Coexpression ◽

Transformation Methods ◽

Network Transformation ◽

Almost All ◽

Coexpression Networks ◽

The Impact

Abstract Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Results Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships. Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

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