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Multi-level Support Vector Regression analysis to identify condition-specific regulatory networks

ABSTRACT: The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification. We present a new approach, namely multi-level support vector regression (ml-SVR), to systematically identify conditionspecific regulatory modules. The approach is built upon a multi-level analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. We applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE20700 | GEO | 2010/03/10

SECONDARY ACCESSION(S): PRJNA124893

REPOSITORIES: GEO

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Multi-level Support Vector Regression analysis to identify condition-specific regulatory networks

Project description:The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification. We present a new approach, namely multi-level support vector regression (ml-SVR), to systematically identify conditionspecific regulatory modules. The approach is built upon a multi-level analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. We applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. Three independent total RNA samples were extracted for each cell line (MCF-7 and MCF-7-stripped) and the samples were arrayed using Affymetrix GeneChip HG-U133A. MCF-7-stripped denotes estrogen-deprived MCF-7 human breast cancer cells, which were grown in the absence of estrogen for 96 hours. We analyzed the enriched motifs and their targets for the genes significantly down-regulated in MCF-7-stripped cells as compared to MCF-7 cells.

2010-03-23 | E-GEOD-20700 | biostudies-arrayexpress

Atonal and Senseless perturbations in Drosophila melanogaster eye-antennal imaginal discs.

2010-06-28 | GSE16713 | GEO

Investigation of cis-regulation of metabolic insecticide resistance related gene expression in Anopheles gambiae using allelic imbalance

Project description:Malaria control relies on insecticides targeting the mosquito vector, but is being increasingly compromised by insecticide resistance. Elevated expression of metabolic enzymes frequently drives resistance. In diploids, gene expression is regulated both in cis, by regulatory sequences on the same chromosome, and by trans acting factors, affecting both alleles equally. Differing levels of transcription can be caused by mutations in cis-regulatory modules, but few cis-regulatory modules controlling the expression of genes that determine insecticide resistance have been identified. Genes potentially under differential cis-regulation between bendiocarb resistant and susceptible Anopheles gambiae strains were identified by counting transcripts produced from maternal and paternal alleles in F1 hybrids of these strains (allelic specific expression). Cis regulatory module sequences controlling gene expression in insecticide resistance relevant tissues such as midgut, Malpighian tubules and legs were predicted using a previously established machine learning method. These predictions included CRM proximal to both genes under differential cis regulation and genes that show consistent differential expression patterns in multiple resistant Anopheles strains.

2023-11-22 | GSE241768 | GEO

Homo sapiens

Project description:A computational genomics approach to identify cis-regulatory modules - a case study using E2F1

| PRJNA96589 | ENA

Homo sapiens

Project description:Identify cis-regulatory modules for OCT4

| PRJNA99651 | ENA

Intrahepatic miRNA/mRNA expression in non-responders to pegylated interferon plus ribavirin combination therapy for chronic hepatitis C (miRNA microarray data)

Project description:Despite advance in interferon-based treatment for chronic hepatitis C, difficult-to-treat patients remain in existence yet. To identify key genes involved in difficult-to-treat characteristics, gene expression patterns of miRNA and RNA were analyzed by profiling pretreatment liver tissues from five sustained virological responders (SVR), three relapsers (R) and four non-responders (NR). Expression levels of miRNA and mRNA were compared between SVR/R and NR groups by using microarray, respectively. Quantitative real-time reverse-transcriptase polymerase chain reaction and statistical analyses validated genes with significantly differential expression levels in 50 liver tissues: proliferation-, inflammation- and anti-apoptosis-related mRNA expression levels increased significantly in NR, compared to SVR/R. Of miRNA with significantly differential expression levels on microarray, several miRNA were correlated inversely with those significant mRNA. In vitro studies by using miRNA inhibitors and mimics verified the inverse correlation between the miRNA and mRNA. These findings enhance our understanding of the difficult-to-treat molecular mechanism and identification of target molecules for novel treatments. Moderated t-statistics based on the empirical Bayes approach were performed to identify miRNA differentially expressed between SVRs/relapses and NVRs using the M-bM-^@M-^\limmaM-bM-^@M-^] package from BioConductor software (http://www.bioconductor.org/) under R statistical software version 2.12.1 (http://www.r-project.org/). Because of multiple hypothesis testing, p values were adjusted by the Benjamini-Hochberg false discovery rate (FDR) method. Related mRNA expression data are in GSE42697.

2014-05-15 | E-GEOD-45179 | biostudies-arrayexpress

Intrahepatic miRNA/mRNA expression in non-responders to pegylated interferon plus ribavirin combination therapy for chronic hepatitis C

Project description:Despite advance in interferon-based treatment for chronic hepatitis C, difficult-to-treat patients remain in existence yet. To identify key genes involved in difficult-to-treat characteristics, gene expression patterns of miRNA and RNA were analyzed by profiling pretreatment liver tissues from five sustained virological responders (SVR), three relapsers (R) and four non-responders (NR). Expression levels of miRNA and mRNA were compared between SVR/R and NR groups by using microarray, respectively. Quantitative real-time reverse-transcriptase polymerase chain reaction and statistical analyses validated genes with significantly differential expression levels in 50 liver tissues: proliferation-, inflammation- and anti-apoptosis-related mRNA expression levels increased significantly in NR, compared to SVR/R. Of miRNA with significantly differential expression levels on microarray, several miRNA were correlated inversely with those significant mRNA. In vitro studies by using miRNA inhibitors and mimics verified the inverse correlation between the miRNA and mRNA. These findings enhance our understanding of the difficult-to-treat molecular mechanism and identification of target molecules for novel treatments. Moderated t-statistics based on the empirical Bayes approach were performed to identify mRNA or miRNA differentially expressed between SVRs/relapses and NVRs using the “limma” package from BioConductor software (http://www.bioconductor.org/) under R statistical software version 2.12.1 (http://www.r-project.org/). Because of multiple hypothesis testing, p values were adjusted by the Benjamini-Hochberg false discovery rate (FDR) method. Related miRNA expression data are in GSE45179.

2014-05-14 | E-GEOD-42697 | biostudies-arrayexpress

Atonal and Senseless perturbations in Drosophila melanogaster eye-antennal imaginal discs.

Project description:The goal of this study is to identify co-expressed genes downstream of Atonal and Senseless. These gene lists are used as candidate target genes (technically: as foreground sets) in computational predictions of cis-regulatory elements using the cisTargetX method (http://med.kuleuven.be/cme-mg/lng/cisTargetX). Together, the gene expression results and cis-regulatory predictions, yield a gene regulatory network underlying the early events in retinal differentiation. Predicted cis-regulatory interactions have been validated extensively in vivo using enhancer reporter assays and genetic perturbations. Six samples were analyzed for Atonal gain-of-function (GOF) in the eye-antennal imaginal discs of third instar larvae of Drosophila melanogaster, namely three biological repeats for two different Gal4 drivers (Gal4/7 and AtoGal), crossed with UASato. Three samples were analyzed for Atonal loss-of-function (LOF) in the eye-antennal imaginal discs, namely three biological repeats of ato-/- larvae. Three samples were analyzed for Senseless GOF, namely three biological repeats of atoGal4 crossed with UASsens. Finally, eight control samples were analyzed, namely two biological repeats of four different lines (atoGal4, Gal4/7, UASato, and cantonS wild type).

2010-07-29 | E-GEOD-16713 | biostudies-arrayexpress

IMPACT: Unraveling intracellular signal transduction and pathway crosstalk by exploring pathway landscape

Project description:Understanding complicated modularization and crosstalk of intracellular signal transduction pathways holds the key to battle against drug resistance in human cancer research. We propose an integrative approach, namely Inferring Modularization of PAthway CrossTalk (IMPACT), to identify aberrant pathway modules and their between-module crosstalk by exploring pathway landscape that is reconstructed from a sampling strategy. The pathway identification method (i.e., IMPACT) was applied to breast cancer data to uncover aberrant pathway modules, which were further investigated with cell line studies to understand drug resistance in breast cancer. The patient datasets we mentioned are published and are available in the GEO database as GSE6532 and GSE17705. The re-processed GSE6532 and GSE17705 patient datasets are linked below as supplementary files. The GSE6532_matrix.txt and GSE17705_matrix.txt are processed by Affy Gene console with plier as normailization. But importantly, we also used 'Combat' method (http://www.bu.edu/jlab/wp-assets/ComBat/Abstract.html) to remove potential institutional batch effect. Four MCF7 derived cell models were included in the study: MCF7-STR, MCF7RR-STR, LCC1 and LCC2 cell lines. MCF7-STR and MCF7RR-STR were grown in phenol red-free IMEM supplemented with 5% charcoal-stripped calf serum and 1nM estradiol for 72 h prior to RNA extraction. LCC1 and LCC2 cells were grown in phenol red-free IMEM supplemented with 5% charcoal-stripped calf serum. The cell line data were used to validate computational results derived from patient datasets.

2013-09-07 | E-GEOD-50564 | biostudies-arrayexpress

High resolution time-lapse proteomic analysis of sporulation

Project description:Bacillus subtilis vegetative cells switch to sporulation upon nutrient limitation. To investigate the proteome changeover during sporulation, a time-lapse proteomic analysis was performed in a cell population that was induced to sporulate synchronously. Here, we are the first to comprehensively investigate the changeover of sporulation regulatory proteins, coat proteins and other proteins involved in sporulation and spore biogenesis. Protein co-expression analysis revealed four co-expressed modules. Modules brown and green are upregulated during sporulation and are annotated as associated with sporulation. Module blue, which is negatively correlated with modules brown and green, comprised ribosomal and metabolic proteins, and module yellow is co-expressed with module blue and modules brown & green. Notably, several proteins not belonging to any of the known transcription regulons were identified as co-expressed with modules brown and green. We speculate that they may also play roles during sporulation. Finally, amounts of some coat proteins, for example morphogenetic coat proteins, decreased late in sporulation. While we speculate on their possible role in guiding or helping assembly of other coat proteins, after which they can be disposed of, such a hypothesis remains to be experimentally addressed.

2021-04-03 | MSV000087154 | MassIVE

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