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maSigPro specifications

Information


Unique identifier OMICS_01990
Name maSigPro
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU Lesser General Public License version 3.0
Computer skills Advanced
Version 1.52.0
Stability Stable
Requirements
stats, graphics, Biobase, grDevices, MASS, mclust, R(>=2.3.1), venn
Maintained Yes

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Publications for maSigPro

maSigPro citations

 (63)
library_books

Time resolved transcriptome and proteome landscape of human regulatory T cell (Treg) differentiation reveals novel regulators of FOXP3

2018
PMCID: 5937035
PMID: 29730990
DOI: 10.1186/s12915-018-0518-3

[…] linear model of the negative binomial family with time as a discrete factor; (2) a deseq2 generalized linear model of the negative binomial family with a natural cubic spline of time; and (3) a masigpro [] third degree polynomial regression. we define the variable describing the different treatments as ‘group’, with the following levels: g01, unstimulated cells; g02, mock-stimulated cells […]

library_books

Transcriptional profiling of human bronchial epithelial cell BEAS 2B exposed to diesel and biomass ultrafine particles

2018
PMCID: 5923024
PMID: 29703138
DOI: 10.1186/s12864-018-4679-9

[…] only the diseases with fdr corrected p-value ≤0.05., groups of genes with a differential temporal expression profile in exposed cells (as compared to controls) were identified using the next-masigpro method of the masigpro [, ] r package (version 1.34.1). briefly, the p.vector function was used to compute a regression fit for each gene in both matrixes of cpm counts for beas-2s exposed […]

library_books

Interactions Among Host–Parasite MicroRNAs During Nosema ceranae Proliferation in Apis mellifera

2018
PMCID: 5902570
PMID: 29692768
DOI: 10.3389/fmicb.2018.00698

[…] for mrnas. instead, a time series analysis method was used to determine if sirna-dicer feeding significantly changed the expression pattern using the data from all 6 days post-infection using masigpro package ()., the target genes of mirnas were predicted using sequence-based algorithms. annotated 3′utrs of protein-coding genes were used for mirna target prediction using the miranda […]

library_books

Luminal lncRNAs Regulation by ERα Controlled Enhancers in a Ligand Independent Manner in Breast Cancer Cells

2018
PMCID: 5855815
PMID: 29462945
DOI: 10.3390/ijms19020593

[…] settings. a gene was considered as differentially expressed if associated with an adjusted p-value < 0.001. to identify genes differentially expressed in the time-course e2-treatment experiment, masigpro r package [] was applied in default setting by considering the adjusted p-value computed using the p.vector function of the package. a gene was considered as differentially expressed […]

library_books

Time course transcriptome analysis of human cellular reprogramming from multiple cell types reveals the drastic change occurs between the mid phase and the late phase

2018
PMCID: 5753469
PMID: 29298685
DOI: 10.1186/s12864-017-4389-8

[…] intensities of each gene in the biological replicates were averaged. next, to extract dynamically expressed genes across all cell types during reprogramming process, we individually proceeded the masigpro package [] in each cell and screened genes which showed the significance in all five cell types (p-value <0.01, fdr < 0.05, r2 > 0.6). the filtration yielded 3615 extracted genes […]

library_books

Transcriptome profiling of aging Drosophila photoreceptors reveals gene expression trends that correlate with visual senescence

2017
PMCID: 5698953
PMID: 29162050
DOI: 10.1186/s12864-017-4304-3

[…] of 32 million high-quality paired-reads for each biological replicate (n = 3). we discarded one sample (day 30 replicate 3) due to poor alignment. we then analyzed the rna-seq time series data using masigpro, which is a generalized linear model-based approach []. utilizing masigpro and multiple time points enabled us to identify genes with robust expression changes that correlate strongly […]


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maSigPro institution(s)
Mathematics Department, University of Alicante, Spain; Genomics of Gene Expression Laboratory, Centro de Investigaciones Prıncipe Felipe, Valencia, Spain; Statistics and Operational Research Department, Politechnical University of Valencia, Valencia, Spain; Microbiology and Cell Science Department, Institute for Food and Agricultural Research, University of Florida, FL, USA
maSigPro funding source(s)
Supported by the European Union Seventh Framework Programme STATegra project under the grant agreement 306000 and the Spanish MINECO grants BIO2012-40244 and BIO2015-71658-R.

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