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GSAASeqSP specifications

Information


Unique identifier OMICS_05582
Name GSAASeqSP
Software type Toolkit/Suite
Interface Command line interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux, Mac OS, Windows
Programming languages Java
Computer skills Advanced
Stability Stable
Maintained Yes

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Publication for GSAASeqSP

GSAASeqSP citations

 (10)
library_books

Variation in DNA Damage Responses to an Inhalational Carcinogen (1,3 Butadiene) in Relation to Strain Specific Differences in Chromatin Accessibility and Gene Transcription Profiles in C57BL/6J and CAST/EiJ Mice

2017
Environ Health Perspect
PMCID: 5944832
PMID: 29038090
DOI: 10.1289/EHP1937

[…] S2). To explore whether strain-specific differences in baseline gene expression profiles reflected changes in higher-level functional classes of genes, we performed pathway enrichment analysis using GSAASeqSP () and the Reactome Pathway Database () (see Excel Table S3). We displayed the significantly altered pathways as a bubble plot, using a combination of distance measures and multidimensional […]

library_books

RBM10 promotes transformation associated processes in small cell lung cancer and is directly regulated by RBM5

2017
PLoS One
PMCID: 5491171
PMID: 28662214
DOI: 10.1371/journal.pone.0180258

[…] ncluding modulation of the immune system and various aspects of cell metabolism.To complement our FIDEA and KEGG pathway analysis, we used the Gene Set Association Analysis with Sequence Permutation (GSAASeqSP) Program, which is specific for RNA-Seq data [], with the Broad Institute’s Molecular Signatures Hallmark Database (MSigDB), which groups genes based on known functions []. As shown in , six […]

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Time Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola

2017
Front Plant Sci
PMCID: 5222877
PMID: 28119706
DOI: 10.3389/fpls.2016.02007
call_split See protocol

[…] sociation analysis for three pairs of samples, 4-2, 6-2, and 8-2 WAP, to identify pathways/gene sets significantly changed during seed development. Gene set association analysis was carried out using GSAASeqSP 2.0 (). RNA-Seq raw counts were normalized by the DESeq normalization (). We chose Signal2 Noise for gene-level differential expression analysis and Weighted_KS for gene set association anal […]

library_books

RBM5 reduces small cell lung cancer growth, increases cisplatin sensitivity and regulates key transformation associated pathways

2016
PMCID: 5133678
PMID: 27957556
DOI: 10.1016/j.heliyon.2016.e00204

[…] p.Pathway enrichment was investigated using the FIDEA (Functional Interpretation of Differential Expression Analysis) program () with the KEGG database (; ). Pathway analysis was also performed using GSAASeqSP (Gene Set Association Analysis for RNA-Seq with Sample Permutation) () with the Molecular Signatures Database (MSigDB) Hallmark gene set collection (). Since each program uses a different al […]

library_books

Genomic profiling is predictive of response to cisplatin treatment but not to PI3K inhibition in bladder cancer patient derived xenografts

2016
Oncotarget
PMCID: 5363516
PMID: 27823983
DOI: 10.18632/oncotarget.13062

[…] . Multiple testing corrections were applied. The list of differentially expressed genes (DEGs) were analyzed for enriched Gene Ontology and/or KEGG pathway term with the GAGE [] Bioconductor package. GSAASeqSP []was also applied for pathway analysis that utilizes p-values from all genes instead of only DEGs. […]

call_split

Characterization of copy number alterations in a mouse model of fibrosis‐associated hepatocellular carcinoma reveals concordance with human disease

2016
PMCID: 4799957
PMID: 26778414
DOI: 10.1002/cam4.606
call_split See protocol

[…] ched nontumor samples from cirrhosis patients were identified using the R package DESeq2 , with Benjamini–Hochberg‐corrected P < 0.1 considered significant. Gene Set Association Analysis for RNA‐Seq (GSAASeqSP) was used to identify enriched genesets among the differentially expressed genes in human HCC. […]

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GSAASeqSP institution(s)
Department of Computer Science and Technology, Department of Statistics, Southwest University, Chongqing, China; Department of Statistical Science, Department of Computer Science, and Department of Mathematics, Duke University, Durham, NC, USA; Department of Genetics, Department of Biology, Lineberger Comprehensive Cancer Center, and Carolina Center for Genomics and Society, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
GSAASeqSP funding source(s)
This work was supported by Key Discipline Fund of National 211 Project (SWU:TR201208-3), The Open Fund of State Key Laboratory of Silkworm Genome Biology (sklsgb2013005), NIH grant 1RC1CA146849, University Cancer Research Fund at UNC-CH, CA123175-01A1, NIH Systems Biology Center Grant, NSF grant DMS-0732260, NSF grant CCF-1049290, and R01 CA125618-01.

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