ArticleNucleic acids research2010
Serial Expression Analysis: a web tool for the analysis of serial gene expression data.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Functional Heatmap: an automated and interactive pattern recognition tool to integrate time with multi-omics assays.BMC bioinformatics · 2019Article
- Pediatric asthma and autism-genomic perspectives.Clinical and translational medicine · 2015Article
- Time-Course Gene Set Analysis for Longitudinal Gene Expression Data.PLoS computational biology · 2015Article
- Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies.BMC bioinformatics · 2014Article
- Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series.Bioinformatics (Oxford, England) · 2014Article
- Co-expression analysis of fetal weight-related genes in ovine skeletal muscle during mid and late fetal development stages.International journal of biological sciences · 2014Article
- The analytical landscape of static and temporal dynamics in transcriptome data.Frontiers in genetics · 2014Review
- Global expression profiling reveals genetic programs underlying the developmental divergence between mouse and human embryogenesis.BMC genomics · 2013Article
- Time series expression analyses using RNA-seq: a statistical approach.BioMed research international · 2013Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Serial transcriptomics experiments investigate the dynamics of gene expression changes associated with a quantitative variable such as time or dosage. The statistical analysis of these data implies the study of global and gene-specific expression trends, the identification of significant serial changes, the comparison of expression profiles and the assessment of transcriptional changes in terms of cellular processes. We have created the SEA (Serial Expression Analysis) suite to provide a complete web-based resource for the analysis of serial transcriptomics data. SEA offers five different algorithms based on univariate, multivariate and functional profiling strategies framed within a user-friendly interface and a project-oriented architecture to facilitate the analysis of serial gene expression data sets from different perspectives. SEA is available at sea.bioinfo.cipf.es.
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Registered trials
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