Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-Change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies Gene Ontology (GO) terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g., weakly, moderately, or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments, and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionally-related gene groups, either weak or strong. Notably, some of the fold-change-specific GO terms are invisible by classical algorithms for functional gene enrichment, Singular Enrichment Analysis (SEA), and Gene Set Enrichment Analysis (GSEA). These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers, and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server.
(1) Background: The widespread application of ChIP-seq technology requires annotation of cis-regulatory modules through the search of co-occurred motifs. (2) Methods: We present the web server Motifs Co-Occurrence Tool (Web-MCOT) that for a single ChIP-seq dataset detects the composite elements (CEs) or overrepresented homo- and heterotypic pairs of motifs with spacers and overlaps, with any mutual orientations, uncovering various similarities to recognition models within pairs of motifs. The first (Anchor) motif in CEs respects the target transcription factor of the ChIP-seq experiment, while the second one (Partner) can be defined either by a user or a public library of Partner motifs being processed. (3) Results: Web-MCOT computes the significances of CEs without reference to motif conservation and those with more conserved Partner and Anchor motifs. Graphic results show histograms of CE abundance depending on orientations of motifs, overlap and spacer lengths; logos of the most common CE structural types with an overlap of motifs, and heatmaps depicting the abundance of CEs with one motif possessing higher conservation than another. (4) Conclusions: Novel capacities of Web-MCOT allow retrieving from a single ChIP-seq dataset with maximal information on the co-occurrence of motifs and potentiates planning of next ChIP-seq experiments.
The transcriptome sequencing experiment (RNA-seq) has become almost a routine procedure for studying both model organisms and crops. As a result of bioinformatics processing of such experimental output, huge heterogeneous data are obtained, representing nucleotide sequences of transcripts, amino acid sequences, and their structural and functional annotation. It is important to present the data obtained to a wide range of researchers in the form of databases. This article proposes a hybrid approach to creating molecular genetic databases that contain information about transcript sequences and their structural and functional annotation. The essence of the approach consists in the simultaneous storing both structured and weakly structured data in the database. The technology was used to implement a database of transcriptomes of agricultural plants. This paper discusses the features of implementing this approach and examples of generating both simple and complex queries to such a database in the SQL language. The OORT database is freely available at https://oort.cytogen.ru/.
Gene expression profiling data contains more information than is routinely extracted with standard approaches. Here we present Fold-change-Specific Enrichment Analysis (FSEA), a new method for functional annotation of differentially expressed genes from transcriptome data with respect to their fold changes. FSEA identifies GO terms, which are shared by the group of genes with a similar magnitude of response, and assesses these changes. GO terms found by FSEA are fold-change-specifically (e.g. weakly, moderately or strongly) affected by a stimulus under investigation. We demonstrate that many responses to abiotic factors, mutations, treatments and diseases occur in a fold-change-specific manner. FSEA analyses suggest that there are two prevailing responses of functionallyrelated gene groups, either weak or strong. Notably, some of the fold-changespecific GO terms are invisible by classical algorithms for functional gene enrichment, SEA and GSEA. These are GO terms not enriched compared to the genome background but strictly regulated by a factor within specific fold-change intervals. FSEA analysis of a cancer-related transcriptome suggested that the gene groups with a tightly coordinated response can be the valuable source to search for possible regulators, markers and therapeutic targets in oncogenic processes. Availability and Implementation: FSEA is implemented as the FoldGO Bioconductor R package and a web-server https://webfsgor.sysbio.cytogen.ru/ .
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