ALE-HSA21 (AnaLysis of Expression of HSA21), is an integrated relational database with a user friendly web interface. It contains detailed information about various aspects of genes mapping on human chromosome 21. In particular, the resource contains:
- Detailed gene descriptions, including (for each splice variant) the reference number, genomic coordinates, information about the encoded protein and the involvement of the gene in human genetic diseases;
- Useful links to databases for gene expression, gene networks, gene ontology analysis and single nucleotide polymorphisms;
- Nucleotide sequences of all the exons, introns, 5' and 3' UTRs and promoters, easily downloadable for each transcript of HSA21 genes. It also hosts novel transcripts for some HSA21 genes, generated through alternative splicing or extended 5' and 3' UTRs, and identified in our RNA-Seq study and validated by RT-PCR;
- Systematic in silico predictions of binding sites for transcription factors' (within gene promoters) and exonic/intronic regulatory splicing proteins;
- Predicted miRNAs' regulatory binding sites in the 3' UTRs (of protein coding genes) and for the entire length of the transcripts (for lincRNAs and pseudogenes);
- Systematic in silico analysis of predicted and validated miRNA target genes and secondary structures (for miRNAs and snoRNAs).
BATS is a user friendly GUI software for Bayesian Analysis of Time Series microarray experiments. It implements a truly functional fully Bayesian approach which allows an user to automatically identify and estimate differentially expressed genes.
RNASeqGUI is a graphical user interface for the analysis of RNA-Seq data, with particular interest toward the identification of differentially expressed genes. It is implemented following and expanding the idea presented in tuxette-chix. RNASeqGUI includes several well known tools for RNA-Seq, available as command line in www.bioconductor.org. RNASeqGUI is divided into five main sections, each dedicated to a step of data analysis. The first section allows to explore the .bam files. The second concerns the counting process of mapped reads against a genes annotation file. The third, focuses on the exploration of count data and on preprocessing of the data, including the normalization procedures. The fourth is about the identification of the differentially expressed genes that can be performed by several methods, such as: DESeq, DESeq2, EdgeR, NOISeq, BaySeq. Finally, the fifth section regards the inspection of the results produced by these methods and the quantitative comparison among them.