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An easy-to-use application for microarray, RNA-Seq and metabolomics analysis. For splicing sensitive platforms (RNA-Seq or Affymetrix Exon, Gene and Junction arrays), AltAnalyze will assess alternative exon (known and novel) expression along protein isoforms, domain composition and microRNA targeting. In addition to splicing-sensitive platforms, AltAnalyze provides comprehensive methods for the analysis of other data (RMA summarization, batch-effect removal, QC, statistics, annotation, clustering, network creation, lineage characterization, alternative exon visualization, gene-set enrichment and more).
Allows to dynamically identify, extract and display complex alternative splicing (AS) events from annotated genes. AStalavista is an alternative splicing transcriptional landscape visualization tool for investigating and comparing types and distributions of the different AS events found whole genome annotations as well as user provided gene sets. Arbitrarily complex combinations of hitherto described AS events can be distinguished, either visually or by representation in a univocal notation system. AStalavista is applicable even if the sequencing/annotation process has not been completed.
GESS / Graph-based Exon-Skipping Scanner
Detects de novo exon-skipping events directly from raw RNA-seq data without prior knowledge of gene-annotation information. First, we build a splicing-site-linking graph from splicing-aware aligned reads using a greedy algorithm. We then iteratively scan this linking graph to obtain those patterns conforming to skipping events. Finally, we apply the MISO model to calculate the ratio of skipping versus inclusion isoforms and determine which is the dominant isoform.
A generalized framework to systematically investigate the synergistic and antagonistic effects of differential splicing and differential expression. dSpliceType detects and prioritizes a list of genes that are differentially expressed and/or spliced. In particular, the multivariate dSpliceType is among the fist to utilize sequential dependency of normalized base-wise read coverage signals and capture biological variability among replicates using a multivariate statistical model.
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