Experimental approaches for genome-wide analysis of alternative splicing. (A) Quantitative splicing microarray profiling and RNA-Seq (middle panel) can be used to measure splicing efficiency and relative isoform usage across different tissue types, stages of development, in response to environmental stimuli, or in wild-type and mutant organisms (left panel). Predictions stemming from either approach can then be confirmed with experimental techniques such as semi-quantitative reverse transcription and PCR (RT-PCR; right panel). In microarray profiling experiments, short oligonucleotide probes annealing to exon body and exon junction sequences are used to monitor alternative splice site or exon usage. In RNA-Seq, short oligonucleotide reads are aligned to exon body and junction sequences, and the number of mapped reads can then be quantified to assess alternative splicing patterns. SF stands for splicing factor. (B) Immuno-affinity-based approaches such as ribonucleoprotein immunoprecipitation (RIP) and cross-linking and immunoprecipitation (CLIP) can be used to purify RNA-binding protein:pre-mRNA complexes (left panel). The purified RNA can then be sequenced, followed by alignment of the resulting short nucleotide reads to a reference genome of interest (middle panel). These genome-wide binding data provide a snapshot of the repertoire, or “regulon,” of a particular RNA-binding protein and can be used to infer functional relationships among genes encoding target transcripts. Alternatively, these data can be combined with RNA-Seq and microarray profiling data to obtain RNA maps (right panel) that correlate binding site positions with splicing regulatory differences observed upon loss or depletion of a given splicing factor.

Regulatory outcomes in alternative splicing networks and cross-talk between factors acting at multiple layers of gene regulation. (A) Coordinated alternative splicing events from an alternative splicing network (isoforms regulated by a specific splicing factor are displayed in red in the top and bottom panels) could result in the modification of protein–protein interactions (top left panel), modulation of genetic interactions within biological pathways (top right panel), altered post-translational modification sites in targeted proteins (bottom left panel), and altered RNA or protein localization in the cell (bottom right panel). Both single candidate gene studies and high-throughput approaches can be used to understand the functional consequences of these regulatory events. (B) Cross-regulation (black lines and arrows) has been observed between various classes of trans-acting regulators, such as chromatin-modifying proteins (blue), splicing factors (red), transcription factors (light blue), RNA stability or localization factors (green), translational regulators (purple), and non-coding RNAs. Cross-regulatory interactions have also been observed between members acting in the same process (gray dashed lines and arrows). The diagram displays both known regulatory events as well as theoretical events. A detailed understanding of how each of these factors interact and regulate each other will be essential for a complete understanding of the underlying regulatory networks they control.