Nature. 2010 May 6;465(7294):53-9. Deciphering the splicing code. Barash Y, Calarco JA, Gao W, Pan Q, Wang X, Shai O, Blencowe BJ, Frey BJ. Deciphering the Splicing code By Leanne Stalker, June 2010 Since the sequencing of the human genome in 2004, a major question on the minds of many scientists has been: “how can an organism so complex originate from a genome of only 20, 000 genes?” The concept of RNA splicing has been popularized as one explanation for this question; that many products of differing expression throughout different tissues may be created from one set of starting material.  Misregulation of this process is known to be involved in human disease with 15%-50% of human disease mutations affecting splice site selection.  Though this concept of alternative splicing has been studied for quite some time, until recently scientists have had no ability to track RNA splicing on a genome wide scale, rendering our ability to garner information from this phenomenon limited. This has all changed.  In their manuscript entitled “Deciphering the splicing code” published in the May 2010 copy of Nature, Barash et al present their solution. Their goal?  Create rules governing how separate parts of a single precursor RNA can be spliced in many different ways.  The result? A computer assisted biological analysis method that can both search and order “codewords” within DNA that will help to predict how each gene can be alternatively spliced into many different RNA products. How did they do it?Starting with 3,665 alternative exons from 27 different murine tissue types, Barash et al studied sequence motifs and divided the sequence features into 4 categories dependent on tissue expression: Central nervous system tissues, muscle tissues, digestive system tissues and Embryonic tissues (including embryonic stem cells). Utilizing their pre determined motifs they were then able to determine if an exon is alternatively spliced and whether the exon’s inclusion level will increase or decrease in a given tissue when compared to another.  The code’s predictions for expression were then backed up by microarray and RT PCR studies and have proven to be extremely accurate. Online tool:An online tool named Website for Alternative Splicing Prediction (WASP) has already been launched to aid researchers in their quest to further understand the regulation of their particular exon of interest. This online tool can perform many tasks including deciphering if an exon is alternatively spliced, mapping putative regulatory elements in the primary sequence and genome wide scans to identify exons with common regulatory patterns. WASP can be accessed here: http://www.psi.toronto.edu/wasp/ So what?This is the first advent of a code with the ability to perform computer-based analysis of RNA splicing.  As errors in RNA splicing are of paramount importance to multiple human diseases, furthering our understanding of this phenomenon may aid in solving questions about gene expression pertinent to disease initiation or progression that were previously unattainable. Whats next?The code presented here represents a murine model of the splicing code. This group has already started moving forward with a human code.