GT to AT transition at a splice donor site causes skipping of the preceding exon in phenylketonuria.

Classical Phenylketonuria (PKU) is an autosomal recessive human genetic disorder caused by a deficiency of hepatic phenylalanine hydroxylase (PAH). We isolated several mutant PAH cDNA clones from a PKU carrier individual and showed that they contained an internal 116 base pair deletion, corresponding precisely to exon 12 of the human chromosomal PAH gene. The deletion causes the synthesis of a truncated protein lacking the C-terminal 52 amino acids. Gene transfer and expression studies using the mutant PAH cDNA indicated that the deletion abolishes PAH activity in the cell as a result of protein instability. To determine the molecular basis of the deletion, the mutant chromosomal PAH gene was isolated from this individual and shown to contain a GT-- greater than AT substitution at the 5' splice donor site of intron 12. Thus, the consequence of the splice donor site mutation in the human liver is the skipping of the preceding exon during RNA splicing

A platform for crowdsourcing the creation of representative, accurate landcover maps

Accurate landcover maps are fundamental to understanding socio-economic and environmental patterns and processes, but existing datasets contain substantial errors. Crowdsourcing map creation may substantially improve accuracy, particularly for discrete cover types, but the quality and representativeness of crowdsourced data is hard to verify. We present an open-sourced platform, DIYlandcover, that serves representative samples of high resolution imagery to an online job market, where workers delineate individual landcover features of interest. Worker mapping skill is frequently assessed, providing estimates of overall map accuracy and a basis for performance-based payments. A trial of DIYlandcover showed that novice workers delineated South African cropland with 91% accuracy, exceeding the accuracy of current generation global landcover products, while capturing important geometric data. A scaling-up assessment suggests the possibility of developing an Africa-wide vector-based dataset of croplands for $2-3 million within 1.2-3.8 years. DIYlandcover can be readily adapted to map other discrete cover types.</jats:p