The CSI method is not without some shortcomings.  In particular, its performance drops if chemical shift assignments are mis-referenced or incomplete.  It is also quite sensitive to the choice of random coil shifts used to calculate the secondary shifts and it generally identifies alpha helices (>85% accuracy) better than beta strands (<75% accuracy) regardless of the choice of random coil shifts.  Furthermore, the CSI method does not identify other kinds of secondary structures, such as β-turns.  Because of these shortcomings, a number of alternative CSI-like approaches have been proposed. These include: 1) a prediction method that employs statistically derived chemical shift/structure potentials (PECAN); 2) a probabilistic approach to secondary structure identification (PSSI); 3) a method that combines secondary structure predictions from sequence data and chemical shift data (PsiCSI), 4) a secondary structure identification approach that uses pre-specified chemical shift patterns (PLATON) and 5) a two-dimensional cluster analysis method known as 2DCSi. The performance of these newer methods is generally slightly better (2-4%) than the original CSI method.