These correlations enable predictions that certain amino acids will appear as neighbors in a sequence. HMMs assume fewer constraints on nucleotide distribution. This hybrid system makes a final prediction based on a combination of methods. Part of the difficulty in predicting structure from sequence is that the data is sparse and unevenly distributed across all possible sequences and structures. Predictive algorithms are constrained by the sufficiency of past associations.
Sparse history leads to weak predictive power.
We have toured the landscape of mapping and sequencing the human genome. The basic problem is summarized as building associations between genes encoded in long DNA sequences and biological functions dictated by activating those genes. Geneticists have developed numerous techniques to collect, validate and distribute data for DNA sequences, protein sequences and protein structures. Computational scientists have developed tools for enhancing these tasks. Working with geneticists, computational scientists will enable widely accessible databases of genetic data.