This tool uses a non-parametric kernel density estimate and a classifier; it identifies the RBP sites based on a combination of T to C mutations and read density.
The Maq mapping software uses a base variation (mutation) rate between reference and reads (the default is 0.001) in its mapping algorithm.
In addition, the database provides statistical summaries, such as region preference, annotation summary, mutation plots, read quality, and read clusters length.
Importantly, using real population genetic data to train our classifier will expose it to heterogeneity in mutation rate, recombination rate, and read depth.
This implies that for data sets which are expected to contain both reads with indels and reads with mutations, a hybrid approach might be the most sensible option, as exemplified by our hybrid method.
This could optimize the balance between allowing multiple T C mutations per read and obtaining unique read mapping to a reference sequence.
Mutation calling tools are used to identify and annotate mutations in reads.
One of these alleles, dRecQ414, was further confirmed by DNA sequencing (Figure 1B) revealing that the start codon mutation and open reading frame shift are as designed.
The criteria for clustering were as follows: All alignments were performed using the Smith-Waterman algorithm with default parameters with an identity threshold of e-100 and a maximum of 10 mutations per read.
