Hi,
Could someone provide a more layman's explanation of how to interpret the feature response curve metric to assess assemblies?
The following explanation is found at http://bioinformatics.nyu.edu/wordpr...esponse-curve/, and the original paper at http://www.plosone.org/article/info%...l.pone.0019175, but I'm still having trouble understanding. Thank you in advance.
Could someone provide a more layman's explanation of how to interpret the feature response curve metric to assess assemblies?
The following explanation is found at http://bioinformatics.nyu.edu/wordpr...esponse-curve/, and the original paper at http://www.plosone.org/article/info%...l.pone.0019175, but I'm still having trouble understanding. Thank you in advance.
Inspired by the standard receiver operating characteristic (ROC) curve, the Feature-Response curve characterizes the sensitivity (coverage) of the sequence assembler output (contigs) as a function of its discrimination threshold (number of features/errors). The AMOS package provides an automated assembly validation pipeline called amosvalidate that analyzes the output of an assembler using a variety of assembly quality metrics (or features). Examples of features include: (M) mate-pair orientations and separations, (K) repeat content by k-mer analysis, (C) depth-of-coverage, (P) correlated polymorphism in the read alignments, and (B) read alignment breakpoints to identify structurally suspicious regions of the assembly. After running amosvalidate on the output of the assembler, each contig is assigned a number of features that correspond to doubtful regions of the sequence. Given any such set of features, the response (quality) of the assembler output is then analyzed as a function of the maximum number of possible errors (features) allowed in the contigs. More specifically, for a fixed feature threshold φ, the contigs are sorted by size and, starting from the longest, only those contigs are tallied, if their sum of features is . For this set of contigs, the corresponding approximate genome coverage is computed, leading to a single point of the Feature-Response curve.
