LOESS correction for length variation in gene set-based genomic sequence analysis

Abstract

Sequence analysis algorithms are often applied to sets of DNA, RNA or protein sequences to identify common or distinguishing features. Controlling for sequence length variation is critical to properly score sequence features and identify true biological signals rather than length-dependent artifacts. Several cis-regulatory module discovery algorithms exhibit a substantial dependence between DNA sequence score and sequence length. Our newly developed LOESS method is flexible in capturing diverse score-length relationships and is more effective in correcting DNA sequence scores for length-dependent artifacts, compared with four other approaches. Application of this method to genes co-expressed during Drosophila melanogaster embryonic mesoderm development or neural development scored by the Lever motif analysis algorithm resulted in successful recovery of their biologically validated cis-regulatory codes. The LOESS length-correction method is broadly applicable, and may be useful not only for more accurate inference of cis-regulatory codes, but also for detection of other types of patterns in biological sequences. Source code and compiled code are available from http://thebrain.bwh.harvard.edu/LM_LOESS/