AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data.

ABSTRACT:

Background

Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC-biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (L

SUBMITTER: Silva JM 

PROVIDER: S-EPMC10716826 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature