A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching.
The identification of nested motifs in genomic sequences is a complex computational problem. The detection of these patterns is important to allow the discovery of transposable element (TE) insertions, incomplete reverse transcripts, deletions, and/or mutations. In this study, a de novo strategy for detecting patterns that represent nested motifs was designed based on exhaustive searches for pairs of motifs and combinatorial pattern analysis. These patterns can be grouped into three categories, motifs within other motifs, motifs flanked by other motifs, and motifs of large size. The methodology used in this study, applied to genomic sequences from the plant speciesAegilops tauschiiandOryza sativa, revealed that it is possible to identify putative nested TEs by detecting these three types of patterns. The results were validated through BLAST alignments, which revealed the efficacy and usefulness of the new method, which is called Mamushka.
Centro de Recursos Naturales Renovables de la Zona Semiárida (CERZOS) - CONICET, Bahía Blanca, Argentina.
José R
JR
Romero
Instituto de Ciencias e Ingeniería de la Computación (ICIC) - CONICET, Universidad Nacional del Sur, Bahía Blanca, Argentina.
Jessica A
JA
Carballido
Centro de Recursos Naturales Renovables de la Zona Semiárida (CERZOS) - CONICET, Bahía Blanca, Argentina.; Departamento de Ciencias de la Salud, Universidad Nacional del Sur, Bahía Blanca, Argentina.
Ingrid
I
Garbus
Centro de Recursos Naturales Renovables de la Zona Semiárida (CERZOS) - CONICET, Bahía Blanca, Argentina.; Departamento de Agronomía, Universidad Nacional del Sur, Bahía Blanca, Argentina.
Viviana C
VC
Echenique
Instituto de Ciencias e Ingeniería de la Computación (ICIC) - CONICET, Universidad Nacional del Sur, Bahía Blanca, Argentina.
Ignacio
I
Ponzoni
exact sequence analysis; nested motifs; repetitive motifs; structural bioinformatics