Logol: Expressive Pattern Matching in Sequences. Application to Ribosomal Frameshift Modeling

First landscape of binding to chromosomes for a domesticated mariner transposase in the human genome: diversity of genomic targets of SETMAR isoforms in two colorectal cell lines

“…These lines display specific expression profiles of SETMAR isoforms: HT29 cells express a single SETMAR isoform (V2) whereas SW48 cells express several (V1, V2, X2, V5 and HSMAR1) [4]. We also revisit the annotation of the human genome for the Hsmar1 element and its associated 80-pb miniature element, MADE1, using logol [23] and BLAST+, and we could detect and annotate ~2500 novel MADE1 copies. We found that SETMAR binding sites occur mostly at MADE1 sequences, although a significant fraction of them (up to ~50%) can also be found at unannotated regions.…”

Learning the Language of Biological Sequences

“…As in linguistics, a solution to get polynomial-time parsing and still represent many of the non-local constraints from genomic sequences is to use mildly context-sensitive languages [9]. Along this way, Searls introduced String Variable Grammars as an expressive formalism for describing the language of DNA that has led to several generic practical parsers: the precursor Genlang [10] and its successors Stan [11], Patscan [12], Patsearch [13] and Logol [14]. Many specialized parsers have also been devised, as for instance RNAMotif [15], RNAbob [16], Hypasearch [17,18], Palingol [19] and Structator [20] tailored to handle efficiently RNA stem-loop secondary structures.…”