Pattern matching for high precision detection of LINE-1s in human genomes

Abstract

Background Long interspersed element 1 (LINE-1 or L1) retrotransposons are mobile elements that constitute 17–20% of the human genome. Strong correlations between abnormal L1 expression and several human diseases have been reported. This has motivated increasing interest in accurate quantification of the number of L1 copies present in any given biologic specimen. A main obstacle toward this aim is that L1s are relatively long DNA segments with regions of high variability, or largely present in the human genome as truncated fragments. These particularities render traditional alignment strategies, such as seed-and-extend inefficient, as the number of segments that are similar to L1s explodes exponentially. This study uses the pattern matching methodology for more accurate identification of L1s. We validate experimentally the superiority of pattern matching for L1 detection over alternative methods and discuss some of its potential applications. Results Pattern matching detected full-length L1 copies with high precision, reasonable computational time, and no prior input information. It also detected truncated and significantly altered copies of L1 with relatively high precision. The method was effectively used to annotate L1s in a target genome and to calculate copy number variation with respect to a reference genome. Crucial to the success of implementation was the selection of a small set of k-mer probes from a set of sequences presenting a stable pattern of distribution in the genome. As in seed-and-extend methods, the pattern matching algorithm sowed these k-mer probes, but instead of using heuristic extensions around the seeds, the analysis was based on distribution patterns within the genome. The desired level of precision could be adjusted, with some loss of recall. Conclusion Pattern matching is more efficient than seed-and-extend methods for the detection of L1 segments whose characterization depends on a finite set of sequences with common areas of low variability. We propose that pattern matching may help establish correlations between L1 copy number and disease states associated with L1 mobilization and evolution.

Improved LINE-1 Detection through Pattern Matching by Increasing Probe Length

“…Because of these adverse effects on health associated with L1s, accurate and efficient L1 detection is important, which is why we developed L1PD (LINE-1 Pattern Detection) [17]. Most L1s are inactive due to rearrangements, point mutations, and truncation [4,5], and that is why L1PD focuses on detecting full-length L1s, which are the most likely to retrotranspose at significant rates.…”

“…L1PD uses what we have termed seed-and-pattern-match, as opposed to seed-and-extend, where we replaced the heuristics of the extend component with a pattern-matching algorithm. Lopez et al [17] discuss the shortcomings of seed-and-extend in the context of L1 detection.…”

“…The seed-and-pattern-match approach established in L1PD [17] displayed promising results of precision and F1 score when matching the generated 50-mer probes back to the human genome. It was hypothesized that perhaps increasing k-mer length would yield better recall, and this hypothesis was reinforced when it was found that another study by Phan et al had previously shown a remarkable improvement in recall, with mrFAST and other aligners, when k-mer size was increased from 50 to 75 or 100 [23].…”

“…The L1PD algorithm [17] receives a fully-assembled genome and uses mrFAST [30] to index the genome and then map the probes against that genome, generating a SAM file. It should be noted that most of the run time of L1PD is taken by these steps.…”

“…To summarize, the pattern-matching algorithm consists of finding patterns of "hits" of probes (in the SAM file) that are in the same order and within the expected distance as specified by the FASTA file that contains the probes. The full algorithm is explained by López et al [17], but Figure 3 provides an overall visualization of L1PD. Note that the edit distance, distance threshold, and min.…”