The quantum hypercube as a k-mer graph.

The application of quantum principles in computing has garnered interest since the 1980s. Today, this concept is not only theoretical, but we have the means to design and execute techniques that leverage the quantum principles to perform calculations. The emergence of the quantum walk search technique exemplifies the practical application of quantum concepts and their potential to revolutionize information technologies. It promises to be versatile and may be applied to various problems. For example, the coined quantum walk search allows for identifying a marked item in a combinatorial search space, such as the quantum hypercube. The quantum hypercube organizes the qubits such that the qubit states represent the vertices and the edges represent the transitions to the states differing by one qubit state. It offers a novel framework to represent k-mer graphs in the quantum realm. Thus, the quantum hypercube facilitates the exploitation of parallelism, which is made possible through superposition and entanglement to search for a marked k-mer. However, as found in the analysis of the results, the search is only sometimes successful in hitting the target. Thus, through a meticulous examination of the quantum walk search circuit outcomes, evaluating what input-target combinations are useful, and a visionary exploration of DNA k-mer search, this paper opens the door to innovative possibilities, laying down the groundwork for further research to bridge the gap between theoretical conjecture in quantum computing and a tangible impact in bioinformatics.

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

Long Interspersed Element-1 (LINE-1 or L1) is an autonomous transposable element that accounts for 17% of the human genome. Strong correlations between abnormal L1 expression and diseases, particularly cancer, have been documented by numerous studies. L1PD (LINE-1 Pattern Detection) had been previously created to detect L1s by using a fixed pre-determined set of 50-mer probes and a pattern-matching algorithm. L1PD uses a novel seed-and-pattern-match strategy as opposed to the well-known seed-and-extend strategy employed by other tools. This study discusses an improved version of L1PD that shows how increasing the size of the k-mer probes from 50 to 75 or to 100 yields better results, as evidenced by experiments showing higher precision and recall when compared to the 50-mers. The probe-generation process was updated and the corresponding software is now shared so that users may generate probes for other reference genomes (with certain limitations). Additionally, L1PD was applied to other non-human genomes, such as dogs, horses, and cows, to further validate the pattern-matching strategy. The improved version of L1PD proves to be an efficient and promising approach for L1 detection.

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

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.

Genomic Insights into Cultivated Mexican Vanilla planifolia Reveal High Levels of Heterozygosity Stemming from Hybridization.

Although vanilla is one of the most valuable spices, there is a lack of understanding of the genomic variability of the main vanilla producing species, Vanilla planifolia, within its cultivated origin, Mexico. High genomic heterozygosity levels within the globally cultivated 'Daphna' genome have raised questions on the possibility of a hybrid origin and analogous genomic signatures of vanilla cultivated within its origin. This study investigated these questions by assessing whether the genomic structure of Mexican V. planifolia reflected domestication events. Whole genome re-sequencing was used to compare genome complexity between 15 cultivated accessions from different regions and gene pools. Results showed high levels of heterozygosity, ranging from 2.48% to 2.85%, in all but one accession, which exhibited a low level (0.403%). Chromosome-level comparative analyses revealed genomic variability among samples, but no signals of chromosome rearrangements. These findings support the hypotheses that cultivated vanilla resulted from hybridization and that multiple domestication events have shaped cultivated vanilla leading to the formation of landraces. High cultural diversity within this region further supports the occurrence of multiple domestication processes. These results may help to improve breeding and conservation efforts aiming to preserve the genetic diversity of this beloved spice threatened by climate change.

Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning.

Transposable elements are mobile sequences that can move and insert themselves into chromosomes, activating under internal or external stimuli, giving the organism the ability to adapt to the environment. Annotating transposable elements in genomic data is currently considered a crucial task to understand key aspects of organisms such as phenotype variability, species evolution, and genome size, among others. Because of the way they replicate, LTR retrotransposons are the most common transposable elements in plants, accounting in some cases for up to 80% of all DNA information. To annotate these elements, a reference library is usually created, a curation process is performed, eliminating TE fragments and false positives and then annotated in the genome using the homology method. However, the curation process can take weeks, requires extensive manual work and the execution of multiple time-consuming bioinformatics software. Here, we propose a machine learning-based approach to perform this process automatically on plant genomes, obtaining up to 91.18% F1-score. This approach was tested with four plant species, obtaining up to 93.6% F1-score (Oryza granulata) in only 22.61 s, where bioinformatics methods took approximately 6 h. This acceleration demonstrates that the ML-based approach is efficient and could be used in massive sequencing projects.

GSER (a Genome Size Estimator using R): a pipeline for quality assessment of sequenced genome libraries through genome size estimation.

The first step in any genome research after obtaining the read data is to perform a due quality control of the sequenced reads. In a de novo genome assembly project, the second step is to estimate two important features, the genome size and 'best k-mer', to start the assembly tests with different de novo assembly software and its parameters. However, the quality control of the sequenced genome libraries as a whole, instead of focusing on the reads only, is frequently overlooked and realized to be important only when the assembly tests did not render the expected results. We have developed GSER, a Genome Size Estimator using R, a pipeline to evaluate the relationship between k-mers and genome size, as a means for quality assessment of the sequenced genome libraries. GSER generates a set of charts that allow the analyst to evaluate the library datasets before starting the assembly. The script which runs the pipeline can be downloaded from http://www.mobilomics.org/GSER/downloads or http://github.com/mobilomics/GSER.

K-mer-based machine learning method to classify LTR-retrotransposons in plant genomes.

Every day more plant genomes are available in public databases and additional massive sequencing projects (i.e., that aim to sequence thousands of individuals) are formulated and released. Nevertheless, there are not enough automatic tools to analyze this large amount of genomic information. LTR retrotransposons are the most frequent repetitive sequences in plant genomes; however, their detection and classification are commonly performed using semi-automatic and time-consuming programs. Despite the availability of several bioinformatic tools that follow different approaches to detect and classify them, none of these tools can individually obtain accurate results. Here, we used Machine Learning algorithms based on k-mer counts to classify LTR retrotransposons from other genomic sequences and into lineages/families with an F1-Score of 95%, contributing to develop a free-alignment and automatic method to analyze these sequences.

Computational Performance Assessment of k-mer Counting Algorithms.

This article is about the assessment of several tools for k-mer counting, with the purpose to create a reference framework for bioinformatics researchers to identify computational requirements, parallelizing, advantages, disadvantages, and bottlenecks of each of the algorithms proposed in the tools. The k-mer counters evaluated in this article were BFCounter, DSK, Jellyfish, KAnalyze, KHMer, KMC2, MSPKmerCounter, Tallymer, and Turtle. Measured parameters were the following: RAM occupied space, processing time, parallelization, and read and write disk access. A dataset consisting of 36,504,800 reads was used corresponding to the 14th human chromosome. The assessment was performed for two k-mer lengths: 31 and 55. Obtained results were the following: pure Bloom filter-based tools and disk-partitioning techniques showed a lesser RAM use. The tools that took less execution time were the ones that used disk-partitioning techniques. The techniques that made the major parallelization were the ones that used disk partitioning, hash tables with lock-free approach, or multiple hash tables.