Algorithms for Molecular Biology最新文献

{"title":"Faster computation of left-bounded shortest unique substrings.","authors":"Larissa L M Aguiar, Felipe A Louza","doi":"10.1186/s13015-025-00287-5","DOIUrl":"10.1186/s13015-025-00287-5","url":null,"abstract":"<p><p>Finding shortest unique substrings (SUS) is a fundamental problem in string processing with applications in bioinformatics. In this paper, we present an algorithm for solving a variant of the SUS problem, the left-bounded shortest unique substrings (LSUS). This variant is particularly important in applications such as PCR primer design. Our algorithm runs in O(n) time using 2n memory words plus n bytes for an input string of length n. Experimental results with real and artificial datasets show that our algorithm is the fastest alternative in practice, being two times faster (on the average) than related works, while using a similar peak memory footprint.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"11"},"PeriodicalIF":1.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructing rearrangement phylogenies of natural genomes.","authors":"Leonard Bohnenkämper, Jens Stoye, Daniel Doerr","doi":"10.1186/s13015-025-00279-5","DOIUrl":"10.1186/s13015-025-00279-5","url":null,"abstract":"<p><strong>Background: </strong>We study the classical problem of inferring ancestral genomes from a set of extant genomes under a given phylogeny, known as the Small Parsimony Problem (SPP). Genomes are represented as sequences of oriented markers, organized in one or more linear or circular chromosomes. Any marker may appear in several copies, without restriction on orientation or genomic location, known as the natural genomes model. Evolutionary events along the branches of the phylogeny encompass large scale rearrangements, including segmental inversions, translocations, gain and loss (DCJ-indel model). Even under simpler rearrangement models, such as the classical breakpoint model without duplicates, the SPP is computationally intractable. Nevertheless, the SPP for natural genomes under the DCJ-indel model has been studied recently, with limited success.</p><p><strong>Methods: </strong>Building on prior work, we present a highly optimized ILP that is able to solve the SPP for sufficiently small phylogenies and gene families. A notable improvement w.r.t. the previous result is an optimized way of handling both circular and linear chromosomes. This is especially relevant to the SPP, since the chromosomal structure of ancestral genomes is unknown and the solution space for this chromosomal structure is typically large.</p><p><strong>Results: </strong>We benchmark our method on simulated and real data. On simulated phylogenies we observe a considerable performance improvement on problems that include linear chromosomes. And even when the ground truth contains only one circular chromosome per genome, our method outperforms its predecessor due to its optimized handling of the solution space. The practical advantage becomes also visible in an analysis of seven Anopheles taxa.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"10"},"PeriodicalIF":1.5,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12144824/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sama: a contig assembler with correctness guarantee.","authors":"Leena Salmela","doi":"10.1186/s13015-025-00280-y","DOIUrl":"10.1186/s13015-025-00280-y","url":null,"abstract":"<p><strong>Background: </strong>In genome assembly the task is to reconstruct a genome based on sequencing reads. Current practical methods are based on heuristics which are hard to analyse and thus such analysis is not readily available.</p><p><strong>Results: </strong>We present a model for estimating the probability of misassembly at each position of a de Bruijn graph based assembly. Unlike previous work, our model also takes into account missing data. We apply our model to produce contigs with correctness guarantee and correctness estimates for each position in the contigs.</p><p><strong>Conclusions: </strong>Our experiments show that when the coverage of k-mers is high enough, our method produces contigs with similar contiguity characteristics as state-of-the-art assemblers which are based on heuristic correction of the de Bruijn graph. Our model may have further applications in downstream analysis of contigs or in any analysis working directly on the de Bruijn graph.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"9"},"PeriodicalIF":1.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12135590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating similarity and distance using FracMinHash.","authors":"Mahmudur Rahman Hera, David Koslicki","doi":"10.1186/s13015-025-00276-8","DOIUrl":"10.1186/s13015-025-00276-8","url":null,"abstract":"<p><strong>Motivation: </strong>The increasing number and volume of genomic and metagenomic data necessitates scalable and robust computational models for precise analysis. Sketching techniques utilizing <math><mi>k</mi></math> -mers from a biological sample have proven to be useful for large-scale analyses. In recent years, FracMinHash has emerged as a popular sketching technique and has been used in several useful applications. Recent studies on FracMinHash proved unbiased estimators for the containment and Jaccard indices. However, theoretical investigations for other metrics are still lacking.</p><p><strong>Theoretical contributions: </strong>In this paper, we present a theoretical framework for estimating similarity/distance metrics by using FracMinHash sketches, when the metric is expressible in a certain form. We establish conditions under which such an estimation is sound and recommend a minimum scale factor s for accurate results. Experimental evidence supports our theoretical findings.</p><p><strong>Practical contributions: </strong>We also present frac-kmc, a fast and efficient FracMinHash sketch generator program. frac-kmc is the fastest known FracMinHash sketch generator, delivering accurate and precise results for cosine similarity estimation on real data. frac-kmc is also the first parallel tool for this task, allowing for speeding up sketch generation using multiple CPU cores - an option lacking in existing serialized tools. We show that by computing FracMinHash sketches using frac-kmc, we can estimate pairwise similarity speedily and accurately on real data. frac-kmc is freely available here: https://github.com/KoslickiLab/frac-kmc/.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"8"},"PeriodicalIF":1.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082993/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AlfaPang: alignment free algorithm for pangenome graph construction.","authors":"Adam Cicherski, Anna Lisiecka, Norbert Dojer","doi":"10.1186/s13015-025-00277-7","DOIUrl":"10.1186/s13015-025-00277-7","url":null,"abstract":"<p><p>The success of pangenome-based approaches to genomics analysis depends largely on the existence of efficient methods for constructing pangenome graphs that are applicable to large genome collections. In the current paper we present AlfaPang, a new pangenome graph building algorithm. AlfaPang is based on a novel alignment-free approach that allows to construct pangenome graphs using significantly less computational resources than state-of-the-art tools. The code of AlfaPang is freely available at https://github.com/AdamCicherski/AlfaPang .</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"7"},"PeriodicalIF":1.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\"><ns0:math><ns0:mrow><ns0:mi>M</ns0:mi> <ns0:mstyle><ns0:mi>C</ns0:mi> <ns0:mi>D</ns0:mi> <ns0:mi>A</ns0:mi> <ns0:mi>G</ns0:mi></ns0:mstyle> </ns0:mrow> </ns0:math> : indexing maximal common subsequences for k strings.","authors":"Giovanni Buzzega, Alessio Conte, Roberto Grossi, Giulia Punzi","doi":"10.1186/s13015-025-00271-z","DOIUrl":"https://doi.org/10.1186/s13015-025-00271-z","url":null,"abstract":"<p><p>Analyzing and comparing sequences of symbols is among the most fundamental problems in computer science, possibly even more so in bioinformatics. Maximal Common Subsequences (MCSs), i.e., inclusion-maximal sequences of non-contiguous symbols common to two or more strings, have only recently received attention in this area, despite being a basic notion and a natural generalization of more common tools like Longest Common Substrings/Subsequences. In this paper we simplify and engineer recent advancements in MCSs into a practical tool called <math><mrow><mi>M</mi> <mstyle><mi>C</mi> <mi>D</mi> <mi>A</mi> <mi>G</mi></mstyle> </mrow> </math> , the first publicly available tool that can index MCSs of real genomic data, and show that its definition can be generalized to multiple strings. We demonstrate that our tool can index pairs of sequences exceeding 10,000 base pairs within minutes, utilizing only 4-7% more than the minimum required nodes. For three or more sequences, we observe experimentally that the minimum index may exhibit a significant increase in the number of nodes.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"6"},"PeriodicalIF":1.5,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008955/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unbiased anchors for reliable genome-wide synteny detection.","authors":"Karl K Käther, Andreas Remmel, Steffen Lemke, Peter F Stadler","doi":"10.1186/s13015-025-00275-9","DOIUrl":"10.1186/s13015-025-00275-9","url":null,"abstract":"<p><p>Orthology inference lies at the foundation of comparative genomics research. The correct identification of loci which descended from a common ancestral sequence is not only complicated by sequence divergence but also duplication and other genome rearrangements. The conservation of gene order, i.e. synteny, is used in conjunction with sequence similarity as an additional factor for orthology determination. Current approaches, however, rely on genome annotations and are therefore limited. Here we present an annotation-free approach and compare it to synteny analysis with annotations. We find that our approach works better in closely related genomes whereas there is a better performance with annotations for more distantly related genomes. Overall, the presented algorithm offers a useful alternative to annotation-based methods and can outperform them in many cases.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"5"},"PeriodicalIF":1.5,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11972476/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The open-closed mod-minimizer algorithm.","authors":"Ragnar Groot Koerkamp, Daniel Liu, Giulio Ermanno Pibiri","doi":"10.1186/s13015-025-00270-0","DOIUrl":"10.1186/s13015-025-00270-0","url":null,"abstract":"<p><p>Sampling algorithms that deterministically select a subset of <math><mi>k</mi></math> -mers are an important building block in bioinformatics applications. For example, they are used to index large textual collections, like DNA, and to compare sequences quickly. In such applications, a sampling algorithm is required to select one <math><mi>k</mi></math> -mer out of every window of w consecutive <math><mi>k</mi></math> -mers. The folklore and most used scheme is the random minimizer that selects the smallest <math><mi>k</mi></math> -mer in the window according to some random order. This scheme is remarkably simple and versatile, and has a density (expected fraction of selected <math><mi>k</mi></math> -mers) of <math><mrow><mn>2</mn> <mo>/</mo> <mo>(</mo> <mi>w</mi> <mo>+</mo> <mn>1</mn> <mo>)</mo></mrow> </math> . In practice, lower density leads to faster methods and smaller indexes, and it turns out that the random minimizer is not the best one can do. Indeed, some schemes are known to approach optimal density 1/w when <math><mrow><mi>k</mi> <mo>→</mo> <mi>∞</mi></mrow> </math> , like the recently introduced mod-minimizer (Groot Koerkamp and Pibiri, WABI 2024). In this work, we study methods that achieve low density when <math><mrow><mi>k</mi> <mo>≤</mo> <mi>w</mi></mrow> </math> . In this small-k regime, a practical method with provably better density than the random minimizer is the miniception (Zheng et al., Bioinformatics 2021). This method can be elegantly described as sampling the smallest closed sycnmer (Edgar, PeerJ 2021) in the window according to some random order. We show that extending the miniception to prefer sampling open syncmers yields much better density. This new method-the open-closed minimizer-offers improved density for small <math><mrow><mi>k</mi> <mo>≤</mo> <mi>w</mi></mrow> </math> while being as fast to compute as the random minimizer. Compared to methods based on decycling sets, that achieve very low density in the small-k regime, our method has comparable density while being computationally simpler and intuitive. Furthermore, we extend the mod-minimizer to improve density of any scheme that works well for small k to also work well when <math><mrow><mi>k</mi> <mo>></mo> <mi>w</mi></mrow> </math> is large. We hence obtain the open-closed mod-minimizer, a practical method that improves over the mod-minimizer for all k.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"4"},"PeriodicalIF":1.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mem-based pangenome indexing for k-mer queries.","authors":"Stephen Hwang, Nathaniel K Brown, Omar Y Ahmed, Katharine M Jenike, Sam Kovaka, Michael C Schatz, Ben Langmead","doi":"10.1186/s13015-025-00272-y","DOIUrl":"10.1186/s13015-025-00272-y","url":null,"abstract":"<p><p>Pangenomes are growing in number and size, thanks to the prevalence of high-quality long-read assemblies. However, current methods for studying sequence composition and conservation within pangenomes have limitations. Methods based on graph pangenomes require a computationally expensive multiple-alignment step, which can leave out some variation. Indexes based on k-mers and de Bruijn graphs are limited to answering questions at a specific substring length k. We present Maximal Exact Match Ordered (MEMO), a pangenome indexing method based on maximal exact matches (MEMs) between sequences. A single MEMO index can handle arbitrary-length queries over pangenomic windows. MEMO enables both queries that test k-mer presence/absence (membership queries) and that count the number of genomes containing k-mers in a window (conservation queries). MEMO's index for a pangenome of 89 human autosomal haplotypes fits in 2.04 GB, 8.8 <math><mo>×</mo></math> smaller than a comparable KMC3 index and 11.4 <math><mo>×</mo></math> smaller than a PanKmer index. MEMO indexes can be made smaller by sacrificing some counting resolution, with our decile-resolution HPRC index reaching 0.67 GB. MEMO can conduct a conservation query for 31-mers over the human leukocyte antigen locus in 13.89 s, 2.5 <math><mo>×</mo></math> faster than other approaches. MEMO's small index size, lack of k-mer length dependence, and efficient queries make it a flexible tool for studying and visualizing substring conservation in pangenomes.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"3"},"PeriodicalIF":1.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11871630/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143538063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding high posterior density phylogenies by systematically extending a directed acyclic graph.","authors":"Chris Jennings-Shaffer, David H Rich, Matthew Macaulay, Michael D Karcher, Tanvi Ganapathy, Shosuke Kiami, Anna Kooperberg, Cheng Zhang, Marc A Suchard, Frederick A Matsen","doi":"10.1186/s13015-025-00273-x","DOIUrl":"10.1186/s13015-025-00273-x","url":null,"abstract":"<p><p>Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a random walk. Previous work explored the possibility of replacing this random walk with a systematic search, but was quickly overwhelmed by the large number of probable trees in the posterior distribution. In this paper we develop methods to sidestep this problem using a recently introduced structure called the subsplit directed acyclic graph (sDAG). This structure can represent many trees at once, and local rearrangements of trees translate to methods of enlarging the sDAG. Here we propose two methods of introducing, ranking, and selecting local rearrangements on sDAGs to produce a collection of trees with high posterior density. One of these methods successfully recovers the set of high posterior density trees across a range of data sets. However, we find that a simpler strategy of aggregating trees into an sDAG in fact is computationally faster and returns a higher fraction of probable trees.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"20 1","pages":"2"},"PeriodicalIF":1.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11869616/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143532146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}