Algorithms for Molecular Biology最新文献

{"title":"ESKEMAP: exact sketch-based read mapping","authors":"Tizian Schulz, Paul Medvedev","doi":"10.1186/s13015-024-00261-7","DOIUrl":"https://doi.org/10.1186/s13015-024-00261-7","url":null,"abstract":"Given a sequencing read, the broad goal of read mapping is to find the location(s) in the reference genome that have a “similar sequence”. Traditionally, “similar sequence” was defined as having a high alignment score and read mappers were viewed as heuristic solutions to this well-defined problem. For sketch-based mappers, however, there has not been a problem formulation to capture what problem an exact sketch-based mapping algorithm should solve. Moreover, there is no sketch-based method that can find all possible mapping positions for a read above a certain score threshold. In this paper, we formulate the problem of read mapping at the level of sequence sketches. We give an exact dynamic programming algorithm that finds all hits above a given similarity threshold. It runs in $$mathcal {O} (|t| + |p| + ell ^2)$$ time and $$mathcal {O} (ell log ell )$$ space, where |t| is the number of $$k$$ -mers inside the sketch of the reference, |p| is the number of $$k$$ -mers inside the read’s sketch and $$ell$$ is the number of times that $$k$$ -mers from the pattern sketch occur in the sketch of the text. We evaluate our algorithm’s performance in mapping long reads to the T2T assembly of human chromosome Y, where ampliconic regions make it desirable to find all good mapping positions. For an equivalent level of precision as minimap2, the recall of our algorithm is 0.88, compared to only 0.76 of minimap2.","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"18 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model","authors":"Yushu Liu, Mohammadamin Edrisi, Zhi Yan, Huw A Ogilvie, Luay Nakhleh","doi":"10.1186/s13015-024-00264-4","DOIUrl":"https://doi.org/10.1186/s13015-024-00264-4","url":null,"abstract":"Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via “bulk sequencing,” the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD’s performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD .","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"48 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting the complexity of and algorithms for the graph traversal edit distance and its variants","authors":"Yutong Qiu, Yihang Shen, Carl Kingsford","doi":"10.1186/s13015-024-00262-6","DOIUrl":"https://doi.org/10.1186/s13015-024-00262-6","url":null,"abstract":"The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al. (2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the computationally costly and error-prone process of genome assembly. Ebrahimpour Boroojeny et al. (2018) propose two ILP formulations for GTED and claim that GTED is polynomially solvable because the linear programming relaxation of one of the ILPs always yields optimal integer solutions. The claim that GTED is polynomially solvable is contradictory to the complexity results of existing string-to-graph matching problems. We resolve this conflict in complexity results by proving that GTED is NP-complete and showing that the ILPs proposed by Ebrahimpour Boroojeny et al. do not solve GTED but instead solve for a lower bound of GTED and are not solvable in polynomial time. In addition, we provide the first two, correct ILP formulations of GTED and evaluate their empirical efficiency. These results provide solid algorithmic foundations for comparing genome graphs and point to the direction of heuristics. The source code to reproduce experimental results is available at https://github.com/Kingsford-Group/gtednewilp/ .","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"75 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast, parallel, and cache-friendly suffix array construction","authors":"Jamshed Khan, Tobias Rubel, Erin Molloy, Laxman Dhulipala, Rob Patro","doi":"10.1186/s13015-024-00263-5","DOIUrl":"https://doi.org/10.1186/s13015-024-00263-5","url":null,"abstract":"String indexes such as the suffix array (sa) and the closely related longest common prefix (lcp) array are fundamental objects in bioinformatics and have a wide variety of applications. Despite their importance in practice, few scalable parallel algorithms for constructing these are known, and the existing algorithms can be highly non-trivial to implement and parallelize. In this paper we present caps-sa, a simple and scalable parallel algorithm for constructing these string indexes inspired by samplesort and utilizing an LCP-informed mergesort. Due to its design, caps-sa has excellent memory-locality and thus incurs fewer cache misses and achieves strong performance on modern multicore systems with deep cache hierarchies. We show that despite its simple design, caps-sa outperforms existing state-of-the-art parallel sa and lcp-array construction algorithms on modern hardware. Finally, motivated by applications in modern aligners where the query strings have bounded lengths, we introduce the notion of a bounded-context sa and show that caps-sa can easily be extended to exploit this structure to obtain further speedups. We make our code publicly available at https://github.com/jamshed/CaPS-SA .","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"75 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pfp-fm: an accelerated FM-index","authors":"Aaron Hong, Marco Oliva, Dominik Köppl, Hideo Bannai, Christina Boucher, Travis Gagie","doi":"10.1186/s13015-024-00260-8","DOIUrl":"https://doi.org/10.1186/s13015-024-00260-8","url":null,"abstract":"FM-indexes are crucial data structures in DNA alignment, but searching with them usually takes at least one random access per character in the query pattern. Ferragina and Fischer [1] observed in 2007 that word-based indexes often use fewer random accesses than character-based indexes, and thus support faster searches. Since DNA lacks natural word-boundaries, however, it is necessary to parse it somehow before applying word-based FM-indexing. In 2022, Deng et al. [2] proposed parsing genomic data by induced suffix sorting, and showed that the resulting word-based FM-indexes support faster counting queries than standard FM-indexes when patterns are a few thousand characters or longer. In this paper we show that using prefix-free parsing—which takes parameters that let us tune the average length of the phrases—instead of induced suffix sorting, gives a significant speedup for patterns of only a few hundred characters. We implement our method and demonstrate it is between 3 and 18 times faster than competing methods on queries to GRCh38, and is consistently faster on queries made to 25,000, 50,000 and 100,000 SARS-CoV-2 genomes. Hence, it seems our method accelerates the performance of count over all state-of-the-art methods with a moderate increase in the memory. The source code for $$texttt {PFP-FM}$$ is available at https://github.com/AaronHong1024/afm .","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"44 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Space-efficient computation of k-mer dictionaries for large values of k","authors":"Diego Díaz-Domínguez, Miika Leinonen, Leena Salmela","doi":"10.1186/s13015-024-00259-1","DOIUrl":"https://doi.org/10.1186/s13015-024-00259-1","url":null,"abstract":"Computing k-mer frequencies in a collection of reads is a common procedure in many genomic applications. Several state-of-the-art k-mer counters rely on hash tables to carry out this task but they are often optimised for small k as a hash table keeping keys explicitly (i.e., k-mer sequences) takes $$O(Nfrac{k}{w})$$ computer words, N being the number of distinct k-mers and w the computer word size, which is impractical for long values of k. This space usage is an important limitation as analysis of long and accurate HiFi sequencing reads can require larger values of k. We propose Kaarme, a space-efficient hash table for k-mers using $$O(N+ufrac{k}{w})$$ words of space, where u is the number of reads. Our framework exploits the fact that consecutive k-mers overlap by $$k-1$$ symbols. Thus, we only store the last symbol of a k-mer and a pointer within the hash table to a previous one, which we can use to recover the remaining $$k-1$$ symbols. We adapt Kaarme to compute canonical k-mers as well. This variant also uses pointers within the hash table to save space but requires more work to decode the k-mers. Specifically, it takes $$O(sigma ^{k})$$ time in the worst case, $$sigma$$ being the DNA alphabet, but our experiments show this is hardly ever the case. The canonical variant does not improve our theoretical results but greatly reduces space usage in practice while keeping a competitive performance to get the k-mers and their frequencies. We compare canonical Kaarme to a regular hash table storing canonical k-mers explicitly as keys and show that our method uses up to five times less space while being less than 1.5 times slower. We also show that canonical Kaarme uses significantly less memory than state-of-the-art k-mer counters when they do not resort to disk to keep intermediate results.","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"31 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140583688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infrared: a declarative tree decomposition-powered framework for bioinformatics.","authors":"Hua-Ting Yao, Bertrand Marchand, Sarah J Berkemer, Yann Ponty, Sebastian Will","doi":"10.1186/s13015-024-00258-2","DOIUrl":"10.1186/s13015-024-00258-2","url":null,"abstract":"<p><strong>Motivation: </strong>Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations.</p><p><strong>Methods: </strong>We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential in the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency.</p><p><strong>Results: </strong>Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled as sets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations and extensions of methods for RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and design of coding sequences. Moreover, we demonstrate multidimensional Boltzmann sampling. These applications of the framework-together with our novel results-underline the practical relevance of Infrared. Remarkably, the achieved complexities are typically equivalent to the ones of specialized algorithms and implementations.</p><p><strong>Availability: </strong>Infrared is available at https://amibio.gitlabpages.inria.fr/Infrared with extensive documentation, including various usage examples and API reference; it can be installed using Conda or from source.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"19 1","pages":"13"},"PeriodicalIF":1.5,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10943887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140141081","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":"Median quartet tree search algorithms using optimal subtree prune and regraft.","authors":"Shayesteh Arasti, Siavash Mirarab","doi":"10.1186/s13015-024-00257-3","DOIUrl":"10.1186/s13015-024-00257-3","url":null,"abstract":"<p><p>Gene trees can be different from the species tree due to biological processes and inference errors. One way to obtain a species tree is to find one that maximizes some measure of similarity to a set of gene trees. The number of shared quartets between a potential species tree and gene trees provides a statistically justifiable score; if maximized properly, it could result in a statistically consistent estimator of the species tree under several statistical models of discordance. However, finding the median quartet score tree, one that maximizes this score, is NP-Hard, motivating several existing heuristic algorithms. These heuristics do not follow the hill-climbing paradigm used extensively in phylogenetics. In this paper, we make theoretical contributions that enable an efficient hill-climbing approach. Specifically, we show that a subtree of size m can be placed optimally on a tree of size n in quasi-linear time with respect to n and (almost) independently of m. This result enables us to perform subtree prune and regraft (SPR) rearrangements as part of a hill-climbing search. We show that this approach can slightly improve upon the results of widely-used methods such as ASTRAL in terms of the optimization score but not necessarily accuracy.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"19 1","pages":"12"},"PeriodicalIF":1.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10938725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140121325","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":"Suffix sorting via matching statistics.","authors":"Zsuzsanna Lipták, Francesco Masillo, Simon J Puglisi","doi":"10.1186/s13015-023-00245-z","DOIUrl":"10.1186/s13015-023-00245-z","url":null,"abstract":"<p><p>We introduce a new algorithm for constructing the generalized suffix array of a collection of highly similar strings. As a first step, we construct a compressed representation of the matching statistics of the collection with respect to a reference string. We then use this data structure to distribute suffixes into a partial order, and subsequently to speed up suffix comparisons to complete the generalized suffix array. Our experimental evidence with a prototype implementation (a tool we call sacamats) shows that on string collections with highly similar strings we can construct the suffix array in time competitive with or faster than the fastest available methods. Along the way, we describe a heuristic for fast computation of the matching statistics of two strings, which may be of independent interest.</p>","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"19 1","pages":"11"},"PeriodicalIF":1.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10935992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140112116","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 maximal exact matches in graphs","authors":"Nicola Rizzo, Manuel Cáceres, Veli Mäkinen","doi":"10.1186/s13015-024-00255-5","DOIUrl":"https://doi.org/10.1186/s13015-024-00255-5","url":null,"abstract":"We study the problem of finding maximal exact matches (MEMs) between a query string Q and a labeled graph G. MEMs are an important class of seeds, often used in seed-chain-extend type of practical alignment methods because of their strong connections to classical metrics. A principled way to speed up chaining is to limit the number of MEMs by considering only MEMs of length at least $$kappa$$ ( $$kappa$$ -MEMs). However, on arbitrary input graphs, the problem of finding MEMs cannot be solved in truly sub-quadratic time under SETH (Equi et al., TALG 2023) even on acyclic graphs. In this paper we show an $$O(ncdot L cdot d^{L-1} + m + M_{kappa ,L})$$ -time algorithm finding all $$kappa$$ -MEMs between Q and G spanning exactly L nodes in G, where n is the total length of node labels, d is the maximum degree of a node in G, $$m = |Q|$$ , and $$M_{kappa ,L}$$ is the number of output MEMs. We use this algorithm to develop a $$kappa$$ -MEM finding solution on indexable Elastic Founder Graphs (Equi et al., Algorithmica 2022) running in time $$O(nH^2 + m + M_kappa )$$ , where H is the maximum number of nodes in a block, and $$M_kappa$$ is the total number of $$kappa$$ -MEMs. Our results generalize to the analysis of multiple query strings (MEMs between G and any of the strings). Additionally, we provide some experimental results showing that the number of graph MEMs is an order of magnitude smaller than the number of string MEMs of the corresponding concatenated collection. We show that seed-chain-extend type of alignment methods can be implemented on top of indexable Elastic Founder Graphs by providing an efficient way to produce the seeds between a set of queries and the graph. The code is available in https://github.com/algbio/efg-mems .","PeriodicalId":50823,"journal":{"name":"Algorithms for Molecular Biology","volume":"40 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140098576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}