Journal of Computational Biology最新文献

{"title":"Sketching Methods with Small Window Guarantee Using Minimum Decycling Sets.","authors":"Guillaume Marçais, Dan DeBlasio, Carl Kingsford","doi":"10.1089/cmb.2024.0544","DOIUrl":"10.1089/cmb.2024.0544","url":null,"abstract":"<p><p>Most sequence sketching methods work by selecting specific <i>k</i>-mers from sequences so that the similarity between two sequences can be estimated using only the sketches. Because estimating sequence similarity is much faster using sketches than using sequence alignment, sketching methods are used to reduce the computational requirements of computational biology software. Applications using sketches often rely on properties of the <i>k</i>-mer selection procedure to ensure that using a sketch does not degrade the quality of the results compared with using sequence alignment. Two important examples of such properties are locality and window guarantees, the latter of which ensures that no long region of the sequence goes unrepresented in the sketch. A sketching method with a window guarantee, implicitly or explicitly, corresponds to a <i>decycling set</i> of the de Bruijn graph, which is a set of unavoidable <i>k</i>-mers. Any long enough sequence, by definition, must contain a <i>k</i>-mer from any decycling set (hence, the unavoidable property). Conversely, a decycling set also defines a sketching method by choosing the <i>k</i>-mers from the set as representatives. Although current methods use one of a small number of sketching method families, the space of decycling sets is much larger and largely unexplored. Finding decycling sets with desirable characteristics (e.g., small remaining path length) is a promising approach to discovering new sketching methods with improved performance (e.g., with small window guarantee). The <i>Minimum Decycling Sets</i> (MDSs) are of particular interest because of their minimum size. Only two algorithms, by Mykkeltveit and Champarnaud, are previously known to generate two particular MDSs, although there are typically a vast number of alternative MDSs. We provide a simple method to enumerate MDSs. This method allows one to explore the space of MDSs and to find MDSs optimized for desirable properties. We give evidence that the Mykkeltveit sets are close to optimal regarding one particular property, the remaining path length. A number of conjectures and computational and theoretical evidence to support them are presented. Code available at https://github.com/Kingsford-Group/mdsscope.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"597-615"},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141563456","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":"Using Attention-UNet Models to Predict Protein Contact Maps.","authors":"V A Jisna, Abhaysing Pawar Ajay, P B Jayaraj","doi":"10.1089/cmb.2023.0102","DOIUrl":"10.1089/cmb.2023.0102","url":null,"abstract":"<p><p>\u0000 <b>Proteins are essential to life, and understanding their intrinsic roles requires determining their structure. The field of proteomics has opened up new opportunities by applying deep learning algorithms to large databases of solved protein structures. With the availability of large data sets and advanced machine learning methods, the prediction of protein residue interactions has greatly improved. Protein contact maps provide empirical evidence of the interacting residue pairs within a protein sequence. Template-free protein structure prediction systems rely heavily on this information. This article proposes UNet-CON, an attention-integrated UNet architecture, trained to predict residue-residue contacts in protein sequences. With the predicted contacts being more accurate than state-of-the-art methods on the PDB25 test set, the model paves the way for the development of more powerful deep learning algorithms for predicting protein residue interactions.</b>\u0000 </p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"691-702"},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141558820","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":"GraphSlimmer: Preserving Read Mappability with the Minimum Number of Variants.","authors":"Neda Tavakoli, Daniel Gibney, Srinivas Aluru","doi":"10.1089/cmb.2024.0601","DOIUrl":"10.1089/cmb.2024.0601","url":null,"abstract":"<p><p>Modern genomic datasets, like those generated under the 1000 Genome Project, contain millions of variants belonging to known haplotypes. Although these datasets are more representative than a single reference sequence and can alleviate issues like reference bias, they are significantly more computationally burdensome to work with, often involving large-indexed genome graph data structures for tasks such as read mapping. The construction, preprocessing, and mapping algorithms can require substantial computational resources depending on the size of these variant sets. Moreover, the accuracy of mapping algorithms has been shown to decrease when working with complete variant sets. Therefore, a drastically reduced set of variants that preserves important properties of the original set is desirable. This work provides a technique for finding a minimal subset of variants <math><mi>S</mi></math> such that for given parameters <i>α</i> and <i>δ</i>, all substrings up to length <i>α</i> in the haplotypes are guaranteed to be still alignable to the appropriate locations with either Hamming or edit distance at most <i>δ</i>, using only <math><mi>S</mi></math>. Our contributions include showing the NP-hardness and inapproximability of these optimization problems and providing Integer Linear Programming (ILP) formulations. Our edit distance ILP formulation carefully decomposes the problem according to variant locations, which allows it to scale to support all of chromosome 22's variants from the 1000 Genome Project. Our experiments also demonstrate a significant reduction in the number of variants. For example, for moderately long reads, e.g., <i>α</i> = 1000, over 75% of the variants can be removed while preserving read mappability with edit distance at most one.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"616-637"},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141590395","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":"Pairwise Distances and the Problem of Multiple Optima.","authors":"Ran Libeskind-Hadas","doi":"10.1089/cmb.2023.0382","DOIUrl":"10.1089/cmb.2023.0382","url":null,"abstract":"<p><p>Discrete optimization problems arise in many biological contexts and, in many cases, we seek to make inferences from the optimal solutions. However, the number of optimal solutions is frequently very large and making inferences from any single solution may result in conclusions that are not supported by other optimal solutions. We describe a general approach for efficiently (polynomial time) and exactly (without sampling) computing statistics on the space of optimal solutions. These statistics provide insights into the space of optimal solutions that can be used to support the use of a single optimum (e.g., when the optimal solutions are similar) or justify the need for selecting multiple optima (e.g., when the solution space is large and diverse) from which to make inferences. We demonstrate this approach on two well-known problems and identify the properties of these problems that make them amenable to this method.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"638-650"},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141579838","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":"Screening Nonlinear miRNA Features of Breast Cancer by Using Ensemble Regularized Polynomial Logistic Regression.","authors":"Juntao Li, Shan Xiang, Xuekun Song","doi":"10.1089/cmb.2023.0289","DOIUrl":"10.1089/cmb.2023.0289","url":null,"abstract":"<p><p>Differentiating breast cancer subtypes based on miRNA data helps doctors provide more personalized treatment plans for patients. This paper explored the interaction between miRNA pairs and developed a novel ensemble regularized polynomial logistic regression method for screening nonlinear features of breast cancer. Three different types of second-order polynomial logistic regression with elastic network penalty (SOPLR-EN) in which each type contains 10 identical models were integrated to determine the most suitable sample set for feature screening by using bootstrap sampling strategy. A single feature and 39 nonlinear features were obtained by screening features that appeared at least 15 times in 30 integrations and were involved in the classification of at least 4 subtypes. The second-order polynomial logistic regression with ridge penalty (SOPLR-R) built on screened feature set achieved 82.30% classification accuracy for distinguishing breast cancer subtypes, surpassing the performance of other six methods. Further, 11 nonlinear miRNA biomarkers were identified, and their significant relevance to breast cancer was illustrated through six types of biological analysis.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"31 7","pages":"670-690"},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141626879","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":"DNASCANNER v2: A Web-Based Tool to Analyze the Characteristic Properties of Nucleotide Sequences.","authors":"Preeti P, Azeen Riyaz, Alakto Choudhury, Priyanka Ray Choudhury, Nischal Pradhan, Abhishek Singh, Mihir Nakul, Chhavi Dudeja, Abhijeet Yadav, Swarsat Kaushik Nath, Vrinda Khanna, Trapti Sharma, Gayatri Pradhan, Simran Takkar, Kamal Rawal","doi":"10.1089/cmb.2023.0227","DOIUrl":"10.1089/cmb.2023.0227","url":null,"abstract":"<p><p>\u0000 <b>Throughout the process of evolution, DNA undergoes the accumulation of distinct mutations, which can often result in highly organized patterns that serve various essential biological functions. These patterns encompass various genomic elements and provide valuable insights into the regulatory and functional aspects of DNA. The physicochemical, mechanical, thermodynamic, and structural properties of DNA sequences play a crucial role in the formation of specific patterns. These properties contribute to the three-dimensional structure of DNA and influence their interactions with proteins, regulatory elements, and other molecules. In this study, we introduce DNASCANNER v2, an advanced version of our previously published algorithm DNASCANNER for analyzing DNA properties. The current tool is built using the FLASK framework in Python language. Featuring a user-friendly interface tailored for nonspecialized researchers, it offers an extensive analysis of 158 DNA properties, including mono/di/trinucleotide frequencies, structural, physicochemical, thermodynamics, and mechanical properties of DNA sequences. The tool provides downloadable results and offers interactive plots for easy interpretation and comparison between different features. We also demonstrate the utility of DNASCANNER v2 in analyzing splice-site junctions, casposon insertion sequences, and transposon insertion sites (TIS) within the bacterial and human genomes, respectively. We also developed a deep learning module for the prediction of potential TIS in a given nucleotide sequence. In the future, we aim to optimize the performance of this prediction model through extensive training on larger data sets.</b>\u0000 </p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"651-669"},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140849825","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":"Estimating Enzyme Expression and Metabolic Pathway Activity in <i>Borreliella</i>-Infected and Uninfected Mice.","authors":"Filipp Martin Rondel, Hafsa Farooq, Roya Hosseini, Akshay Juyal, Sergey Knyazev, Serghei Mangul, Artem S Rogovskyy, Alexander Zelikovsky","doi":"10.1089/cmb.2024.0564","DOIUrl":"https://doi.org/10.1089/cmb.2024.0564","url":null,"abstract":"<p><p>Evaluating changes in metabolic pathway activity is essential for studying disease mechanisms and developing new treatments, with significant benefits extending to human health. Here, we propose EMPathways2, a maximum likelihood pipeline that is based on the expectation-maximization algorithm, which is capable of evaluating enzyme expression and metabolic pathway activity level. We first estimate enzyme expression from RNA-seq data that is used for simultaneous estimation of pathway activity levels using enzyme participation levels in each pathway. We implement the novel pipeline to RNA-seq data from several groups of mice, which provides a deeper look at the biochemical changes occurring as a result of bacterial infection, disease, and immune response. Our results show that estimated enzyme expression, pathway activity levels, and enzyme participation levels in each pathway are robust and stable across all samples. Estimated activity levels of a significant number of metabolic pathways strongly correlate with the infected and uninfected status of the respective rodent types.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141457117","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":"Toward the Reconciliation of Inconsistent Molecular Structures from Biochemical Databases.","authors":"Casper Asbjørn Eriksen, Jakob Lykke Andersen, Rolf Fagerberg, Daniel Merkle","doi":"10.1089/cmb.2024.0520","DOIUrl":"10.1089/cmb.2024.0520","url":null,"abstract":"<p><p><b>Information on the structure of molecules, retrieved via biochemical databases, plays a pivotal role in various disciplines, including metabolomics, systems biology, and drug discovery. No such database can be complete and it is often necessary to incorporate data from several sources. However, the molecular structure for a given compound is not necessarily consistent between databases. This article presents StructRecon, a novel tool for resolving unique molecular structures from database identifiers. Currently, identifiers from BiGG, ChEBI,</b> <i>Escherichia coli</i> Metabolome Database <b>(ECMDB), MetaNetX, and PubChem are supported. StructRecon traverses the cross-links between entries in different databases to construct what we call identifier graphs. The goal of these graphs is to offer a more complete view of the total information available on a given compound across all the supported databases. To reconcile discrepancies met during the traversal of the databases, we develop an extensible model for molecular structure supporting multiple independent levels of detail, which allows standardization of the structure to be applied iteratively. In some cases, our standardization approach results in multiple candidate structures for a given compound, in which case a random walk-based algorithm is used to select the most likely structure among incompatible alternatives. As a case study, we applied StructRecon to the <i>EColiCore2</i> model. We found at least one structure for 98.66% of its compounds, which is more than twice as many as possible when using the databases in more standard ways not considering the complex network of cross-database references captured by our identifier graphs. StructRecon is open-source and modular, which enables support for more databases in the future.</b></p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"498-512"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140957701","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":"Enhanced Compression of <i>k</i>-Mer Sets with Counters via de Bruijn Graphs.","authors":"Enrico Rossignolo, Matteo Comin","doi":"10.1089/cmb.2024.0530","DOIUrl":"10.1089/cmb.2024.0530","url":null,"abstract":"<p><p>An essential task in computational genomics involves transforming input sequences into their constituent <i>k</i>-mers. The quest for an efficient representation of <i>k</i>-mer sets is crucial for enhancing the scalability of bioinformatic analyses. One widely used method involves converting the <i>k</i>-mer set into a de Bruijn graph (dBG), followed by seeking a compact graph representation via the smallest path cover. This study introduces USTAR* (Unitig STitch Advanced constRuction), a tool designed to compress both a set of <i>k</i>-mers and their associated counts. USTAR leverages the connectivity and density of dBGs, enabling a more efficient path selection for constructing the path cover. The efficacy of USTAR is demonstrated through its application in compressing real read data sets. USTAR improves the compression achieved by UST (Unitig STitch), the best algorithm, by percentages ranging from 2.3% to 26.4%, depending on the <i>k</i>-mer size, and it is up to <math><mn>7</mn><mo>×</mo></math> times faster.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"524-538"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141183858","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":"Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks.","authors":"Gatis Melkus, Andrejs Sizovs, Peteris Rucevskis, Sandra Silina","doi":"10.1089/cmb.2024.0515","DOIUrl":"10.1089/cmb.2024.0515","url":null,"abstract":"<p><p>\u0000 <b>Chromatin conformation capture technologies permit the study of chromatin spatial organization on a genome-wide scale at a variety of resolutions. Despite the increasing precision and resolution of high-throughput chromatin conformation capture (Hi-C) methods, it remains challenging to conclusively link transcriptional activity to spatial organizational phenomena. We have developed a clique-based approach for analyzing Hi-C data that helps identify chromosomal hotspots that feature considerable enrichment of chromatin annotations for transcriptional start sites and, building on previously published work, show that these chromosomal hotspots are not only significantly enriched in RNA polymerase II binding sites as identified by the ENCODE project, but also identify a noticeable increase in FANTOM5 and GTEx transcription within our identified cliques across a variety of tissue types. From the obtained data, we surmise that our cliques are a suitable method for identifying transcription factories in Hi-C data, and outline further extensions to the method that may make it useful for locating regions of increased transcriptional activity in datasets where in-depth expression or polymerase data may not be available.</b>\u0000 </p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"589-596"},"PeriodicalIF":1.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141071020","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}