Journal of Bioinformatics and Computational Biology最新文献_第9页

EdClust: A heuristic sequence clustering method with higher sensitivity. EdClust:一种灵敏度较高的启发式序列聚类方法。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-12-23 DOI: 10.1142/S0219720021500360

Ming Cao, Qinke Peng, Ze-Gang Wei, Fei Liu, Yi-Fan Hou

{"title":"EdClust: A heuristic sequence clustering method with higher sensitivity.","authors":"Ming Cao, Qinke Peng, Ze-Gang Wei, Fei Liu, Yi-Fan Hou","doi":"10.1142/S0219720021500360","DOIUrl":"https://doi.org/10.1142/S0219720021500360","url":null,"abstract":"The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"20 1","pages":"2150036"},"PeriodicalIF":1.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39751492","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}

引用次数: 2

Bioinformatics and Computational Biology: A Primer for Biologists 生物信息学和计算生物学:生物学家入门

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-01-01 DOI: 10.1007/978-981-16-4241-8

B. Tiwary

引用次数: 2

A Novel Method for Predicting DNA N4-Methylcytosine Sites Based on Deep Forest Algorithm 基于深度森林算法的DNA n4 -甲基胞嘧啶位点预测新方法

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-01-01 DOI: 10.2139/ssrn.4062895

Yonglin Zhang, Mei Hu, Qi Mo, Wenli Gan, Jiesi Luo

引用次数: 0

Introduction to the Special Issue of the 18th Annual International RECOMB Satellite Workshop on Comparative Genomics. 第18届国际RECOMB卫星比较基因组学研讨会特刊简介。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2021-12-01 Epub Date: 2021-12-13 DOI: 10.1142/S0219720021020030

Rohan B H Williams, Louxin Zhang

引用次数: 0

Small parsimony for natural genomes in the DCJ-indel model. 自然基因组在DCJ-indel模型中的微小简约。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2021-12-01 Epub Date: 2021-11-19 DOI: 10.1142/S0219720021400096

Daniel Doerr, Cedric Chauve

引用次数: 2

A symmetry-inclusive algebraic approach to genome rearrangement. 基因组重排的对称包涵代数方法。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2021-12-01 Epub Date: 2021-11-19 DOI: 10.1142/S0219720021400151

Venta Terauds, Joshua Stevenson, Jeremy Sumner

{"title":"A symmetry-inclusive algebraic approach to genome rearrangement.","authors":"Venta Terauds, Joshua Stevenson, Jeremy Sumner","doi":"10.1142/S0219720021400151","DOIUrl":"https://doi.org/10.1142/S0219720021400151","url":null,"abstract":"Of the many modern approaches to calculating evolutionary distance via models of genome rearrangement, most are tied to a particular set of genomic modeling assumptions and to a restricted class of allowed rearrangements. The \"position paradigm\", in which genomes are represented as permutations signifying the position (and orientation) of each region, enables a refined model-based approach, where one can select biologically plausible rearrangements and assign to them relative probabilities/costs. Here, one must further incorporate any underlying structural symmetry of the genomes into the calculations and ensure that this symmetry is reflected in the model. In our recently-introduced framework of genome algebras, each genome corresponds to an element that simultaneously incorporates all of its inherent physical symmetries. The representation theory of these algebras then provides a natural model of evolution via rearrangement as a Markov chain. Whilst the implementation of this framework to calculate distances for genomes with \"practical\" numbers of regions is currently computationally infeasible, we consider it to be a significant theoretical advance: one can incorporate different genomic modeling assumptions, calculate various genomic distances, and compare the results under different rearrangement models. The aim of this paper is to demonstrate some of these features.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"19 6","pages":"2140015"},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39734890","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}

引用次数: 2

The monoploid chromosome complement of reconstructed ancestral genomes in a phylogeny. 系统发育中重建祖先基因组的单倍体染色体补体。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2021-12-01 Epub Date: 2021-11-19 DOI: 10.1142/S0219720021400084

Qiaoji Xu, Xiaomeng Zhang, Yue Zhang, Chunfang Zheng, James H Leebens-Mack, Lingling Jin, David Sankoff

引用次数: 0

Incorporating intergenic regions into reversal and transposition distances with indels. 将基因间区与索引结合到反转和转位距离中。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2021-12-01 Epub Date: 2021-11-13 DOI: 10.1142/S0219720021400114

Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Ulisses Dias, Zanoni Dias

{"title":"Incorporating intergenic regions into reversal and transposition distances with indels.","authors":"Alexsandro Oliveira Alexandrino, Andre Rodrigues Oliveira, Ulisses Dias, Zanoni Dias","doi":"10.1142/S0219720021400114","DOIUrl":"https://doi.org/10.1142/S0219720021400114","url":null,"abstract":"Problems in the genome rearrangement field are often formulated in terms of pairwise genome comparison: given two genomes [Formula: see text] and [Formula: see text], find the minimum number of genome rearrangements that may have occurred during the evolutionary process. This broad definition lacks at least two important considerations: the first being which features are extracted from genomes to create a useful mathematical model, and the second being which types of genome rearrangement events should be represented. Regarding the first consideration, seminal works in the genome rearrangement field solely used gene order to represent genomes as permutations of integer numbers, neglecting many important aspects like gene duplication, intergenic regions, and complex interactions between genes. Regarding the second consideration, some rearrangement events are widely studied such as reversals and transpositions. In this paper, we shed light on the first consideration and created a model that takes into account gene order and the number of nucleotides in intergenic regions. In addition, we consider events of reversals, transpositions, and indels (insertions and deletions) of genomic material. We present a 4-approximation algorithm for reversals and indels, a [Formula: see text]-approximation algorithm for transpositions and indels, and a 6-approximation for reversals, transpositions, and indels.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"19 6","pages":"2140011"},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39889211","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}

引用次数: 3

DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost. DNN-Boost:利用深度神经网络和XGBoost对肿瘤全外显子组测序数据进行体细胞突变鉴定。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2021-12-01 Epub Date: 2021-12-13 DOI: 10.1142/S0219720021400175

Firda Aminy Maruf, Rian Pratama, Giltae Song

{"title":"DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.","authors":"Firda Aminy Maruf, Rian Pratama, Giltae Song","doi":"10.1142/S0219720021400175","DOIUrl":"https://doi.org/10.1142/S0219720021400175","url":null,"abstract":"Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches require exome sequencing for both tumor and normal samples. However, it is more common to sequence exomes for tumor samples only without the paired normal samples. To include these types of data for extensive studies on the process of tumorigenesis, it is necessary to develop an approach for identifying somatic mutations using tumor exome sequencing data only. In this study, we designed a machine learning approach using Deep Neural Network (DNN) and XGBoost to identify somatic mutations in tumor-only exome sequencing data and we integrated this into a pipeline called DNN-Boost. The XGBoost algorithm is used to extract the features from the results of variant callers and these features are then fed into the DNN model as input. The XGBoost algorithm resolves issues of missing values and overfitting. We evaluated our proposed model and compared its performance with other existing benchmark methods. We noted that the DNN-Boost classification model outperformed the benchmark method in classifying somatic mutations from paired tumor-normal exome data and tumor-only exome data.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":"19 6","pages":"2140017"},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39716735","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}

引用次数: 3

Comparing the topology of phylogenetic network generators. 比较系统发育网络生成器的拓扑结构。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2021-12-01 Epub Date: 2021-12-06 DOI: 10.1142/S0219720021400126

Remie Janssen, Pengyu Liu

引用次数: 11