Journal of Bioinformatics and Computational Biology最新文献_第10页

Mining sponge phenomena in RNA expression data. 挖掘RNA表达数据中的海绵现象。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-11-18 DOI: 10.1142/S0219720021500220

Fabrizio Angiulli, Teresa Colombo, Fabio Fassetti, Angelo Furfaro, Paola Paci

{"title":"Mining sponge phenomena in RNA expression data.","authors":"Fabrizio Angiulli, Teresa Colombo, Fabio Fassetti, Angelo Furfaro, Paola Paci","doi":"10.1142/S0219720021500220","DOIUrl":"https://doi.org/10.1142/S0219720021500220","url":null,"abstract":"In the last few years, the interactions among competing endogenous RNAs (ceRNAs) have been recognized as a key post-transcriptional regulatory mechanism in cell differentiation, tissue development, and disease. Notably, such sponge phenomena substracting active microRNAs from their silencing targets have been recognized as having a potential oncosuppressive, or oncogenic, role in several cancer types. Hence, the ability to predict sponges from the analysis of large expression data sets (e.g. from international cancer projects) has become an important data mining task in bioinformatics. We present a technique designed to mine sponge phenomena whose presence or absence may discriminate between healthy and unhealthy populations of samples in tumoral or normal expression data sets, thus providing lists of candidates potentially relevant in the pathology. With this aim, we search for pairs of elements acting as ceRNA for a given miRNA, namely, we aim at discovering miRNA-RNA pairs involved in phenomena which are clearly present in one population and almost absent in the other one. The results on tumoral expression data, concerning five different cancer types, confirmed the effectiveness of the approach in mining interesting knowledge. Indeed, 32 out of 33 miRNAs and 22 out of 25 protein-coding genes identified as top scoring in our analysis are corroborated by having been similarly associated with cancer processes in independent studies. In fact, the subset of miRNAs selected by the sponge analysis results in a significant enrichment of annotation for the KEGG32 pathway \"microRNAs in cancer\" when tested with the commonly used bioinformatic resource DAVID. Moreover, often the cancer datasets where our sponge analysis identified a miRNA as top scoring match the one reported already in the pertaining literature.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39636898","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: 0

O-glycosylation site prediction for Homo sapiens by combining properties and sequence features with support vector machine. 基于属性与序列特征结合的智人o -糖基化位点预测。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-11-19 DOI: 10.1142/S0219720021500293

Yan Zhu, Shuwan Yin, Jia Zheng, Yixia Shi, Cangzhi Jia

{"title":"O-glycosylation site prediction for Homo sapiens by combining properties and sequence features with support vector machine.","authors":"Yan Zhu, Shuwan Yin, Jia Zheng, Yixia Shi, Cangzhi Jia","doi":"10.1142/S0219720021500293","DOIUrl":"https://doi.org/10.1142/S0219720021500293","url":null,"abstract":"O-glycosylation is a protein posttranslational modification important in regulating almost all cells. It is related to a large number of physiological and pathological phenomena. Recognizing O-glycosylation sites is the key to further investigating the molecular mechanism of protein posttranslational modification. This study aimed to collect a reliable dataset on Homo sapiens and develop an O-glycosylation predictor for Homo sapiens, named Captor, through multiple features. A random undersampling method and a synthetic minority oversampling technique were employed to deal with imbalanced data. In addition, the Kruskal-Wallis (K-W) test was adopted to optimize feature vectors and improve the performance of the model. A support vector machine, due to its optimal performance, was used to train and optimize the final prediction model after a comprehensive comparison of various classifiers in traditional machine learning methods and deep learning. On the independent test set, Captor outperformed the existing O-glycosylation tool, suggesting that Captor could provide more instructive guidance for further experimental research on O-glycosylation. The source code and datasets are available at https://github.com/YanZhu06/Captor/.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39645905","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

Amino acid environment affinity model based on graph attention network. 基于图关注网络的氨基酸环境亲和模型。

IF 1 4区生物学

Journal of Bioinformatics and Computational Biology Pub Date : 2022-02-01 Epub Date: 2021-11-13 DOI: 10.1142/S0219720021500323

Xueheng Tong, Shuqi Liu, Jiawei Gu, Chunguo Wu, Yanchun Liang, Xiaohu Shi

{"title":"Amino acid environment affinity model based on graph attention network.","authors":"Xueheng Tong, Shuqi Liu, Jiawei Gu, Chunguo Wu, Yanchun Liang, Xiaohu Shi","doi":"10.1142/S0219720021500323","DOIUrl":"https://doi.org/10.1142/S0219720021500323","url":null,"abstract":"Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that can be identified by three-dimensional locations and local neighborhoods in which the structure or function exists. Understanding the amino acid environment affinity is essential for additional protein structural or functional studies, such as mutation analysis and functional site detection. In this study, an amino acid environment affinity model based on the graph attention network was developed. Initially, we constructed a protein graph according to the distance between amino acid pairs. Then, we extracted a set of structural features for each node. Finally, the protein graph and the associated node feature set were set to input the graph attention network model and to obtain the amino acid affinities. Numerical results show that our proposed method significantly outperforms a recent 3DCNN-based method by almost 30%.","PeriodicalId":48910,"journal":{"name":"Journal of Bioinformatics and Computational Biology","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39875262","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: 0

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":null,"pages":null},"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":null,"pages":null},"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