Journal of Integrative Bioinformatics最新文献

{"title":"Special Issue of the 1st International Applied Bioinformatics Conference (iABC'21).","authors":"Jens Allmer, Mourad Elloumi, Matteo Comin, Ralf Hofestädt","doi":"10.1515/jib-2021-0042","DOIUrl":"https://doi.org/10.1515/jib-2021-0042","url":null,"abstract":"Diseases can be tied to changes at the molecular level within affected cells. This can be concerning transcription, translation, or any other mechanism involved in gene expression, such as post-transcriptional regulation. Instrumentation for the measurement of such molecular changes is readily available and produces large amounts of data. For example, DNA and RNA sequencing, as well as protein quantitation, and sequencing can be achieved via next-generation sequencing andmass spectrometry, respectively. One current challenge is the analysis and integration of the resulting heterogeneous and large datasets. Bioinformatics is the field of study which produces algorithms and integrative approaches to attempt suchdata analyses. The primary aim in algorithmic bioinformatics is, however, the development of algorithms and not their application. Typically, novel algorithms are introduced with a proof of principle, and they are applied to some data for that purpose, but usually not comprehensively. Their data might slightly differ from the proof of principle, inducing further data analysis challenges. Additionally, applying such algorithms to their data may be involved for researchers from the biomedical domain. The 1st International Applied Bioinformatics Conference was conceived to bring together representatives from all research fields involved to increase knowledge transfer. First planned for 2020 and then deferred to 2021 due to the pandemic caused by the Coronavirus [1], the conference was held online. Despite the virtual nature of the conference, attentionwas great.We receivedmany goodmanuscripts and invited a few to submit their full versions to this special issue. The range of topics was extensive, but many submissions concerned the interface of bioinformatics and its application. The selected papers for this special issue also discuss various topics such as sequence alignment and gene network reconstruction. The first paper in this special issue concerns a challenging issue in bioinformatics, the usage of pangenomes instead of single reference genomes and offers a fast variation-aware read mapping algorithm [2]. Mapping is also vital to investigate gene expression, which is essential for the secondmanuscript. It discusses how microRNA and mRNA expression profiles can be investigated [3]. From this, modular networks are inferred, describing post-transcriptional regulatory networks. Such networks are challenging to visualize, which is the focus of the third paper [4]. The work summarizes the state-of-the-art in bicluster visualization and is also based on gene expression data. Next, we move from transcriptomics to metabolomics. A disparity filter was applied to perform network analysis for colorectal cancer as a proof of principle [5]. The final two manuscripts focus more on practical application in cancer. First, the prostate, ovary, testes, and embryo","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39729371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the possible effect of miR-203a-3p and miR-29a-3p on <i>DNMT3B</i> and <i>GAS7</i> genes expression.","authors":"Afgar Ali,&nbsp;Sattarzadeh Bardsiri Mahla,&nbsp;Vahidi Reza,&nbsp;Farsinejad Alireza","doi":"10.1515/jib-2021-0016","DOIUrl":"https://doi.org/10.1515/jib-2021-0016","url":null,"abstract":"<p><p>Aberrant expression of genes involved in methylation, including DNA methyltransferase 3 Beta (<i>DNMT3B</i>), can cause hypermethylation of various tumor suppressor genes. In this regard, various molecular factors such as microRNAs can play a critical role in regulating these methyltransferase enzymes and eventually downstream genes such as growth arrest specific 7 (<i>GAS7</i>). Accordingly, in the present study we aimed to predict regulatory effect of miRNAs on <i>DNMT3B</i> and <i>GAS7</i> genes expression in melanoma cell line. hsa-miR-203a-3p and hsa-miR-29a-3p were predicted and selected using bioinformatics software. The Real-time PCR technique was performed to investigate the regulatory effect of these molecules on the <i>DNMT3B</i> and <i>GAS7</i> genes expression. Expression analysis of <i>DNMT3B</i> gene in A375 cell line showed that there was a significant increase compared to control (<i>p</i> value = 0.0015). Analysis of hsa-miR-203a-3p and hsa-miR-29a-3p indicated the insignificant decreased expression in melanoma cell line compared to control (<i>p</i> value < 0.05). Compared to control, the expression of GAS7 gene in melanoma cells showed a significant decrease (<i>p</i> value = 0.0323). Finally, our findings showed that the decreased expression of hsa-miR-203a-3p and hsa-miR-29a-3p can hypothesize that their aberrant expression caused <i>DNMT3B</i> dysfunction, possible methylation of the <i>GAS7</i> gene, and ultimately decreased its expression. However, complementary studies are necessary to definite comment.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9069646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39731426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis.","authors":"Nisar Wani,&nbsp;Debmalya Barh,&nbsp;Khalid Raza","doi":"10.1515/jib-2021-0029","DOIUrl":"https://doi.org/10.1515/jib-2021-0029","url":null,"abstract":"<p><p>Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39640284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics.","authors":"Silvia Sabatini,&nbsp;Amalia Gastaldelli","doi":"10.1515/jib-2021-0030","DOIUrl":"https://doi.org/10.1515/jib-2021-0030","url":null,"abstract":"<p><p>Differential network analysis has become a widely used technique to investigate changes of interactions among different conditions. Although the relationship between observed interactions and biochemical mechanisms is hard to establish, differential network analysis can provide useful insights about dysregulated pathways and candidate biomarkers. The available methods to detect differential interactions are heterogeneous and often rely on assumptions that are unrealistic in many applications. To address these issues, we develop a novel method for differential network analysis, using the so-called disparity filter as network reduction technique. In addition, we propose a classification model based on the inferred network interactions. The main novelty of this work lies in its ability to preserve connections that are statistically significant with respect to a null model without favouring any resolution scale, as a hard threshold would do, and without Gaussian assumptions. The method was tested using a published metabolomic dataset on colorectal cancer (CRC). Detected hub metabolites were consistent with recent literature and the classifier was able to distinguish CRC from polyp and healthy subjects with great accuracy. In conclusion, the proposed method provides a new simple and effective framework for the identification of differential interaction patterns and improves the biological interpretation of metabolomics data.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39635704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>In silico</i> approach to understand epigenetics of POTEE in ovarian cancer.","authors":"Sahar Qazi,&nbsp;Khalid Raza","doi":"10.1515/jib-2021-0028","DOIUrl":"https://doi.org/10.1515/jib-2021-0028","url":null,"abstract":"<p><p>Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog mRNA sequence to identify major motifs and also performed its enrichment analysis. We identified 6 motifs of varying lengths, out of which only three motifs, including CTTCCAGCAGATGTGGATCA, GGAACTGCC, and CGCCACATGCAGGC were most likely to be present in the nucleotide sequence of POTEE. By enrichment and occurrences identification analyses, we rectified the best match motif as CTTCCAGCAGATGT. Since there is no experimentally verified structure of POTEE paralog, thus, we predicted the POTEE structure using an automated workflow for template-based modeling using the power of a deep neural network. Additionally, to validate our predicted model we used AlphaFold predicted POTEE structure and observed that the residual stretch starting from 237-958 had a very high confidence per residue. Furthermore, POTEE predicted model stability was evaluated using replica exchange molecular dynamic simulation for 50 ns. Our network-based epigenetic analysis discerns only 10 highly significant, direct, and physical associators of POTEE. Our finding aims to provide new insights about the POTEE paralog.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39631904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast alignment of reads to a variation graph with application to SNP detection.","authors":"Maurilio Monsu,&nbsp;Matteo Comin","doi":"10.1515/jib-2021-0032","DOIUrl":"https://doi.org/10.1515/jib-2021-0032","url":null,"abstract":"<p><p>Sequencing technologies has provided the basis of most modern genome sequencing studies due to its high base-level accuracy and relatively low cost. One of the most demanding step is mapping reads to the human reference genome. The reliance on a single reference human genome could introduce substantial biases in downstream analyses. Pangenomic graph reference representations offer an attractive approach for storing genetic variations. Moreover, it is possible to include known variants in the reference in order to make read mapping, variant calling, and genotyping variant-aware. Only recently a framework for variation graphs, <i>vg</i> [Garrison E, Adam MN, Siren J, et al. Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9], have improved variation-aware alignment and variant calling in general. The major bottleneck of <i>vg</i> is its high cost of reads mapping to a variation graph. In this paper we study the problem of SNP calling on a variation graph and we present a fast reads alignment tool, named VG SNP-Aware. VG SNP-Aware is able align reads exactly to a variation graph and detect SNPs based on these aligned reads. The results show that VG SNP-Aware can efficiently map reads to a variation graph with a speedup of 40× with respect to <i>vg</i> and similar accuracy on SNPs detection.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39895114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glioblastoma gene network reconstruction and ontology analysis by online bioinformatics tools.","authors":"Natalya V Gubanova,&nbsp;Nina G Orlova,&nbsp;Arthur I Dergilev,&nbsp;Nina Y Oparina,&nbsp;Yuriy L Orlov","doi":"10.1515/jib-2021-0031","DOIUrl":"https://doi.org/10.1515/jib-2021-0031","url":null,"abstract":"<p><p>Glioblastoma is the most aggressive type of brain tumors resistant to a number of antitumor drugs. The problem of therapy and drug treatment course is complicated by extremely high heterogeneity in the benign cell populations, the random arrangement of tumor cells, and polymorphism of their nuclei. The pathogenesis of gliomas needs to be studied using modern cellular technologies, genome- and transcriptome-wide technologies of high-throughput sequencing, analysis of gene expression on microarrays, and methods of modern bioinformatics to find new therapy targets. Functional annotation of genes related to the disease could be retrieved based on genetic databases and cross-validated by integrating complementary experimental data. Gene network reconstruction for a set of genes (proteins) proved to be effective approach to study mechanisms underlying disease progression. We used online bioinformatics tools for annotation of gene list for glioma, reconstruction of gene network and comparative analysis of gene ontology categories. The available tools and the databases for glioblastoma gene analysis are discussed together with the recent progress in this field.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39895113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evaluation study of biclusters visualization techniques of gene expression data.","authors":"Haithem Aouabed,&nbsp;Mourad Elloumi,&nbsp;Rodrigo Santamaría","doi":"10.1515/jib-2021-0019","DOIUrl":"https://doi.org/10.1515/jib-2021-0019","url":null,"abstract":"<p><p><i>Biclustering</i> is a non-supervised data mining technique used to analyze gene expression data, it consists to classify subgroups of genes that have similar behavior under subgroups of conditions. The classified genes can have independent behavior under other subgroups of conditions. Discovering such co-expressed genes, called <i>biclusters</i>, can be helpful to find specific biological features such as gene interactions under different circumstances. Compared to clustering, biclustering has two main characteristics: <i>bi-dimensionality</i> which means grouping both genes and conditions simultaneously and <i>overlapping</i> which means allowing genes to be in more than one bicluster at the same time. Biclustering algorithms, which continue to be developed at a constant pace, give as output a large number of overlapping biclusters. Visualizing groups of biclusters is still a non-trivial task due to their overlapping. In this paper, we present the most interesting techniques to visualize groups of biclusters and evaluate them.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709740/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39558960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Specifications of standards in systems and synthetic biology: status and developments in 2021.","authors":"Falk Schreiber, Padraig Gleeson, Martin Golebiewski, Thomas E Gorochowski, Michael Hucka, Sarah M Keating, Matthias König, Chris J Myers, David P Nickerson, Björn Sommer, Dagmar Waltemath","doi":"10.1515/jib-2021-0026","DOIUrl":"10.1515/jib-2021-0026","url":null,"abstract":"<p><p>This special issue of the <i>Journal of Integrative Bioinformatics</i> contains updated specifications of COMBINE standards in systems and synthetic biology. The 2021 special issue presents four updates of standards: Synthetic Biology Open Language Visual Version 2.3, Synthetic Biology Open Language Visual Version 3.0, Simulation Experiment Description Markup Language Level 1 Version 4, and OMEX Metadata specification Version 1.2. This document can also be consulted to identify the latest specifications of all COMBINE standards.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39540482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic biology open language visual (SBOL visual) version 3.0.","authors":"Hasan Baig,&nbsp;Pedro Fontanarossa,&nbsp;James McLaughlin,&nbsp;James Scott-Brown,&nbsp;Prashant Vaidyanathan,&nbsp;Thomas Gorochowski,&nbsp;Goksel Misirli,&nbsp;Jacob Beal,&nbsp;Chris Myers","doi":"10.1515/jib-2021-0013","DOIUrl":"https://doi.org/10.1515/jib-2021-0013","url":null,"abstract":"<p><p>People who engineer biological organisms often find it useful to draw diagrams in order to communicate both the structure of the nucleic acid sequences that they are engineering and the functional relationships between sequence features and other molecular species. Some typical practices and conventions have begun to emerge for such diagrams. SBOL Visual aims to organize and systematize such conventions in order to produce a coherent language for expressing the structure and function of genetic designs. This document details version 3.0 of SBOL Visual, a new major revision of the standard. The major difference between SBOL Visual 3 and SBOL Visual 2 is that diagrams and glyphs are defined with respect to the SBOL 3 data model rather than the SBOL 2 data model. A byproduct of this change is that the use of dashed undirected lines for subsystem mappings has been removed, pending future determination on how to represent general SBOL 3 constraints; in the interim, this annotation can still be used as an annotation. Finally, deprecated material has been removed from collection of glyphs: the deprecated \"insulator\" glyph and \"macromolecule\" alternative glyphs have been removed, as have the deprecated BioPAX alternatives to SBO terms.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8560346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39532713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}