A Bioinformatics Tutorial for Comparative Development Genomics in Diverse Meiofauna

Sevigny, Joseph L.; Norenburg, Jon L.; Leasi, Francesca

doi:10.1007/978-1-0716-0974-3_19

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Phospholipids include platelet-activating factor (PAF), phosphatidylcholine, and lysophosphatidic acid (LPA). Previous studies (31,32) have shown that PAF induces rat fibroblasts to secrete type I and IV collagen and fibronectin tubular cells, leading to renal fibrosis. Furthermore, LPA exerts therapeutic effects on myoblasts and induces significant expression of connective tissue growth factor.…”

Section: Discussionmentioning

confidence: 95%

Bioinformatics analysis of the potential regulatory mechanisms of renal fibrosis and the screening and identification of factors related to human renal fibrosis

Wang¹,

Wu²,

Li³

et al. 2022

Transl Androl Urol

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Background: This paper aimed to identify the key genes and potential mechanisms of renal fibrosis, and provide methods of evaluation and new ideas for the early diagnosis and treatment of renal fibrosis. Methods:The GSE102515 dataset was searched from the Gene Expression Omnibus (GEO) database was searched, the differential genes were screened out, and the down-regulated and up-regulated genes were identified. Enrichment analysis of differential genes in the development of renal fibrosis was carried out using the DAVID database, differential genes were analyzed using the STRING database, and Cytoscape software was used for visual processing.Results: Eighteen up-regulated genes and ten down-regulated genes were screened. Differential genes are mainly involved in the integrin-mediated signaling pathway and mitotic sister chromatid binding, etc. We found that the molecular functions (MFs) of the differential genes are phospholipid binding and regulatory region DNA binding, etc. Moreover, the cellular components (CCs) of the differential genes are mainly related to low-density lipoprotein (LDL) particles and nuclei. Screening revealed that ADM, ARRB1, AVPR2, CCR1, MTNR1A, PTH, and S1PR2 were core genes in the interaction network of renal fibrosis riskrelated proteins.Conclusions: In this study, the differential genes in the occurrence of renal fibrosis were screened out via dataset analysis. It was found that ADM, ARRB1, AVPR2, CCR1, MTNR1A, PTH, and S1PR2 may be important participants in the development of renal fibrosis, which provides analytical support for the identification of valuable markers of renal fibrosis.

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Section: Discussionmentioning

confidence: 95%

Bioinformatics analysis of the potential regulatory mechanisms of renal fibrosis and the screening and identification of factors related to human renal fibrosis

Wang¹,

Wu²,

Li³

et al. 2022

Transl Androl Urol

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“…Previous studies have found that target genes can inhibit the migration, proliferation, and metastasis of colon cancer and thyroid cancer ( 17 ). MiR-135b can promote the proliferation and metastasis of breast cancer, gastric cancer, and other tumors through the effect of target genes, and inhibit the occurrence, development, and metastasis of melanoma and other tumors ( 18 - 20 ).…”

Section: Discussionmentioning

confidence: 99%

The potential mechanisms of neuroblastoma in children based on bioinformatics big data

Chen¹,

Wu²,

Chen³

et al. 2022

Transl Pediatr

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Background: In recent years, miRNAs have become a research hotspot, which is related to the occurrence and development of a variety of malignant tumors, but there are few studies in neuroblastoma. In this study, the differentially expressed microRNAs (miRNAs) in neuroblastoma were identified and analyzed using bioinformatics, and their biological functions and related signaling pathways were examined. Methods:The neuroblastoma miRNA chip GSE121513 was obtained from the Gene Expression Omnibus (GEO) database and the data of 95 neuroblastoma samples and normal fetal adrenal neuroblastoma samples were analyzed to screen the differential miRNAs. The target genes of the differentially expressed miRNAs were predicted using |log fold change (FC)| ≥4. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were performed to construct a protein-protein interaction network and identify the core target genes.Results: A total of 91 differentially expressed miRNAs were identified (P<0.05, |logFC| ≥1), including 52 upregulated and 39 downregulated miRNAs. The target genes of the differential miRNAs (P<0.05, |logFC| ≥4) were pretested, and 602 target genes were obtained. Functional analysis showed that these genes were mainly located in the extracellular matrix region of proteins, and were involved in the negative regulation of cytoplasmic translation, mRNA 3'-untranslated region (UTR) binding, and binding to nucleic acid to inhibit the activity of translation factors. They were also involved in RNA degradation, adhesion pathways, and the phosphatidylinositol-3-kinase (PI3K)-Akt signaling pathway. Ten key target genes were identified via protein interaction network screening. Conclusions:The differential miRNAs may be related to the occurrence of neuroblastoma were screened.

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“…Trimming involves cropping sequence vectors with low quality and removing adapters from the sequences. As per NCBI and the literature, we used Trimmomatic for sequence trimming [28].…”

Section: ) Files Trimmingmentioning

confidence: 99%

A Novel Approach of Transcriptomic microRNA Analysis Using Text Mining Methods: An Early Detection of Multiple Sclerosis Disease

Ali¹,

Shaheen

Mabrouk

et al. 2021

IEEE Access

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Multiple sclerosis is an autoimmune disease that causes psychological impacts and severe physical disabilities, including motor disabilities and partial blindness. This work introduces an early detection method for multiple sclerosis disease by analyzing transcriptomic microRNA data. By transforming this phenotype classification problem into a text mining problem, multiple sclerosis disease biomarkers can be obtained. To our knowledge, text mining methods have not been introduced previously in transcriptomic data analysis of multiple sclerosis disease. Hence, this work presents a complete predictive model by combining consecutive transcriptomic data preprocessing procedures, followed by the proposed KmerFIDF method as a feature extraction method and linear discriminant analysis for dimensionality reduction. Predictive machine learning methods can then be obtained accordingly. This study describes experimental work on a transcriptomic dataset of noncoding microRNA sequences denoted from relapsingremitting multiple sclerosis patients before fingolimod treatment and after six consecutive months of treatment. The experimental results of the predictive methods with the proposed model report sensitivity, specificity, F1-score, and average accuracy scores of 96.4, 96.47, 95.6, and 97% with random forest, 92.89, 92.78, 93.2, and 94% with support vector machine and 91.95, 92.2, 93.1, and 94% with logistic regression, respectively. These promising results support the introduced model and the proposed KmerFIDF method in transcriptomic data analysis. Moreover, comparative experiments are conducted with two referenced studies. The obtained results show that the average reported accuracy scores of the proposed model outperform the referenced literature work.

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A Bioinformatics Tutorial for Comparative Development Genomics in Diverse Meiofauna

Cited by 3 publications

References 23 publications

Bioinformatics analysis of the potential regulatory mechanisms of renal fibrosis and the screening and identification of factors related to human renal fibrosis

Bioinformatics analysis of the potential regulatory mechanisms of renal fibrosis and the screening and identification of factors related to human renal fibrosis

The potential mechanisms of neuroblastoma in children based on bioinformatics big data

A Novel Approach of Transcriptomic microRNA Analysis Using Text Mining Methods: An Early Detection of Multiple Sclerosis Disease

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