BMT: Bioinformatics mini toolbox for comprehensive DNA and protein analysis

Iqbal, Muhammad Nasir; Rasheed, Muhammad; Awais, Muhammad; Chammam, Wathek; Kanwal, Sumaira; Khan, Sami Ullah; Saddick, Salina; Tlili, Iskander

doi:10.1016/j.ygeno.2020.08.010

Cited by 13 publications

(11 citation statements)

References 39 publications

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“…Contemporary, the rapid development of bioinformatics approaches enabled us to gain a deeper understanding of the pathobiology biology of diseases in the genetic perspective [13]. The methods of bioinformatics including gene sequence analysis, RNA structure prediction, molecular evolution direction, signal pathway direction research, gene expression regulation analysis and biological network analysis [14]. Nowadays, bioinformatics analysis is increasingly applied in the eld of non-tumor diseases, which was more used in the cancer and oncology research previously.…”

Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Screening and identification of the potential diagnostic genes for Parkinson's Disease based on Machine Learning algorithm

Peng

Chen

et al. 2023

Preprint

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Background: Parkinson’s disease (PD) is one of the common neurodegenerative diseases, which have an increasingly morbidity rate, and the genetic markers of PD are receiving increasing attention currently. Method: From the Gene Expression Omnibus (GEO) database, we downloaded five array datasets which from the brain tissue of patients with PD and healthy people. We screened the differentially expressed genes (DEGs) between PD and healthy control samples while performed intersection analysis with Weighted Gene Coexpression Network Analysis (WGCNA) genes. Then three machine learning algorithms were employed the least absolute shrinkage and selection operator (LASSO) regression, supported vector machine recursive feature elimination (SVM-RFE) and the Random Forest (RF) analyze to selected the hub gene of PD. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of hub gene. Subsequently, the reverse transcription quantitative polymerase chain reaction (RT-qPCR) experimentwas utilized to confirm the expression of hub gene based on the clinic samples. Finally, the correlations between the expression of hub gene and immune cell ratios were investigated using the CIBERSORT algorithm. Results: Based on the GEO database, 254 overlapping genes were screened from DEG and the blue modules of WGCNA. Functional enrichment analysis showed that these candidate disease genes were associated with PD and might participated in the progression of PD through the MAPK signaling pathway. Two hub genes (EAF2 and HTR1F) were subsequently selected through machine learning algorithms, and the ROC analysis showed that the area under curve (AUC) of both genes were all greater than 0.7 in the experimental datasets and validation datasets. Afterwards, the results of the RT-qPCR experiments demonstrated that the mRNA expression of EAF2 and HTR1F were lower in patients with PD, which consistent with the gene expression results in the GEO dataset. Finally, CIBERSORT analysis indicated the changes of the immune environment in PD patients, and revealed the types of immune cells associated with the expression of EAF2and HTR1F. Conclusion: Two hub genes of PD, EAF2 and HTR1F, were identified by machine learning algorithms, which might provide a significant target for potential diagnosis and personalized treatment of PD in the future.

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Section: Introductionmentioning

confidence: 99%

WITHDRAWN: Screening and identification of the potential diagnostic genes for Parkinson's Disease based on Machine Learning algorithm

Peng

Chen

et al. 2023

Preprint

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“…Many tools are available to carry out primary DNA and protein analysis, and most of them are compatible with various platforms, and many remain undetected. 25 However, the different bioinformatics tools preserved a complicated sequence of uses; thus, the designation of biomarker could not be handled more efficiently. Hence, we constructed the identification of virus ssDNA probe started with analysis with Tools for bioinformatics have been of significant relevance and were used in various fields of life sciences.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the bioinformatics tools were very beneficial for such new and emerging research studies as an accessory or alternative tool. Furthermore, experimental technologies include time and cost implications, so that bioinformatics tools as an alternative methodology complement biologists in their research studies to predict protein structures and their functions 25 …”

Section: Introductionmentioning

confidence: 99%

“…There were vast wide opportunities on multiple constructive sequences of DNA probes for biosensing application. Many tools were available to carry out primary DNA and protein analysis, and most of them were compatible with various platforms, and many remain undetected 25 . However, the different bioinformatics tools preserved a complicated sequence of uses; thus, the designation of biomarker could not be handled more efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…Fur-thermore, experimental technologies include time and cost implications, so that bioinformatics tools as an alternative methodology complement biologists in their research studies to predict protein structures and their functions. 25 The short length of virus ssDNA-designed results in higher stability during the hybridization process. The hybridization process is responsible for detecting the target sequences of the virus caused by HPV 18 and 58.…”

Section: Introductionmentioning

confidence: 99%

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Designing DNA probe from HPV 18 and 58 in the E6 region for sensing element in the development of genosensor‐based gold nanoparticles

Jaapar

Parmin

Halim

et al. 2021

Biotech and App Biochem

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The E6 region has higher protuberant probability annealing than consensus probe focusing on another region in the human papillomavirus (HPV) genome in terms of detection and screening method. Here, we designed the first multiple virus single‐stranded deoxyribonucleic acid (ssDNA) for multiple detections in an early phase of screening for cervical cancer in the E6 region and became a fundamental evolution of detection electrochemical HPV biosensor. Gene profiling of the virus ssDNA sequences has been carried by high‐end bioinformatics tools such as GenBank, Basic Local Alignment Searching Tools (BLAST), and Clustal OMEGA in a row. The output from bioinformatics tools resulted in 100% of similarities between our virus ssDNA probe and HPV complete genome in the databases. The cross‐validation between HPV genome and our designed virus ssDNA provided high specificity and selectivity during screening methods compared with Pap smear. The DNA probe for HPV 18, 5′ COOH‐GAT CCA GAA GGT ACA GAC GGG GAG GGC ACG 3′, while 5′COOH‐GGG CGC TGT GCA GTG TGT TGG AGA CCC CGA3′ as DNA probe for HPV 58 designed with 66.77% guanine (G) and cytosine (C) content for both. Our virus ssDNA probe for the HPV biosensor promises high sensitivity, specificity, selectivity, repeatability, low fluid consumption, and will be useful in mini‐size diagnostic devices for cervical cancer detection.

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In silico discovery of potential androgen receptor and cytochrome P450 17A1 inhibitors from Camellia sinensis for prostate cancer treatment

Salaam,

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et al. 2024

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BMT: Bioinformatics mini toolbox for comprehensive DNA and protein analysis

Cited by 13 publications

References 39 publications

WITHDRAWN: Screening and identification of the potential diagnostic genes for Parkinson's Disease based on Machine Learning algorithm

WITHDRAWN: Screening and identification of the potential diagnostic genes for Parkinson's Disease based on Machine Learning algorithm

Designing DNA probe from HPV 18 and 58 in the E6 region for sensing element in the development of genosensor‐based gold nanoparticles

In silico discovery of potential androgen receptor and cytochrome P450 17A1 inhibitors from Camellia sinensis for prostate cancer treatment

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