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Influence of Artificial Intelligence in Clinical and Genomic Diagnostics
Published in P. Kaliraj, T. Devi, Artificial Intelligence Theory, Models, and Applications, 2021
E. Kiruba Nesamalar, J. Satheeshkumar, T. Amudha
Once the DNA set has been collected, pre-processing is said to be performed for further analysis. First, DNA sequencing is defined as the process to determine nucleotide order in a specific DNA molecule that performs in order to understand its functions and its effects on an organism. It also involves alignment and merging of fragments to reconstruct the DNA into smaller pieces, so that genome can be analyzed easily. Second, the process of genome assembly takes a considerable quantity of small DNA strand sequences from DNA sequencing and puts them together to create the actual chromosomes from where the DNA originated. Next, binning is an unsupervised approach of clustering DNA which reads sequences into an individual genome by appropriate ML methods to identify feature patterns of DNA sequences. AI can be implemented in various subsets to have an incredible value to the genomic industry. Regulatory genomics predicts the enhancement of gene expression, functional genomics helps in classification of mutants in functional and classification of activity, and structural genomics helps to classify structures of protein structures and make connection establishment with it. The analysis of the DNA phase brings discoveries and knowledge of genomes that can be applied in relevant situations. Gene prediction refers to the process of identifying regions of DNA genomes that are used for encoding and also includes protein sequences such as RNA genes which is useful for the predictions of functional DNA elements. Gene annotation is the process of identifying the location of genes and their relevant code in a specific genome that may process the possible function of genes. Finally, gene alignment is a way of arranging the DNA or protein sequences that is useful for identifying regions based on the types of genomic analysis and alignment based on the structure. It is considered to be the final step of DNA analysis that makes discoveries of genomic data [3].
Nanobubble technology applications in environmental and agricultural systems: Opportunities and challenges
Published in Critical Reviews in Environmental Science and Technology, 2023
Kyle Rafael Marcelino, Li Ling, Sumeth Wongkiew, Hua Thai Nhan, K. C. Surendra, Ty Shitanaka, Hui Lu, Samir Kumar Khanal
NB research should expand to the biological frontier, analyzing changes in microbial communities, gene regulation, and biomolecular interactions through metagenomics, proteomics, and metabolomics. Mapping gene expression in agriculture, especially for plants and fish, is critical for understanding how NB affect underlying biological processes. Studies should focus on functional gene prediction, microbial shotgun sequencing, gene expressions, and labeling isotope studies of specific biomarkers. Additionally, future studies should be carried out to track nutrient uptake over time to optimize and further predict biological responses when NB-aeration is applied. For example, radiolabeled carbon and oxygen could elucidate the pathways NB traverse in specific aquaculture or other agriculture applications.
miR-30d-5p represses the proliferation, migration, and invasion of lung squamous cell carcinoma via targeting DBF4
Published in Journal of Environmental Science and Health, Part C, 2021
Yitian Qi, Yi Hou, Liangchen Qi
Isoform expression quantification data of miRNA-seq and HTSeq-Counts data of RNA-seq associated with LUSC were downloaded from TCGA (https://portal.gdc.cancer.gov/) on December 1, 2019. In total, miRNA data including 45 adjacent normal tissue samples and 473 cancer tissue samples were obtained, while mRNA data consisted of 49 adjacent normal tissue samples and 497 cancer samples. R package edgeR was used to perform differential analysis (|logFC|>2, FDR < 0.05) to obtain differentially expressed miRNAs and mRNAs. The edgeR was also used for normalization. The mirDIP (http://ophid.utoronto.ca/mirDIP/index.jsp#r), miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/index.php), miRDB (http://mirdb.org/), starBase (http://starbase.sysu.edu.cn/), and TargetScan (http://www.targetscan.org/vert_72/) databases were used for predicting the target gene of the miRNA of interest. The database parameters of miRTarBase, miRDB, and TargetScan were based on the default settings for target gene prediction. The parameter of the mirDIP database was MinimumScore:VeryHigh. The thresholds of starBase were clipExpNum = 1, degraExpNum = 0, pancancerNum = 0, programNum = 1, program = None, target = all, and cellType = all. Then, the predicted results were intersected with upregulated differentially expressed mRNAs. The R language cor function was utilized for correlation calculation, and the correlation coefficient was calculated with “Pearson” by default.
Enhanced shortcut nitrogen removal and metagenomic analysis of functional microbial communities in a double sludge system treating ammonium-rich wastewater
Published in Environmental Technology, 2020
Jia Miao, Yunhong Shi, Danfei Zeng, Guangxue Wu
To analyse the functional genes and metabolic pathways, the Illumina HiSeq platform was applied to analyse the metagenome of the activated sludge [25]. After sequencing, the raw reads were optimized by quality control (splitting, cutting and pollution removal) to get clean reads. Then the clean reads were used for assembly and gene prediction [26]. The obtained genes were annotated and classified by functions and species using the Non-redundant (NR) database, the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.