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Impact of Integrated Omics Technologies for Identification of Key Genes and Enhanced Artemisinin Production in Artemisia annua L.
Published in Tariq Aftab, M. Naeem, M. Masroor, A. Khan, Artemisia annua, 2017
Shashi Pandey-Rai, Neha Pandey, Anjana Kumari, Deepika Tripathi, Sanjay Kumar Rai
Genomics is the systematic study of an organism’s genome with the help of molecular tools. Traditionally, genes have been analyzed individually, but microarray technology has advanced substantially in recent years. Various steps of genome analysis involve (1) genome sequencing, (2) identification of repetitive as well as unique sequences, (3) gene prediction, (4) identification of functional expressed sequence tags (ESTs) and complementary DNA (cDNA) sequences, and (5) genome annotation and gene location/gene mapping. Recently, DNA microarray techniques have evolved as a powerful tool, which has the potential to measure differences in DNA sequences between individuals and the expression of thousands of genes simultaneously.
DNA methylation analysis using bisulfite sequencing data
Published in Altuna Akalin, Computational Genomics with R, 2020
The regions of interest obtained through differential methylation or segmentation analysis often need to be integrated with genome annotation datasets. Without this type of integration, differential methylation or segmentation results will be hard to interpret in biological terms. The most common annotation task is to see where regions of interest land in relation to genes and gene parts and regulatory regions: Do they mostly occupy promoter, intronic or exonic regions? Do they overlap with repeats? Do they overlap with other epigenomic markers or long-range regulatory regions? These questions are not specific to methylation −nearly all regions of interest obtained via genome-wide studies have to deal with such questions. Thus, there are already multiple software tools that can produce such annotations. One is the Bioconductor package genomation13(Akalin et al., 2015). It can be used to annotate DMRs/DMCs and it can also be used to integrate methylation proportions over the genome with other quantitative information and produce meta-gene plots or heatmaps. Below, we are reading a BED file for transcripts and using that to annotate DMCs with promoter/intron/exon/intergenic annotation. The genomation::readTranscriptFeatures() function reads a BED12 file, calculates the coordinates of promoters, exons, and introns and the subsequent function uses that information for annotation.
Identification and validation of a novel prognostic circadian rhythm-related gene signature for stomach adenocarcinoma
Published in Chronobiology International, 2023
Lei Qian, Xiaochen Ding, Xiaoyan Fan, Shisen Li, Yihuan Qiao, Xiaoqun Zhang, Jipeng Li
Gene set functional annotation was performed using the R package “clusterProfiler.” This package supports functional characteristics of both coding and non-coding genomic data for thousands of species with up-to-date gene annotation. Specifically, biological terms from Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to annotate the biological function of the given gene set. GO analysis can reveal details about how genes work, whereas KEGG pathway analysis reveals the potential signaling pathways present in a genome. Overall, the symbols representing the examined genes were analyzed using the Database for Annotation, Visualization and Integrated Discovery tool while selecting Homo sapiens as the target species. Then, the functional annotation tools GO-BP-DIRECT, GO-CC-DIRECT, GO-MF-DIRECT, and KEGG PATHWAY were selected, using default settings for all other parameters.
High throughput genome scale modeling predicts microbial vitamin requirements contribute to gut microbiome community structure
Published in Gut Microbes, 2022
Juan P. Molina Ortiz, Mark Norman Read, Dale David McClure, Andrew Holmes, Fariba Dehghani, Erin Rose Shanahan
In our analysis, we find that 43 to 74% of strains in our sample can synthesize one of the assessed vitamins. A previous analysis based on genome annotation reported similar outcomes (40–65%).20 Although similar, our analysis also allowed us to identify vitamins that are non-nutrients for a portion of strains. Furthermore, thanks to the granular level of control GSMs offer over culture media, we can confidently assert that tested strains had no access to any of the vitamins, or analogues, when these were being examined, complicated to guarantee during in vitro assessments. Importantly, however, our results are highly dependent on the nutritional contexts generated from our UDM and alterations to such will change the observed outcomes. Nevertheless, GEMNAST can be applied to explore new nutritional contexts as GSMs readily allow modeling of diets with detailed nutrient composition.
Transcriptomic Profiling of Circulating HLA-DR– Myeloid Cells, Compared with HLA-DR+ Myeloid Antigen-presenting Cells
Published in Immunological Investigations, 2021
Reem Saleh, Rowaida Z Taha, Varun Sasidharan Nair, Salman M Toor, Nehad M Alajez, Eyad Elkord
Quality-trimmed pair end reads were aligned to the hg19 human reference genome in CLC Genomics Workbench 12 (Qiagen, Hilden, Germany) with default settings, as previously described (Sasidharan Nair et al. 2020b; Vishnubalaji et al. 2019). The abundance of the transcript expression was determined as the score of Transcripts Per Million (TPM). Differential gene expression analysis, hierarchical clustering, and Principal component analysis (PCA) were performed on expression data, as we have previously described (Shaath et al. 2019), using 2.0-fold change and P value cutoff <0.05. Ingenuity Pathways Analysis (IPA) software (Ingenuity Systems; www.ingenuity.com) was used to obtain canonical pathways and functional regulatory networks of differentially expressed genes, as previously described (Vishnubalaji et al. 2019). Additionally, functional gene annotation analyses were performed on Database for Annotation, visualization, and Integrated Discovery (DAVID) platform, as previously described (Sasidharan Nair et al. 2020a). “KEGG pathway” and “BioCarta” were obtained from significantly up/downregulated genes using DAVID platform. For heatmaps, the Z-score was calculated, as previously described (Malone et al. 2011), to show the fold change of each gene in CD33+HLA-DR– myeloid cells, compared to CD33+HLA-DR+ APCs. Protein–protein interaction (PPI) networks among the significantly up/downregulated genes were determined by web-based online tool, STRING V11.0 (http://string-db.org) (Szklarczyk et al. 2015).