e SAM alignment was normalized to reduce high coverage specifically in the rRNA gene region

e SAM alignment was normalized to reduce high coverage specifically in the rRNA gene region followed by consensus generation applying the samtools mpile up and bcftools [19]. The draft mitogenome assembly was annotated and used for phylogenetic analysis as previously described [1].2.5. Annotation of unigenes The protein coding sequences have been extracted employing TransDecoder v.5.5.0 followed by clustering at 98 protein similarity working with cdhit v4.7 (-g 1 -c 98). The non-redundant predicted protein dataset was annotated applying eggNOG mapper (evolutionary genealogy of genes: Non-supervised Orthologous Groups) having a minimum E-value of 0.001. Functional annotation of unigenes was executed by mapping against the 3 PKCδ Synonyms databases, GO (Gene Ontology), KEGG (Kyoto Encyclopedia of Genes and Genomes) and COG (the Clusters of Orthologous Groups).Ethics Statement All experiments comply with all the ARRIVE suggestions and were carried out in accordance with the U.K. Animals (Scientific Procedures) Act, 1986 and linked suggestions, EU Directive 2010/63/EU for animal experiments, or the National Institutes of Health guide for the care and use of Laboratory animals (NIH Publications No. 8023, revised 1978).Declaration of Competing Interest The authors declare that they have no recognized competing financial interests or individual relationships which have or may be perceived to possess influenced the operate reported within this article.M.M.L. Lau, L.W.K. Lim and H.H. Chung et al. / Information in Short 39 (2021)CRediT Author Statement Melinda Mei Lin Lau: Writing original draft, Information curation, Conceptualization; Leonard Whye Kit Lim: Information curation, Writing original draft, Conceptualization; Hung Hui Chung: Conceptualization, Funding acquisition, Writing critique editing; Han Ming Gan: Methodology, Conceptualization, Writing evaluation editing.Acknowledgments The perform was funded by Sarawak Research and Development Council by way of the Analysis Initiation Grant Scheme with grant number RDCRG/RIF/2019/13 awarded to H. H. Chung.
nature/scientificreportsOPENA machine learning framework for predicting drug rug interactionsSuyu Mei1 Kun Zhang2Understanding drug rug interactions is an important step to decrease the danger of adverse drug events before clinical drug co-prescription. Current methods, frequently integrating heterogeneous information to raise model performance, frequently suffer from a high model complexity, As such, the way to elucidate the molecular mechanisms underlying drug rug interactions though preserving rational biological interpretability is often a challenging task in computational modeling for drug discovery. In this study, we try to investigate drug rug interactions via the associations between genes that two drugs target. For this objective, we propose a basic f drug target profile representation to depict drugs and drug pairs, from which an l2-regularized logistic regression model is built to predict drug rug interactions. Moreover, we define several statistical metrics inside the context of human proteinprotein interaction networks and signaling mTOR Biological Activity pathways to measure the interaction intensity, interaction efficacy and action range amongst two drugs. Large-scale empirical research such as each cross validation and independent test show that the proposed drug target profiles-based machine studying framework outperforms current data integration-based approaches. The proposed statistical metrics show that two drugs easily interact in the cases that they target widespread genes; or their target genes