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Morphine addiction-NAc_RNA sequencing

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NIAID Data Ecosystem2026-03-13 收录
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https://www.ncbi.nlm.nih.gov/sra/SRP367828
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The current study aimed at addressing two questions: 1) How the expression of key lncRNAs is altered in the NAc during the morphine-induced addiction? 2) Which is/are the target gene(s) and what is/are the regulatory mechanism(s) of gene(s) in response to the candidate lncRNAs? To answer these two questions, the morphine addiction model was first established by using the conditioned place preference (CPP) paradigm. Then, the aberrant expression of lncRNAs was identified in the NAc tissue by RNA-sequencing. Overall design: The NAc tissues from C57BL/6N mice from the two groups that were subjected to morphine or saline training CPP model were harvested; three samples (6 mice) from each group were used for total RNA isolation. RNA-seq was performed by BioMarker (Beijing, China). In short, following RNA quantification and qualification, RNA integrity was examined with the RNA Nano 6000 Assay Kit and the Agilent Bioanalyzer 2100 library preparation for RNA sequencing system (Agilent Technologies, CA, USA). Any library preparations in this study were sequenced on an an Illumina platform. The known lncRNAs were differentiated from the assembled transcripts if the sequencing species has known lncRNA annotations. The remaining transcripts(unknown transcripts) were used to screen for putative lncRNAs.Three computational approaches include CPC/CNCI/Pfam/CPAT were combined to sort non-protein codingRNA candidates from putative protein-coding RNAs in the unknown transcripts. Putative protein-coding RNAs were filtered out using aminimum length and exon number threshold. Transcripts with lengths more than 200 nt and have more than two exons were selected as lncRNA candidates and further screened using CPC/CNCI/Pfam/CPAT that have the power to distinguish the protein-coding genes from the non-coding genes. As well as the different types of lncRNAs include lincRNA, intronic lncRNA, anti-sense lncRNA ,sense lncRNA were selected using gffcompare.
创建时间:
2022-07-10
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