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Non-coding RNAs have been shown to play vital roles in muscle development. However, the biological roles of long non-coding natural antisense transcripts, antisense lncRNAs (ASlncRNAs), are largely unknown in embryonic muscle development. Here, we identified a total of 466 ASlncRNAs in the longissimus dorsi muscle. And 48 differentially expressed ASlncRNAs were identified based on pairwise comparisons (P < 0.05), sixteen of which were validated by qPCR. Additionally, 466 ASlncRNAs were predicted to target 335 protein coding genes based on complementary base-pairing. Enrichment analysis suggests that ASlncRNAs may be involved in muscle development by negatively regulating the expression of target genes. Furthermore, 170 ASlncRNAs were identified as potential miRNA precursors, suggesting that these ASlncRNAs may be involved in the regulation of muscle development by producing miRNA precursors. Our results provide a catalog of goat muscle-related ASlncRNAs, and will contribute to a fuller understanding of the roles of ASlncRNAs in muscle development.
Pubmed ID: 35124172
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Software that estimates expression at transcript-level resolution and controls for variability evident across replicate libraries.
View all literature mentionsSoftware tool to rapidly search for short interactions between two long RNAs.
View all literature mentionsA tool to design target-specific primers for polymerase chain reaction (PCR). It uses Primer3 to design PCR primers and then uses BLAST and global alignment algorithm to screen primers against user-selected database in order to avoid primer pairs (all combinations including forward-reverse primer pair, forward-forward as well as reverse-reverse pairs) that can cause non-specific amplifications.
View all literature mentionsA database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).
View all literature mentionsA SVM (support vector machine-based software tool for prediction of most probable microRNA coding regions in genome scale sequences.
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View all literature mentionsBioconductor software package for Empirical analysis of Digital Gene Expression data in R. Used for differential expression analysis of RNA-seq and digital gene expression data with biological replication.
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