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Long non-coding RNAs (lncRNAs) are a recently discovered group of non-coding RNAs that play a crucial role in the regulation of various human diseases, especially in the study of nervous system diseases which has garnered significant attention. However, there is limited knowledge on the identification and function of lncRNAs in hepatolenticular degeneration (HLD). The objective of this study was to identify novel lncRNAs and determine their involvement in the networks associated with HLD. We conducted a comprehensive analysis of RNA sequencing (RNA-seq) data, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and computational biology to identify novel lncRNAs and explore their potential mechanisms in HLD. We identified 212 differently expressed lncRNAs, with 98 upregulated and 114 downregulated. Additionally, 32 differently expressed mRNAs were found, with 15 upregulated and 17 downregulated. We obtained a total of 1131 pairs of co-expressed lncRNAs and mRNAs by Pearson correlation test and prediction and annotation of the lncRNA-targeted miRNA-mRNA network. The differential lncRNAs identified in this study were found to be involved in various biological functions and signaling pathways. These include translational initiation, motor learning, locomotors behavior, dioxygenase activity, integral component of postsynaptic membrane, neuroactive ligand-receptor interaction, nuclear factor-kappa B (NF-κB) signaling pathway, cholinergic synapse, sphingolipid signaling pathway, and Parkinson's disease signaling pathway, as revealed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Six lncRNAs, including XR_001782921.1 (P < 0.01), XR_ 001780581.1 (P < 0.01), ENSMUST_00000207119 (P < 0.01), XR_865512.2 (P < 0.01), TCONS_00005916 (P < 0.01), and TCONS_00020683 (P < 0.01), showed significant differences in expression levels between the model group and normal group by RT-qPCR. Among these, four lncRNAs (TCONS_00020683, XR_865512.2, XR_001780581.1, and ENSMUST00000207119) displayed a high degree of conservation. This study provides a unique perspective for the pathogenesis and therapy of HLD by constructing the lncRNA-miRNA-mRNA network. This insight provides a foundation for future exploration in this field.
Pubmed ID: 37759104 RIS Download
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A 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).
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented January 29, 2018.
From website: "Note that the eXpress software is also no longer being developed. We recommend you use kallisto instead." Kallisto can be found at http://pachterlab.github.io/kallisto/.
Software for streaming quantification for high-throughput DNA/RNA sequencing.
Can be used in any application where abundances of target sequences need to be estimated from short reads sequenced from them.
Software Java pipeline for trimming tasks for Illumina paired end and single ended data. Flexible Trimmer for Illumina Sequence Data. Pair aware preprocessing tool optimized for Illumina next generation sequencing data. Includes several processing steps for read trimming and filtering. Operating systems Unix/Linux, Mac OS, Windows.
View all literature mentionsScript distributed with the HT-Seq Python framework for processing RNA-seq or DNA-seq data.
View all literature mentionsSoftware tool for transcriptome assembly and differential expression analysis for RNA-Seq. Includes script called cuffmerge that can be used to merge together several Cufflinks assemblies. It also handles running Cuffcompare as well as automatically filtering a number of transfrags that are likely to be artifacts. If the researcher has a reference GTF file, the researcher can provide it to the script to more effectively merge novel isoforms and maximize overall assembly quality.
View all literature mentionsGraph-based alignment of next generation sequencing reads to a population of genomes.
View all literature mentionsThe main purpose of Cufflinks.cuffmerge is to merge together several Cufflinks assemblies, making it easier to produce an assembly GTF file suitable for use with Cufflinks.cuffdiff. Cufflinks.cuffmerge also runs Cuffcompare in the background and automatically filters out transcribed fragments (transfrags) that are likely to be artifacts. Trapnell C, Hendrickson D,Sauvageau S, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature Biotechnology. 2013;31:46-53.
View all literature mentionsSoftware application for assembling of RNA-Seq alignments into potential transcripts. It enables improved reconstruction of a transcriptome from RNA-seq reads. This transcript assembling and quantification program is implemented in C++ .
View all literature mentionsThe SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
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