ETENLNC: An end to end lncRNA identification and analysis framework to facilitate construction of known and novel lncRNA regulatory networks.

Long non-coding RNAs (lncRNAs) play crucial roles in the regulation of gene expression and maintenance of genomic integrity through various interactions with DNA, RNA, and proteins. The availability of large-scale sequence data from various high-throughput platforms has opened possibilities to identify, predict, and functionally annotate lncRNAs. As a result, there is a growing demand for an integrative computational framework capable of identifying known lncRNAs, predicting novel lncRNAs, and inferring the downstream regulatory interactions of lncRNAs at the genome-scale. We present ETENLNC (End-To-End-Novel-Long-NonCoding), a user-friendly, integrative, open-source, scalable, and modular computational framework for identifying and analyzing lncRNAs from raw RNA-Seq data. ETENLNC employs six stringent filtration steps to identify novel lncRNAs, performs differential expression analysis of mRNA and lncRNA transcripts, and predicts regulatory interactions between lncRNAs, mRNAs, miRNAs, and proteins. We benchmarked ETENLNC against six existing tools and optimized it for desktop workstations and high-performance computing environments using data from three different species. ETENLNC is freely available on GitHub: https://github.com/EvolOMICS-TU/ETENLNC.

Integrative ceRNA network analysis identifies unique and shared molecular signatures in Bipolar Disorder and Schizophrenia.

Bipolar Disorder (BPD) and Schizophrenia (SCZ) are complex psychiatric disorders with shared symptomatology and genetic risk factors. Understanding the molecular mechanisms underlying these disorders is crucial for refining diagnostic criteria and guiding targeted treatments. In this study, publicly available RNA-seq data from post-mortem samples of the basal ganglia's striatum were analyzed using an integrative computational approach to identify differentially expressed (DE) transcripts associated with SCZ and BPD. The analysis aimed to reveal both shared and distinct genes and long non-coding RNAs (lncRNAs) and to construct competitive endogenous RNA (ceRNA) networks within the striatum. Furthermore, the functional implications of these identified transcripts are explored, alongside their presence in established databases such as BipEx and SCHEMA. A significant outcome of our analysis was the identification of 21 DEmRNAs and 1 DElncRNA shared between BPD and SCZ across the Caudate, Putamen, and Nucleus Accumbens. Another noteworthy finding was the identification of Hub nodes within the ceRNA networks that were linked to major psychosis. Particularly, MED19, HNRNPC, MAGED4B, KDM5A, GOLGA7, CHASERR, hsa-miR-4778-3p, hsa-miR-4739, and hsa-miR-4685-5p emerged as potential biomarkers. These findings shed light on the common and unique molecular signatures underlying BPD and SCZ, offering significant potential for the advancement of diagnostic and therapeutic strategies tailored to these psychiatric disorders.

Integrative network analysis identifies differential regulation of neuroimmune system in Schizophrenia and Bipolar disorder.

Background: Neuropsychiatric disorders such as Schizophrenia (SCZ) and Bipolar disorder (BPD) pose a broad range of problems with different symptoms mainly characterized by some combination of abnormal thoughts, emotions, behaviour, etc. However, in depth molecular and pathophysiological mechanisms among different neuropsychiatric disorders have not been clearly understood yet. We have used RNA-seq data to investigate unique and overlapping molecular signatures between SCZ and BPD using an integrative network biology approach. Methods: RNA-seq count data were collected from NCBI-GEO database generated on post-mortem brain tissues of controls (n = 24) and patients of BPD (n = 24) and SCZ (n = 24). Differentially expressed genes (DEGs) were identified using the consensus of DESeq2 and edgeR tools and used for downstream analysis. Weighted gene correlation networks were constructed to find non-preserved (NP) modules for SCZ, BPD and control conditions. Topological analysis and functional enrichment analysis were performed on NP modules to identify unique and overlapping expression signatures during SCZ and BPD conditions. Results: We have identified four NP modules from the DEGs of BPD and SCZ. Eleven overlapping genes have been identified between SCZ and BPD networks, and they were found to be highly enriched in inflammatory responses. Among these eleven genes, TNIP2, TNFRSF1A and AC005840.1 had higher sum of connectivity exclusively in BPD network. In addition, we observed that top five genes of NP module from SCZ network were downregulated which may be a key factor for SCZ disorder. Conclusions: Differential activation of the immune system components and pathways may drive the common and unique pathogenesis of the BPD and SCZ.