LncRTPred: Predicting RNA-RNA mode of interaction mediated by lncRNA.

Long non-coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA-mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real-time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML-based lncRNA-mRNA target prediction model- 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non-interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease-specific lncRNA-mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA-mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.

LncRBase V.2: an updated resource for multispecies lncRNAs and ClinicLSNP hosting genetic variants in lncRNAs for cancer patients.

The recent discovery of long non-coding RNA as a regulatory molecule in the cellular system has altered the concept of the functional aptitude of the genome. Since our publication of the first version of LncRBase in 2014, there has been an enormous increase in the number of annotated lncRNAs of multiple species other than Human and Mouse. LncRBase V.2 hosts information of 549,648 lncRNAs corresponding to six additional species besides Human and Mouse, viz. Rat, Fruitfly, Zebrafish, Chicken, Cow and C.elegans. It provides additional distinct features such as (i) Transcription Factor Binding Site (TFBS) in the lncRNA promoter region, (ii) sub-cellular localization pattern of lncRNAs (iii) lnc-pri-miRNAs (iv) Possible small open reading frames (sORFs) within lncRNA. (v) Manually curated information of interacting target molecules and disease association of lncRNA genes (vi) Distribution of lncRNAs across multiple tissues of all species. Moreover, we have hosted ClinicLSNP within LncRBase V.2. ClinicLSNP has a comprehensive catalogue of lncRNA variants present within breast, ovarian, and cervical cancer inferred from 561 RNA-Seq data corresponding to these cancers. Further, we have checked whether these lncRNA variants overlap with (i)Repeat elements,(ii)CGI, (iii)TFBS within lncRNA loci (iv)SNP localization in trait-associated Linkage Disequilibrium(LD) region, (v)predicted the potentially pathogenic variants and (vi)effect of SNP on lncRNA secondary structure. Overall, LncRBaseV.2 is a user-friendly database to survey, search and retrieve information about multi-species lncRNAs. Further, ClinicLSNP will serve as a useful resource for cancer specific lncRNA variants and their related information. The database is freely accessible and available at http://dibresources.jcbose.ac.in/zhumur/lncrbase2/.

Elucidating the gene regulatory networks modulating cancer stem cells and non-stem cancer cells in high grade serous ovarian cancer.

The origin and pathogenesis of epithelial ovarian cancer have perplexed investigators for decades. The most prevalent type of it is the high-grade serous ovarian carcinoma (HGSOv) which is a highly aggressive disease with high relapse rates and insurgence of chemo-resistance at later stages of treatment. These are driven by a rare population of stem cell like cancer cells called cancer stem cells (CSCs). We have taken up a systems approach to find out the common gene interaction paths between non-CSC tumor cells (CCs) and CSCs in HGSOv. Detailed investigation reveals a set of 17 Transcription Factors (named as pivot-TFs) which can govern changes in the mode of gene regulation along these paths. Overall, this work highlights a divergent road map of functional information relayed by these common key players in the two cell states, which might aid towards designing novel therapeutic measures to target the CSCs for ovarian cancer therapy.

Small RNA sequencing revealed dysregulated piRNAs in Alzheimer's disease and their probable role in pathogenesis.

PIWI-interacting RNAs (piRNAs), ∼23-36 nucleotide-long small non-coding RNAs, earlier believed to be germline-specific, have now been identified in somatic cells including neural cells. However, piRNAs have not yet been studied in the human brain (HB) and Alzheimer's disease (AD)-affected brain. In this study, by next-generation small RNA sequencing, 564 and 451 piRNAs were identified in the HB and AD-affected brain respectively. The majority of the neuronal piRNAs have intronic origin wherein primary piRNAs are mostly from the negative strand. piRNAs originating from the coding sequence of mRNAs and tRNAs are highly conserved compared to other genomic contexts. We found 1923 mRNAs significantly down-regulated in AD as the predicted targets of 125 up-regulated piRNAs. The filtering of targets based on our criteria coupled with pathway enrichment analysis of all the predicted targets resulted in five most significant AD-associated pathways enriched with four genes (CYCS, LIN7C, KPNA6, and RAB11A) found to be regulated by four piRNAs. The qRT-PCR study verified the reciprocal expression of piRNAs and their targets. This study provides the first evidence of piRNAs in the HB and AD which will provide the foundation for future studies to unravel the regulatory role of piRNAs in the human brain and associated diseases. The sequencing data have been submitted to the GEO database (Accession no. GSE85075).