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Background: Long non-coding Ribonucleic Acids (lncRNAs) can be localized to different cellular compartments, such as the nuclear and the cytoplasmic regions. Their biological functions are influenced by the region of the cell where they are located. Compared to the vast number of lncRNAs, only a relatively small proportion have annotations regarding their subcellular localization. It would be helpful if those few annotated lncRNAs could be leveraged to develop predictive models for localization of other lncRNAs. Methods: Conventional computational methods use q-mer profiles from lncRNA sequences and train machine learning models such as support vector machines and logistic regression with the profiles. These methods focus on the exact q-mer. Given possible sequence mutations and other uncertainties in genomic sequences and their role in biological function, a consideration of these variabilities might improve our ability to model lncRNAs and their localization. Thus, we build on inexact q-mers and use machine learning/deep learning techniques to study three specific problems in lncRNA subcellular localization, namely, prediction of lncRNA localization using inexact q-mers, the issue of whether lncRNA localization is cell-type-specific, and the notion of switching (lncRNA) genes. Results: We performed our analysis using data on lncRNA localization across 15 cell lines. Our results showed that using inexact q-mers (with q = 6) can improve the lncRNA localization prediction performance compared to using exact q-mers. Further, we showed that lncRNA localization, in general, is not cell-line-specific. We also identified a category of LncRNAs which switch cellular compartments between different cell lines (we call them switching lncRNAs). These switching lncRNAs complicate the problem of predicting lncRNA localization using machine learning models, showing that lncRNA localization is still a major challenge. 

Abstract Capsicum chinense (habanero pepper) exhibits substantial variation in fruit pungency, color, and flavor due to its rich secondary metabolite composition, including capsaicinoids, carotenoids, and volatile organic compounds (VOCs). To dissect the genetic and regulatory basis of these traits, we conducted an integrative analysis across 244 diverse accessions using metabolite profiling, genome-wide association studies (GWAS), and transcriptome-wide association studies (TWAS). GWAS identified 507 SNPs for capsaicinoids, 304 for carotenoids, and 1176 for VOCs, while TWAS linked gene expression to metabolite levels, highlighting biosynthetic and regulatory genes in phenylpropanoid, fatty acid, and terpenoid pathways. Segmental RNA sequencing across fruit tissues of contrasting accessions revealed 7034 differentially expressed genes, including MYB31, 3-ketoacyl-CoA synthase, phytoene synthase, and ABC transporters. Notably, AP2 transcription factors and Pentatrichopeptide repeat (PPR) emerged as central regulators, co-expressed with carotenoid and VOC biosynthetic genes. High-resolution spatial transcriptomics (Stereo-seq) identified 74 genes with tissue-specific expression that overlap with GWAS and TWAS loci, reinforcing their regulatory relevance. To validate these candidates, we employed CRISPR/Cas9 to knock out AP2 and PPR genes in tomato. Widely targeted metabolomics and carotenoid profiling revealed major metabolic shifts: AP2 mutants accumulated higher levels of β-carotene and lycopene. In contrast, PPR mutants altered xanthophyll ester and apocarotenoid levels, supporting their roles in carotenoid flux and remodeling. This study provides the first integrative GWAS–TWAS–spatial transcriptomics in C. chinense, revealing key regulators of fruit quality traits. These findings lay the groundwork for precision breeding and metabolic engineering to enhance nutritional and sensory attributes in peppers. 

Abstract The lncATLAS database quantifies the relative cytoplasmic versus nuclear abundance of long non-coding RNAs (lncRNAs) observed in 15 human cell lines. The literature describes several machine learning models trained and evaluated on these and similar datasets. These reports showed moderate performance, e.g. 72–74% accuracy, on test subsets of the data withheld from training. In all these reports, the datasets were filtered to include genes with extreme values while excluding genes with values in the middle range and the filters were applied prior to partitioning the data into training and testing subsets. Using several models and lncATLAS data, we show that this ‘middle exclusion’ protocol boosts performance metrics without boosting model performance on unfiltered test data. We show that various models achieve only about 60% accuracy when evaluated on unfiltered lncRNA data. We suggest that the problem of predicting lncRNA subcellular localization from nucleotide sequences is more challenging than currently perceived. We provide a basic model and evaluation procedure as a benchmark for future studies of this problem. 

This is a mini-review capturing the views and opinions of selected participants at the 2021 IEEE BIBM 3rd Annual LncRNA Workshop, held in Dubai, UAE. The views and opinions are expressed on five broad themes related to problems in lncRNA, namely, challenges in the computational analysis of lncRNAs, lncRNAs and cancer, lncRNAs in sports, lncRNAs and COVID-19, and lncRNAs in human brain activity. 

Multiple myeloma is the second most hematological cancer. RUVBL1 and RUVBL2 form a subcomplex of many chromatin remodeling complexes implicated in cancer progression. As an inhibitor specific to the RUVBL1/2 complex, CB-6644 exhibits remarkable anti-tumor activity in xenograft models of Burkitt’s lymphoma and multiple myeloma (MM). In this work, we defined transcriptional signatures corresponding to CB-6644 treatment in MM cells and determined underlying epigenetic changes in terms of chromatin accessibility. CB-6644 upregulated biological processes related to interferon response and downregulated those linked to cell proliferation in MM cells. Transcriptional regulator inference identified E2Fs as regulators for downregulated genes and MED1 and MYC as regulators for upregulated genes. CB-6644-induced changes in chromatin accessibility occurred mostly in non-promoter regions. Footprinting analysis identified transcription factors implied in modulating chromatin accessibility in response to CB-6644 treatment, including ATF4/CEBP and IRF4. Lastly, integrative analysis of transcription responses to various chemical compounds of the molecular signature genes from public gene expression data identified CB-5083, a p97 inhibitor, as a synergistic candidate with CB-6644 in MM cells, but experimental validation refuted this hypothesis.