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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 BackgroundParent-of-origin allele-specific gene expression (ASE) can be detected in interspecies hybrids by virtue of RNA sequence variants between the parental haplotypes. ASE is detectable by differential expression analysis (DEA) applied to the counts of RNA-seq read pairs aligned to parental references, but aligners do not always choose the correct parental reference. ResultsWe used public data for species that are known to hybridize. We measured our ability to assign RNA-seq read pairs to their proper transcriptome or genome references. We tested software packages that assign each read pair to a reference position and found that they often favored the incorrect species reference. To address this problem, we introduce a post process that extracts alignment features and trains a random forest classifier to choose the better alignment. On each simulated hybrid dataset tested, our machine-learning post-processor achieved higher accuracy than the aligner by itself at choosing the correct parent-of-origin per RNA-seq read pair. ConclusionsFor the parent-of-origin classification of RNA-seq, machine learning can improve the accuracy of alignment-based methods. This approach could be useful for enhancing ASE detection in interspecies hybrids, though RNA-seq from real hybrids may present challenges not captured by our simulations. We believe this is the first application of machine learning to this problem domain. 

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.