Search bioRxiv⌕ Search

bioRxiv · 10.1101/2021.02.08.430280

SALTS: SURFR (small noncoding RNA) And LAGOOn (long noncoding RNA) Transcriptomics Suite

Abstract

The widespread utilization of high-throughput sequencing technologies has unequivocally demonstrated that eukaryotic transcriptomes consist primarily (>98%) of non-coding RNA (ncRNA) transcripts significantly more diverse than their protein-coding counterparts. ncRNAs are typically divided into two categories based on their length. (1) ncRNAs less than 200 nucleotides (nt) long are referred as small non-coding RNAs (sncRNAs) and include microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), small nucleolar RNAs (snoRNAs), transfer ribonucleic RNAs (tRNAs), etc., and the majority of these are thought to function primarily in controlling gene expression. That said, the full repertoire of sncRNAs remains fairly poorly defined as evidenced by two entirely new classes of sncRNAs only recently being reported, i.e., snoRNA-derived RNAs (sdRNAs) and tRNA-derived fragments (tRFs). (2) ncRNAs longer than 200 nt long are known as long ncRNAs (lncRNAs). lncRNAs represent the 2nd largest transcriptional output of the cell (behind only ribosomal RNAs), and although functional roles for several lncRNAs have been reported, most lncRNAs remain largely uncharacterized due to a lack of predictive tools aimed at guiding functional characterizations. Importantly, whereas the cost of high-throughput transcriptome sequencing is now feasible for most active research programs, tools necessary for the interpretation of these sequencings typically require significant computational expertise and resources markedly hindering widespread utilization of these datasets. In light of this, we have developed a powerful new ncRNA transcriptomics suite, SALTS, which is highly accurate, markedly efficient, and extremely user-friendly. SALTS stands for SURFR (sncRNA) And LAGOOn (lncRNA) Transcriptomics Suite and offers platforms for comprehensive sncRNA and lncRNA profiling and discovery, ncRNA functional prediction, and the identification of significant differential expressions among datasets. Notably, SALTS is accessed through an intuitive Web-based interface, can be used to analyze either user-generated, standard next-generation sequencing (NGS) output file uploads (e.g., FASTQ) or existing NCBI Sequence Read Archive (SRA) data, and requires absolutely no dataset pre-processing or knowledge of library adapters/oligonucleotides. SALTS constitutes the first publically available, Web-based, comprehensive ncRNA transcriptomic NGS analysis platform designed specifically for users with no computational background, providing a much needed, powerful new resource capable of enabling more widespread ncRNA transcriptomic analyses. The SALTS WebServer is freely available online at http://salts.soc.southalabama.edu.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kasukurthi, M. V., Houserova, D., Huang, Y. M., Barchie, A. A., Roberts, J. T., Li, D., Wu, B., Huang, J., Borchert, G. M.. 2021-02-10. SALTS: SURFR (small noncoding RNA) And LAGOOn (long noncoding RNA) Transcriptomics Suite. https://doi.org/10.1101/2021.02.08.430280

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗