Search bioRxiv⌕ Search

bioRxiv · 10.1101/2022.09.30.510283

Novel and simple simulation method to design and development of antisense template

Abstract

Antisense technology is emerging as potential therapeutics against lethal infections. Basically, Antisense-mRNA complex inhibits the protein translation of pathogens and thus it is used for treatment. Based on previous online tools and literatures and difficulties for designing antisense template, finding high conserved regions from large number of long sequences, by taking all those factors in consideration, we proposed new innovative offline target simulation methods i.e. Deletion of unwanted region from viral sequence alignment (DURVA) and Most frequent region (MFR) for designing and developing antisense template from large number of long sequence or genomic data. Based on current pandemic crisis and long genomic sequence of SARS-CoV-2, we chose coronavirus for simulation. Initially, we hypothesized that DURVA-MFR would find stable region from large annotated sequencing data. As per Chan et.al. guidelines for antisense designing and development, we designed couple of algorithms and python scripts to process the data of approximately 30kbp sequence length and 1Gb file size in short turnaround time. The steps involved were as: 1) Simplifying whole genome sequence in single line; 2) Deletion of unwanted region from Virus sequence alignment(DURVA); 3)Most frequent antisense target region(MFR) and 4)Designing and development of antisense template. This simulation method is identifying most frequent regions between 20-30bp long, GC count[≥]10. Our study concluded that targets were highly identical with large population and similar with high number of remaining sequences. In addition, designed antisense sequences were stable and each sequence is having tighter binding with targets. After studying each parameter, here we suggested that our proposed method would be helpful for finding best antisense against all present and upcoming lethal infection.The initial design of this logic was published in Indian Patent Office Journal No.08/2021withApplication number202121005964A. Simple summaryThe antisense development is state of the art for modern therapeutics. There are number of online soft-wares and open sources for designing of antisense template. But all other tools did not consider frequency as major factor for designing antisense. Also; all sources excepting our simulation approach does not process large file or long sequences. Therefore; we designed an offline innovative simulation method which deletes the unwanted region from sequences and stores the data which are fulfilled antisense criteria. Further; the calculation of frequency from these short listed target regions; the most frequent region is desire antisense target and further antisense template will be designed according to Watson-Crick model. This article explained all information about how our new approach is best for designing antisense template against SARS-CoV-2 and many lethal infectious viruses etc.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Deo, D., Shaikh, N.. 2022-09-30. Novel and simple simulation method to design and development of antisense template. https://doi.org/10.1101/2022.09.30.510283

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↗