bioRxiv ScienceSearch

bioRxiv · 10.1101/2020.07.12.199661

Number of mismatches and length of longest match correlate with alignment score in swalign built-in function in MATLAB

Abstract

Understanding how one sequence relates to another at the nucleotide or amino acid level allows the derivation of new knowledge regarding the provenance of particular sequence as well as the determination of consensus sequence motifs that informs biological conservation at the sequence level. To this end, local or multiple sequence alignments tools in bioinformatics have been developed to automatically profile two or more nucleotide or amino acid sequence in search of matches in stretches of nucleotides or amino acid sequence that yield an alignment. While alignment score is a common metric for assessing alignment quality, relative difference between alignment scores does not readily correlate with concrete measures such as number of mismatches and length of longest match in alignment. Thus, using swalign local sequence alignment function in MATLAB on 200 alignments between RNA-seq sequence read and reference Escherichia coli K-12 MG1655 genome sequence in the sense and antisense direction, this work sought to shed some light on how alignment score from swalign correlates with number of mismatches and length of longest match. Results revealed that number of mismatches negatively correlate with alignment score; thereby, validating theoretical predictions that larger number of mismatches would result in a poorer alignment and lower alignment score. However, dependence of alignment score on other factors such as length of longest match and gap penalty from opening an alignment gap prevents linear relationship to be obtained between number of mismatches and alignment score. On the other hand, length of longest match was found to positively correlate with alignment score as predicted from theoretical understanding. But, data obtained revealed that clusters of data points gather at two regions of the scatter plot involving short matches and low alignment score, as well as long matches and high alignment score. Such clustering and sparseness of data points between the two clusters preclude the elucidation of a linear quantitative relationship between length of longest match and alignment score. Overall, dependence of alignment score of swalign on number of mismatches and length of longest match in alignment match theoretical predictions; thereby, validating the utility of alignment score in indicating the qualitative quality of alignment. However, given that alignment score inherently depends on a multitude of factors, users could not easily discern the quantitative difference in mismatches and length of longest match from relative differences between two alignment scores. Such problems are unlikely to be resolved given the near impossibility of obtaining quantitative linear relationship correlating either number of mismatches or length of longest match with alignment score of a sequence alignment tool. HighlightsO_LINumber of mismatches in alignment negatively correlates with alignment score. C_LIO_LILength of longest match positively correlates with alignment score. C_LIO_LIQuantitative linear relationship could not be obtained for alignment score with either number of mismatches or length of longest match. C_LIO_LIResults validate that swalign tool in MATLAB could quantitatively detect differences in alignment quality and expressed it using alignment score. C_LIO_LIBut, relative alignment score of two alignments remains a nebulous concept with regards to differences in number of mismatches and length of longest match. C_LI

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ng, W.. 2020-07-13. Number of mismatches and length of longest match correlate with alignment score in swalign built-in function in MATLAB. https://doi.org/10.1101/2020.07.12.199661

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