bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/760140

PeSA: A Software Tool for Peptide Specificity Analysis

Topcu, E.·Biggar, K. K.
View original source ↗Export metadata ↓

Abstract

The discovery of molecular interactions is crucial towards a better understanding of complex biological functions. Particularly protein-protein interactions (i.e., PPIs), which are responsible for a variety of cellular functions from epigenetic modifications to enzyme-substrate specificity, have been studied extensively over the past decades. Position-specific scoring matrices (PSSM) in particular are used extensively to help determine interaction specificity or candidate interaction motifs. However, not all studies successfully report their results as a candidate interaction motif. In many cases, this is the result of a lack of analysis tools for simple analysis and motif generation. Peptide Specificity Analyst (PeSA) is developed with the goal of filling this gap and providing an analysis software to aid peptide array analysis and subsequent motif generation. PeSA utilizes two models of motif creation: (1) frequency-based using a peptide list, and (2) weight-based using a quantified matrix. The ability to generate motifs effortlessly will make analyzing, interpreting and sharing peptide specificity study results in a simple and straightforward process.\n\nGRAPHICAL ABSTRACT\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC=\"FIGDIR/small/760140v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (20K):\norg.highwire.dtl.DTLVardef@1668389org.highwire.dtl.DTLVardef@145bbfeorg.highwire.dtl.DTLVardef@1392eaeorg.highwire.dtl.DTLVardef@127e2c3_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIBiological motifs are widely used representations for peptide specificity analysis.\nC_LIO_LIPeSA populates a list of peptides matching a set threshold from a quantified matrix.\nC_LIO_LIFrequency-based motif using a peptide list to spot residue patterns.\nC_LIO_LIUse of quantified matrices to create weight-based motifs using residue positions.\nC_LI

Source connections

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Topcu, E., Biggar, K. K.. 2019-09-08. PeSA: A Software Tool for Peptide Specificity Analysis. https://doi.org/10.1101/760140

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

KEEP EXPLORING

Related preprints

bioRxiv2026

Uncovering Viral Signals Across Cell Populations in Colorectal Cancer

Viral sequences have been detected in colorectal cancer, but their cellular and anatomical distribution remains poorly resolved. We aimed to establish a cell-type-resolved map of viral reference-associated RNA signals across colorectal cancer, tumour-adjacent normal tissue, and healthy colon. We repurposed human-unmapped reads from single-cell RNA sequencing libraries generated in ten publicly available studies. We retained cell-barcode and unique molecular identifier information, removed duplicate molecules, and aligned reads to a curated human-associated viral reference using three aligners. Viral signals were analysed across cell types, tissue conditions, and colorectal segments. We used participant-grouped repeated cross-validation to evaluate tissue-condition classification and screened an independent paired colorectal cancer proteomics cohort for compatible peptide matches. The study included 547 libraries from 112 participants, including 49 with matched colorectal cancer and tumour-adjacent normal samples, and approximately 415,000 cells spanning ten major cell populations. Viral reference-associated RNA signals differed across epithelial, immune, stromal, and vascular compartments, tissue conditions, and anatomical regions of the colon. Of 318 independent healthy-reference viral-load comparisons, seven involving six virus-cell-type groups remained significant after Benjamini-Hochberg correction, including plasma-cell HHV-7 and HHV-6B and mast-cell HCV-2. Cell-type-resolved viral features distinguished colorectal cancer, tumour-adjacent normal tissue, and healthy colon in participant-grouped validation; random forest achieved a macro-AUC of 0.957 (SD 0.028), with HERV-K113 as the leading quantitative feature. An independent proteomic screen identified peptide matches compatible with 20 candidate viral groups. We established a cell-type-resolved map of viral reference-associated RNA signals across colorectal tissue conditions and anatomical regions. By identifying candidate viral signals in epithelial and immune compartments, this map provides a basis for investigating virus-host pathways involved in tumour biology, immune regulation, and antigen-presentation states, and could help prioritise future combination strategies pairing virus-directed or virus-mimetic approaches with standard treatment or immune checkpoint blockade.

bioinformatics↗
bioRxiv2026

Statistical significance testing for spatial domain detection in spatial transcriptomics

In spatial transcriptomics, domain-detection methods group platform-specific spatial measurement units--cells, bins or multicellular spots--into spatial domains for comparing molecular programs, cellular composition and interactions among tissue regions. However, the number of candidate domains detected in the same tissue can vary with user-specified parameters or model-selection criteria. Whether candidate domains should be merged can be framed as a test of a single-domain null hypothesis, under which each gene has constant mean expression across all spatial units in the combined region. However, testing candidate domains on the same gene-expression data used to detect them may induce post-selection bias and yield anti-conservative p-values, increasing the risk of oversegmentation. To address these challenges, we introduce SigDoTS (statistical Significance testing for Domain deTection in Spatial transcriptomics), a hypothesis-testing framework for spatial domain detection. SigDoTS uses hierarchical testing to determine which candidate domains to retain or merge, with adjustment for family-wise error control. It accounts for gene-gene covariance and spatial dependence and computes p-values by rerunning the upstream domain-detection pipeline on datasets simulated under the fitted single-domain null. In simulations, SigDoTS controlled the type I error rate at the root node under the single-domain null and improved recovery of the true spatial domains when multiple domains were present. In human dorsolateral prefrontal cortex, SigDoTS retained superficial and deep Layer 6 domains but merged Layer 3 candidate domains across different initial numbers of candidate domains. In ovarian and colorectal cancers, SigDoTS retained epithelial domains with different gene-expression programs and local immune environments, together with immune, stromal and vascular domains.

bioinformatics↗
bioRxiv2026

ASD Neuro Agentic (ANA): An Agentic Framework for Feature Discovery and Cross Site Generalization in Autism fMRI Classification

Classifiers of autism spectrum disorder (ASD) from resting state functional MRI rarely generalize across sites, and many reported accuracies are inflated by data leakage.We show that an agentic artificial intelligence framework, ASD Neuro Agentic (ANA), whose language model agents propose features from the literature and researcher input while fixed tools perform every calculation, can search auditably for site independent classifiers while controlling the leakage. In 1,028 participants from 17 ABIDE I centers, ANA tested 71 pipelines and selected a ComBat harmonized ensemble of tangent space connectivity models from six parcellations and 98 public connectivity derivatives. In leave one center out testing, the ensemble reached an area under the curve of 0.814 (95% CI, 0.773 to 0.841), outperforming ASD DiagNet and ASD SAENet. Predictive connectivity was mainly reduced in ASD. Agentic AI offers a systematic and efficient approach to exploring, evaluating, and identifying pipelines as model selection grows complex.

bioinformatics↗