bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.06.10.658975

CLUES2 Companion: Computational pipelines to estimate, visualize, and date selection on multi-locus sites

Abstract

SummaryStatistical methods that quantify the selection coefficient associated with alleles provide critical insights into the evolutionary processes underlying organismal adaptation. Among these approaches, CLUES2 was recently developed to estimate selection coefficients for alleles using a statistical framework that captures the maximum amount of information present in genomic data, making it a state-of-the-art method for identifying variation under selection. However, before executing this approach, users first need to apply Relate, a genealogy-based approach, to their data to generate the input files for CLUES2. Moreover, completing this pre-processing step inherently assumes that users have sufficient expertise to successfully run the Relate software. Here, we present the CLUES2 Companion package, which contains user-friendly pipelines that seamlessly apply Relate to multiple sites within a target genomic region and then execute the CLUES2 software to estimate selection coefficients for these sites. CLUES2 Companion also has the capability to present the output of CLUES2 analyses in tabular and graphical formats. In addition, as a new feature, we adapted Relate and CLUES2 to estimate the age of onset of a selective sweep of derived variation, expanding the functionality of our package. ResultsTo demonstrate the utility of our approach, we applied CLUES2 Companion to polymorphisms in the MCM6 gene on Chromosome 2 (including the known variants associated with lactase persistence) in the European Finnish, Middle Eastern Bedouin, and East African Maasai populations from the 1000 Genomes Project, the Human Genome Diversity Project (HGDP), and the haplotype map (HapMap) Project Phase 3, respectively. Our analyses uncovered significant selection coefficient estimates at the persistence-associated T-13910 allele (rs4988235; s = 0.09986, CI: 0.08678 - 0.11294) in the Finnish, the G-13915 allele (rs41380347; s = 0.09981, CI: 0.06515 - 0.13448) in the Bedouin, and the C-14010 allele (rs145946881; s = 0.09981, CI: 0.08799 - 0.11163) in the Maasai, indicative of a classic selective sweep. Furthermore, we inferred the age of onset of selection at these alleles to be 9,100 years ago (CI: 6,552 - 10,612 years ago) in the Finnish, 7,700 years ago (CI: 1,864 - 8,064 years ago) in the Bedouin, and 4,900 years ago (CI: 3,864 - 5,936 years ago) in the Maasai, respectively, which coincide well with other estimates based on genetic and archaeological data. To further validate our dating method, we simulated several datasets containing SNPs with known ages of onset of selection, s estimates, and genomic positions using a selective sweep framework implemented in msprime and then applied CLUES2 Companion to the simulated datasets. Using this approach, CLUES2 Companion produced similar estimates of selection onset as the ones specified in the simulations, corroborating the dependability of our method. Overall, CLUES2 Companion is a versatile package that enables users to efficiently explore, interpret, and report evidence of selection in genomic datasets, complementing the CLUES2 software. Availability and ImplementationCLUES2 Companion is free and open source on GitHub (https://github.com/alisi1989/CLUES2-Companion) and on DropBox (https://www.dropbox.com/scl/fo/m5y6aek0twd1jz9grg4p3/ALxMgIljUJRIZZNQXaGU-OE?rlkey=mbbh36ondftnqg0x07eao57eg&st=9jzsl5um&dl=0). Contactalisi@usc.edu; mc44680@usc.edu

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lisi, A., Campbell, M. C.. 2025-06-16. CLUES2 Companion: Computational pipelines to estimate, visualize, and date selection on multi-locus sites. https://doi.org/10.1101/2025.06.10.658975

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

KEEP EXPLORING

Related preprints

Accounting for pseudo-replication of Linkage Disequilibrium for contemporary Ne estimation

The Linkage Disequilibrium (LD) of unlinked loci can be used to estimate contemporary effective population size (Ne) of one to a few generations ago. In genomic datasets loci on different chromosomes are considered unlinked, but there are many more pairs of unlinked loci than there are independent pairs of chromosomes, resulting to confidence intervals (C.I.) being too narrow if the non-independence is not taken into account. Simulations were run to investigate the correlation structure among LD of unlinked loci, which can be expressed by the LD of loci along the same chromosomes, based on a discovery of a novel Random Probe LD estimator. We classify the correlation into two categories: overlapping of loci and disjoint pairs. The former is induced from the same locus being considered twice and is the stronger form of correlation. These correlations feed into {rho}, a parameter to quantify the degree of pseudo-replication in a dataset, and further a correction formula from which C.I. can be properly inferred. We demonstrate the use of our method via an analysis of genomic data from the malaria-transmitting Anopheles gambiae s.s mosquitoes. Apart from the point and C.I. estimates, we find that Var((r^2 ) ) is inflated by about 550 times due to pseudo-replication, highlighting the danger of not handling genetic correlation properly.

bioinformatics↗

Accurate and scalable decontamination of imaging-based spatial transcriptomics via optimal transport

Imaging-based spatial transcriptomics enables molecule-resolved profiling of gene expression and tissue organization in situ. However, segmentation errors, transcript spillover and three-dimensional cell overlap can introduce misassigned transcripts into cell-level expression profiles, compromising biological interpretation and obscuring genuine signals. Existing methods either remove suspect expression at the cost of signal loss or lack a biologically grounded criterion for transcript assignment. Here we present CellDot, an optimal-transport framework that determines the fate of each transcript by retaining it in its host cell, reassigning it to a plausible neighboring cell or removing it as background. By integrating reference-guided expression compatibility with spatial information and data-adaptive constraints, CellDot enables accurate and traceable molecule-level correction while preserving biologically meaningful variation. In evaluations across multiple human tumor datasets, CellDot exhibited superior performance compared to existing decontamination methods, successfully restoring spatial expression patterns that matched independent cross-platform measurements. Moreover, it significantly enhanced the recovery of cellular states, intercellular communication, and spatial niche programs. Our experiments using real data demonstrated CellDot's scalability and established it as the only method applicable to a whole-transcriptome Atera dataset, underscoring its distinct advantages in the field of spatial transcriptomics.

bioinformatics↗

Interpretable Machine Learning Reveals Complementary Age-Related Signatures in the Oral and Gut Microbiome

Whether combining microbiome data from multiple body sites improves prediction, and whether different sites carry complementary or redundant information, are distinct questions that most studies conflate into a single accuracy metric. This work makes two contributions, one methodological and one biological, using paired stool and oral cavity microbiome samples from 44 subjects across two age groups, healthy adults and newborns (Ferretti et al., 2018). Methodologically, we show that a subject-matched fusion design combined with SHAP-based (SHapley Additive exPlanations) site attribution can detect complementary information between body sites even when no measurable accuracy gain results. This is a pattern that conventional model comparison would misread as a null result. Gut (stool) composition alone achieved near-perfect classification (area under the receiver operating characteristic curve, AUC = 1.00), and combined stool-oral models never exceeded this ceiling. A null baseline, bootstrap confidence intervals, and preprocessing sensitivity checks confirmed that this ceiling reflects genuine biological signal rather than an artifact. Despite the flat accuracy curve, SHAP analysis of the fused model showed that oral cavity features carried more total feature importance than stool features (58.1% versus 41.9%), indicating that the model draws on real, non-redundant information from both sites. Biologically, the taxa driving this pattern include Malassezia restricta, Staphylococcus epidermidis, and Prevotella melaninogenica. These taxa behave in a manner consistent with their established roles as early colonizers of the neonatal gut, skin, and oral cavity, once their model-specific behavior is verified directly against abundance data rather than inferred from the literature alone. An independent, substantially larger paired-cohort study using a different analytical method reports a compatible pattern. Together, these results support a model of oral-gut microbiome maturation as two distinct, complementary processes, and demonstrate that detecting this kind of relationship requires examining a model's internal reasoning rather than its accuracy alone.

bioinformatics↗