bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.10.07.511371

vamos: VNTR annotation using efficient motif sets

Abstract

MotivationRoughly 3% of the human genome is composed of variable-number tandem repeats (VNTRs): tandemly repeated arrays of motifs at least six bases. These loci are highly polymorphic: over 61% of insertion and deletion variants at least 50 bases found from long-read assemblies are inside VNTRs. Furthermore, long-read assemblies reveal that VNTR loci are multiallelic, and can vary by both motif composition and copy number. Current approaches that define and merge variants based on alignment breakpoints do not capture this complexity of variation. A natural alternative approach is to instead define the motif composition of VNTR sequences from samples, and to detect differences based on comparisons of repeat composition. However, due to the complexity of VNTR sequences, it is difficult to establish a common reference set of motif sequences that may be used to describe variation in large sequencing studies. ResultsHere we present a method vamos: VNTR Annotation using efficient Motif Sets that for any VNTR locus selects a set of representative motifs from all motifs observed at that locus that may be used to encode VNTR sequences within a bounded edit distance of the original sequence. We use our method to characterize VNTR variation in 32 haplotype-resolved human genomes. In contrast to current studies that merge multi-allelic calls, we estimate an average of 3.1-4.0 alleles per locus. Availabilitygithub.com/chaissonlab/vamos, zenodo.org/record/7158427 Contactmchaisso@usc.edu

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ren, J., Gu, B., Chaisson, M.. 2022-10-08. vamos: VNTR annotation using efficient motif sets. https://doi.org/10.1101/2022.10.07.511371

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

KEEP EXPLORING

Related preprints

StableProt: Structure-Aware Deep Learning for Protein Thermostability (Tm) and Environmental Adaptation (OGT) Prediction

Predicting protein melting temperature (Tm) and organismal optimal growth temperature (OGT) is essential for enzyme engineering, biocatalyst design, and metagenomic bioprospecting, yet computational prediction faces several fundamental bottlenecks: sequence-only representations miss tertiary packing; dual-task coupling induces destructive gradient conflict; natural database skew (>87% mesophiles) drives mesophilic collapse; and existing tools produce uncalibrated point predictions. To address these challenges, we present StableProt, a structure-aware framework that couples amino acid sequences with Foldseek 3Di structural tokens via a frozen SaProt backbone to capture 3D contacts. Disjoint heads condition thermodynamic stability on ecological context without gradient conflict, while targeted 14% mesophilic subsampling expands thermophile representation to eliminate mesophilic collapse. Calibrated ensemble modeling reduces calibration error to 1.92%, providing dependable 95% confidence intervals. Evaluating models across 10{degrees}C temperature intervals reveals severe baseline collapse at thermal extremes, whereas StableProt maintains robust accuracy across regimes. On ProThermDB and zero-shot FireProtDB, StableProt achieves state-of-the-art probabilistic accuracy (CRPS = 4.52{degrees}C and 8.71{degrees}C) while recovering 92.0% of hyperthermophiles ([≥]80{degrees}C), and in prospective validation, its calibrated intervals capture both enzyme stability (75.3% coverage) and host environmental growth temperatures (78.1% coverage). StableProt is freely accessible as an interactive web server at https://stableprot.dixitlab.org/.

bioinformatics↗

RoDPAA: A Robust Dissimilarity-Based Principal Angle Analysis Framework for Multi-View Microbiome Data Integration

Multiview microbiome studies profile the same participants across body sites or molecular layers, but integration is hampered by sparse, compositional data, modest sample sizes and methods that recognize only exact sharing. We present RoDPAA (Robust Dissimilarity-based Principal Angle Analysis), a framework that represents each view through any data-appropriate dissimilarity, such as Bray--Curtis, without log-ratio transformation or likelihood assumptions. RoDPAA aligns view-specific signal subspaces through principal angles, captures complete and partial sharing, and tests by permutation how many components are shared, using only eigendecomposition and little tuning. In Gaussian simulations, RoDPAA matched JIVE and MOFA given true ranks, ran faster and alone recovered the shared dimension in every replicate. In two inflammatory bowel disease (IBD) cohorts with sparse, zero-inflated data, Bray--Curtis-based RoDPAA recovered shared structure that was stable to prevalence filtering, whereas log-ratio-based alternatives were not. Its leading microbiome--metabolome factors, learned without disease labels, were the only ones associated with IBD.

bioinformatics↗

Deducing splicing events from transcript sets with splicelogic

Long-read RNA sequencing captures a greater proportion of full-length transcripts, enabling more accurate isoform identification, discovery of novel isoforms, and more precise differential transcript usage analysis. However, comparing transcripts, whether between conditions or between sets such as novel and annotated isoforms, yields complex isoform changes that are hard to manipulate, summarize, and visualize. This is compounded by vertical integration of some tools, where downstream analysis depends on the specific output of a particular upstream tool. To address this gap, we have developed splicelogic, an R/Bioconductor package that makes transcript-set comparisons biologically interpretable by turning them into discrete splicing events, such as exon skipping, alternative splice sites, intron retention, and alternative transcription start and end sites. We designed splicelogic to integrate easily with existing RNA-seq workflows and tools. The splicelogic functions are modular, readable, and extensible, making it easy for users to adapt core logic to their own custom scientific questions or novel splicing definitions. By obtaining interpretable splicing events from transcript changes, splicelogic enables downstream analyses, including annotation of RNA-binding protein recognition sites, alongside summary statistics and visualization. The splicelogic package is publicly available and distributed through Bioconductor.

bioinformatics↗