bioRxiv Science⌕ Search

bioRxiv · 10.1101/2020.04.01.018481

Succinct Dynamic de Bruijn Graphs

Abstract

MotivationThe de Bruijn graph is one of the fundamental data structures for analysis of high throughput sequencing data. In order to be applicable to population-scale studies, it is essential to build and store the graph in a space- and time-efficient manner. In addition, due to the ever-changing nature of population studies, it has become essential to update the graph after construction e.g. add and remove nodes and edges. Although there has been substantial effort on making the construction and storage of the graph efficient, there is a limited amount of work in building the graph in an efficient and mutable manner. Hence, most space efficient data structures require complete reconstruction of the graph in order to add or remove edges or nodes. ResultsIn this paper we present DynamicBOSS, a succinct representation of the de Bruijn graph that allows for an unlimited number of additions and deletions of nodes and edges. We compare our method with other competing methods and demonstrate that DynamicBOSS is the only method that supports both addition and deletion and is applicable to very large samples (e.g. greater than 15 billion k-mers). Competing dynamic methods e.g., FDBG (Crawford et al., 2018) cannot be constructed on large scale datasets, or cannot support both addition and deletion e.g., BiFrost (Holley and Melsted, 2019). AvailabilityDynamicBOSS is publicly available at https://github.com/baharpan/dynboss. Contactbaharpan@ufl.edu

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alipanahi, B., Kuhnle, A., Puglisi, S., Salmela, L., Boucher, C.. 2020-04-03. Succinct Dynamic de Bruijn Graphs. https://doi.org/10.1101/2020.04.01.018481

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

KEEP EXPLORING

Related preprints

scRM: A single-cell foundation model trained on scRNA-seq reads

To our knowledge, scRM is the first single-cell foundation model trained on sequencing reads, demonstrating that raw data pretraining is not only feasible but competitive at a fraction of the computational cost. We anticipate that scaling this framework and exploring alternative architectures will unlock biological information inaccessible to count models and extend the dataset-specific pretraining it already makes possible. The source code is available at https://github.com/altos-labs/scRM.

bioinformatics↗

Local fractal dimension as an indicator of functional reorganization in molecular interaction networks

Changes in protein abundance incompletely describe how molecular perturbations reshape cellular organization. Here we introduce DIFFRACT, a framework that integrates quantitative proteomics with protein-protein interaction networks to identify reorganization through changes in local fractal dimension. Molecular associations define effective distances between connected proteins, allowing the geometry of their surrounding network neighborhoods to be compared across conditions. Analyses across four organisms (E. coli, A. thaliana, C. elegans, and H. sapiens) and three interaction resources (STRING, BioGRID, and IntAct) establish heterogeneous local scaling, while controlled perturbations demonstrate sensitivity to regional changes and invariance to uniform distance rescaling. Applied to RAVER1-deficient A549 cells, DIFFRACT identifies components associated with established RNA-regulatory functions and candidate roles in tRNA biology, mitochondrial organization and ciliogenesis. Selected RNA-regulatory and ciliary themes are not significantly enriched by abundance-ranked gene set analysis, supporting complementary information from network geometry. Selected functional annotations also recur in HEK293T cells. Guided by the ciliary predictions, we show that RAVER1 depletion increases the proportion of ciliated A549 cells 2.57-fold and increases cilium length. DIFFRACT thus connects changes in molecular network geometry to experimentally testable biological responses, extending the interpretation of proteomic perturbations beyond differential abundance.

bioinformatics↗

Peripheral Exercise Transcriptomes Do Not Converge on Glutamatergic or Neuroinflammatory Signatures: A Cross-Platform Meta-Analysis

Executive dysfunction following physical overtraining has been linked to glutamatergic dysregulation in the lateral prefrontal cortex (LPFC), but whether this central mechanism is reflected in peripheral gene expression is unknown. This study tested whether peripheral transcriptomic responses to exercise converge on the glutamatergic or neuroinflammatory pathways implicated in LPFC dysfunction. Three publicly available GEO datasets spanning skeletal muscle, whole blood, and circulating neutrophils were analyzed across four exercise groups using limma, with a discovery threshold of nominal p < 0.05 and absolute log2 fold change above 0.5. Differential expression analysis identified 7,311 unique differentially expressed genes (DEGs) across the four contrasts, but overlap between datasets was limited: 7 genes were shared between skeletal muscle and blood, 38 between skeletal muscle and the GSE43856 contrasts, and 90 between blood and the GSE43856 contrasts, with none significant across all three studies. Gene Ontology and KEGG enrichment of the DEG union identified ribonucleoprotein complex biogenesis, ribosome biogenesis, protein folding, and T cell activation as the dominant biological processes; no glutamatergic or neuroinflammatory terms reached significance. Cross-tissue concordance between skeletal muscle and blood was absent (r = 0). Effect sizes among the genes driving the top enriched pathways were dominated by ribosomal proteins, heat shock proteins, and translation initiation factors rather than glutamate receptors or transporters. These findings indicate that peripheral transcriptomic responses to exercise are tissue-specific and dominated by proteostasis, translational, and immune programs rather than glutamatergic signaling. Direct assessment of central nervous system tissue or fluid-based glutamatergic biomarkers may be required to link overtraining-induced fatigue to LPFC dysfunction.

bioinformatics↗