bioRxiv Science⌕ Search

bioRxiv · 10.1101/092478

Detection and characterization of low and high genome coverage regions using an efficient running median and a double threshold approach.

Abstract

MotivationNext Generation Sequencing (NGS) provides researchers with powerful tools to investigate both prokaryotic and eukaryotic genetics. An accurate assessment of reads mapped to a specific genome consists of inspecting the genome coverage as number of reads mapped to a specific genome location. Most current methods use the average of the genome coverage (sequencing depth) to summarize the overall coverage. This metric quickly assess the sequencing quality but ignores valuable biological information like the presence of repetitive regions or deleted genes. The detection of such information may be challenging due to a wide spectrum of heterogeneous coverage regions, a mixture of underlying models or the presence of a non-constant trend along the genome. Using robust statistics to systematically identify genomic regions with unusual coverage is needed to characterize these regions more precisely.\n\nResultsWe implemented an efficient running median algorithm to estimate the genome coverage trend. The distribution of the normalized genome coverage is then estimated using a Gaussian mixture model. A z-score statistics is then assigned to each base position and used to separate the central distribution from the regions of interest (ROI) (i.e., under and over-covered regions). Finally, a double threshold mechanism is used to cluster the genomic ROIs. HTML reports provide a summary with interactive visual representations of the genomic ROIs.\n\nAvailabilityAn implementation of the genome coverage characterization is available within the Sequana project. The standalone application is called sequana_coverage. The source code is available on GitHub (http://github.com/sequana/sequana), and documentation on ReadTheDocs (http://sequana.readtheodcs.org). An example of HTML report is provided on http://sequana.github.io.\n\nContactdimitri.desvillechabrol@pasteur.fr, thomas.cokelaer@pasteur.fr

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Desvillechabrol, D., Bouchier, C., Kennedy, S., Cokelaer, T.. 2016-12-08. Detection and characterization of low and high genome coverage regions using an efficient running median and a double threshold approach.. https://doi.org/10.1101/092478

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

KEEP EXPLORING

Related preprints

Cross-modal transfer enables zero-shot prediction of drug responses across cell types

Genetic perturbation responses capture the functional consequences of gene-level interventions, providing functional information for drug-response prediction beyond baseline gene expression. Within the same cellular context, genetic and chemical perturbations engage shared regulatory networks, providing a biological basis for transferring response information between perturbation modalities. Here we introduce GRAFT, a framework that combines genetic-to-chemical transfer with cross-context prediction to infer drug-induced transcriptional responses without drug measurements in the target context. Across benchmarked LINCS and external contexts, GRAFT improved response correlation, directional agreement and magnitude reconstruction over competing methods, particularly for drugs with context-dependent responses. GRAFT-Adapt further refines predictions of unmeasured responses using a small number of drug measurements from the target context. GRAFT-Atlas spans 13 cellular contexts and 7,856 drug catalog entries, comprising 102,128 predictions for drug-context combinations. The atlas captured recognizable pharmacological neighborhoods, shared functional programs, context-dependent transcriptional changes and structural-functional associations supported by molecular docking. Across ten iPSC lines from distinct donors, GRAFT identified candidate inter-donor differences, with functional support for selected candidates from measured genetic perturbation responses. Together, GRAFT enables perturbation-informed prediction and comparison of drug responses across cellular contexts.

bioinformatics↗

Pycea: a scverse toolkit for single-cell lineage tracing analysis

Lineage tracing enables reconstruction of cellular histories in development, regeneration, and disease, but integrating lineage data into single-cell analysis workflows remains challenging. We introduce Pycea, a Python toolkit for lineage analysis and visualization, and TreeData, an extension of AnnData that stores lineage trees alongside cellular measurements. TreeData is compatible with Scanpy and other scverse tools, enabling integrated analysis without converting between formats. Pycea provides scalable methods for tree visualization, clade identification, distance comparisons, ancestral state inference, and lineage statistics. This software is open source and available at https://github.com/YosefLab/pycea.

bioinformatics↗

Transcript quantification using context-aware MAPQ modeling

Long-read RNA-sequencing enables accurate transcript-level quantification, but prior static filtering of MAPping Quality (MAPQ) can ignore transcriptome-intrinsic ambiguity, and discarding reads on this basis distorts abundance estimates. We present CALMeR (Context-Aware Long-read MAPQ-based Rescuer), which conditions MAPQ filtering on transcript properties rather than a fixed cutoff. CALMeR fits a zero-one inflated beta regression on various transcript and read-level features to model the expected MAPQ distribution for each alignment. Reads whose MAPQ fits this context, such as low-MAPQ reads from multi-isoform genes and paralog families are retained; reads with anomalously low MAPQ are removed as likely artifacts. Retained reads can be summed per transcript or passed to any downstream quantifier. Compared with a MAPQ [≥] 1 filter and expectation-maximization-based transcript quantification methods, CALMeR recovers more spike-in transcripts and better matches known spike-in concentrations. CALMeR is available at https://github.com/sparthib/CALMeR.

bioinformatics↗