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Schaefer, R. A.

Publications and source records attributed to Schaefer, R. A..

2 recordsLinked to original sources

Analysis of isoform complexity in pan-transcriptome graphs with atroplex

MotivationAlternative splicing of precursor mRNA lets a single gene encode multiple isoforms by joining exons in different combinations. Long-read sequencing resolves this isoform diversity across tissues, cohorts, and conditions. However, the resulting pan-transcriptomes are structurally complex, and their analysis requires repeatedly searching the full catalogue, which is impractical without a queryable index. As splicing patterns differ across conditions, a structure is needed that captures the connectivity between exons, not just their coordinates, so isoforms can be compared by structure across cohorts. ResultsWe present atroplex, a framework that indexes pan-transcriptome annotations and transcript isoforms in a combined spatial index and graph overlay, capturing both exon coordinates and splice connectivity. atroplex classifies query transcripts against the index, tracks per-sample isoform presence, and enables crosscohort isoform comparison. We indexed 21,005 samples spanning multiple reference resources into a single queryable structure, yielding a comprehensive map of isoform complexity that supports improved transcript discovery and structural comparison across cohorts.

bioinformatics↗

A Comprehensive Benchmark of Tools for Efficient Genomic Interval Querying

Efficiently querying genomic intervals is fundamental to modern bioinformatics, enabling researchers to extract and analyze specific regions from large genomic datasets. While various tools have been developed for this purpose, there lacks a comprehensive comparison of their performance, memory usage, and practical utility. We present a systematic evaluation of genomic interval query tools using simulated datasets of varying sizes. Our benchmarking framework, segmeter, assesses both basic and complex interval queries, examining runtime performance, memory efficiency, and query precision across different tools and data structures. This comprehensive analysis provides insights into the strengths and limitations of different approaches to genomic interval querying, offering guidance for tool selection based on specific use cases and data requirements. The segmeter framework and all benchmark data are freely available, facilitating reproducibility and enabling researchers to conduct their own comparative analyses.

bioinformatics↗