Browsing by Author "Majidian, Sina"
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Item Genomic variant benchmark: if you cannot measure it, you cannot improve it(Springer Nature, 2023) Majidian, Sina; Agustinho, Daniel Paiva; Chin, Chen-Shan; Sedlazeck, Fritz J.; Mahmoud, MedhatGenomic benchmark datasets are essential to driving the field of genomics and bioinformatics. They provide a snapshot of the performances of sequencing technologies and analytical methods and highlight future challenges. However, they depend on sequencing technology, reference genome, and available benchmarking methods. Thus, creating a genomic benchmark dataset is laborious and highly challenging, often involving multiple sequencing technologies, different variant calling tools, and laborious manual curation. In this review, we discuss the available benchmark datasets and their utility. Additionally, we focus on the most recent benchmark of genes with medical relevance and challenging genomic complexity.Item Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree(Springer Nature, 2023) Dylus, David; Altenhoff, Adrian; Majidian, Sina; Sedlazeck, Fritz J.; Dessimoz, ChristopheCurrent methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes and bypasses traditional steps in phylogeny inference, such as genome assembly, annotation and all-versus-all sequence comparisons, while retaining accuracy. In a benchmark encompassing a broad variety of datasets, Read2Tree is 10–100 times faster than assembly-based approaches and in most cases more accurate—the exception being when sequencing coverage is high and reference species very distant. Here, to illustrate the broad applicability of the tool, we reconstruct a yeast tree of life of 435 species spanning 590 million years of evolution. We also apply Read2Tree to >10,000 Coronaviridae samples, accurately classifying highly diverse animal samples and near-identical severe acute respiratory syndrome coronavirus 2 sequences on a single tree. The speed, accuracy and versatility of Read2Tree enable comparative genomics at scale.