Browsing by Author "Majidian, Sina"

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    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, Medhat
    Genomic 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.
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    The GIAB genomic stratifications resource for human reference genomes
    (Springer Nature, 2024) Dwarshuis, Nathan; Kalra, Divya; McDaniel, Jennifer; Sanio, Philippe; Alvarez Jerez, Pilar; Jadhav, Bharati; Huang, Wenyu (Eddy); Mondal, Rajarshi; Busby, Ben; Olson, Nathan D.; Sedlazeck, Fritz J.; Wagner, Justin; Majidian, Sina; Zook, Justin M.
    Despite the growing variety of sequencing and variant-calling tools, no workflow performs equally well across the entire human genome. Understanding context-dependent performance is critical for enabling researchers, clinicians, and developers to make informed tradeoffs when selecting sequencing hardware and software. Here we describe a set of “stratifications,” which are BED files that define distinct contexts throughout the genome. We define these for GRCh37/38 as well as the new T2T-CHM13 reference, adding many new hard-to-sequence regions which are critical for understanding performance as the field progresses. Specifically, we highlight the increase in hard-to-map and GC-rich stratifications in CHM13 relative to the previous references. We then compare the benchmarking performance with each reference and show the performance penalty brought about by these additional difficult regions in CHM13. Additionally, we demonstrate how the stratifications can track context-specific improvements over different platform iterations, using Oxford Nanopore Technologies as an example. The means to generate these stratifications are available as a snakemake pipeline at https://github.com/usnistgov/giab-stratifications. We anticipate this being useful in enabling precise risk-reward calculations when building sequencing pipelines for any of the commonly-used reference genomes.
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    Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree
    (Springer Nature, 2023) Dylus, David; Altenhoff, Adrian; Majidian, Sina; Sedlazeck, Fritz J.; Dessimoz, Christophe
    Current 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.