More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19.
A method of Transitional Automorphic Mapping of the Genome on Itself (TAMGI) is aimed at combining detection and reconstruction of correlational and quasi-periodic motifs in the viral genomic RNA/DNA sequences. The motifs reconstructed by TAMGI are robust with respect to indels and point mutations and can be tried as putative therapeutic targets. We developed and tested the relevant theory and statistical criteria for TAMGI applications. The applications of TAMGI are illustrated by the study of motifs in the genomes of the severe acute respiratory syndrome coronaviruses SARS-CoV and SARS-CoV-2…
Methods for evaluating the quality of genomic and metagenomic data are essential to aid genome assembly and to correctly interpret the results of subsequent analyses. BUSCO estimates the completeness and redundancy of processed genomic data based on universal single-copy orthologs. Here we present new functionalities and major improvements of the BUSCO software, as well as the renewal and expansion of the underlying datasets in sync with the OrthoDB v10 release. Among the major novelties, BUSCO now enables phylogenetic placement of the input sequence to automatically select the most appropriat…
Reconstructing components of a genomic mixture from data obtained by means of DNA sequencing is a challenging problem encountered in a variety of applications including single individual haplotyping and studies of viral communities. High-throughput DNA sequencing platforms oversample mixture components to provide massive amounts of reads whose relative positions can be determined by mapping the reads to a known reference genome; assembly of the components, however, requires discovery of the reads' origin -- an NP-hard problem that the existing methods struggle to solve with the required level …
We propose a frame-based representation of k-mers for detecting sequencing errors and rare variants in next generation sequencing data obtained from populations of closely related genomes. Frames are sets of non-orthogonal basis functions, traditionally used in signal processing for noise removal. We define a frame for genomes and sequenced reads to consist of discrete spatial signals of every k-mer of a given size. We show that each k-mer in the sequenced data can be projected onto multiple frames and these projections are maximized for spatial signals corresponding to the k-mer's substrings.…
Unlocking Noisy Real-World Corpora for Foundation Model Pre-Training via Quality-Aware Tokenization
Broad Institute of MIT and Harvard, Cambridge, MA, USA（麻省理工学院与哈佛大学Broad研究所）；Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院）；Koch Institute for Integrative Cancer Research, MIT, Cambridge, MA, USA（麻省理工学院Koch整合癌症研究 institute）；Department of Neurology and Neurosurgery, McGill University, Montreal, Canada（麦吉尔大学神经学与神经外科系）；The Montreal Neurological Hospital-Institute, Montreal, Canada（蒙特利尔神经科学医院-研究所）
Recently, an elegant approach in phylogenetics was introduced by Billera-Holmes-Vogtmann that allows a systematic comparison of different evolutionary histories using the metric geometry of tree spaces. In many problem settings one encounters heavily populated phylogenetic trees, where the large number of leaves encumbers visualization and analysis in the relevant evolutionary moduli spaces. To address this issue, we introduce tree dimensionality reduction, a structured approach to reducing large phylogenetic trees to a distribution of smaller trees. We prove a stability theorem ensuring that …
Ultrafast topological data analysis reveals pandemic-scale dynamics of convergent evolution
Heidelberg University（海德堡大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Columbia University（哥伦比亚大学）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Centre Inria d’Université Côte d’Azur（蔚蓝海岸大学Inria中心）；TUM Department of Mathematics and Munich Data Science Institute（慕尼黑工业大学数学系与慕尼黑数据科学研究所）；Wellcome Trust Sanger Institute（惠康信托桑格研究所）
