The amount of sequencing data for SARS-CoV-2 is several orders of magnitude
larger than any virus. This will continue to grow geometrically for SARS-CoV-2,
and other viruses, as many countries heavily finance genomic surveillance
efforts. Hence, we need methods for processing large amounts of sequence data
to allow for effective yet timely decision-making. Such data will come from
heterogeneous sources: aligned, unaligned, or even unassembled raw nucleotide
or amino acid sequencing reads pertaining to the whole genome or regions (e.g.,
spike) of interest. In this work, we propose \emph{ViralVectors}, a compact
feature vector generation from virome sequencing data that allows effective
downstream analysis. Such generation is based on \emph{minimizers}, a type of
lightweight "signature" of a sequence, used traditionally in assembly and read
mapping -- to our knowledge, the first use minimizers in this way. We validate
our approach on different types of sequencing data: (a) 2.5M SARS-CoV-2 spike
sequences (to show scalability); (b) 3K Coronaviridae spike sequences (to show
robustness to more genomic variability); and (c) 4K raw WGS reads sets taken
from nasal-swab PCR tests (to show the ability to process unassembled reads).
Our results show that ViralVectors outperforms current benchmarks in most
classification and clustering tasks.

本文介绍了一种名为 ViralVectors 的新型序列特征提取方法，该方法基于 minmizers 轻量级 “签名” 技术，可用于处理大量异构序列数据，能够在 SARS-CoV-2 spike 序列、Coronaviridae spike 序列和鼻咽拭子 PCR 测序数据集等多种样本数据的分类和聚类任务中优于当前基准结果。

ViralVectors：紧凑且可扩展的无需比对的宏基因组病毒特征生成

ViralVectors: Compact and Scalable Alignment-free Virome Feature  Generation

Extracting genetic information from a full range of sequencing data is
important for understanding diseases. We propose a novel method to effectively
explore the landscape of genetic mutations and aggregate them to predict cancer
type. We used multinomial logistic regression, nonsmooth non-negative matrix
factorization (nsNMF), and support vector machine (SVM) to utilize the full
range of sequencing data, aiming at better aggregating genetic mutations and
improving their power in predicting cancer types. Specifically, we introduced a
classifier to distinguish cancer types using somatic mutations obtained from
whole-exome sequencing data. Mutations were identified from multiple cancers
and scored using SIFT, PP2, and CADD, and grouped at the individual gene level.
The nsNMF was then applied to reduce dimensionality and to obtain coefficient
and basis matrices. A feature matrix was derived from the obtained matrices to
train a classifier for cancer type classification with the SVM model. We have
demonstrated that the classifier was able to distinguish the cancer types with
reasonable accuracy. In five-fold cross-validations using mutation counts as
features, the average prediction accuracy was 77.1% (SEM=0.1%), significantly
outperforming baselines and outperforming models using mutation scores as
features. Using the factor matrices derived from the nsNMF, we identified
multiple genes and pathways that are significantly associated with each cancer
type. This study presents a generic and complete pipeline to study the
associations between somatic mutations and cancers. The discovered genes and
pathways associated with each cancer type can lead to biological insights. The
proposed method can be adapted to other studies for disease classification and
pathway discovery.

该研究提出了一种新的方法，利用多项逻辑回归、非平滑非负矩阵分解 (nsNMF) 和支持向量机 (SVM) 等算法，从全序列数据中提取遗传信息以更好地预测癌症类型。实验表明，该方法在突变计数、突变得分等方面均表现优异，可用于其他疾病分类和通路发现研究。