Continuous chromatin state feature annotation of the human epigenome.

MOTIVATION: Segmentation and genome annotation (SAGA) algorithms are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing-based assays of epigenomic activity, such as ChIP-seq measurements of histone modification and transcription factor binding. They output an annotation of the genome that assigns a chromatin state label to each genomic position. Existing SAGA methods have several limitations caused by the discrete annotation framework: such annotations cannot easily represent varying strengths of genomic elements, and they cannot easily represent combinatorial elements that simultaneously exhibit multiple types of activity. To remedy these limitations, we propose an annotation strategy that instead outputs a vector of chromatin state features at each position rather than a single discrete label. Continuous modeling is common in other fields, such as in topic modeling of text documents. We propose a method, epigenome-ssm-nonneg, that uses a non-negative state space model to efficiently annotate the genome with chromatin state features. We also propose several measures of the quality of a chromatin state feature annotation and we compare the performance of several alternative methods according to these quality measures. RESULTS: We show that chromatin state features from epigenome-ssm-nonneg are more useful for several downstream applications than both continuous and discrete alternatives, including their ability to identify expressed genes and enhancers. Therefore, we expect that these continuous chromatin state features will be valuable reference annotations to be used in visualization and downstream analysis. AVAILABILITY AND IMPLEMENTATION: Source code for epigenome-ssm is available at https://github.com/habibdanesh/epigenome-ssm and Zenodo (DOI: 10.5281/zenodo.6507585). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Target capture sequencing of SARS-CoV-2 genomes using the ONETest Coronaviruses Plus.

We introduce a target capture next-generation sequencing methodology, the ONETest Coronaviruses Plus, to sequence the SARS-CoV-2 genome and select loci of other respiratory viruses. We applied the ONETest on 70 respiratory samples (collected in Florida, USA between May and July, 2020), in which SARS-CoV-2 had been detected by a PCR assay. For 48 of the samples, we also applied the ARTIC protocol. Of the 70 ONETest libraries, 45 (64%) had a (near-)complete sequence (>29,000 bases and >90% covered by >9 reads). Of the 48 ARTIC libraries, 25 (52%) had a (near-)complete sequence. In 19 out of 25 (76%) samples in which both the ONETest and ARTIC yielded (near-)complete sequences, the lineages assigned were identical. As a target capture approach, the ONETest is less prone to loss of sequence coverage than amplicon approaches, and thus can provide complete genomic information more often to track and monitor SARS-CoV-2 variants.