Evaluating the performance of tools used to call minority variants from whole genome short-read data

An overview of next generation sequencing strategies and genomics tools used for tuberculosis research

Tuberculosis (TB) is a grave public health concern and is considered the foremost contributor to human mortality resulting from infectious disease. Due to the stringent clonality and extremely restricted genomic diversity, conventional methods prove inefficient for in-depth exploration of minor genomic variations and the evolutionary dynamics operating in Mycobacterium tuberculosis (M.tb) populations. Until now, the majority of reviews have primarily focused on delineating the application of whole-genome sequencing (WGS) in predicting antibiotic resistant genes, surveillance of drug resistance strains, and M.tb lineage classifications. Despite the growing use of next generation sequencing (NGS) and WGS analysis in TB research, there are limited studies that provide a comprehensive summary of there role in studying macroevolution, minor genetic variations, assessing mixed TB infections, and tracking transmission networks at an individual level. This highlights the need for systematic effort to fully explore the potential of WGS and its associated tools in advancing our understanding of TB epidemiology and disease transmission. We delve into the recent bioinformatics pipelines and NGS strategies that leverage various genetic features and simultaneous exploration of host-pathogen protein expression profile to decipher the genetic heterogeneity and host-pathogen interaction dynamics of the M.tb infections. This review highlights the potential benefits and limitations of NGS and bioinformatics tools and discusses their role in TB detection and epidemiology. Overall, this review could be a valuable resource for researchers and clinicians interested in NGS-based approaches in TB research.

Long-Read Genome Assembly and Gene Model Annotations for the Rodent Malaria Parasite Plasmodium yoelii 17XNL

Malaria causes over 600 thousand fatalities each year, with most cases attributed to the human-infectious Plasmodium falciparum species. Many rodent-infectious Plasmodium species, like Plasmodium berghei and Plasmodium yoelii, have been used as model species that can expedite studies of this pathogen. P. yoelii is an especially good model for investigating the mosquito and liver stages of parasite development because key attributes closely resemble those of P. falciparum. Because of its importance, in 2002 the 17XNL strain of P. yoelii was the first rodent malaria parasite to be sequenced. While a breakthrough effort, the assembly consisted of >5000 contiguous sequences that adversely impacted the annotated gene models. While other rodent malaria parasite genomes have been sequenced and annotated since then, including the related P. yoelii 17X strain, the 17XNL strain has not. As a result, genomic data for 17X has become the de facto reference genome for the 17XNL strain while leaving open questions surrounding possible differences between the 17XNL and 17X genomes. In this work, we present a high-quality genome assembly for P. yoelii 17XNL using PacBio DNA sequencing. In addition, we use Nanopore and Illumina RNA sequencing of mixed blood stages to create complete gene models that include coding sequences, alternate isoforms, and UTR designations. A comparison of the 17X and this new 17XNL assembly revealed biologically meaningful differences between the strains due to the presence of coding sequence variants. Taken together, our work provides a new genomic framework for studies with this commonly used rodent malaria model species.

Long-Read Genome Assembly and Gene Model Annotations for the Rodent Malaria Parasite Plasmodium yoelii 17XNL

Malaria causes over 200 million infections and over 600 thousand fatalities each year, with most cases attributed to a human-infectious Plasmodium species, Plasmodium falciparum . Many rodent-infectious Plasmodium species, like Plasmodium berghei, Plasmodium chabaudi , and Plasmodium yoelii , have been used as genetically tractable model species that can expedite studies of this pathogen. In particular, P. yoelii is an especially good model for investigating the mosquito and liver stages of parasite development because key attributes closely resemble those of P. falciparum . Because of its importance to malaria research, in 2002 the 17XNL strain of P. yoelii was the first rodent malaria parasite to be sequenced. While sequencing and assembling this genome was a breakthrough effort, the final assembly consisted of >5000 contiguous sequences that impacted the creation of annotated gene models. While other important rodent malaria parasite genomes have been sequenced and annotated since then, including the related P. yoelii 17X strain, the 17XNL strain has not. As a result, genomic data for 17X has become the de facto reference genome for the 17XNL strain while leaving open questions surrounding possible differences between the 17XNL and 17X genomes. In this work, we present a high-quality genome assembly for P. yoelii 17XNL using HiFi PacBio long-read DNA sequencing. In addition, we use Nanopore long-read direct RNA-seq and Illumina short-read sequencing of mixed blood stages to create complete gene models that include not only coding sequences but also alternate transcript isoforms, and 5' and 3' UTR designations. A comparison of the 17X and this new 17XNL assembly revealed biologically meaningful differences between the strains due to the presence of coding sequence variants. Taken together, our work provides a new genomic and gene expression framework for studies with this commonly used rodent malaria model species.

Genomic epidemiology of SARS-CoV-2 under an elimination strategy in Hong Kong

Hong Kong employed a strategy of intermittent public health and social measures alongside increasingly stringent travel regulations to eliminate domestic SARS-CoV-2 transmission. By analyzing 1899 genome sequences (>18% of confirmed cases) from 23-January-2020 to 26-January-2021, we reveal the effects of fluctuating control measures on the evolution and epidemiology of SARS-CoV-2 lineages in Hong Kong. Despite numerous importations, only three introductions were responsible for 90% of locally-acquired cases. Community outbreaks were caused by novel introductions rather than a resurgence of circulating strains. Thus, local outbreak prevention requires strong border control and community surveillance, especially during periods of less stringent social restriction. Non-adherence to prolonged preventative measures may explain sustained local transmission observed during wave four in late 2020 and early 2021. We also found that, due to a tight transmission bottleneck, transmission of low-frequency single nucleotide variants between hosts is rare.

Viral variant visualizer (VVV): A novel bioinformatic tool for rapid and simple visualization of viral genetic diversity

Here a bioinformatic pipeline VVV has been developed to analyse viral populations in a given sample from Next Generation Sequencing (NGS) data. To date, handling large amounts of data from NGS requires the expertise of bioinformaticians, both for data processing and result analysis. Consequently, VVV was designed to help non-bioinformaticians to perform these tasks. By providing only the NGS data file, the developed pipeline generated consensus sequences and determined the composition of the viral population for an avian Metapneumovirus (AMPV) and three different animal coronaviruses (Porcine Epidemic Diarrhea Virus (PEDV), Turkey Coronavirus (TCoV) and Infectious Bronchitis Virus (IBV)). In all cases, the pipeline produced viral consensus genomes corresponding to known consensus sequence and made it possible to highlight the presence of viral genetic variants through a single graphic representation. The method was validated by comparing the viral populations of an AMPV field sample, and of a copy of this virus produced from a DNA clone. VVV demonstrated that the cloned virus population was homogeneous (as designed) at position 2934 where the wild-type virus demonstrated two variant populations at a ratio of almost 50:50. A total of 18, 10, 3 and 28, viral genetic variants were detected for AMPV, PEDV, TCoV and IBV respectively. The simplicity of this pipeline makes the study of viral genetic variants more accessible to a wide variety of biologists, which should ultimately increase the rate of understanding of the mechanisms of viral genetic evolution.

Evaluating assembly and variant calling software for strain-resolved analysis of large DNA viruses

Infection with human cytomegalovirus (HCMV) can cause severe complications in immunocompromised individuals and congenitally infected children. Characterizing heterogeneous viral populations and their evolution by high-throughput sequencing of clinical specimens requires the accurate assembly of individual strains or sequence variants and suitable variant calling methods. However, the performance of most methods has not been assessed for populations composed of low divergent viral strains with large genomes, such as HCMV. In an extensive benchmarking study, we evaluated 15 assemblers and 6 variant callers on 10 lab-generated benchmark data sets created with two different library preparation protocols, to identify best practices and challenges for analyzing such data. Most assemblers, especially metaSPAdes and IVA, performed well across a range of metrics in recovering abundant strains. However, only one, Savage, recovered low abundant strains and in a highly fragmented manner. Two variant callers, LoFreq and VarScan2, excelled across all strain abundances. Both shared a large fraction of false positive variant calls, which were strongly enriched in T to G changes in a 'G.G' context. The magnitude of this context-dependent systematic error is linked to the experimental protocol. We provide all benchmarking data, results and the entire benchmarking workflow named QuasiModo, Quasispecies Metric determination on omics, under the GNU General Public License v3.0 (https://github.com/hzi-bifo/Quasimodo), to enable full reproducibility and further benchmarking on these and other data.