ObjectivesThis study aimed to characterize the genomic epidemiology of human adenoviruses (HAdVs) in Hubei, China, using metagenomic next-generation sequencing (mNGS).MethodsIn total, 25 HAdV-positive samples collected from 21 pediatric patients were sequenced and subjected to mNGS using the NextSeq 550 and GenoLab M sequencing platforms. The metagenomic data were assembled de novo for molecular typing, phylogenetic and recombination analyzes.ResultsWe assembled 50 HAdV genomes, 88% (22/25) genomes from GenoLab M, and 84% (21/25) genomes from NextSeq 550 have perfect alignments to reference genomes with greater than 90%. The most fully assembled 25 genomes were categorized into 7 HAdV genotypes, the most abundant of which were HAdV-B3 (9/25) and HAdV-C2 (6/25). Phylogenetic analyzes revealed that the newly isolated HAdV-B3 strains diverged into separate clusters according to their genotypes. Vigilance is needed that HAdV-B3 isolates have begun to form new distinct clusters. High nucleotide identity was observed in the whole genome level within the same HAdV genotypes, while marked differences of three capsid genes across HAdV genotypes were noted. The high nucleotide diversity regions were concordant with the reported hypervariable regions. Further, three recombinant strains were identified: S64 and S71 originated from the parental strains HAdV-B14 and HAdV-B11, and S28 originated from HAdV-C1, HAdV-C5, and HAdV-CBJ113. GenoLab M and NextSeq 550 showed comparable performance with respect to data yield, duplication rate, human ratio, and assembly completeness.ConclusionThe sequencing quality and assembly accuracy showed that mNGS assembled genomes can be used for subsequently HAdV genotyping and genomic characterization. The high nucleotide diversity of capsid genes and high frequency of recombination events has highlighted the necessity for HAdV epidemiological surveillance in China.

Algal genomics research contributes to a deeper understanding of algal evolution and provides useful genomics inferences correlated with various functions. Published algal genome sequences are very limited owing to genome assembly challenges. Because genome data of freshwater algae are rapidly increasing with the recent boom in next-generation sequencing and bioinformatics, an interface to store, interlink, and display these data is needed. To provide a substantial genomic resource specifically for freshwater algae, we developed the Freshwater Algae Database (FWAlgaeDB), a user-friendly, constantly updated online repository for integrating genomic data and annotation information. This database, which includes information on 204 freshwater algae, allows easy access to gene repertoires and gene clusters of interest and facilitates potential applications. Three functional modules are integrated into FWAlgaeDB: a Basic Local Alignment Search Tool tool for similarity analyses, a Search tool for rapid data retrieval, and a Download function for data downloads. This database tool is freely available at http://www.fwalagedb.com/#/home. To demonstrate the utility of FWAlgaeDB, we also individually mapped metagenomic sequencing reads of 10 water samples to FWAlgaeDB and Nt algae databases we constructed to obtain taxonomic composition information. According to the mapping results, FWAlgaeDB may be a better choice for identifying algal species in freshwater samples, with fewer potential false positives because of its focus on freshwater algal species. FWAlgaeDB can therefore serve as an open-access, sustained platform to provide genomic data and molecular analysis tools specifically for freshwater algae.

Background Targeted genomic sequencing (TS) greatly benefits precision oncology by rapidly detecting genetic variations with better accuracy and sensitivity owing to its high sequencing depth. Multiple sequencing platforms and variant calling tools are available for TS, making it excruciating for researchers to choose. Therefore, benchmarking study across different platforms and pipelines available for TS is imperative. In this study, we performed a TSof Reference OncoSpan FFPE (HD832) sample enriched by TSO500 panel using four commercially available sequencers, and analyzed the output 50 datasets using five commonly-used bioinformatics pipelines. We systematically investigated the sequencing quality and variant detection sensitivity, expecting to provide optimal recommendations for future research. Results Four sequencing platforms returned highly concordant results in terms of base quality (Q20>94%), sequencing coverage (>97%) and depth (>2000×). Benchmarking revealed good concordance of variant calling across different platforms and pipelines, among which, FASTASeq 300 platform showed the highest sensitivity (100%) in high-confidence variants calling when analyzed by SNVer and VarScan 2 algorithms. Furthermore, this sequencer demonstrated the shortest sequencing time (~21 hr) at the sequencing mode PE150. Through the intersection of 50 datasets generated in this study, we recommended a novel set of variant genes outside the truth set published by HD832, expecting to replenish HD832 for future research of tumor variant diagnosis. Considering the dissimilarity of variant calls across different pipelines for datasets from the same platform, we recommended an integration of multiple tools to improve variant calling sensitivity and accuracy for the cancer genome. Conclusions Illumina and GeneMind technologies can be used independently or together by public health laboratories performing tumor TS. FASTASeq 300 platform performs better regarding variant detection sensitivity under SNVer and VarScan 2 algorithms along with the shortest turnaround time. Our study provides a standardized target sequencing resource to benchmark new bioinformatics protocols and sequencing platforms.