Background The human microbiota are complex systems with important roles in our physiological activities and diseases. Sequencing the microbial genomes in the microbiota can help in our interpretation of their activities. The vast majority of the microbes in the microbiota cannot be isolated for individual sequencing. Current metagenomics practices use short-read sequencing to simultaneously sequence a mixture of microbial genomes. However, these results are in ambiguity during genome assembly, leading to unsatisfactory microbial genome completeness and contig continuity. Linked-read sequencing is able to remove some of these ambiguities by attaching the same barcode to the reads from a long DNA fragment (10–100 kb), thus improving metagenome assembly. However, it is not clear how the choices for several parameters in the use of linked-read sequencing affect the assembly quality. Results We first examined the effects of read depth (C) on metagenome assembly from linked-reads in simulated data and a mock community. The results showed that C positively correlated with the length of assembled sequences but had little effect on their qualities. The latter observation was corroborated by tests using real data from the human gut microbiome, where C demonstrated minor impact on the sequence quality as well as on the proportion of bins annotated as draft genomes. On the other hand, metagenome assembly quality was susceptible to read depth per fragment (CR) and DNA fragment physical depth (CF). For the same C, deeper CR resulted in more draft genomes while deeper CF improved the quality of the draft genomes. We also found that average fragment length (μFL) had marginal effect on assemblies, while fragments per partition (NF/P) impacted the off-target reads involved in local assembly, namely, lower NF/P values would lead to better assemblies by reducing the ambiguities of the off-target reads. In general, the use of linked-reads improved the assembly for contig N50 when compared to Illumina short-reads, but not when compared to PacBio CCS (circular consensus sequencing) long-reads. Conclusions We investigated the influence of linked-read sequencing parameters on metagenome assembly comprehensively. While the quality of genome assembly from linked-reads cannot rival that from PacBio CCS long-reads, the case for using linked-read sequencing remains persuasive due to its low cost and high base-quality. Our study revealed that the probable best practice in using linked-reads for metagenome assembly was to merge the linked-reads from multiple libraries, where each had sufficient CR but a smaller amount of input DNA.
Stony corals form the foundation of coral reefs, which are of prominent ecological and economic significance. A robust workflow for investigating the coral proteome is essential in understanding coral biology. Here we investigated different preparative workflows and characterized the proteome of Platygyra carnosa, a common stony coral of the South China Sea. We found that a combination of bead homogenization with suspension trapping (S-Trap) preparation could yield more than 2700 proteins from coral samples. Annotation using a P. carnosa transcriptome database revealed that the majority of proteins were from the coral host cells (2140, 212, and 427 proteins from host coral, dinoflagellate, and other compartments, respectively). Label-free quantification and functional annotations indicated that a high proportion were involved in protein and redox homeostasis. Furthermore, the S-Trap method achieved good reproducibility in quantitative analysis. Although yielding a low symbiont:host ratio, the method is efficient in characterizing the coral host proteomic landscape, which provides a foundation to explore the molecular basis of the responses of coral host tissues to environmental stressors.
Viruses change constantly during replication, leading to high intra-species diversity. Although many changes are neutral or deleterious, some can confer on the virus different biological properties such as better adaptability. In addition, viral genotypes often have associated metadata, such as host residence, which can help with inferring viral transmission during pandemics. Thus, subspecies analysis can provide important insights into virus characterization. Here, we present VirStrain, a tool taking short reads as input with viral strain composition as output. We rigorously test VirStrain on multiple simulated and real virus sequencing datasets. VirStrain outperforms the state-of-the-art tools in both sensitivity and accuracy.
1AbstractGenome epidemiology, which uses genomic data to analyze the source and spread of infectious diseases, provides important information beyond interview-based methods. Given fast accumulation of sequenced viral genomes, a basic need in genome epidemiology is to identify which reference genomes are identical or closest to the ones in a sequenced sample. Then the associated metadata such as the geographical locations can be utilized to infer the transmission network. In this work, we deliver VirStrain, a fast and accurate tool for conducting strain-level analysis from short reads. By using a greedy covering algorithm, we are able to derive unique k-mer combinations for highly similar reference genomes. VirStrain is able to detect the most possible strain and also multiple strains that may simultaneously infect the same host. We tested VirStrain on three types of RNA viruses whose reference genomes have different similarity distributions. For each types of virus, we assessed VirStrain across multiple benchmark datasets of different properties and complexity. The experimental results on both simulated and real sequencing data show that VirStrain outperforms other strain identification tools.
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