The human respiratory microbiome plays a crucial role in respiratory health, but there is no comprehensive respiratory genome catalogue (RGC) for studying the microbiome. In this study, we collected whole-metagenome shotgun sequencing data from 4067 samples and sequenced long reads of 124 samples, yielding 9.08 and 0.42 Tbp of short- and long-read data, respectively. By submitting these data with a novel assembly algorithm, we obtained a comprehensive human RGC. This high-quality RGC contains 190,443 contigs over 1 kbps and an N50 length exceeding 13 kbps; it comprises 159 high-quality and 393 medium-quality genomes, including 117 previously uncharacterized respiratory bacteria. Moreover, the RGC contains 209 respiratory-specific species not captured by the unified human gastrointestinal genome. Using the RGC, we revisited a study on a pediatric pneumonia dataset and identified 17 pneumonia-specific respiratory pathogens, reversing an inaccurate etiological conclusion due to the previous incomplete reference. Furthermore, we applied the RGC to the data of 62 participants with a clinical diagnosis of infection. Compared to the Nucleotide database, the RGC yielded greater specificity (0 versus 0.444, respectively) and sensitivity (0.852 versus 0.881, respectively), suggesting that the RGC provides superior sensitivity and specificity for the clinical diagnosis of respiratory diseases.

MOTIVATION: The microbial community plays an essential role in human diseases and physiological activities. The functions of microbes can differ due to strain-level differences in the genome sequences. Shotgun metagenomic sequencing allows us to profile the strains in microbial communities practically. However, current methods are underdeveloped due to the highly similar sequences among strains. We observe that strains genotypes at the same single nucleotide variant (SNV) locus can be speculated by the genotype frequencies. Also, the variants in different loci covered by the same reads can provide evidence that they reside on the same strain. RESULTS: These insights inspire us to design PStrain, an optimization method that utilizes genotype frequencies and the reads which cover multiple SNV loci to profile strains iteratively based on SNVs in a set of MetaPhlAn2 marker genes. Compared to the state-of-art methods, PStrain, on average, improved the performance of inferring strains abundances and genotypes by 87.75% and 59.45%, respectively. We have applied the PStrain package to the dataset with two cohorts of colorectal cancer (CRC) and found that the sequences of Bacteroides coprocola strains are significantly different between CRC and control samples, which is the first time to report the potential role of B.coprocola in the gut microbiota of CRC. AVAILABILITYAND IMPLEMENTATION: https://github.com/wshuai294/PStrain. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Genome assembly is fundamental for de novo genome analysis. Hybrid assembly, utilizing various sequencing technologies increases both contiguity and accuracy. While such approaches require extra costly sequencing efforts, the information provided millions of existed whole-genome sequencing data have not been fully utilized to resolve the task of scaffolding. Genetic recombination patterns in population data indicate non-random association among alleles at different loci, can provide physical distance signals to guide scaffolding. RESULTS: In this paper, we propose LDscaff for draft genome assembly incorporating linkage disequilibrium information in population data. We evaluated the performance of our method with both simulated data and real data. We simulated scaffolds by splitting the pig reference genome and reassembled them. Gaps between scaffolds were introduced ranging from 0 to 100 KB. The genome misassembly rate is 2.43% when there is no gap. Then we implemented our method to refine the Giant Panda genome and the donkey genome, which are purely assembled by NGS data. After LDscaff treatment, the resulting Panda assembly has scaffold N50 of 3.6 MB, 2.5 times larger than the original N50 (1.3 MB). The re-assembled donkey assembly has an improved N50 length of 32.1 MB from 23.8 MB. CONCLUSIONS: Our method effectively improves the assemblies with existed re-sequencing data, and is an potential alternative to the existing assemblers required for the collection of new data.