The human reference genome is still incomplete, especially for those population-specific or individual-specific regions, which may have important functions. Here, we developed a HUman Pan-genome ANalysis (HUPAN) system to build the human pan-genome. We applied it to 185 deep sequencing and 90 assembled Han Chinese genomes and detected 29.5 Mb novel genomic sequences and at least 188 novel protein-coding genes missing in the human reference genome (GRCh38). It can be an important resource for the human genome-related biomedical studies, such as cancer genome analysis. HUPAN is freely available at http://cgm.sjtu.edu.cn/hupan/ and https://github.com/SJTU-CGM/HUPAN . Electronic supplementary material The online version of this article (10.1186/s13059-019-1751-y) contains supplementary material, which is available to authorized users.

Background Diversity-generating retroelements (DGRs) are a unique family of retroelements that generate sequence diversity of DNA to benefit their hosts by introducing variations and accelerating the evolution of target proteins. They exist widely in bacteria, archaea, phage and plasmid. However, our understanding about DGRs in natural environments was still very limited. Results We developed an efficient computational algorithm to identify DGRs, and applied it to characterize DGRs in more than 80,000 sequenced bacterial genomes as well as more than 4,000 human metagenome datasets. In total, we identified 948 non-redundant DGRs, which expanded the number of known DGRs in bacterial genomes and human microbiomes by about 55%, and provided a much more comprehensive reference for the study of DGRs. Phylogenetic analysis was done for identified DGRs. The putative target genes of DGRs were searched, and the functions of these target genes were investigated with a comprehensive alignment against the nr database. Conclusions DGR system is a powerful and universal mechanism to generate diversity. DGR evolution is closely associated with the living environment and their cassette structures. Furthermore, it may impact a wide range of functional processes in addition to receptor-binding. These results significantly improved our understanding about DGRs. Electronic supplementary material The online version of this article (10.1186/s12864-019-5951-3) contains supplementary material, which is available to authorized users.

BackgroundMany methods have been developed for metagenomic sequence classification, and most of them depend heavily on genome sequences of the known organisms. A large portion of sequencing sequences may be classified as unknown, which greatly impairs our understanding of the whole sample.ResultHere we present MetaBinG2, a fast method for metagenomic sequence classification, especially for samples with a large number of unknown organisms. MetaBinG2 is based on sequence composition, and uses GPUs to accelerate its speed. A million 100 bp Illumina sequences can be classified in about 1 min on a computer with one GPU card. We evaluated MetaBinG2 by comparing it to multiple popular existing methods. We then applied MetaBinG2 to the dataset of MetaSUB Inter-City Challenge provided by CAMDA data analysis contest and compared community composition structures for environmental samples from different public places across cities.ConclusionCompared to existing methods, MetaBinG2 is fast and accurate, especially for those samples with significant proportions of unknown organisms.ReviewersThis article was reviewed by Drs. Eran Elhaik, Nicolas Rascovan, and Serghei Mangul.Electronic supplementary materialThe online version of this article (10.1186/s13062-018-0220-y) contains supplementary material, which is available to authorized users.