The Genome Sequence Archive (GSA) is a data repository for archiving raw sequence data, which provides data storage and sharing services for worldwide scientific communities. Considering explosive data growth with diverse data types, here we present the GSA family by expanding into a set of resources for raw data archive with different purposes, namely, GSA (https://ngdc.cncb.ac.cn/gsa/), GSA for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/), and Open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix/). Compared with the 2017 version, GSA has been significantly updated in data model, online functionalities, and web interfaces. GSA-Human, as a new partner of GSA, is a data repository specialized in human genetics-related data with controlled access and security. OMIX, as a critical complement to the two resources mentioned above, is an open archive for miscellaneous data. Together, all these resources form a family of resources dedicated to archiving explosive data with diverse types, accepting data submissions from all over the world, and providing free open access to all publicly available data in support of worldwide research activities.

Since the proposal for pangenomic study, there have been a dozen software tools actively in use for pangenomic analysis. By the end of 2014, Panseq and the pan-genomes analysis pipeline (PGAP) ranked as the top two most popular packages according to cumulative citations of peer-reviewed scientific publications. The functions of the software packages and tools, albeit variable among them, include categorizing orthologous genes, calculating pangenomic profiles, integrating gene annotations, and constructing phylogenies. As epigenomic elements are being gradually revealed in prokaryotes, it is expected that pangenomic databases and toolkits have to be extended to handle information of detailed functional annotations for genes and non-protein-coding sequences including non-coding RNAs, insertion elements, and conserved structural elements. To develop better bioinformatic tools, user feedback and integration of novel features are both of essence.

To unravel the genetic mechanisms of disease and physiological traits, it requires comprehensive sequencing analysis of large sample size in Chinese populations. Here, we report the primary results of the Chinese Academy of Sciences Precision Medicine Initiative (CASPMI) project launched by the Chinese Academy of Sciences, including the de novo assembly of a northern Han reference genome (NH1.0) and whole genome analyses of 597 healthy people coming from most areas in China. Given the two existing reference genomes for Han Chinese (YH and HX1) were both from the south, we constructed NH1.0, a new reference genome from a northern individual, by combining the sequencing strategies of PacBio, 10× Genomics, and Bionano mapping. Using this integrated approach, we obtained an N50 scaffold size of 46.63 Mb for the NH1.0 genome and performed a comparative genome analysis of NH1.0 with YH and HX1. In order to generate a genomic variation map of Chinese populations, we performed the whole-genome sequencing of 597 participants and identified 24.85 million (M) single nucleotide variants (SNVs), 3.85 M small indels, and 106,382 structural variations. In the association analysis with collected phenotypes, we found that the T allele of rs1549293 in KAT8 significantly correlated with the waist circumference in northern Han males. Moreover, significant genetic diversity in MTHFR, TCN2, FADS1, and FADS2, which associate with circulating folate, vitamin B12, or lipid metabolism, was observed between northerners and southerners. Especially, for the homocysteine-increasing allele of rs1801133 (MTHFR 677T), we hypothesize that there exists a “comfort” zone for a high frequency of 677T between latitudes of 35–45 degree North. Taken together, our results provide a high-quality northern Han reference genome and novel population-specific data sets of genetic variants for use in the personalized and precision medicine.

Defense systems are vital weapons for prokaryotes to resist heterologous DNA and survive from the constant invasion of viruses, and they are widely used in biochemistry investigation and antimicrobial drug research. So far, numerous types of defense systems have been discovered, but there is no comprehensive defense systems database to organize prokaryotic defense gene datasets. To fill this gap, we unveil the prokaryotic antiviral defense system (PADS) Arsenal (https://bigd.big.ac.cn/padsarsenal), a public database dedicated to gathering, storing, analyzing and visualizing prokaryotic defense gene datasets. The initial version of PADS Arsenal integrates 18 distinctive categories of defense system with the annotation of 6 600 264 genes retrieved from 63,701 genomes across 33 390 species of archaea and bacteria. PADS Arsenal provides various ways to retrieve defense systems related genes information and visualize them with multifarious function modes. Moreover, an online analysis pipeline is integrated into PADS Arsenal to facilitate annotation and evolutionary analysis of defense genes. PADS Arsenal can also visualize the dynamic variation information of defense genes from pan-genome analysis. Overall, PADS Arsenal is a state-of-the-art open comprehensive resource to accelerate the research of prokaryotic defense systems.

Compared with conventional comparative genomics, the recent studies in pan-genomics have provided further insights into species genomic dynamics, taxonomy and identification, pathogenicity and environmental adaptation. To better understand genome characteristics of species of interest and to fully excavate key metabolic and resistant genes and their conservations and variations, here we present ProPan (https://ngdc.cncb.ac.cn/propan), a public database covering 23 archaeal species and 1,481 bacterial species (in a total of 51,882 strains) for comprehensively profiling prokaryotic pan-genome dynamics. By analyzing and integrating these massive datasets, ProPan offers three major aspects for the pan-genome dynamics of the species of interest: 1) the evaluations of various species' characteristics and composition in pan-genome dynamics; 2) the visualization of map association, the functional annotation and presence/absence variation for all contained species' gene clusters; 3) the typical characteristics of the environmental adaptation, including resistance genes prediction of 126 substances (biocide, antimicrobial drug and metal) and evaluation of 31 metabolic cycle processes. Besides, ProPan develops a very user-friendly interface, flexible retrieval and multi-level real-time statistical visualization. Taken together, ProPan will serve as a weighty resource for the studies of prokaryotic pan-genome dynamics, taxonomy and identification as well as environmental adaptation.