Comparative genomic sequencing to characterize Mycoplasma pneumoniae genome, typing, and drug resistance.

To analyze the characteristics of Mycoplasma pneumoniae as well as macrolide antibiotic resistance through whole-genome sequencing and comparative genomics. Thirteen clinical strains isolated from 2003 to 2019 were selected, 10 of which were resistant to erythromycin (MIC >64 µg/mL), including 8 P1-type I and 2 P1-type II. Three were sensitive (<1 µg/mL) and P1-type II. One resistant strain had an A→G point mutation at position 2064 in region V of the 23S rRNA, the others had it at position 2063, while the three sensitive strains had no mutation here. Genome assembly and comparative genome analysis revealed a high level of genome consistency within the P1 type, and the primary differences in genome sequences concentrated in the region encoding the P1 protein. In P1-type II strains, three specific gene mutations were identified: C162A and A430G in L4 gene and T1112G mutation in the CARDS gene. Clinical information showed seven cases were diagnosed with severe pneumonia, all of which were infected with drug-resistant strains. Notably, BS610A4 and CYM219A1 exhibited a gene multi-copy phenomenon and shared a conserved functional domain with the DUF31 protein family. Clinically, the patients had severe refractory pneumonia, with pleural effusion, necessitating treatment with glucocorticoids and bronchoalveolar lavage. The primary variations between strains occur among different P1-types, while there is a high level of genomic consistency within P1-types. Three mutation loci associated with specific types were identified, and no specific genetic alterations directly related to clinical presentation were observed.IMPORTANCEMycoplasma pneumoniae is an important pathogen of community-acquired pneumonia, and macrolide resistance brings difficulties to clinical treatment. We analyzed the characteristics of M. pneumoniae as well as macrolide antibiotic resistance through whole-genome sequencing and comparative genomics. The work addressed primary variations between strains that occur among different P1-types, while there is a high level of genomic consistency within P1-types. In P1-type II strains, three specific gene mutations were identified: C162A and A430G in L4 gene and T1112G mutation in the CARDS gene. All the strains isolated from severe pneumonia cases were drug-resistant, two of which exhibited a gene multi-copy phenomenon, sharing a conserved functional domain with the DUF31 protein family. Three mutation loci associated with specific types were identified, and no specific genetic alterations directly related to clinical presentation were observed.

Plant genomic resources at National Genomics Data Center: assisting in data-driven breeding applications.

Genomic data serve as an invaluable resource for unraveling the intricacies of the higher plant systems, including the constituent elements within and among species. Through various efforts in genomic data archiving, integrative analysis and value-added curation, the National Genomics Data Center (NGDC), which is a part of the China National Center for Bioinformation (CNCB), has successfully established and currently maintains a vast amount of database resources. This dedicated initiative of the NGDC facilitates a data-rich ecosystem that greatly strengthens and supports genomic research efforts. Here, we present a comprehensive overview of central repositories dedicated to archiving, presenting, and sharing plant omics data, introduce knowledgebases focused on variants or gene-based functional insights, highlight species-specific multiple omics database resources, and briefly review the online application tools. We intend that this review can be used as a guide map for plant researchers wishing to select effective data resources from the NGDC for their specific areas of study. Supplementary Information: The online version contains supplementary material available at 10.1007/s42994-023-00134-4.

TCOD: an integrated resource for tropical crops.

Tropical crops are vital for tropical agriculture, with resource scarcity, functional diversity and extensive market demand, providing considerable economic benefits for the world's tropical agriculture-producing countries. The rapid development of sequencing technology has promoted a milestone in tropical crop research, resulting in the generation of massive amount of data, which urgently needs an effective platform for data integration and sharing. However, the existing databases cannot fully satisfy researchers' requirements due to the relatively limited integration level and untimely update. Here, we present the Tropical Crop Omics Database (TCOD, https://ngdc.cncb.ac.cn/tcod), a comprehensive multi-omics data platform for tropical crops. TCOD integrates diverse omics data from 15 species, encompassing 34 chromosome-level de novo assemblies, 1 255 004 genes with functional annotations, 282 436 992 unique variants from 2048 WGS samples, 88 transcriptomic profiles from 1997 RNA-Seq samples and 13 381 germplasm items. Additionally, TCOD not only employs genes as a bridge to interconnect multi-omics data, enabling cross-species comparisons based on homology relationships, but also offers user-friendly online tools for efficient data mining and visualization. In short, TCOD integrates multi-species, multi-omics data and online tools, which will facilitate the research on genomic selective breeding and trait biology of tropical crops.

Calibration of Linear Time-Varying Frequency Errors for Distributed ISAR Imaging Based on the Entropy Minimization Principle.

The inevitable frequency errors owing to the frequency mismatch of a transmitter and receiver oscillators could seriously deteriorate the imaging performance in distributed inverse synthetic aperture radar (ISAR) system. In this paper, for this issue, a novel method is proposed to calibrate the linear time-varying frequency errors (LTFE) between the transmitting node and the receiving node. The cost function is constructed based on the entropy minimization principle and the problem of LTFE calibration is transformed into cost function optimization. The frequency error coefficient, which minimizes the image entropy, is obtained by searching optimum solution in the solution space of cost function. Then, the original signal is calibrated by the frequency error coefficient. Finally, the effectiveness of the proposed method is demonstrated by simulation and real-data experiments.