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Here, we present the complete genome sequence and annotation of Ralstonia syzygii subsp. indonesiensis strain LLRS-1, which caused bacterial wilt on flue-cured tobacco in Yunnan province, southwest China. Strain LLRS-1 is the first R. syzygii strain identified to be pathogenic to tobacco in China. The completely assembled genome of strain LLRS-1 consists of a 3648314-bp circular chromosome and a 2046405-bp megaplasmid with 5190 protein-coding genes, 55 tRNAs, 28 sRNAs, 3 structural RNAs ( 5S, 16S, and 23S), and has a G+C content of 67.05%.

A Gram-negative, aerobic, motile with paired polar flagella and rod-shaped bacterium strain (56D2T) was isolated from tobacco planting soil in Yunnan, PR China. Major fatty acids were C16  :  1 ω7c (summed feature 3), C16  :  0 and C18  :  1  ω7c (summed feature 8). The polar lipid profile of strain 56D2T consisted of diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, one unidentified aminophospholipid and one unidentified glycolipid. Moreover, strain 56D2T contained ubiquinone Q-8 as the sole respiratory quinone. 16S rRNA gene sequence analysis showed that strain 56D2T was closely related to members of the genus Ralstonia and the two type strains with the highest sequence identities were R. mannitolilytica LMG 6866T (98.36 %) and R. pickettii K-288T (98.22 %). The 16S rRNA gene sequence identities between strain 56D2T and other members of the genus Ralstonia were below 98.00 %. Genome sequencing revealed a genome size of 5.87 Mb and a G+C content of 63.7 mol%. The average nucleotide identity values between strain 56D2T and R. pickettii K-288T, R. mannitolilytica LMG 6866 T and R. insidiosa CCUG 46789T were less than 95 %, and the in silico DNA–DNA hybridization values (yielded by formula 2) were less than 70 %. Based on these data, we conclude that strain 56D2T represents a novel species of the genus Ralstonia , for which the name Ralstonia wenshanensis sp. nov. is proposed. The type strain of Ralstonia wenshanensis sp. nov. is 56D2T (=CCTCC AB 2021466T=GDMCC 1.2886T=JCM 35178T).

The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS. Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.