Liang Sun

It is a challenge to classify protein-coding or non-coding transcripts, especially those re-constructed from high-throughput sequencing data of poorly annotated species. This study developed and evaluated a powerful signature tool, Coding-Non-Coding Index (CNCI), by profiling adjoining nucleotide triplets to effectively distinguish protein-coding and non-coding sequences independent of known annotations. CNCI is effective for classifying incomplete transcripts and sense-antisense pairs. The implementation of CNCI offered highly accurate classification of transcripts assembled from whole-transcriptome sequencing data in a cross-species manner, that demonstrated gene evolutionary divergence between vertebrates, and invertebrates, or between plants, and provided a long non-coding RNA catalog of orangutan. CNCI software is available at http://www.bioinfo.org/software/cnci.

NONCODE (http://www.bioinfo.org/noncode/) is a systematic database that is dedicated to presenting the most complete collection and annotation of non-coding RNAs (ncRNAs), especially long non-coding RNAs (lncRNAs). Since NONCODE 2016 was released two years ago, the amount of novel identified ncRNAs has been enlarged by the reduced cost of next-generation sequencing, which has produced an explosion of newly identified data. The third-generation sequencing revolution has also offered longer and more accurate annotations. Moreover, accumulating evidence confirmed by biological experiments has provided more comprehensive knowledge of lncRNA functions. The ncRNA data set was expanded by collecting newly identified ncRNAs from literature published over the past two years and integration of the latest versions of RefSeq and Ensembl. Additionally, pig was included in the database for the first time, bringing the total number of species to 17. The number of lncRNAs in NONCODEv5 increased from 527 336 to 548 640. NONCODEv5 also introduced three important new features: (i) human lncRNA-disease relationships and single nucleotide polymorphism-lncRNA-disease relationships were constructed; (ii) human exosome lncRNA expression profiles were displayed; (iii) the RNA secondary structures of NONCODE human transcripts were predicted. NONCODEv5 is also accessible through http://www.noncode.org/.

Recently, the pharmaceutical industry has heavily emphasized phenotypic drug discovery (PDD), which relies primarily on knowledge about phenotype changes associated with diseases. Traditional Chinese medicine (TCM) provides a massive amount of information on natural products and the clinical symptoms they are used to treat, which are the observable disease phenotypes that are crucial for clinical diagnosis and treatment. Curating knowledge of TCM symptoms and their relationships to herbs and diseases will provide both candidate leads and screening directions for evidence-based PDD programs. Therefore, we present SymMap, an integrative database of traditional Chinese medicine enhanced by symptom mapping. We manually curated 1717 TCM symptoms and related them to 499 herbs and 961 symptoms used in modern medicine based on a committee of 17 leading experts practicing TCM. Next, we collected 5235 diseases associated with these symptoms, 19 595 herbal constituents (ingredients) and 4302 target genes, and built a large heterogeneous network containing all of these components. Thus, SymMap integrates TCM with modern medicine in common aspects at both the phenotypic and molecular levels. Furthermore, we inferred all pairwise relationships among SymMap components using statistical tests to give pharmaceutical scientists the ability to rank and filter promising results to guide drug discovery. The SymMap database can be accessed at http://www.symmap.org/ and https://www.bioinfo.org/symmap.

NONCODE (http://www.noncode.org/) is a comprehensive database of collection and annotation of noncoding RNAs, especially long non-coding RNAs (lncRNAs) in animals. NONCODEV6 is dedicated to providing the full scope of lncRNAs across plants and animals. The number of lncRNAs in NONCODEV6 has increased from 548 640 to 644 510 since the last update in 2017. The number of human lncRNAs has increased from 172 216 to 173 112. The number of mouse lncRNAs increased from 131 697 to 131 974. The number of plant lncRNAs is 94 697. The relationship between lncRNAs in human and cancer were updated with transcriptome sequencing profiles. Three important new features were also introduced in NONCODEV6: (i) updated human lncRNA-disease relationships, especially cancer; (ii) lncRNA annotations with tissue expression profiles and predicted function in five common plants; iii) lncRNAs conservation annotation at transcript level for 23 plant species. NONCODEV6 is accessible through http://www.noncode.org/.

OBJECTIVE: Elevated lipopolysaccharide-binding protein (LBP), a marker of subclinical endotoxemia, may be involved in the pathogenesis of obesity and metabolic risk. We aimed to investigate the association between plasma LBP and metabolic disorders in apparently healthy Chinese. RESEARCH DESIGN AND METHODS: A population-based study including 559 overweight/obese (BMI >or=24.0 kg/m(2)) and 500 normal-weight (18.0 <or= BMI <24.0 kg/m(2)) subjects aged 35-54 years was conducted in Shanghai, China. Fasting plasma glucose, lipid profile, LBP, high-sensitivity C-reactive protein, interleukin-6, high-molecular-weight (HMW) adiponectin, leptin, hepatic enzymes, and body composition were measured. Metabolic syndrome was defined by the updated National Cholesterol Education Program Adult Treatment Panel III criterion for Asian Americans. RESULTS: LBP levels were significantly higher in overweight/obese individuals than in normal-weight individuals (geometric mean 27.6 [95% CI 25.2-30.3] vs. 10.0 [9.1-11.1] microg/ml; P < 0.001). After multiple adjustments including BMI, the odds ratios were 3.54 (95% CI 2.05-6.09) and 5.53 (95% CI 2.64-11.59) for metabolic syndrome and type 2 diabetes, respectively, comparing the highest with the lowest LBP quartile. Further adjustments for inflammatory markers almost abolished the significant association of LBP with metabolic syndrome but not that with type 2 diabetes, and controlling for adipokines and hepatic enzymes did not substantially alter the results. CONCLUSIONS: Elevated circulating LBP was associated with obesity, metabolic syndrome, and type 2 diabetes in apparently healthy Chinese. These findings suggested a role of lipopolysaccharide via initiation of innate immune mechanism(s) in metabolic disorders. Prospective studies are needed to confirm these results.