Biomedical subjects

Runsheng Chen

Publications and source records attributed to Runsheng Chen.

39 records · Page 3Linked to original sources

PubMed2002

Proteome-wide analysis of protein function composition reveals the clustering and phylogenetic properties of organisms.

A 17-dimensional vector named the proteome vector is defined to represent an organism. The components of the vector reflect the relative contents of protein-encoding genes of the 17 cluster of orthologous groups of proteins (COGs) classes in the whole genome of the relevant organism. Based on the definition of this proteome vector, the fuzzy clustering of 36 completely sequenced organisms (8 archaea, 24 bacteria, and 4 eukarya) was performed and a proteome tree was constructed. Our results show that (1) the 36 organisms can be 100% correctly classified into three clusters corresponding to the three primary kingdoms, (2) our proteome tree is remarkably similar to that derived from 16S rRNA, and (3) the chromosomes and/or plasmids belonging to the same organism have very similar gene composition. Based on these results, we argue that the 17-dimensional proteome vector could be a good criterion for clustering approaches and to a large extent reveals the phylogenetic properties of organisms; the Three Primary Kingdoms Hypothesis is trustworthy although the existence of lateral gene transfer (LGT) brings controversy to the construction of the "universal tree of life."

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PubMed2002

A complete sequence of the T. tengcongensis genome.

Thermoanaerobacter tengcongensis is a rod-shaped, gram-negative, anaerobic eubacterium that was isolated from a freshwater hot spring in Tengchong, China. Using a whole-genome-shotgun method, we sequenced its 2,689,445-bp genome from an isolate, MB4(T) (Genbank accession no. AE008691). The genome encodes 2588 predicted coding sequences (CDS). Among them, 1764 (68.2%) are classified according to homology to other documented proteins, and the rest, 824 CDS (31.8%), are functionally unknown. One of the interesting features of the T. tengcongensis genome is that 86.7% of its genes are encoded on the leading strand of DNA replication. Based on protein sequence similarity, the T. tengcongensis genome is most similar to that of Bacillus halodurans, a mesophilic eubacterium, among all fully sequenced prokaryotic genomes up to date. Computational analysis on genes involved in basic metabolic pathways supports the experimental discovery that T. tengcongensis metabolizes sugars as principal energy and carbon source and utilizes thiosulfate and element sulfur, but not sulfate, as electron acceptors. T. tengcongensis, as a gram-negative rod by empirical definitions (such as staining), shares many genes that are characteristics of gram-positive bacteria whereas it is missing molecular components unique to gram-negative bacteria. A strong correlation between the G + C content of tDNA and rDNA genes and the optimal growth temperature is found among the sequenced thermophiles. It is concluded that thermophiles are a biologically and phylogenetically divergent group of prokaryotes that have converged to sustain extreme environmental conditions over evolutionary timescale.

Bacillaceae↗

PubMedDate not supplied

Predicting molecular formulas of fragment ions with isotope patterns in tandem mass spectra.

A number of different approaches have been proposed to predict elemental component formulas (or molecular formulas) of molecular ions in low and medium resolution mass spectra. Most of them rely on isotope patterns, enumerate all possible formulas for an ion, and exclude certain formulas violating chemical constraints. However, these methods cannot be well generalized to the component prediction of fragment ions in tandem mass spectra. In this paper, a new method, FFP (Fragment ion Formula Prediction), is presented to predict elemental component formulas of fragment ions. In the FFP method, the prediction of the best formulas is converted into the minimization of the distance between theoretical and observed isotope patterns. And, then, a novel local search model is proposed to generate a set of candidate formulas efficiently. After the search, FFP applies a new multiconstraint filtering to exclude as many invalid and improbable formulas as possible. FFP is experimentally compared with the previous enumeration methods, and shown to outperform them significantly. The results of this paper can help to improve the reliability of de novo in the identification of peptide sequences.

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