PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-3 (3)
 

Clipboard (0)
None
Journals
Authors
Year of Publication
Document Types
author:("Yin, yanbian")
1.  The percentage of bacterial genes on leading versus lagging strands is influenced by multiple balancing forces 
Nucleic Acids Research  2012;40(17):8210-8218.
The majority of bacterial genes are located on the leading strand, and the percentage of such genes has a large variation across different bacteria. Although some explanations have been proposed, these are at most partial explanations as they cover only small percentages of the genes and do not even consider the ones biased toward the lagging strand. We have carried out a computational study on 725 bacterial genomes, aiming to elucidate other factors that may have influenced the strand location of genes in a bacterium. Our analyses suggest that (i) genes of some functional categories such as ribosome have higher preferences to be on the leading strands; (ii) genes of some functional categories such as transcription factor have higher preferences on the lagging strands; (iii) there is a balancing force that tends to keep genes from all moving to the leading and more efficient strand and (iv) the percentage of leading-strand genes in an bacterium can be accurately explained based on the numbers of genes in the functional categories outlined in (i) and (ii), genome size and gene density, indicating that these numbers implicitly contain the information about the percentage of genes on the leading versus lagging strand in a genome.
doi:10.1093/nar/gks605
PMCID: PMC3458553  PMID: 22735706
2.  dbCAN: a web resource for automated carbohydrate-active enzyme annotation 
Nucleic Acids Research  2012;40(Web Server issue):W445-W451.
Carbohydrate-active enzymes (CAZymes) are very important to the biotech industry, particularly the emerging biofuel industry because CAZymes are responsible for the synthesis, degradation and modification of all the carbohydrates on Earth. We have developed a web resource, dbCAN (http://csbl.bmb.uga.edu/dbCAN/annotate.php), to provide a capability for automated CAZyme signature domain-based annotation for any given protein data set (e.g. proteins from a newly sequenced genome) submitted to our server. To accomplish this, we have explicitly defined a signature domain for every CAZyme family, derived based on the CDD (conserved domain database) search and literature curation. We have also constructed a hidden Markov model to represent the signature domain of each CAZyme family. These CAZyme family-specific HMMs are our key contribution and the foundation for the automated CAZyme annotation.
doi:10.1093/nar/gks479
PMCID: PMC3394287  PMID: 22645317
3.  Integration of sequence-similarity and functional association information can overcome intrinsic problems in orthology mapping across bacterial genomes 
Nucleic Acids Research  2011;39(22):e150.
Existing methods for orthologous gene mapping suffer from two general problems: (i) they are computationally too slow and their results are difficult to interpret for automated large-scale applications when based on phylogenetic analyses; or (ii) they are too prone to making mistakes in dealing with complex situations involving horizontal gene transfers and gene fusion due to the lack of a sound basis when based on sequence similarity information. We present a novel algorithm, Global Optimization Strategy (GOST), for orthologous gene mapping through combining sequence similarity and contextual (working partners) information, using a combinatorial optimization framework. Genome-scale applications of GOST show substantial improvements over the predictions by three popular sequence similarity-based orthology mapping programs. Our analysis indicates that our algorithm overcomes the intrinsic issues faced by sequence similarity-based methods, when orthology mapping involves gene fusions and horizontal gene transfers. Our program runs as efficiently as the most efficient sequence similarity-based algorithm in the public domain. GOST is freely downloadable at http://csbl.bmb.uga.edu/~maqin/GOST.
doi:10.1093/nar/gkr766
PMCID: PMC3239196  PMID: 21965536

Results 1-3 (3)