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1.  Leaderless genes in bacteria: clue to the evolution of translation initiation mechanisms in prokaryotes 
BMC Genomics  2011;12:361.
Shine-Dalgarno (SD) signal has long been viewed as the dominant translation initiation signal in prokaryotes. Recently, leaderless genes, which lack 5'-untranslated regions (5'-UTR) on their mRNAs, have been shown abundant in archaea. However, current large-scale in silico analyses on initiation mechanisms in bacteria are mainly based on the SD-led initiation way, other than the leaderless one. The study of leaderless genes in bacteria remains open, which causes uncertain understanding of translation initiation mechanisms for prokaryotes.
Here, we study signals in translation initiation regions of all genes over 953 bacterial and 72 archaeal genomes, then make an effort to construct an evolutionary scenario in view of leaderless genes in bacteria. With an algorithm designed to identify multi-signal in upstream regions of genes for a genome, we classify all genes into SD-led, TA-led and atypical genes according to the category of the most probable signal in their upstream sequences. Particularly, occurrence of TA-like signals about 10 bp upstream to translation initiation site (TIS) in bacteria most probably means leaderless genes.
Our analysis reveals that leaderless genes are totally widespread, although not dominant, in a variety of bacteria. Especially for Actinobacteria and Deinococcus-Thermus, more than twenty percent of genes are leaderless. Analyzed in closely related bacterial genomes, our results imply that the change of translation initiation mechanisms, which happens between the genes deriving from a common ancestor, is linearly dependent on the phylogenetic relationship. Analysis on the macroevolution of leaderless genes further shows that the proportion of leaderless genes in bacteria has a decreasing trend in evolution.
PMCID: PMC3160421  PMID: 21749696
2.  A Spike-Timing Pattern Based Neural Network Model for the Study of Memory Dynamics 
PLoS ONE  2009;4(7):e6247.
It is well accepted that the brain's computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timing patterns that give both reliable memory of firing activities and precise memory of firing timings. The relationship between memory states and spike-timing patterns has been studied empirically with large-scale recording of neuron population in recent years. Here, by using a recurrent neural network model with dynamics at two time scales, we construct a dynamical memory network model which embeds both fast neural and synaptic variation and slow learning dynamics. A state vector is proposed to describe memory states in terms of spike-timing patterns of neural population, and a distance measure of state vector is defined to study several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli. We show that the distance measure can capture the timing difference of memory states. In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states. Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.
PMCID: PMC2710501  PMID: 19629179
3.  Dating the time of viral subtype divergence 
Precise dating of viral subtype divergence enables researchers to correlate divergence with geographic and demographic occurrences. When historical data are absent (that is, the overwhelming majority), viral sequence sampling on a time scale commensurate with the rate of substitution permits the inference of the times of subtype divergence. Currently, researchers use two strategies to approach this task, both requiring strong conditions on the molecular clock assumption of substitution rate. As the underlying structure of the substitution rate process at the time of subtype divergence is not understood and likely highly variable, we present a simple method that estimates rates of substitution, and from there, times of divergence, without use of an assumed molecular clock. We accomplish this by blending estimates of the substitution rate for triplets of dated sequences where each sequence draws from a distinct viral subtype, providing a zeroth-order approximation for the rate between subtypes. As an example, we calculate the time of divergence for three genes among influenza subtypes A-H3N2 and B using subtype C as an outgroup. We show a time of divergence approximately 100 years ago, substantially more recent than previous estimates which range from 250 to 3800 years ago.
PMCID: PMC2443812  PMID: 18541033
4.  Computational evaluation of TIS annotation for prokaryotic genomes 
BMC Bioinformatics  2008;9:160.
Accurate annotation of translation initiation sites (TISs) is essential for understanding the translation initiation mechanism. However, the reliability of TIS annotation in widely used databases such as RefSeq is uncertain due to the lack of experimental benchmarks.
Based on a homogeneity assumption that gene translation-related signals are uniformly distributed across a genome, we have established a computational method for a large-scale quantitative assessment of the reliability of TIS annotations for any prokaryotic genome. The method consists of modeling a positional weight matrix (PWM) of aligned sequences around predicted TISs in terms of a linear combination of three elementary PWMs, one for true TIS and the two others for false TISs. The three elementary PWMs are obtained using a reference set with highly reliable TIS predictions. A generalized least square estimator determines the weighting of the true TIS in the observed PWM, from which the accuracy of the prediction is derived. The validity of the method and the extent of the limitation of the assumptions are explicitly addressed by testing on experimentally verified TISs with variable accuracy of the reference sets. The method is applied to estimate the accuracy of TIS annotations that are provided on public databases such as RefSeq and ProTISA and by programs such as EasyGene, GeneMarkS, Glimmer 3 and TiCo. It is shown that RefSeq's TIS prediction is significantly less accurate than two recent predictors, Tico and ProTISA. With convincing proofs, we show two general preferential biases in the RefSeq annotation, i.e. over-annotating the longest open reading frame (LORF) and under-annotating ATG start codon. Finally, we have established a new TIS database, SupTISA, based on the best prediction of all the predictors; SupTISA has achieved an average accuracy of 92% over all 532 complete genomes.
Large-scale computational evaluation of TIS annotation has been achieved. A new TIS database much better than RefSeq has been constructed, and it provides a valuable resource for further TIS studies.
PMCID: PMC2362131  PMID: 18366730
5.  A dynamic Bayesian network approach to protein secondary structure prediction 
BMC Bioinformatics  2008;9:49.
Protein secondary structure prediction method based on probabilistic models such as hidden Markov model (HMM) appeals to many because it provides meaningful information relevant to sequence-structure relationship. However, at present, the prediction accuracy of pure HMM-type methods is much lower than that of machine learning-based methods such as neural networks (NN) or support vector machines (SVM).
In this paper, we report a new method of probabilistic nature for protein secondary structure prediction, based on dynamic Bayesian networks (DBN). The new method models the PSI-BLAST profile of a protein sequence using a multivariate Gaussian distribution, and simultaneously takes into account the dependency between the profile and secondary structure and the dependency between profiles of neighboring residues. In addition, a segment length distribution is introduced for each secondary structure state. Tests show that the DBN method has made a significant improvement in the accuracy compared to other pure HMM-type methods. Further improvement is achieved by combining the DBN with an NN, a method called DBNN, which shows better Q3 accuracy than many popular methods and is competitive to the current state-of-the-arts. The most interesting feature of DBN/DBNN is that a significant improvement in the prediction accuracy is achieved when combined with other methods by a simple consensus.
The DBN method using a Gaussian distribution for the PSI-BLAST profile and a high-ordered dependency between profiles of neighboring residues produces significantly better prediction accuracy than other HMM-type probabilistic methods. Owing to their different nature, the DBN and NN combine to form a more accurate method DBNN. Future improvement may be achieved by combining DBNN with a method of SVM type.
PMCID: PMC2266706  PMID: 18218144
6.  ProTISA: a comprehensive resource for translation initiation site annotation in prokaryotic genomes 
Nucleic Acids Research  2007;36(Database issue):D114-D119.
Correct annotation of translation initiation site (TIS) is essential for both experiments and bioinformatics studies of prokaryotic translation initiation mechanism as well as understanding of gene regulation and gene structure. Here we describe a comprehensive database ProTISA, which collects TIS confirmed through a variety of available evidences for prokaryotic genomes, including Swiss-Prot experiments record, literature, conserved domain hits and sequence alignment between orthologous genes. Moreover, by combining the predictions from our recently developed TIS post-processor, ProTISA provides a refined annotation for the public database RefSeq. Furthermore, the database annotates the potential regulatory signals associated with translation initiation at the TIS upstream region. As of July 2007, ProTISA includes 440 microbial genomes with more than 390 000 confirmed TISs. The database is available at
PMCID: PMC2238952  PMID: 17942412
7.  MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes 
BMC Bioinformatics  2007;8:97.
Despite a remarkable success in the computational prediction of genes in Bacteria and Archaea, a lack of comprehensive understanding of prokaryotic gene structures prevents from further elucidation of differences among genomes. It continues to be interesting to develop new ab initio algorithms which not only accurately predict genes, but also facilitate comparative studies of prokaryotic genomes.
This paper describes a new prokaryotic genefinding algorithm based on a comprehensive statistical model of protein coding Open Reading Frames (ORFs) and Translation Initiation Sites (TISs). The former is based on a linguistic "Entropy Density Profile" (EDP) model of coding DNA sequence and the latter comprises several relevant features related to the translation initiation. They are combined to form a so-called Multivariate Entropy Distance (MED) algorithm, MED 2.0, that incorporates several strategies in the iterative program. The iterations enable us to develop a non-supervised learning process and to obtain a set of genome-specific parameters for the gene structure, before making the prediction of genes.
Results of extensive tests show that MED 2.0 achieves a competitive high performance in the gene prediction for both 5' and 3' end matches, compared to the current best prokaryotic gene finders. The advantage of the MED 2.0 is particularly evident for GC-rich genomes and archaeal genomes. Furthermore, the genome-specific parameters given by MED 2.0 match with the current understanding of prokaryotic genomes and may serve as tools for comparative genomic studies. In particular, MED 2.0 is shown to reveal divergent translation initiation mechanisms in archaeal genomes while making a more accurate prediction of TISs compared to the existing gene finders and the current GenBank annotation.
PMCID: PMC1847833  PMID: 17367537

Results 1-7 (7)