RNA secondary structure prediction using conditional random fields model.

Non-coding RNAs (ncRNAs) have important biological functions in living cells dependent on their conserved secondary structures. Here, we focus on computational RNA secondary structure prediction by exploring primary sequences and complementary base pair interactions using the Conditional Random Fields (CRFs) model, which treats RNA prediction as a sequence labelling problem. Proposing suitable feature extraction from known RNA secondary structures, we developed a feature extraction based on natural RNA's loop and stem characteristics. Our CRFs models can predict the secondary structures of the test RNAs with optimal F-score prediction between 56.61 and 98.20% for different RNA families.

HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening.

BACKGROUND: High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. RESULTS: We propose an automated analysis method with novel descriptors of neuromorphology features for analyzing HCS-based multi-neuron images, called HCS-neurons. To observe multiple phenotypic changes of neurons, we propose two kinds of descriptors which are neuron feature descriptor (NFD) of 13 neuromorphology features, e.g., neurite length, and generic feature descriptors (GFDs), e.g., Haralick texture. HCS-neurons can 1) automatically extract all quantitative phenotype features in both NFD and GFDs, 2) identify statistically significant phenotypic changes upon drug treatments using ANOVA and regression analysis, and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method. To evaluate HCS-neurons, we treated P19 neurons with nocodazole (a microtubule depolymerizing drug which has been shown to impair neurite development) at six concentrations ranging from 0 to 1000 ng/mL. The experimental results show that all the 13 features of NFD have statistically significant difference with respect to changes in various levels of nocodazole drug concentrations (NDC) and the phenotypic changes of neurites were consistent to the known effect of nocodazole in promoting neurite retraction. Three identified features, total neurite length, average neurite length, and average neurite area were able to achieve an independent test accuracy of 90.28% for the six-dosage classification problem. This NFD module and neuron image datasets are provided as a freely downloadable MatLab project at http://iclab.life.nctu.edu.tw/HCS-Neurons. CONCLUSIONS: Few automatic methods focus on analyzing multi-neuron images collected from HCS used in drug discovery. We provided an automatic HCS-based method for generating accurate classifiers to classify neurons based on their phenotypic changes upon drug treatments. The proposed HCS-neurons method is helpful in identifying and classifying chemical or biological molecules that alter the morphology of a group of neurons in HCS.

Synonymous codon usage bias dependent on local nucleotide context in the class Deinococci.

To study the evolution of mutation biased synonymous codon usage, we examined nucleotide co-occurrence patterns in the Deinococcus radiodurans, D. geothermalis, and Thermus thermophilus genomes for nucleotide replacement dependent on the surrounding nucleotide context. Nucleotides on the third codon site were found to be strongly correlated with nucleotide sites at most six nucleotides away in all three species, where abundance patterns were dependent on whether two nucleotides share the same purine(R)/pyrimidine(Y) status. In the class Deinococci adjacent third site nucleotides were strongly correlated, where NNR|NNR and NNY|NNY codon pairs were overabundant while NNR|NNY and NNY|NNR codon pairs were underabundant. By far the largest deviations in all three species occur for NN(YR)|(YR)NN codon pairs. In the Thermus species, the NNY|YNN and NNR|RNN codon pairs were overabundant versus the underabundant NNY|RNN and NNR|YNN codon pairs, whereas in the Deinococcus species the opposite over-/underabundance relationship held for adjacent (GC) bases. We also observed a weaker overabundance of NNR|NRN and NNY|NYN codon pairs versus the underabundant NNR|NYN and NNY|NRN codon pairs. The perfect purine/pyrimidine symmetry of each of these cases, plus the lack of significant deviations for nucleotide pairs on other length scales up to 20 codons apart demonstrates that a pervasive pattern of nucleotide replacement dependent on local nucleotide context, and not codon bias, has occurred in these species. This nucleotide replacement has led to modified synonymous codon usage within the class Deinococci that affects which codons are positioned at particular codon sites dependent on the local nucleotide context.

Convergent host-parasite codon usage between honeybee and bee associated viral genomes.

By correlating the codon usage in four insects (the honeybee, red flour beetle, mosquito and fruit fly) with six honeybee host specific viruses, we found that the codon usage patterns of the bee viruses were strongly related to that of the honeybee and only weakly related to the red flour beetle. The insects shared varying degrees of codon usage similarity which roughly follow the known phylogenetic relatedness. All of the codon usage similarity can be described by relatedness-by-descent except for the high codon usage similarity between the honeybee and honeybee associated viruses. This evidence for the convergent evolution of the honeybee viruses toward the codon usage of the honeybee suggests that small host specific viral genomes have the freedom to quickly optimize codon usage to successfully parasitize their preferred host. The codon usage co-evolution of the six host specific honeybee viruses towards the codon usage of the honeybee described in this paper is the first evidence for codon usage correlation between an insect host and a single stranded RNA virus.

The effect of local nucleotides on synonymous codon usage in the honeybee (Apis mellifera L.) genome.

Using all currently predicted coding regions in the honeybee genome, a novel form of synonymous codon bias is presented that affects the usage of particular codons dependent on the surrounding nucleotides in the coding region. Nucleotides at the third codon site are correlated, dependent on their weak (adenine [A] or thyamine [T]) versus strong (guanine [G] or cytosine [C]) status, to nucleotides on the first codon site which are dependent on their purine (A/G) versus pyrimidine (C/T) status. In particular, for adjacent third and first site nucleotides, weak-pyrimidine and strong-purine nucleotide combinations occur much more frequently than the underabundant weak-purine and strong-pyrimidine nucleotide combinations. Since a similar effect is also found in the noncoding regions, but is present for all adjacent nucleotides, this coding effect is most likely due to a genome-wide context-dependent mutation error correcting mechanism in combination with selective constraints on adjacent first and second nucleotide pairs within codons. The position-dependent relationship of synonymous codon usage is evidence for a novel form of codon position bias which utilizes the redundancy in the genetic code to minimize the effect of nucleotide mutations within coding regions.