Optimized Fluorescence-Based Detection in Single Molecule Synthesis Process.

Single molecule sequencing is imperative to overall genetic analysis in areas such as genomics, transcriptomics, clinical test, drug development, and cancer screening. In addition, fluorescence-based sequencing is primarily applied in single molecule sequencing besides other methods, precisely in the fields of DNA sequencing. Modern-day fluorescence labeling methods exploit a charge-coupled device camera to capture snapshots of a number of pixels on the single molecule sequencing. The method discussed in this article involves fluorescence labeling detection with a single pixel, outrivals in high accuracy and low resource requirement under low signal-to-noise ratio conditions, as well as benefits from higher throughput comparing with others. Through discussion in this article, we explore the single molecule synthesis process modeling using negative binomial distributions. Furthermore, incorporating the method of maximum likelihood and Viterbi algorithm in this modeling enhances the signal detection accuracy. The fluorescence-based model benefits in simulating actual experiment processes and assisting in understanding relations between the fluorescence emission and the signal receiving events. Last but not least, the model offers potential candidates on fluorescence dye selection that yields more accurate experiment results.

The Agility of a Neuron: Phase Shift Between Sinusoidal Current Input and Firing Rate Curve.

The response of a neuron when receiving a periodic input current signal is a periodic spike firing rate signal. The frequency of an input sinusoidal current and the surrounding environment such as background noises are two important factors that affect the firing rate output signal of a neuron model. This study focuses on the phase shift between input and output signals, and here we present a new concept: the agility of a neuron, to describe how fast a neuron can respond to a periodic input signal. In this study, we derived three agility score functions for the balanced leaky integrate-and-fire model, the Hodgkin-Huxley model, and the Connor-Stevens neuron model. By applying the score of agility, we are capable of characterizing the surrounding environment; once the frequency of the periodic input signal is given, the actual angle of phase shift can then be determined and, therefore, different neuron models can be normalized and compared with each other.

Extracting transcription factor binding sites from unaligned gene sequences with statistical models.

BACKGROUND: Transcription factor binding sites (TFBSs) are crucial in the regulation of gene transcription. Recently, chromatin immunoprecipitation followed by cDNA microarray hybridization (ChIP-chip array) has been used to identify potential regulatory sequences, but the procedure can only map the probable protein-DNA interaction loci within 1-2 kb resolution. To find out the exact binding motifs, it is necessary to build a computational method to examine the ChIP-chip array binding sequences and search for possible motifs representing the transcription factor binding sites. RESULTS: We developed a program to find out accurate motif sites from a set of unaligned DNA sequences in the yeast genome. Compared with MDscan, the prediction results suggest that, overall, our algorithm outperforms MDscan since the predicted motifs are more consistent with previously known specificities reported in the literature and have better prediction ranks. Our program also outperforms the constraint-less Cosmo program, especially in the elimination of false positives. CONCLUSION: In this study, an improved sampling algorithm is proposed to incorporate the binomial probability model to build significant initial candidate motif sets. By investigating the statistical dependence between base positions in TFBSs, the method of dependency graphs and their expanded Bayesian networks is combined. The results show that our program satisfactorily extract transcription factor binding sites from unaligned gene sequences.

Prediction of splice sites with dependency graphs and their expanded bayesian networks.

MOTIVATION: Owing to the complete sequencing of human and many other genomes, huge amounts of DNA sequence data have been accumulated. In bioinformatics, an important issue is how to predict the complete structure of genes from the genomic DNA sequence, especially the human genome. A crucial part in the gene structure prediction is to determine the precise exon-intron boundaries, i.e. the splice sites, in the coding region. RESULTS: We have developed a dependency graph model to fully capture the intrinsic interdependency between base positions in a splice site. The establishment of dependency between two position is based on a chi2-test from known sample data. To facilitate statistical inference, we have expanded the dependency graph (which is usually a graph with cycles that make probabilistic reasoning very difficult, if not impossible) into a Bayesian network (which is a directed acyclic graph that facilitates statistical reasoning). When compared with the existing models such as weight matrix model, weight array model, maximal dependence decomposition, Cai et al.'s tree model as well as the less-studied second-order and third-order Markov chain models, the expanded Bayesian networks from our dependency graph models perform the best in nearly all the cases studied. AVAILABILITY: Software (a program called DGSplicer) and datasets used are available at http://csrl.ee.nthu.edu.tw/bioinf/ CONTACT: cclu@ee.nthu.edu.tw.