Background: In recent years, DNA barcoding has become an important tool for biologists to identify species and understand their natural biodiversity. The complexity of barcode data makes it difficult to analyze quickly and effectively. Manual classification of this data cannot keep up to the rate of increase of available data. Results: In this study, we propose a new method for DNA barcode classification based on the distribution of nucleotides within the sequence. By adding the covariance of nucleotides to the original natural vector, this augmented 18-dimensional natural vector makes good use of the available information in the DNA sequence. The accurate classification results we obtained demonstrate that this new 18-dimensional natural vector method, together with the random forest classifier algorthm, can serve as a computationally efficient identification tool for DNA barcodes. We performed phylogenetic analysis on the genus Megacollybia to validate our method. We also studied how effective our method was in determining the genetic distance within and between species in our barcoding dataset. Conclusions: The classification performs well on the fungi barcode dataset with high and robust accuracy. The reasonable phylogenetic trees we obtained further validate our methods. This method is alignment-free and does not depend on any model assumption, and it will become a powerful tool for classification and evolutionary analysis.

The interplay between excitatory and inhibitory neurons imparts rich functions of the brain. To understand the synaptic mechanisms underlying neuronal computations, a fundamental approach is to study the dynamics of excitatory and inhibitory synaptic inputs of each neuron. The traditional method of determining input conductance, which has been applied for decades, employs the synaptic current-voltage (I-V) relation obtained via voltage clamp. Due to the space clamp effect, the measured conductance is different from the local conductance on the dendrites. Therefore, the interpretation of the measured conductance remains to be clarified. Using theoretical analysis, electrophysiological experiments, and realistic neuron simulations, here we demonstrate that there does not exist a transform between the local conductance and the conductance measured by the traditional method, due to the neglect of a nonlinear interaction between the clamp current and the synaptic current in the traditional method. Consequently, the conductance determined by the traditional method may not correlate with the local conductance on the dendrites, and its value could be unphysically negative as observed in experiment. To circumvent the challenge of the space clamp effect and elucidate synaptic impact on neuronal information processing, we propose the concept of effective conductance which is proportional to the local conductance on the dendrite and reflects directly the functional influence of synaptic inputs on somatic membrane potential dynamics, and we further develop a framework to determine the effective conductance accurately. Our work suggests re-examination of previous studies involving conductance measurement and provides a reliable approach to assess synaptic influence on neuronal computation.

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This study quantitatively validates the principle that the biological properties associated with a given genotype are determined by the distribution of amino acids. In order to visualize this central law of molecular biology, each protein was represented by a point in 250-dimensional space based on its amino acid distribution. Proteins from the same family are found to cluster together, leading to the principle that the convex hull surrounding protein points from the same family do not intersect with the convex hulls of other protein families. This principle was verified computationally for all available and reliable protein kinases and human proteins. In addition, we generated 2,328,761 figures to show that the convex hulls of different families were disjoint from each other. The classification performs well with high and robust accuracy (95.75% and 97.5%) together with reasonable phylogenetic trees validate our methods further.

HIV-1 is the most common and pathogenic strain of human immunodeficiency virus consisting of many subtypes. To study the difference among HIV-1 subtypes in infection, diagnosis and drug design, it is important to identify HIV-1 subtypes from clinical HIV-1 samples. In this work, we propose an effective numeric representation called Subsequence Natural Vector (SNV) to encode HIV-1 sequences. Using the representation, we introduce an improved linear discriminant analysis method to classify HIV-1 viruses correctly. SNV is based on distribution of nucleotides in HIV-1 viral sequences. It not only computes the number of nucleotides, but also describes the position and variance of nucleotides in viruses. To validate our alignment-free method, 6902 complete genomes and 11,668 pol gene sequences of HIV-1 subtypes were collected from the up-to-date Los Alamos HIV database. SNV outperforms the three popular methods, Kameris, Comet and REGA, with almost 100% Sensitivity and Specificity, also with much less time. Our subtyping algorithm especially works better for circulating recombinant forms (CRFs) consisting of a few sequences. Our approach is also powerful to separate unique recombinant forms (URFs) from other subtypes with 100% Sensitivity and Specificity. Moreover, phylogenetic trees based on SNV representation are constructed using full-length HIV-1 genomes and pol genes respectively, where viruses from the same subtype are clustered together correctly.