QuickMod: A tool for open modification spectrum library searches

MS2 library spectra are rich in reproducible information about peptide fragmentation patterns compared to theoretical spectra modeled by a sequence search tool. So far, spectrum library searches are mostly applied to detect peptides as they are present in the library. However, they also allow finding modified variants of the library peptides if the search is done with a large precursor mass window and an adapted Spectrum-Spectrum Match (SSM) scoring algorithm. We perform a thorough evaluation on the use of library spectra as opposed to theoretical peptide spectra for the identification of PTMs, analyzing spectra of a well-annotated modification-rich test data set compiled from public data repositories. These initial studies motivate the development of our modification tolerant spectrum library search tool QuickMod, designed to identify modified variants of the peptides listed in the spectrum library without any prior input from the user estimating the modifications present in the sample. We built the search algorithm of QuickMod after carefully testing different SSM similarity scores. The final spectrum scoring scheme uses a support vector machine (SVM) on a selection of scoring features to classify correct and incorrect SSM. After identification of a list of modified peptides at a given False Discovery Rate (FDR), the modifications need to be positioned on the peptide sequence. We present a rapid modification site assignment algorithm and evaluate its positioning accuracy. Finally, we demonstrate that QuickMod performs favorably in terms of speed and identification rate when compared to other software solutions for PTM analysis.     

Determination of window size for analyzing DNA sequences

Summary DNA sequences are generally not random sequences. To show such nonrandomness visually, DNA sequence data are often plotted as moving averages for a certain length of window slid along a sequence. Here a simple algorithm is presented for determining the window size and for finding a nonrandom region of sequence.     

Adaptive optimization of a continuous culture with the use of a bilevel forgetting factor

An adaptive optimization algorithm using a dynamic identification scheme with a bilevel forgetting factor (BFF) has been developed. The simulation results show superiority of this method to other methods when applied to maximize the cellular productivity of a continuous culture of baker's yeast, Saccharomyces cerievisiae. Within the limited ranges of tuning parameters tested the BFF algorithm is found to be superior in terms of initial optimization speed and accuracy and reoptimization speed and accuracy when there is an external change and long term stability (removal of "blowing up" phenomena). Algorithms tested include those based on a constant forgetting factor, an adaptive variable forgetting factor (VFF) and moving window (MW) identification.     

Algorithms for optical mapping.

Optical mapping is a novel technique for determining the restriction sites on a DNA molecule by directly observing a number of partially digested copies of the molecule under a light microscope. The problem is complicated by uncertainty as to the orientation of the molecules and by erroneous detection of cuts. In this paper we study the problem of constructing a restriction map based on optical mapping data. We give several variants of a polynomial reconstruction algorithm, as well as an algorithm that is exponential in the number of cut sites, and hence is appropriate only for small number of cut sites. We give a simple probabilistic model for data generation and for the errors and prove probabilistic upper and lower bounds on the number of molecules needed by each algorithm in order to obtain a correct map, expressed as a function of the number of cut sites and the error parameters. To the best of our knowledge, this is the first probabilistic analysis of algorithms for the problem. We also provide experimental results confirming that our algorithms are highly effective on simulated data.     

Detection and correction of erroneous eye movements by the brain error detector while tracking moving object

The models for research of function of the brain error detector of eye movements are described. The quantitative estimation of temporary parameters of erroneous eye saccades detection and correction of the healthy people is given. The data on distinctions in duration of the latency, speed and other parameters of erroneous and correctional saccades, are used for discussion on types of sensory signals used by the brain detector for revealing and correction of erroneous eye movements.     

Structure of lipid bilayers

The quantitative experimental uncertainty in the structure of fully hydrated, biologically relevant, fluid (L(alpha)) phase lipid bilayers has been too large to provide a firm base for applications or for comparison with simulations. Many structural methods are reviewed including modern liquid crystallography of lipid bilayers that deals with the fully developed undulation fluctuations that occur in the L(alpha) phase. These fluctuations degrade the higher order diffraction data in a way that, if unrecognized, leads to erroneous conclusions regarding bilayer structure. Diffraction measurements at high instrumental resolution provide a measure of these fluctuations. In addition to providing better structural determination, this opens a new window on interactions between bilayers, so the experimental determination of interbilayer interaction parameters is reviewed briefly. We introduce a new structural correction based on fluctuations that has not been included in any previous studies. Updated measurements, such as for the area compressibility modulus, are used to provide adjustments to many of the literature values of structural quantities. Since the gel (L(beta)') phase is valuable as a stepping stone for obtaining fluid phase results, a brief review is given of the lower temperature phases. The uncertainty in structural results for lipid bilayers is being reduced and best current values are provided for bilayers of five lipids.     

The modelling of interactions between organs and medical tools: a volumetric mass-spring chain algorithm

This paper adds volume deformation capability to the mass-spring chain method using tetrahedral elements in order to obtain more realistic deformations, which occur during the interactions between medical tools and soft tissues. The mass-spring chain method originally does not consider volume information and performs deformation by moving and deforming individual springs of a deformable model. However, most of the applications in computer graphics require volume modelling using tetrahedrons. In the proposed method, the deformation algorithm loops through tetrahedrons and performs deformation based on defined rules similar to those of the original mass-spring chain method. This method can handle not only ordinary deformation applications but also those with topology changes, such as cutting and tearing, as it does not rely on any pre-computed quantities. A method to preserve the volume and the shape of the tetrahedral elements is also developed. In order to speed up the new version of the algorithm, a tetrahedral propagation for deformation is developed. The detailed implementation of the algorithm and the various applications of the organ–surgery tool interactions are presented. The paper also provides the animations of the different models obtained by the proposed method.     

Misuse of nonlinear Scatchard plots

Scatchard plots--plots of bound/free ligand vs bound ligand--are a common graphical presentation of binding data. They are often nonlinear. Despite examples of correct usage and several articles calling attention to incorrect treatment of Scatchard plots, erroneous interpretations of nonlinear Scatchard plots remain frequent; plots are resolved incorrectly into two or more linear components which have no relation to an acceptable binding model. Correct analysis requires determination, usually by computer, of numerical values of the binding parameters that give the best nonlinear fit to an appropriate model, examples of which are specified.     

A distance-type measure approach to the analysis of copy number variation in DNA sequencing data

Background

The next generation sequencing technology allows us to obtain a large amount of short DNA sequence (DNA-seq) reads at a genome-wide level. DNA-seq data have been increasingly collected during the recent years. Count-type data analysis is a widely used approach for DNA-seq data. However, the related data pre-processing is based on the moving window method, in which a window size need to be defined in order to obtain count-type data. Furthermore, useful information can be reduced after data pre-processing for count-type data.

Results

In this study, we propose to analyze DNA-seq data based on the related distance-type measure. Distances are measured in base pairs (bps) between two adjacent alignments of short reads mapped to a reference genome. Our experimental data based simulation study confirms the advantages of distance-type measure approach in both detection power and detection accuracy. Furthermore, we propose artificial censoring for the distance data so that distances larger than a given value are considered potential outliers. Our purpose is to simplify the pre-processing of DNA-seq data. Statistically, we consider a mixture of right censored geometric distributions to model the distance data. Additionally, to reduce the GC-content bias, we extend the mixture model to a mixture of generalized linear models (GLMs). The estimation of model can be achieved by the Newton-Raphson algorithm as well as the Expectation-Maximization (E-M) algorithm. We have conducted simulations to evaluate the performance of our approach. Based on the rank based inverse normal transformation of distance data, we can obtain the related z-values for a follow-up analysis. For an illustration, an application to the DNA-seq data from a pair of normal and tumor cell lines is presented with a change-point analysis of z-values to detect DNA copy number alterations.

Conclusion

Our distance-type measure approach is novel. It does not require either a fixed or a sliding window procedure for generating count-type data. Its advantages have been demonstrated by our simulation studies and its practical usefulness has been illustrated by an experimental data application.

     

The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series

This paper investigates the utility of the Lomb–Scargle periodogram for the analysis of biological rhythms. This method is particularly suited to detect periodic components in unequally sampled time-series and data sets with missing values, but restricts all calculations to actually measured values. The Lomb-Scargle method was tested on both real and simulated time-series with even and uneven sampling, and compared to a standard method in biomedical rhythm research, the Chi-square periodogram. Results indicate that the Lomb–Scargle algorithm shows a clearly better detection efficiency and accuracy in the presence of noise, and avoids possible bias or erroneous results that may arise from replacement of missing data by interpolation techniques. Hence, the Lomb–Scargle periodogram may serve as a useful method for the study of biological rhythms, especially when applied to telemetrical or observational time-series obtained from free-living animals, i.e., data sets that notoriously lack points.     

Computational identification of three-way junctions in folded RNAs: a case study in Arabidopsis

Three-way junctions in folded RNAs have been investigated both experimentally and computationally. The interest in their analysis stems from the fact that they have significantly been found to possess a functional role. In recent work, three-way junctions have been categorized into families depending on the relative lengths of the segments linking the three helices. Here, based on ideas originating from computational geometry, an algorithm is proposed for detecting three-way junctions in data sets of genes that are related to a metabolic pathway of interest. In its current implementation, the algorithm relies on a moving window that performs energy minimization folding predictions, and is demonstrated on a set of genes that are involved in purine metabolism in plants. The pattern matching algorithm can be extended to other organisms and other metabolic cycles of interest in which three-way junctions have been or will be discovered to play an important role. In the test case presented here with, the computational prediction of a three-way junction in Arabidopsis that was speculated to have an interesting functional role is verified experimentally.     

The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series

This paper investigates the utility of the Lomb-Scargle periodogram for the analysis of biological rhythms. This method is particularly suited to detect periodic components in unequally sampled time-series and data sets with missing values, but restricts all calculations to actually measured values. The Lomb-Scargle method was tested on both real and simulated time-series with even and uneven sampling, and compared to a standard method in biomedical rhythm research, the Chi-square periodogram. Results indicate that the Lomb-Scargle algorithm shows a clearly better detection efficiency and accuracy in the presence of noise, and avoids possible bias or erroneous results that may arise from replacement of missing data by interpolation techniques. Hence, the Lomb-Scargle periodogram may serve as a useful method for the study of biological rhythms, especially when applied to telemetrical or observational time-series obtained from free-living animals, i.e., data sets that notoriously lack points.     

Individualised automated lameness detection in dairy cows and the impact of historical window length on algorithm performance

Lameness is an important economic problem in the dairy sector, resulting in production loss and reduced welfare of dairy cows. Given the modern-day expansion of dairy herds, a tool to automatically detect lameness in real-time can therefore create added value for the farmer. The challenge in developing camera-based tools is that one system has to work for all the animals on the farm despite each animal having its own individual lameness response. Individualising these systems based on animal-level historical data is a way to achieve accurate monitoring on farm scale. The goal of this study is to optimise a lameness monitoring algorithm based on back posture values derived from a camera for individual cows by tuning the deviation thresholds and the quantity of the historical data being used. Back posture values from a sample of 209 Holstein Friesian cows in a large herd of over 2000 cows were collected during 15 months on a commercial dairy farm in Sweden. A historical data set of back posture values was generated for each cow to calculate an individual healthy reference per cow. For a gold standard reference, manual scoring of lameness based on the Sprecher scale was carried out weekly by a single skilled observer during the final 6 weeks of data collection. Using an individual threshold, deviations from the healthy reference were identified with a specificity of 82.3%, a sensitivity of 79%, an accuracy of 82%, and a precision of 36.1% when the length of the healthy reference window was not limited. When the length of the healthy reference window was varied between 30 and 250 days, it was observed that algorithm performance was maximised with a reference window of 200 days. This paper presents a high-performing lameness detection system and demonstrates the importance of the historical window length for healthy reference calculation in order to ensure the use of meaningful historical data in deviation detection algorithms.     

Genetic algorithm solution for double digest problem

The strongly NP-Hard Double Digest Problem, for reconstructing the physical map of DNA sequence, in now using for efficient genotyping. Most of the existing methods are inefficient in tackling large instances due to the large search space for the problem which grows as a factorial function (a!)(b!) of the numbers a and b of the DNA fragments generated by the two restriction enzymes. Also, none of the existing methods are able to handle the erroneous data. In this paper, we develop a novel method based on genetic algorithm for solving this problem and it is adapted to handle the erroneous data. Our genetic algorithm is implemented and compared with the other well-known existing algorithms. The obtained results show the efficiency (speedup) of our algorithm with respect to the other methods, specially for erroneous data.     

Assessment of control ability in the physically handicapped

A tracking task was developed in order to obtain parameters relevant to the design of control interfaces for the physically handicapped. Principles of construction and operation are given, and methods for obtaining parameters are described. Results are presented from evaluator use with a number of subjects covering a wide range of control abilities. Overall performance in the tracking task is compared to general physical ability and experience with other control devices. One task required a response to a target which moved between two possible positions after a constant or variable time interval; correlation was observed between variables representing overall speed and accuracy. For multi-level tasks, scores equal to total time on target were obtained for tasks of differing complexity. Their values are shown to contain information on both speed and accuracy of control. Factors affecting performance are discussed and useful parameters suggested. In particular, information derived from the first, simpler task was shown to correlate well with that from the more complex tests. This type of test provides a useful general method for the interactive design and assessment of control interfaces.     

Dynamically Tunable Electromagnetically Induced Transparency in Graphene and Split-Ring Hybrid Metamaterial

In this letter, a novel hybrid metamaterial consisting of periodic array of graphene nano-patch and gold split-ring resonator has been theoretically proposed to realize an active control of the electromagnetically induced transparency analog in the mid-infrared regime. A narrow transparency window occurs over a wide absorption band due to the coupling of the high-quality factor mode provided by graphene dipolar resonance and the low-quality factor mode of split-ring resonator magnetic resonance, which is interpreted in terms of the phase change and surface charge distribution. In addition to the obvious dependence of the spectral feature on the geometric parameters of the elements and the surrounding environmental dielectric constant, our proposed metamaterial shows great tunabilities to the transparency window by tuning the Fermi energy of the graphene nano-patch through electric gating and its electronic mobility without changing the geometric parameters. Furthermore, our proposed metamaterial combines low losses with very large group index associated with the resonance response in the transparency window, showing it suitable for slow light applications and nanophotonic devices for light filter and biosensing.     

NQ-Flipper: validation and correction of asparagine/glutamine amide rotamers in protein crystal structures

The error rate of asparagine (Asn) and glutamine (Gln) amide rotamers in protein crystal structures is in the order of 20% and as a consequence the current Protein Database (PDB) contains approximately half a million incorrect Asn and Gln side-chain rotamers. Here we present NQ-Flipper, a web service based on knowledge-based potentials of mean force to automatically detect and correct erroneous rotamers. We achieve excellent agreement with expert curated data.     

A computational approach to motion perception

In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.     

Multi-scale parametric spectral analysis for exon detection in DNA sequences based on forward-backward linear prediction and singular value decomposition of the double-base curves

This paper presents a new method for exon detection in DNA sequences based on multi-scale parametric spectral analysis. A forward-backward linear prediction (FBLP) with the singular value decomposition (SVD) algorithm FBLP-SVD is applied to the double-base curves (DB-curves) of a DNA sequence using a variable moving window sizes to estimate the signal spectrum at multiple scales. Simulations are done on short human genes in the range of 11bp to 2032bp and the results show that our proposed method out-performs the classical Fourier transform method. The multi-scale approach is shown to be more effective than using a single scale with a fixed window size. In addition, our method is flexible as it requires no training data.