A new scoring function for top-down spectral deconvolution

BACKGROUND: Top-down mass spectrometry plays an important role in intact protein identification and characterization. Top-down mass spectra are more complex than bottom-up mass spectra because they often contain many isotopomer envelopes from highly charged ions, which may overlap with one another. As a result, spectral deconvolution, which converts a complex top-down mass spectrum into a monoisotopic mass list, is a key step in top-down spectral interpretation. RESULTS: In this paper, we propose a new scoring function, L-score, for evaluating isotopomer envelopes. By combining L-score with MS-Deconv, a new software tool, MS-Deconv+, was developed for top-down spectral deconvolution. Experimental results showed that MS-Deconv+ outperformed existing software tools in top-down spectral deconvolution. CONCLUSIONS: L-score shows high discriminative ability in identification of isotopomer envelopes. Using L-score, MS-Deconv+ reports many correct monoisotopic masses missed by other software tools, which are valuable for proteoform identification and characterization

Effects of Search Volume Index On Return Of Hospitality Industry Stock

This study proposes a new indicator of stock price in hospitality industry by using the search volume index from search engine. The search volume index of a hotel is calculated by the customer’s search frequencies and can be treated as a proxy of customers’ attentions on the hotel. Therefore, search volume index is proposed to be used to predict the returns of the hotel’s stock. Monthly returns of all the stocks of hospitality industry in America market has been collected. A panel data model has been applied to analyze the effect of the search volume index of these hotels from Google Trends on their stocks returns. The result indicates that the hotel’s stocks will have excessive returns when it has gotten an abnormal search volume inde

Systematic Evaluation of Protein Sequence Filtering Algorithms for Proteoform Identification Using Top-Down Mass Spectrometry

Complex proteoforms contain various primary structural alterations resulting from variations in genes, RNA, and proteins. Top-down mass spectrometry is commonly used for analyzing complex proteoforms because it provides whole sequence information of the proteoforms. Proteoform identification by top-down mass spectral database search is a challenging computational problem because the types and/or locations of some alterations in target proteoforms are in general unknown. Although spectral alignment and mass graph alignment algorithms have been proposed for identifying proteoforms with unknown alterations, they are extremely slow to align millions of spectra against tens of thousands of protein sequences in high throughput proteome level analyses. Many software tools in this area combine efficient protein sequence filtering algorithms and spectral alignment algorithms to speed up database search. As a result, the performance of these tools heavily relies on the sensitivity and efficiency of their filtering algorithms. Here, we propose two efficient approximate spectrum-based filtering algorithms for proteoform identification. We evaluated the performances of the proposed algorithms and four existing ones on simulated and real top-down mass spectrometry data sets. Experiments showed that the proposed algorithms outperformed the existing ones for complex proteoform identification. In addition, combining the proposed filtering algorithms and mass graph alignment algorithms identified many proteoforms missed by ProSightPC in proteome-level proteoform analyses

Complex Proteoform Identification Using Top-Down Mass Spectrometry

Indiana University-Purdue University Indianapolis (IUPUI)Proteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers.2019-06-2

Complex Proteoform Identification Using Top-Down Mass Spectrometry

Indiana University-Purdue University Indianapolis (IUPUI)Proteoforms are distinct protein molecule forms created by variations in genes, gene expression, and other biological processes. Many proteoforms contain multiple primary structural alterations, including amino acid substitutions, terminal truncations, and posttranslational modifications. These primary structural alterations play a crucial role in determining protein functions: proteoforms from the same protein with different alterations may exhibit different functional behaviors. Because top-down mass spectrometry directly analyzes intact proteoforms and provides complete sequence information of proteoforms, it has become the method of choice for the identification of complex proteoforms. Although instruments and experimental protocols for top-down mass spectrometry have been advancing rapidly in the past several years, many computational problems in this area remain unsolved, and the development of software tools for analyzing such data is still at its very early stage. In this dissertation, we propose several novel algorithms for challenging computational problems in proteoform identification by top-down mass spectrometry. First, we present two approximate spectrum-based protein sequence filtering algorithms that quickly find a small number of candidate proteins from a large proteome database for a query mass spectrum. Second, we describe mass graph-based alignment algorithms that efficiently identify proteoforms with variable post-translational modifications and/or terminal truncations. Third, we propose a Markov chain Monte Carlo method for estimating the statistical signi ficance of identified proteoform spectrum matches. They are the first efficient algorithms that take into account three types of alterations: variable post-translational modifications, unexpected alterations, and terminal truncations in proteoform identification. As a result, they are more sensitive and powerful than other existing methods that consider only one or two of the three types of alterations. All the proposed algorithms have been incorporated into TopMG, a complete software pipeline for complex proteoform identification. Experimental results showed that TopMG significantly increases the number of identifications than other existing methods in proteome-level top-down mass spectrometry studies. TopMG will facilitate the applications of top-down mass spectrometry in many areas, such as the identification and quantification of clinically relevant proteoforms and the discovery of new proteoform biomarkers.2019-06-2

Common Replenishment Strategies in Supply Chain under Uncertainty Demand Environment

This paper is based on the model proposed by Viswanathan and continue to analyze the benefit of supply chain inventories through the use of common replenishment epochs. We studied a one-vendor, multi-buyer supply chain for a single product under uncertainty demand environment. The vendor specifies common replenishment periods and asks all buyers to replenish only at those time period and the price discount to be offered by the vendor are determined by the solution to a Stackelberg game. A numerical study is conducted to evaluate the benefit of the strategy by simulation

Simplicity of Kmeans versus Deepness of Deep Learning: A Case of Unsupervised Feature Learning with Limited Data

We study a bio-detection application as a case study to demonstrate that Kmeans -- based unsupervised feature learning can be a simple yet effective alternative to deep learning techniques for small data sets with limited intra-as well as inter-class diversity. We investigate the effect on the classifier performance of data augmentation as well as feature extraction with multiple patch sizes and at different image scales. Our data set includes 1833 images from four different classes of bacteria, each bacterial culture captured at three different wavelengths and overall data collected during a three-day period. The limited number and diversity of images present, potential random effects across multiple days, and the multi-mode nature of class distributions pose a challenging setting for representation learning. Using images collected on the first day for training, on the second day for validation, and on the third day for testing Kmeans -- based representation learning achieves 97% classification accuracy on the test data. This compares very favorably to 56% accuracy achieved by deep learning and 74% accuracy achieved by handcrafted features. Our results suggest that data augmentation or dropping connections between units offers little help for deep-learning algorithms, whereas significant boost can be achieved by Kmeans -- based representation learning by augmenting data and by concatenating features obtained at multiple patch sizes or image scales

Deeply Virtual Compton Scattering at Future Electron-Ion Colliders

The study of hadronic structure has been carried out for many years. Generalized parton distribution functions (GPDs) give broad information on the internal structure of hadrons. Combining GPDs and high-energy scattering experiments, we expect yielding three-dimensional physical quantities from experiments. Deeply Virtual Compton Scattering (DVCS) process is a powerful tool to study GPDs. It is one of the important experiments of Electron Ion Collider (EIC) and Electron ion collider at China (EicC) in the future. In the initial stage, the proposed EicC will have $3 \sim 5$ GeV polarized electrons on $12 \sim 25$ GeV polarized protons, with luminosity up to $1 \sim 2 \times 10^{33}$cm$^{-2}$s$^{-1}$. EIC will be constructed in coming years, which will cover the variable c.m. energies from 30 to 50 GeV, with the luminosity about $10^{33} \sim 10^{34}$cm$^{-2}$s$^{-1}$. In this work we present a detailed simulation of DVCS to study the feasibility of experiments at EicC and EIC. Referring the method used by HERMES Collaboration, and comparing the model calculations with pseudo data of asymmetries attributed to the DVCS, we obtained a model-dependent constraint on the total angular momentum of up and down quarks in the proton.Comment: 12 pages, 18 figures, 3 Table

Mass graphs and their applications in top-down proteomics

Although proteomics has made rapid progress in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a "bird view" of intact proteoforms. The combinatorial explosion of possible proteoforms, which may result in billions of possible proteoforms for one protein, makes proteoform identification a challenging computational problem. Here we propose a new data structure, called the mass graph, for efficiently representing proteoforms. In addition, we design mass graph alignment algorithms for proteoform identification by top-down mass spectrometry. Experiments on a histone H4 mass spectrometry data set showed that the proposed methods outperformed MS-Align-E in identifying complex proteoforms

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a 'bird's eye view' of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry datasets showed that TopMG outperformed existing methods in identifying complex proteoforms