Changes in mineral fraction and pore morphology of coal with acidification treatment: contribution of clay minerals to methane adsorption.

The accurate calculation of the contribution which provided by clay minerals in coal on methane adsorption not only bares a significant importance for evaluating the effectiveness of acid stimulation in improving permeability and estimating the coalbed methane reserves but also serves a guide for the governance and utilization of methane resources. In this study, hydrochloric acid (HCl) and hydrofluoric acid (HF) were used to remove specific minerals in Qingdong coal samples. We firstly analyzed the mineral compositions of coal samples with different acidification treatments based on the X-ray diffraction (XRD) experiments, together with analysis of the changes in pore morphology and adsorption capacity. The results showed that acidification did not significantly change the shape of the pores, which remained slit-/plate-like pore. However, the altered adsorption capacity of the coal samples was attributed to changes in pore structure and mineral distribution. Acid erosion of mesopores promoted the transition from mesopores to macropores, contributing to an increase of 8.4% and 24.36% in the percentage of macropores in coal samples treated with HCl and HF, respectively. Fractal dimension D1 grew from 2.2193 to 2.3888 and 2.2572, respectively, but D2 decreased from 2.6146 to 2.5814 and 2.5433, indicating an increment in pore surface roughness and a simplification of the pore structure. The mineral richness of the coal seams should be taken into consideration when applying acid stimulation to increase permeability due to that the acidification products may block the passage of gas migration when the mineral content is slight, which can hinder gas extraction. The aim of this study is to quantitatively determine the contribution rate of clay minerals in coal to methane adsorption with a calculation method is provided by combining pore parameters and limit adsorption capacity, resulting in a contribution rate of 15%.

Assessment of the clinical efficacy of simultaneous transurethral resection of both bladder cancer and the prostate: a systematic review and meta-analysis.

OBJECTIVE: In this study, we aimed to examine the clinical efficacy of simultaneous transurethral resection of bladder cancer and the prostate (TURBT + TURP) in non-muscle invasive bladder cancer (NMIBC) and benign prostatic hyperplasia (BPH) patients. METHOD: We conducted systematic research in PubMed, EMBASE, and Cochrane Library databases to identify retrospective studies and prospective randomized controlled trials (RCTs) comparing patient outcomes between TURBT + TURP and TURBT-only patients. The meta-analysis was conducted using Review Manager 5.3. RESULTS: We identified eight relevant studies involving a total of 1032 patients. We found that patients that underwent TURBT + TURP exhibited significantly lower recurrence rates [odds ratio (OR), 0.70; 95% confidence interval (CI), 0.53-0.93; p = .01] and increased maximal urinary flow rate (Qmax) (WMD, 5.92; 95% CI, 4.67-7.16; p < .001) compared with patients that underwent TURBT-only. However, rates of recurrence at the prostatic urethra/bladder neck and bladder tumor progression, as well as the time to recurrence did not differ significantly between these two groups. CONCLUSIONS: Simultaneous TURBT + TURP can be safely performed in patients with NMIBC and BPH and improves patient quality of life, without any risk of increasing tumor recurrence or metastasis rates. Comprehensive RCTs are needed to confirm the results of this study.

An Improved Approach for N-Linked Glycan Structure Identification from HCD MS/MS Spectra.

Glycosylation is a frequently observed post-translational modification on proteins. Currently, tandem mass spectrometry (MS/MS) serves as an efficient analytical technique for characterizing structures of oligosaccharides. However, developing effective computational approaches for identifying glycan structures from mass spectra is still a great challenge in glycoproteomics research. In this study, we proposed an approach for matching the input spectra with glycan structures acquired from a glycan structure database by incorporating a de novo sequencing assisted ranking scheme. The proposed approach is implemented as a software tool, GlycoNovoDB, for automated glycan structure identification from HCD MS/MS of glycopeptides. Experimental results showed that GlycoNovoDB can identify glycans effectively and has better performance than our previously proposed de novo sequencing algorithm as well as another software GlycoMaster DB.

An approach for N-linked glycan identification from MS/MS spectra by target-decoy strategy.

Glycan structure determination serves as an essential step for the thorough investigation of the structure and function of protein. Currently, appropriate sample preparation followed by tandem mass spectrometry has emerged as the dominant technique for the characterization of glycans and glycopeptides. Although extensive efforts have been made to the development of computational approaches for the automated interpretation of glycopeptide spectra, the previously appeared methods lack a reasonable quality control strategy for the statistical validation of reported results. In this manuscript, we introduced a novel method that constructed a decoy glycan database based on the glycan structures in the target database, and searched the experimental spectra against both the target and decoy databases to find the best matched glycans. Specifically, a two-layer scoring scheme for calculating a normalized matching score is applied in the search procedure which enables the unbiased ranking of the matched glycans. Experimental analysis showed that our proposed method can report more structures with high confidence compared with previous approaches.

DISC: DISulfide linkage Characterization from tandem mass spectra.

MOTIVATION: Enzymatic digestion under appropriate reducing conditions followed by mass spectrometry analysis has emerged as the primary method for disulfide bond analysis. The large amount of mass spectral data collected in the mass spectrometry experiment requires effective computational approaches to automate the interpretation process. Although different approaches have been developed for such purpose, they always choose to ignore the frequently observed internal ion fragments and they lack a reasonable quality control strategy and calibrated scoring scheme for the statistical validation and ranking of the reported results. RESULTS: In this research, we present a new computational approach, DISC (DISulfide bond Characterization), for matching an input MS/MS spectrum against the putative disulfide linkage structures hypothetically constructed from the protein database. More specifically, we consider different ion types including a variety of internal ions that frequently observed in mass spectra resulted from disulfide linked peptides, and introduce an effective two-layer scoring scheme to evaluate the significance of the matching between spectrum and structure, based on which we have also developed a useful target-decoy strategy for providing quality control and reporting false discovery rate in the final results. Systematic experiments conducted on both low-complexity and high-complexity datasets demonstrated the efficiency of our proposed method for the identification of disulfide bonds from MS/MS spectra, and showed its potential in characterizing disulfide bonds at the proteome scale instead of just a single protein. AVAILABILITY AND IMPLEMENTATION: Software is available for downloading at http://www.csd.uwo.ca/yliu766/. CONTACT: yliu766@uwo.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Approach for Peptide Identification by De Novo Sequencing of Mixture Spectra.

Mixture spectra occur quite frequently in a typical wet-lab mass spectrometry experiment, which result from the concurrent fragmentation of multiple precursors. The ability to efficiently and confidently identify mixture spectra is essential to alleviate the existent bottleneck of low mass spectra identification rate. However, most of the traditional computational methods are not suitable for interpreting mixture spectra, because they still take the assumption that the acquired spectra come from the fragmentation of a single precursor. In this manuscript, we formulate the mixture spectra de novo sequencing problem mathematically, and propose a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra data sets to verify the merits of the proposed algorithm.

De Novo Sequencing Assisted Approach for Characterizing Mixture MS/MS Spectra.

Extensive research has been conducted for the computational analysis of mass spectrometry based proteomics data. However, there are still remaining challenges, among which, one particular challenge is the low identification rate of the collected spectral data. A specific contributing factor is the existence of mixture spectra in the collected MS/MS spectra which are generated by the concurrent fragmentation of multiple precursors in one sequencing attempt. The quite frequently observed mixture spectra necessitates the development of effective computational approaches to characterize those non-conventional spectral data. In this research, we proposed an approach for matching the query mixture spectra with a pair of peptide sequences acquired from the protein database by incorporating a special de novo assisted filtration strategy. The experiment results on two different datasets of MS/MS spectra containing mixed ion fragments from multiple peptides demonstrated the efficiency of the integrated filtration strategy in reducing examination space and verified the effectiveness of the proposed matching scheme as well.

An Effective Approach for Glycan Structure De Novo Sequencing From HCD Spectra.

Mass spectrometry has become a widely used analytical technique for proteomics study because of its high throughput and sensitivity. Among those applications, a specific one is to characterize glycan structure. Glycosylation is a frequently occurred post-translational modification of proteins which is relevant to humans' health. Therefore, it is significant to develop effective computational methods to automate the identification of glycan structures from mass spectral data. In our research, we mathematically formulated the glycan de novo sequencing problem and proposed a heuristic algorithm for glycan de novo sequencing from HCD MS/MS spectra of N-linked glycopeptides. The algorithm proceeds in a carefully designate pathway to construct the best matched tree structure from MS/MS spectrum. Experimental results showed that our proposed approach can effectively identify glycan structures from HCD MS/MS spectra.

Spectra library assisted de novo peptide sequencing for HCD and ETD spectra pairs.

BACKGROUND: De novo peptide sequencing via tandem mass spectrometry (MS/MS) has been developed rapidly in recent years. With the use of spectra pairs from the same peptide under different fragmentation modes, performance of de novo sequencing is greatly improved. Currently, with large amount of spectra sequenced everyday, spectra libraries containing tens of thousands of annotated experimental MS/MS spectra become available. These libraries provide information of the spectra properties, thus have the potential to be used with de novo sequencing to improve its performance. RESULTS: In this study, an improved de novo sequencing method assisted with spectra library is proposed. It uses spectra libraries as training datasets and introduces significant scores of the features used in our previous de novo sequencing method for HCD and ETD spectra pairs. Two pairs of HCD and ETD spectral datasets were used to test the performance of the proposed method and our previous method. The results show that this proposed method achieves better sequencing accuracy with higher ranked correct sequences and less computational time. CONCLUSIONS: This paper proposed an advanced de novo sequencing method for HCD and ETD spectra pair and used information from spectra libraries and significant improved previous similar methods.

Optimizing Spaced k-mer Neighbors for Efficient Filtration in Protein Similarity Search.

Large-scale comparison or similarity search of genomic DNA and protein sequence is of fundamental importance in modern molecular biology. To perform DNA and protein sequence similarity search efficiently, seeding (or filtration) method has been widely used where only sequences sharing a common pattern or "seed" are subject to detailed comparison. Therefore these methods trade search sensitivity with search speed. In this paper, we introduce a new seeding method, called spaced k-mer neighbors, which provides a better tradeoff between the sensitivity and speed in protein sequence similarity search. With the method of spaced k-mer neighbors, for each spaced k-mer, a set of spaced k-mers is selected as its neighbors. These pre-selected spaced k-mer neighbors are then used to detect hits between query sequence and database sequences. We propose an efficient heuristic algorithm for the spaced neighbor selection. Our computational experimental results demonstrate that the method of spaced k-mer neighbors can improve the overall tradeoff efficiency over existing seeding methods.

A method for discovering common patterns from two RNA secondary structures and its application to structural repeat detection.

We propose an ab initio method, named DiscoverR, for finding common patterns from two RNA secondary structures. The method works by representing RNA secondary structures as ordered labeled trees and performs tree pattern discovery using an efficient dynamic programming algorithm. DiscoverR is able to identify and extract the largest common substructures from two RNA molecules having different sizes without prior knowledge of the locations and topologies of these substructures. We also extend DiscoverR to find repeated regions in an RNA secondary structure, and apply this extended method to detect structural repeats in the 3'-untranslated region of a protein kinase gene. We describe the biological significance of a repeated hairpin found by our method, demonstrating the usefulness of the method. DiscoverR is implemented in Java; a jar file including the source code of the program is available for download at http://bioinformatics.njit.edu/DiscoverR.

DomPep--a general method for predicting modular domain-mediated protein-protein interactions.

Protein-protein interactions (PPIs) are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains.

On comparing two structured RNA multiple alignments.

We present a method, called BlockMatch, for aligning two blocks, where a block is an RNA multiple sequence alignment with the consensus secondary structure of the alignment in Stockholm format. The method employs a quadratic-time dynamic programming algorithm for aligning columns and column pairs of the multiple alignments in the blocks. Unlike many other tools that can perform pairwise alignment of either single sequences or structures only, BlockMatch takes into account the characteristics of all the sequences in the blocks along with their consensus structures during the alignment process, thus being able to achieve a high-quality alignment result. We apply BlockMatch to phylogeny reconstruction on a set of 5S rRNA sequences taken from fifteen bacteria species. Experimental results showed that the phylogenetic tree generated by our method is more accurate than the tree constructed based on the widely used ClustalW tool. The BlockMatch algorithm is implemented into a web server, accessible at http://bioinformatics.njit.edu/blockmatch. A jar file of the program is also available for download from the web server.

Predicting consensus structures for RNA alignments via pseudo-energy minimization.

Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http://datalab.njit.edu/biology/RSpredict.

Prediction of phosphotyrosine signaling networks using a scoring matrix-assisted ligand identification approach.

Systematic identification of binding partners for modular domains such as Src homology 2 (SH2) is important for understanding the biological function of the corresponding SH2 proteins. We have developed a worldwide web-accessible computer program dubbed SMALI for scoring matrix-assisted ligand identification for SH2 domains and other signaling modules. The current version of SMALI harbors 76 unique scoring matrices for SH2 domains derived from screening oriented peptide array libraries. These scoring matrices are used to search a protein database for short peptides preferred by an SH2 domain. An experimentally determined cut-off value is used to normalize an SMALI score, therefore allowing for direct comparison in peptide-binding potential for different SH2 domains. SMALI employs distinct scoring matrices from Scansite, a popular motif-scanning program. Moreover, SMALI contains built-in filters for phosphoproteins, Gene Ontology (GO) correlation and colocalization of subject and query proteins. Compared to Scansite, SMALI exhibited improved accuracy in identifying binding peptides for SH2 domains. Applying SMALI to a group of SH2 domains identified hundreds of interactions that overlap significantly with known networks mediated by the corresponding SH2 proteins, suggesting SMALI is a useful tool for facile identification of signaling networks mediated by modular domains that recognize short linear peptide motifs.

Complexities and algorithms for glycan sequencing using tandem mass spectrometry.

Determining glycan structures is vital to comprehend cell-matrix, cell-cell, and even intracellular biological events. Glycan sequencing, which determines the primary structure of a glycan using tandem mass spectrometry (MS/MS), remains one of the most important tasks in proteomics. Analogous to peptide de novo sequencing, glycan de novo sequencing determines the structure without the aid of a known glycan database. We show in this paper that glycan de novo sequencing is NP-hard. We then provide a heuristic algorithm and develop a software program to solve the problem in practical cases. Experiments on real MS/MS data of glycopeptides demonstrate that our heuristic algorithm gives satisfactory results on practical data.

Locality and gaps in RNA comparison.

Locality is an important and well-studied notion in comparative analysis of biological sequences. Similarly, taking into account affine gap penalties when calculating biological sequence alignments is a well-accepted technique for obtaining better alignments. When dealing with RNA, one has to take into consideration not only sequential features, but also structural features of the inspected molecule. This makes the computation more challenging, and usually prohibits the comparison only to small RNAs. In this paper we introduce two local metrics for comparing RNAs that extend the Smith-Waterman metric and its normalized version used for string comparison. We also present a global RNA alignment algorithm which handles affine gap penalties. Our global algorithm runs in O(m(2)n(1 + lg n/m)) time, while our local algorithms run in O(m(2)n(1 + lg n/m)) and O(n(2)m) time, respectively, where m

An algorithm for searching RNA motifs in genomic sequences.

RNA molecules, which are found in all living cells, fold into characteristic structures that account for their diverse functional activities. Many of these RNA structures consist of a collection of fundamental RNA motifs. The various combinations of RNA basic components form different RNA classes and define their unique structural and functional properties. The availability of many genome sequences makes it possible to search computationally for functional RNAs. Biological experiments indicate that functional RNAs have characteristic RNA structural motifs represented by specific combinations of base pairings and conserved nucleotides in the loop regions. The searching for those well-ordered RNA structures and their homologues in genomic sequences is very helpful for the understanding of RNA-based gene regulation. In this paper, we consider the following problem: given an RNA sequence with a known secondary structure, efficiently determine candidate segments in genomic sequences that can potentially form RNA secondary structures similar to the given RNA secondary structure. Our new bottom-up approach searches all potential stem-loops similar to ones of the given RNA secondary structure first, and then based on located stem-loops, detects potential homologous structural RNAs in genomic sequences.

Improving the sensitivity and specificity of protein homology search by incorporating predicted secondary structures.

In this paper, we improve the homology search performance by the combination of the predicted protein secondary structures and protein sequences. Previous research suggested that the straightforward combination of predicted secondary structures did not improve the homology search performance, mostly because of the errors in the structure prediction. We solved this problem by taking into account the confidence scores output by the prediction programs.