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1.
Background
Network alignment is one of the most common biological network comparison methods. Aligning protein-protein interaction (PPI) networks of different species is of great important to detect evolutionary conserved pathways or protein complexes across species through the identification of conserved interactions, and to improve our insight into biological systems. Global network alignment (GNA) problem is NP-complete, for which only heuristic methods have been proposed so far. Generally, the current GNA methods fall into global heuristic seed-and-extend approaches. These methods can not get the best overall consistent alignment between networks for the opinionated local seed. Furthermore These methods are lost in maximizing the number of aligned edges between two networks without considering the original structures of functional modules.Methods
We present a novel seed selection strategy for global network alignment by constructing the pairs of hub nodes of networks to be aligned into multiple seeds. Beginning from every hub seed and using the membership similarity of nodes to quantify to what extent the nodes can participate in functional modules associated with current seed topologically we align the networks by modules. By this way we can maintain the functional modules are not damaged during the heuristic alignment process. And our method is efficient in resolving the fatal problem of most conventional algorithms that the initialization selected seeds have a direct influence on the alignment result. The similarity measures between network nodes (e.g., proteins) include sequence similarity, centrality similarity, and dynamic membership similarity and our algorithm can be called Multiple Hubs-based Alignment (MHA).Results
When applying our seed selection strategy to several pairs of real PPI networks, it is observed that our method is working to strike a balance, extending the conserved interactions while maintaining the functional modules unchanged. In the case study, we assess the effectiveness of MHA on the alignment of the yeast and fly PPI networks. Our method outperforms state-of-the-art algorithms at detecting conserved functional modules and retrieves in particular 86% more conserved interactions than IsoRank.Conclusions
We believe that our seed selection strategy will lead us to obtain more topologically and biologically similar alignment result. And it can be used as the reference and complement of other heuristic methods to seek more meaningful alignment results.2.
Background
Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types.Methods
Distributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction.Results
The experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource.Conclusions
THAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.3.
Background
Database search has been the main approach for proteoform identification by top-down tandem mass spectrometry. However, when the target proteoform that produced the spectrum contains post-translational modifications (PTMs) and/or mutations, it is quite time consuming to align a query spectrum against all protein sequences without any PTMs and mutations in a large database. Consequently, it is essential to develop efficient and sensitive filtering algorithms for speeding up database search.Results
In this paper, we propose a spectrum graph matching (SGM) based protein sequence filtering method for top-down mass spectral identification. It uses the subspectra of a query spectrum to generate spectrum graphs and searches them against a protein database to report the best candidates. As the sequence tag and gaped tag approaches need the preprocessing step to extract and select tags, the SGM filtering method circumvents this preprocessing step, thus simplifying data processing. We evaluated the filtration efficiency of the SGM filtering method with various parameter settings on an Escherichia coli top-down mass spectrometry data set and compared the performances of the SGM filtering method and two tag-based filtering methods on a data set of MCF-7 cells.Conclusions
Experimental results on the data sets show that the SGM filtering method achieves high sensitivity in protein sequence filtration. When coupled with a spectral alignment algorithm, the SGM filtering method significantly increases the number of identified proteoform spectrum-matches compared with the tag-based methods in top-down mass spectrometry data analysis.4.
Background
Microbial communities are influenced by environmental factors including host genetics. We investigated the relationship between host bitter taste receptor genotype hTAS2R38 and oral microbiota, together with the influence of geographical location.Methods
hTAS2R38 polymorphisms and 16S bacterial gene sequencing from oral samples were analyzed from a total of 45 healthy volunteers from different geographical locations.Results
Genetic variation in the bitter taste receptor TAS2R38 reflected in the microbial composition of oral mucosa in Finnish and Spanish subjects. Multivariate analysis showed significant differences in the microbial composition between country and also dependent on taste genotype. Oral microbiota was shown to be more stable to the geographical location impact among AVI-homozygotes than PAV-homozygotes or heterozygotes (PAV/AVI).Conclusion
Geographical location and genetic variation in the hTAS2R38 taste receptor impact oral mucosa microbial composition. These findings provide an advance in the knowledge regarding the interactions between taste receptor genes and oral microbiota. This study suggests the role of host-microbiota interactions on the food taste perception in food choices, nutrition, and eating behavior.5.
Background
Spaced-seeds, i.e. patterns in which some fixed positions are allowed to be wild-cards, play a crucial role in several bioinformatics applications involving substrings counting and indexing, by often providing better sensitivity with respect to k-mers based approaches. K-mers based approaches are usually fast, being based on efficient hashing and indexing that exploits the large overlap between consecutive k-mers. Spaced-seeds hashing is not as straightforward, and it is usually computed from scratch for each position in the input sequence. Recently, the FSH (Fast Spaced seed Hashing) approach was proposed to improve the time required for computation of the spaced seed hashing of DNA sequences with a speed-up of about 1.5 with respect to standard hashing computation.Results
In this work we propose a novel algorithm, Fast Indexing for Spaced seed Hashing (FISH), based on the indexing of small blocks that can be combined to obtain the hashing of spaced-seeds of any length. The method exploits the fast computation of the hashing of runs of consecutive 1 in the spaced seeds, that basically correspond to k-mer of the length of the run.Conclusions
We run several experiments, on NGS data from simulated and synthetic metagenomic experiments, to assess the time required for the computation of the hashing for each position in each read with respect to several spaced seeds. In our experiments, FISH can compute the hashing values of spaced seeds with a speedup, with respect to the traditional approach, between 1.9x to 6.03x, depending on the structure of the spaced seeds.6.
Armelle Darrasse Matthieu Barret Sophie Cesbron Stéphane Compant Marie-Agnès Jacques 《Plant and Soil》2018,422(1-2):115-128
Aims
Seeds are vectors of a diversified microbiota including plant pathogens. To better understand transmission of common bacterial blight (CBB) agents to bean seeds, we analyzed the role of non-pathogenic xanthomonads on seed transmission efficiency and investigated the location of Xanthomonas citri pv. fuscans (Xcf) into seeds and plantlets.Methods
Competition between CBB and NP strains was initially assessed in vitro and then extended in planta to monitor the impact of co-inoculation on Xcf seed transmission. Moreover, location of Xcf strains in seeds and seedlings was visualized using a combination of gfp-tagged strain and DOPE-FISH/CSLM.Results
Whereas CBB agent growth was inhibited in vitro by some seed-borne non-pathogenic xanthomonads strains, these strains did not transmit efficiently to seed through floral pathway and did not affect Xcf seed transmission. Xcf cells were observed entering seed through vascular elements and parenchyma of funiculus, but also micropyle and testa. Xcf cells were observed, moreover, among other bacteria on radicle surfaces, especially tip, in cotyledons, and plumules.Conclusions
CBB agents are more efficient than non-pathogenic xanthomonads in using the floral route to colonize seeds. CBB agents are located within different niches in the seed tissues up to the embryonic axis.7.
Background
Literature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined.Methods
We present a detailed analysis of the quantity of hidden knowledge produced by an LBD system and the effect of various filtering approaches upon this. The investigation of filtering combined with single or multi-step linking term chains is carried out on all articles in PubMed.Results
The evaluation is carried out using both replication of existing discoveries, which provides justification for multi-step linking chain knowledge in specific cases, and using timeslicing, which gives a large scale measure of performance.Conclusions
While the quantity of hidden knowledge generated by LBD can be vast, we demonstrate that (a) intelligent filtering can greatly reduce the number of hidden knowledge pairs generated, (b) for a specific term, the number of single step connections can be manageable, and (c) in the absence of single step hidden links, considering multiple steps can provide valid links.8.
Background
Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias.Results
We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting GC-bias at SCNAs level. Pre-SCNAClonal first corrects GC bias using Markov chain Monte Carlo probability model, then accurately locates baseline DNA segments (not containing any SCNAs) with a hierarchy clustering model. We show Pre-SCNAClonal’s superiority to exsiting GC-bias correction methods at any level of subclonal population.Conclusions
Pre-SCNAClonal could be run independently as well as serving as pre-processing/gc-correction step in conjuntion with exsiting SCNA-based subclonal inferring tools.9.
Erfan Soliman Fabio Bianchi James N. Sleigh Julian H. George M. Zameel Cader Zhanfeng Cui Hua Ye 《Biotechnology letters》2018,40(3):601-607
Objectives
To test a 3D approach for neural network formation, alignment, and patterning that is reproducible and sufficiently stable to allow for easy manipulation.Results
A novel cell culture system was designed by engineering a method for the directional growth of neurons. This uses NG108-15 neuroblastoma x glioma hybrid cells cultured on suspended and aligned electrospun fibers. These fiber networks improved cellular directionality, with alignment angle standard deviations significantly lower on fibers than on regular culture surfaces. Morphological studies found nuclear aspect ratios and cell projection lengths to be unchanged, indicating that cells maintained neural morphology while growing on fibers and forming a 3D network. Furthermore, fibronectin-coated fibers enhanced neurite extensions for all investigated time points. Differentiated neurons exhibited significant increases in average neurite lengths 96 h post plating, and formed neurite extensions parallel to suspended fibers, as visualized through scanning electron microscopy.Conclusions
The developed model has the potential to serve as the basis for advanced 3D studies, providing an original approach to neural network patterning and setting the groundwork for further investigations into functionality.10.
Sonia Liggi Christine Hinz Zoe Hall Maria Laura Santoru Simone Poddighe John Fjeldsted Luigi Atzori Julian L. Griffin 《Metabolomics : Official journal of the Metabolomic Society》2018,14(4):52
Introduction
Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.Objectives
Merge in the same platform the steps required for metabolomics data processing.Methods
KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.Results
The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.Conclusion
KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.11.
Rashid H. Kazmi Leo A. J. Willems Ronny V. L. Joosen Noorullah Khan Wilco Ligterink Henk W. M. Hilhorst 《Metabolomics : Official journal of the Metabolomic Society》2017,13(12):145
Introduction
Seed germination is inherently related to seed metabolism, which changes throughout its maturation, desiccation and germination processes. The metabolite content of a seed and its ability to germinate are determined by underlying genetic architecture and environmental effects during development.Objective
This study aimed to assess an integrative approach to explore genetics modulating seed metabolism in different developmental stages and the link between seed metabolic- and germination traits.Methods
We have utilized gas chromatography-time-of-flight/mass spectrometry (GC-TOF/MS) metabolite profiling to characterize tomato seeds during dry and imbibed stages. We describe, for the first time in tomato, the use of a so-called generalized genetical genomics (GGG) model to study the interaction between genetics, environment and seed metabolism using 100 tomato recombinant inbred lines (RILs) derived from a cross between Solanum lycopersicum and Solanum pimpinellifolium.Results
QTLs were found for over two-thirds of the metabolites within several QTL hotspots. The transition from dry to 6 h imbibed seeds was associated with programmed metabolic switches. Significant correlations varied among individual metabolites and the obtained clusters were significantly enriched for metabolites involved in specific biochemical pathways.Conclusions
Extensive genetic variation in metabolite abundance was uncovered. Numerous identified genetic regions that coordinate groups of metabolites were detected and these will contain plausible candidate genes. The combined analysis of germination phenotypes and metabolite profiles provides a strong indication for the hypothesis that metabolic composition is related to germination phenotypes and thus to seed performance.12.
Aims
We investigated potential mechanisms by which a seed microbiome recruited from vermicomposted dairy manure alters Pythium aphanidermatum zoospore mediated pathogenesis in cucumber.Methods
Bioassays were conducted to measure arrival of zoospores at the seed surface via qPCR and subsequent seedling disease incidence. Seed exudates were collected at relevant time points for use in zoospore microscopy assays. Metabolomic analysis was used to characterize seed exudates.Results
Microbes recruited by the germinating seed from a disease suppressive substrate within 8 hours of sowing prevented zoospore arrival at the seed surface, modified seed exudates and reduced disease incidence. In vitro exposure to microbially modified seed exudates altered zoospore homing responses and reduced both encystment and germination compared to control exudates. Combining modified and control exudates failed to restore zoospore attraction to levels observed with control exudates. Observed zoosporolytic activity of the modified exudates was unique to the ethyl acetate fraction and metabolomic analysis revealed several putative zoosporolytic compounds present at higher relative abundance when compared to control exudates.Conclusions
The observed disease suppression was likely due to the production of a specific zoosporolytic compound or set of compounds in the spermosphere by one or more members of the seed-recruited vermicompost microbiome.13.
D. Jacob C. Deborde M. Lefebvre M. Maucourt A. Moing 《Metabolomics : Official journal of the Metabolomic Society》2017,13(4):36
Introduction
Concerning NMR-based metabolomics, 1D spectra processing often requires an expert eye for disentangling the intertwined peaks.Objectives
The objective of NMRProcFlow is to assist the expert in this task in the best way without requirement of programming skills.Methods
NMRProcFlow was developed to be a graphical and interactive 1D NMR (1H & 13C) spectra processing tool.Results
NMRProcFlow (http://nmrprocflow.org), dedicated to metabolic fingerprinting and targeted metabolomics, covers all spectra processing steps including baseline correction, chemical shift calibration and alignment.Conclusion
Biologists and NMR spectroscopists can easily interact and develop synergies by visualizing the NMR spectra along with their corresponding experimental-factor levels, thus setting a bridge between experimental design and subsequent statistical analyses.14.
Samir Rezki Claire Campion Philippe Simoneau Marie-Agnès Jacques Ashley Shade Matthieu Barret 《Plant and Soil》2018,422(1-2):67-79
Background and aims
Seeds are involved in the transmission of microorganisms from one plant generation to another and consequently may act as the initial inoculum source for the plant microbiota. In this work, we assessed the structure and composition of the seed microbiota of radish (Raphanus sativus) across three successive plant generations.Methods
Structure of seed microbial communities were estimated on individual plants through amplification and sequencing of genes that are markers of taxonomic diversity for bacteria (gyrB) and fungi (ITS1). The relative contribution of dispersal and ecological drift in inter-individual fluctuations were estimated with a neutral community model.Results
Seed microbial communities of radish display a low heritability across plant generations. Fluctuations in microbial community profiles were related to changes in community membership and composition across plant generations, but also to variation between individual plants. Ecological drift was an important driver of the structure of seed bacterial communities, while dispersal was involved in the assembly of the fungal fraction of the seed microbiota.Conclusions
These results provide a first glimpse of the governing processes driving the assembly of the seed microbiota.15.
Bjorn J. M. Robroek Remy J. H. Albrecht Samuel Hamard Adrian Pulgarin Luca Bragazza Alexandre Buttler Vincent EJ Jassey 《Plant and Soil》2016,399(1-2):135-146
Aims
The readily available global rock phosphate (P) reserves may be depleted within the next 50–130 years warranting careful use of this finite resource. We develop a model that allows us to assess a range of P fertiliser and soil management strategies for Barley in order to find which one maximises plant P uptake under certain climate conditions.Methods
Our model describes the development of the P and water profiles within the soil. Current cultivation techniques such as ploughing and reduced till gradient are simulated along with fertiliser options to feed the top soil or the soil right below the seed.Results
Our model was able to fit data from two barley field trials, achieving a good fit at early growth stages but a poor fit at late growth stages, where the model underestimated plant P uptake. A well-mixed soil (inverted and 25 cm ploughing) is important for optimal plant P uptake and provides the best environment for the root system.Conclusions
The model is sensitive to the initial state of P and its distribution within the soil profile; experimental parameters which are sparsely measured. The combination of modelling and experimental data provides useful agricultural predictions for site specific locations.16.
Soo-Jin?Cho Kyung-Ho?Yu Mi?Sun?Oh San?Jung Ju-Hun?Lee Im-Seok?Koh Hee-Joon?Bae Yeonwook?Kang Byung-Chul?Lee
Background
The American Stroke Association/American Heart Association recommended the criteria for diagnosis of vascular cognitive impairment and memory impairment (MI) is a feature in the classification of vascular mild cognitive impairment (VaMCI). VaMCI patients with MI may differ in terms of infarct location or demographic features, so we evaluated the clinical characteristics associated with MI in patients with VaMCI.Methods
A prospective multicenter study enrolled 353 acute ischemic stroke patients who underwent evaluation using the Korean Vascular Cognitive Impairment Harmonization Standard Neuropsychological Protocol at three months after onset. The association between MI and demographic features, stroke risk factors, and infarct location was assessed.Results
VaMCI was diagnosed in 141 patients, and 58 (41.1%) exhibited MI. Proportions of men and of left side infarcts were higher in VaMCI with MI than those without (75.9 vs. 57.8%, P?=?0.03, 66.7 vs. 47%, P?=?0.02). Multiple logistic analyses revealed that male sex (odds ratio [OR] 3.07, 95% confidence interval [95% CI] 1.12-8.42), left-side infarcts (OR 3.14, 95% CI 1.37-7.20), and basal ganglia/internal capsule infarcts (OR 4.53, 95% CI 1.55-13.22) were associated with MI after adjusting other demographic variables, vascular risk factors, and subtypes of stroke.Conclusions
MI is associated with sex and infarct location in VaMCI patients.17.
Dimitrios J. Floros Paul R. Jensen Pieter C. Dorrestein Nobuhiro Koyama 《Metabolomics : Official journal of the Metabolomic Society》2016,12(9):145
Introduction
Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections.Objective
Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs.Method
In this work we utilize untargeted LC–MS/MS based metabolomics together with molecular networking to inventory the chemistries associated with 1000 marine microorganisms.Result
This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B.Conclusion
Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.18.
Qibo Zhang Lisa A. Ford Anne M. Evans Douglas R. Toal 《Metabolomics : Official journal of the Metabolomic Society》2017,13(8):92
Introduction
A major bottleneck in metabolomic studies is metabolite identification from accurate mass spectrometric data. Metabolite x17299 was identified in plasma as an unknown in a metabolomic study using a compound-centric approach where the associated ion features of the compound were used to determine the true molecular mass.Objectives
The aim of this work is to elucidate the chemical structure of x17299, a new compound by de novo interpretation of mass spectrometric data.Methods
An Orbitrap Elite mass spectrometer was used for acquisition of mass spectra up to MS4 at high resolution. Synthetic standards of N,N,N-trimethyl-l-alanyl-l-proline betaine (l,l-TMAP), a diastereomer, and an enantiomer were chemically prepared.Results
The planar structure of x17299 was successfully proposed by de novo mechanistic interpretation of mass spectrometric data without any laborious purification and nuclear magnetic resonance spectroscopic analysis. The proposed structure was verified by deuterium exchanged mass spectrometric analysis and confirmed by comparison to a synthetic standard. Relative configuration of x17299 was determined by direct chromatographic comparison to a pair of synthetic diastereomers. Absolute configuration was assigned after derivatization of x17299 with a chiral auxiliary group followed by its chromatographic comparison to a pair of synthetic standards.Conclusion
The chemical structure of metabolite x17299 was determined to be l,l-TMAP.19.
Yuji Sawada Hirokazu Tsukaya Yimeng Li Muneo Sato Kensuke Kawade 《Metabolomics : Official journal of the Metabolomic Society》2017,13(6):75
Introduction
In plant metabolomics, metabolite contents are often normalized by sample weight. However, accurate weighing of very small samples, such as individual Arabidopsis thaliana seeds (approximately 20 µg), is difficult, which may lead to irreproducible results.Objectives
We aimed to establish alternative normalization methods for seed-grain-based comparative metabolomics of A. thaliana.Methods
Arabidopsis thaliana seeds were assumed to have a prolate spheroid shape. Using a microscope image of each seed, the lengths of major and minor axes were measured by fitting a projected 2-dimensional shape of each seed as an ellipse. Metabolic profiles of individual diploid or tetraploid A. thaliana seeds were measured by our highly sensitive protocol (“widely targeted metabolomics”) that uses liquid chromatography coupled with tandem quadrupole mass spectrometry. Mass spectrometric analysis of 1 µL of solution extract identified more than 100 metabolites. The data were normalized by various seed-size measures, including seed volume (single-grain-based analysis). For comparison, metabolites were extracted from 4 mg of diploid and tetraploid A. thaliana seeds and their metabolic profiles were analyzed by normalization of weight (weight-based analysis).Results
A small number of metabolites showed statistically significant differences in the single-grain-based analysis compared to weight-based analysis. A total of 17 metabolites showed statistically different accumulation between ploidy types with similar fold changes in both analyses.Conclusion
Seed-size measures obtained by microscopic imaging were useful for data normalization. Single-grain-based analysis enables evaluation of metabolism of each seed and elucidates the metabolic profiles of precious bioresources by using small amounts of samples.20.
Miguel F. Astudillo Hessam AzariJafari 《The International Journal of Life Cycle Assessment》2018,23(7):1512-1516