蛋白质组学的研究方法

近年来,蛋白质组学的研究在生物医学领域正逐步深入,研究方法也正逐步完善。本文综述了蛋白质组学的几种研究方法,指出了各种方法的优点及局限性,并且对蛋白质组学的研究方法和前景进行了展望。     

基于物种的大尺度生物多样性热点研究方法

生物多样性热点是建立保护区、制定保护决策的依据,是生物保护研究的热点问题之一。基于物种的研究方法是大尺度陆地生物多样性热点的主要研究方法,但数据的缺乏限制了直接根据物种丰富度确定热点的方法,因此研究中经常采用其他方法间接的反映物种情况,介绍了4种主要的基于物种的替代方法:指示种、高级分类单元、环境模型和景观异质性,详细阐述了各种方法存在的利弊,并从数据的可获取性、操作的便捷性和对物种特征的反映3个方面对各种方法进行了评价。任何单一的方法都无法准确反映出生物多样性热点的真实分布。合适的研究方法是权衡研究目的、时间和资金的结果,建议选择优势互补的多种方法。     

从一般系统论到后基因组时代的系统生物学

论述了贝塔朗菲的一般系统论的思想起源、主要内容,基于一般系统论的系统生物学的产生、研究思路和方法,阐述了生物学由还原论的研究方法过渡到系统论的研究方法,以及系统生物学未来的发展进行了评价。     

城市建筑代谢研究方法及其展望

建筑代谢是当前城市代谢研究领域中的一个新兴热点问题,其研究着重关注建筑系统中物质能量流动可能对周围环境产生的压力及其有害影响。本文在阐明城市建筑代谢内涵的基础上,综述了国内外建筑代谢研究方法的进展情况,分析了目前城市建筑代谢研究方法的适用范围及其特点,指出了城市建筑代谢研究方法在未来应从以下几个方面发展:开发跨城市边界的代谢研究方法;注重建筑代谢中非物质性代谢流研究;制定适合乡土建筑地域特点的代谢研究方法;综合物质量和价值量的建筑代谢研究,全面认识和评价建筑的可持续性。     

中药药效物质的研究方法

中药药效物质的研究是中药现代化研究的核心,科学阐明中药临床治疗的物质基础,是中药药效学研究的最终目标。现阶段中药药效物质基础研究方法主要有化学分离分析、生物色谱法和虚拟筛选等方法。本文对现有中药药效物质基础的几种研究方法进行了分类,并对其特点进行了介绍。     

多肽固相合成的研究进展

多肽固相化学合成法是蛋白质研究领域非常重要的研究方法之一,主要可以分为Boc方法和Fmoc方法,在生物药物、蛋白质工程、免疫学等研究中得到了广泛的应用。该文论述了多肽固相合成法的原理,比较了两种典型合成方法的优缺点,介绍了适用于该方法的多肽种类,提出了多肽合成过程中存在的问题与解决策略,最后展望了多肽合成法的应用前景,以供相关领域的研究人员参考。     

陆地生态系统碳平衡主要研究方法评述

陆地生态系统碳平衡是全球变化科学中的核心问题之一,目前也是生态科学中的前沿与热点问题,而陆地生态系统的复杂性与不确定性决定了对陆地生态系统碳平衡估测的复杂性和不确定性。为研究这一复杂性问题,已发展了许多研究方法。可分为“自下而上”与“自上而下”两种,各种方法都有其自身的优势和劣势。相关方向也已经有了大量的研究报道,但是,不同的研究由于在方法、时间与空间尺度等存在的差异,使得许多研究结果和预测很难被有效的整合或适用于大范围甚至全球水平。综述了陆地碳平衡的主要研究方法,分析和比较了各方法的特点,指出在研究中对不同方法的结果进行分析和比较,以及采用综合方法的必要性。     

不同层面上微生物多样性研究方法

在讨论不同层面上微生物多样性研究的特征和对比各主要研究方法的基础上,探讨了在不同层面上研究微生物多样性时对方法的选择性应用,分析了微生物多样性研究技术体系的发展趋势。     

系统生物学研究及其在医学中的应用

目前,系统生物学研究已初显端倪。生物学正从分子生物学走向系统生物学,由精细的分解研究转向系统的整体研究,由还原论的研究方法过渡到系统论的研究方法。简要论述了系统生物学的产生背景、结构和内容、研究思路和方法、与医学的关系等,重点介绍系统生物学的内容和研究方法,以及在疾病治疗和药物开发中的研究进展。     

昆虫系统发育重建的常用方法及步骤

本文介绍了昆虫系统发育重建研究的步骤,常用方法及相关软件的使用,指出了不同研究方法的优缺点及适用范围,分析了系统发育重建存在的问题,从而为相关研究的开展提供了参考。     

A simulation-based evaluation of methods for inferring linear barriers to gene flow

Different analytical techniques used on the same data set may lead to different conclusions about the existence and strength of genetic structure. Therefore, reliable interpretation of the results from different methods depends on the efficacy and reliability of different statistical methods. In this paper, we evaluated the performance of multiple analytical methods to detect the presence of a linear barrier dividing populations. We were specifically interested in determining if simulation conditions, such as dispersal ability and genetic equilibrium, affect the power of different analytical methods for detecting barriers. We evaluated two boundary detection methods (Monmonier's algorithm and WOMBLING), two spatial Bayesian clustering methods (TESS and GENELAND), an aspatial clustering approach (STRUCTURE), and two recently developed, non-Bayesian clustering methods [PSMIX and discriminant analysis of principal components (DAPC)]. We found that clustering methods had higher success rates than boundary detection methods and also detected the barrier more quickly. All methods detected the barrier more quickly when dispersal was long distance in comparison to short-distance dispersal scenarios. Bayesian clustering methods performed best overall, both in terms of highest success rates and lowest time to barrier detection, with GENELAND showing the highest power. None of the methods suggested a continuous linear barrier when the data were generated under an isolation-by-distance (IBD) model. However, the clustering methods had higher potential for leading to incorrect barrier inferences under IBD unless strict criteria for successful barrier detection were implemented. Based on our findings and those of previous simulation studies, we discuss the utility of different methods for detecting linear barriers to gene flow.     

Differential expression analyses for single-cell RNA-Seq: old questions on new data

Background: Single-cell RNA sequencing (scRNA-seq) is an emerging technology that enables high resolution detection of heterogeneities between cells. One important application of scRNA-seq data is to detect differential expression (DE) of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed. Results: In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations. Conclusions: DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.     

Spatial analysis of two-species interactions

Summary In this paper, I present and discuss some methods for the analysis of univariate and bivariate spatial point pattern data. Examples of such data in ecology include x-y coordinates of organisms in mapped field plots. I illustrate the methods with analyses of data from mapped field plots on Mount St. Helens, Washington state, USA. The statistical methods I emphasize are graphical methods that rely on analysis of distances between organisms. Hypothesis testing for methods like these is easily done using Monte Carlo methods, which I also discuss. For both univariate and bivariate analyses, I find that second-order methods such as K-function plots are often preferable to first-order methods (i.e., QQ-plots). However, for multivariate analyses, these second-order methods are more sensitive to small sample sizes than first-order analyses.     

磷酸化蛋白质组学研究中磷酸化肽段富集与分离方法研究进展

对磷酸化蛋白质组（phosphoproteome）进行系统深入的研究依赖于高重复性和特异性的磷酸化肽段富集与分离方法。目前发展了多种不同原理的磷酸化肽段富集方法,它们往往具有不同的选择性和特异性,因此,根据不同的研究目的选择最适合的富集方法显得尤为重要。本文综述了基于亲和色谱法（affinity chromatography）、免疫沉淀法（immunoprecipitation）、化学衍生法（chemical derivatization）、色谱法（chromatography）和其他新发展方法的磷酸化肽段富集方法,详细介绍了各自的优缺点及相关的优化与改进策略。此外,还简单介绍了磷酸化肽段富集与预分方法的不同组合的研究进展。     

Methods for gas chromatography-olfactometry: a review

Gas chromatography-olfactometry methods are used in flavor research to determine the odor active compounds in foods. In this review, the four major methods for gas chromatography-olfactometry are described and their potentials and limitations discussed. The methods include dilution analysis, detection frequency methods, posterior intensity methods and time-intensity methods. The value of gas chromatography olfactometry data is shown to depend directly on the gas chromatography-olfactometry method, as well as on sample preparation and analytical conditions. Each of the methods has been used frequently and has its advantages and disadvantages. However, on the methodological side, a considerable area is still to be explored, which would contribute to the interpretation of the data and would improve the value of these techniques for both fundamental and applied research.     

临泽盐渍化草地空间格局的可塑性面积单元问题

通过对临泽盐渍化草地群落取样资料的分析表明,草地空间格局的分布对样地尺度变化和划区方案选择均存在敏感性.样地面积在12500m2以下时,尺度增聚效应表现非常明显.在同一取样面积单元下不同的划区方案对草地的空间分布都有一定的影响,但随着取样面积的增大,区划方案的影响也是减少的.因此在研究草地空间分布格局时,要力求寻找一个最佳的区划方案和取样尺度,使得面积单元内的差异最小是非常有必要的.     

Registration of pedobarographic image data in the frequency domain

Image registration has been used to support pixel-level data analysis on pedobarographic image data sets. Some registration methods have focused on robustness and sacrificed speed, but a recent approach based on external contours offered both high computational processing speed and high accuracy. However, since contours can be influenced by local perturbations, we sought more global methods. Thus, we propose two new registration methods based on the Fourier transform, cross-correlation and phase correlation which offer high computational speed. We found out that both proposed methods revealed high accuracy for the similarity measures considered, using control geometric transformations. Additionally, both methods revealed high computational processing speed which, combined with their accuracy and robustness, allows their implementation in near-real-time applications. Furthermore, we found that the current methods were robust to moderate levels of noise, and consequently, do not require noise removal procedure like the contours method does.     

A note on standard methods of investigating the hygrothermal microclimate (subjective and objective evaluation of the indoor climate)

The methods described for investigating the microclimate are based on the so-called complex system of evaluation of the microclimate, according to which the response of man, induced by the environment, is decisive, i.e. the microclimate being assessed on the basis of the thermal load on man. The methods are applied in two stages. The first stage, the so-called methods of anamnestic examination, is based on the subjective data, provided by persons, on the microclimate which they experience; the second stage, the objective methods of examination establish guidelines on where, when, what and by what methods to measure the environment under examination in relation to the type of thermal load on man.     

挥发性有机化合物(VOCs)采样方法的研究进展

对近年来国内外挥发性有机化合物（VOCs）采集方法的研究进展进行了综合和概括。对VOCs几种主要的采样方法包括顶空收集法、液液萃取法、超临界流体萃取、吸附剂收集法、吹扫捕集法、画相微萃取等技术进行了阐述和比较,提出应根据实验材料的特性选择合适的采样方法,旨在为应用VOCs的采集方法及相关研究提供参考和借鉴。     

A Coarse-Grained Elastic Network Atom Contact Model and Its Use in the Simulation of Protein Dynamics and the Prediction of the Effect of Mutations

Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with C_α−only representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.