昆虫表皮中碳氢化合物在昆虫分类中的应用

碳氢化合物是昆虫表皮蜡层中的主要成分,利用气相色谱技术或气谱质谱联用技术对其进行分析,并以此为依据对昆虫进行分类鉴定,是近十几年来昆虫分类学发展的一个方面,主要用于近缘种及种下类群的研究。该文概述了这类化合物的研究方法及 应用情况     

荧光定量PCR技术在植物研究中的应用

荧光定量PCR技术是近年发展起来的一种用于基因定量分析的PCR方法,自同世以来在基础科学研究、临床诊断、疾病研究及药物研发等领域应用较为广泛.在植物研究方面应用起步较晚,现已开始用于植物基因表达分析、外源基因基因鉴定等方面的研究.介绍了实时荧光定量PCR技术的原理、优缺点和试验中潜在问题和条件的优化,并对其在植物研究中的应用及前景作了探讨.     

扩增片段长度多态性实验技术及在动物遗传多样性研究中的应用

扩增片段长度多态性(AFLP)是一种有效的分子遗传标记方法,具有经济、简便、模板需要量少、重复性高、结果可靠等优点。目前AFLP在动物方面的应用还不是很多,处于初级阶段,主要用于鉴定分类关系、种群遗传多样性分析、遗传连锁图谱构建等方面。     

隐马尔科夫过程在基因识别中的应用

用非数学语言描述了隐马尔科夫过程（hidden mark-ov model,HMM),介绍了HMM用于基因识别的原理及基于HMM开发的,比较常用的基因识别程序。     

土地系统动态模拟方法研究进展

土地系统动态模拟对土地利用规划、国土资源管理具有重要指导价值.基于经验统计的方法、多智能主体分析的方法、栅格邻域关系分析的方法以及近年来发展起来的土地系统动态模拟系统(DLS)方法是当前开展土地系统动态模拟的主要手段.本文综述了上述4种方法的原理、特点与应用案例,概括了相关方法在理论依据和推广应用等方面的优点与不足,展望了DLS在土地系统动态模拟方面的广阔应用前景.     

DHPLC系统工作原理及其应用

变性高效液相色谱(DHPLC)是一种高通量筛选DNA序列变异的新技术,从该仪器设备的组成、工作原理、基本操作方法、主要技术特点等作一综述,并对其在基因组领域的应用如SNP分析、双链DNA片段分析、微卫星分析、mRNA定量分析、引物纯度检测等方面及在医学、遗传学方面的应用作了较详细的综述。     

自体红细胞凝集试验研究进展

自体红细胞凝集试验是一种快速、简便,成本低廉的免疫学检测方法。用于自体红细胞凝集试验的主要成分是一种双功能性抗体。介绍了自体红细胞凝集试验的基本原理和主要特点,以及如何建立自体红细胞凝集试验检测体系;简要综述了双功能性抗体的活性、稳定性、特异性、敏感性等方面的研究进展,以及自体红细胞凝集试验检测方法的应用前景。     

常用的生物统计方法及其R语言实现

统计分析是科学研究中一个极其重要的环节.本文以昆虫学研究为实例,利用模拟数据,总结了14种常用的生物统计方法及其R语言实现,重点强调了如何根据科学问题和样本数据的具体情形选取合适的统计方法.这些统计方法包括可用于均值比较分析的符号检验、Wilcoxon符号秩检验、t-检验、Wilcoxon秩和检验、Kruskal-Wa...     

溶菌酶的研究进展

溶菌酶普遍存在于动物、植物和微生物中。溶菌酶是一种小分子碱性蛋白,长期以来一直被作为一种“模型”体系．用于研究蛋白质的空间构象、酶动力学及其与分子进化、分子免疫间的关系。介绍了溶菌酶的来源、结构、性质、作用机制。并对近年来其在食品工业、医学和酶工程中的应用进行了综述;分析了溶菌酶应用中存在的主要问题,并对其应用前景进行了展望。     

动物DNA条形码之分析方法研究进展

DNA条形码是一段可用于物种鉴定的DNA序列。本文综述了近年来多种基于DNA条形码的分析方法及其在物种鉴定和隐存种发现中的应用,主要包括遗传距离法、进化树法、相似性比对法、诊断法和统计分类法等,旨在为这一技术的广泛应用提供参考。     

Hidden Markov Models and their Applications in Biological Sequence Analysis

Hidden Markov models (HMMs) have been extensively used in biological sequence analysis. In this paper, we give a tutorial review of HMMs and their applications in a variety of problems in molecular biology. We especially focus on three types of HMMs: the profile-HMMs, pair-HMMs, and context-sensitive HMMs. We show how these HMMs can be used to solve various sequence analysis problems, such as pairwise and multiple sequence alignments, gene annotation, classification, similarity search, and many others.Key Words: Hidden Markov model (HMM), pair-HMM, profile-HMM, context-sensitive HMM (csHMM), profile-csHMM, sequence analysis.     

Applications of generalized pair hidden Markov models to alignment and gene finding problems.

Hidden Markov models (HMMs) have been successfully applied to a variety of problems in molecular biology, ranging from alignment problems to gene finding and annotation. Alignment problems can be solved with pair HMMs, while gene finding programs rely on generalized HMMs in order to model exon lengths. In this paper, we introduce the generalized pair HMM (GPHMM), which is an extension of both pair and generalized HMMs. We show how GPHMMs, in conjunction with approximate alignments, can be used for cross-species gene finding and describe applications to DNA-cDNA and DNA-protein alignment. GPHMMs provide a unifying and probabilistically sound theory for modeling these problems.     

The August Krogh principle applies to plants

The Krogh principle refers to the use of a large number of animals to study the large number of physiological problems, rather than limiting study to a particular organism for all problems. There may be organisms that are more suited to study of a particular problem than others. This same principle applies to plants. The authors are concerned with the recent trend in plant biology of using Arabidopsis thaliana as the "organism of choice." Arabidopsis is an excellent organism for molecular genetic research, but other plants are superior models for other research areas of plant biology. The authors present examples of the successful use of the Krogh principle in plant cell biology research, emphasizing the particular characteristics of the selected research organisms that make them the appropriate choice.     

BALL--rapid software prototyping in computational molecular biology. Biochemicals Algorithms Library

MOTIVATION: Rapid software prototyping can significantly reduce development times in the field of computational molecular biology and molecular modeling. Biochemical Algorithms Library (BALL) is an application framework in C++ that has been specifically designed for this purpose. RESULTS: BALL provides an extensive set of data structures as well as classes for molecular mechanics, advanced solvation methods, comparison and analysis of protein structures, file import/export, and visualization. BALL has been carefully designed to be robust, easy to use, and open to extensions. Especially its extensibility which results from an object-oriented and generic programming approach distinguishes it from other software packages. BALL is well suited to serve as a public repository for reliable data structures and algorithms. We show in an example that the implementation of complex methods is greatly simplified when using the data structures and functionality provided by BALL.     

Accelerated Profile HMM Searches

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.     

MRFalign: Protein Homology Detection through Alignment of Markov Random Fields

Sequence-based protein homology detection has been extensively studied and so far the most sensitive method is based upon comparison of protein sequence profiles, which are derived from multiple sequence alignment (MSA) of sequence homologs in a protein family. A sequence profile is usually represented as a position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This paper presents a new homology detection method MRFalign, consisting of three key components: 1) a Markov Random Fields (MRF) representation of a protein family; 2) a scoring function measuring similarity of two MRFs; and 3) an efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning two MRFs. Compared to HMM that can only model very short-range residue correlation, MRFs can model long-range residue interaction pattern and thus, encode information for the global 3D structure of a protein family. Consequently, MRF-MRF comparison for remote homology detection shall be much more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that MRFalign outperforms several popular HMM or PSSM-based methods in terms of both alignment accuracy and remote homology detection and that MRFalign works particularly well for mainly beta proteins. For example, tested on the benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM succeed on 48% and 52% of proteins, respectively, at superfamily level, and on 15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign succeeds on 57.3% and 42.5% of proteins at superfamily and fold level, respectively. This study implies that long-range residue interaction patterns are very helpful for sequence-based homology detection. The software is available for download at http://raptorx.uchicago.edu/download/. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2–5.     

Nanostructures of APOBEC3G support a hierarchical assembly model of high molecular mass ribonucleoprotein particles from dimeric subunits

Human APOBEC3G (hA3G) is a cytidine deaminase that restricts human immunodeficiency virus (HIV)-1 infection in a vif (the virion infectivity factor from HIV)-dependent manner. hA3G from HIV-permissive activated CD4+ T-cells exists as an inactive, high molecular mass (HMM) complex that can be transformed in vitro into an active, low molecular mass (LMM) variant comparable with that of HIV-non-permissive CD4+ T-cells. Here we present low resolution structures of hA3G in HMM and LMM forms determined by small angle x-ray scattering and advanced shape reconstruction methods. The results show that LMM particles have an extended shape, dissimilar to known cytidine deaminases, featuring novel tail-to-tail dimerization. Shape analysis of LMM and HMM structures revealed how symmetric association of dimers could lead to minimal HMM variants. These observations imply that the disruption of cellular HMM particles may require regulation of protein-RNA, as well as protein-protein interactions, which has implications for therapeutic development.     

Hidden Markov models incorporating fuzzy measures and integrals for protein sequence identification and alignment

Profile hidden Markov models （HMMs） based on classical HMMs have been widely applied for protein sequence identification. The formulation of the forward and backward variables in profile HMMs is made under statistical independence assumption of the probability theory. We propose a fuzzy profile HMM to overcome the limitations of that assumption and to achieve an improved alignment for protein sequences belonging to a given family. The proposed model fuzzifies the forward and backward variables by incorporating Sugeno fuzzy measures and Choquet integrals, thus further extends the generalized HMM. Based on the fuzzified forward and backward variables, we propose a fuzzy Baum-Welch parameter estimation algorithm for profiles. The strong correlations and the sequence preference involved in the protein structures make this fuzzy architecture based model as a suitable candidate for building profiles of a given family, since the fuzzy set can handle uncertainties better than classical methods.     

Molecular-genetic imaging: a nuclear medicine-based perspective

Molecular imaging is a relatively new discipline, which developed over the past decade, initially driven by in situ reporter imaging technology. Noninvasive in vivo molecular-genetic imaging developed more recently and is based on nuclear (positron emission tomography [PET], gamma camera, autoradiography) imaging as well as magnetic resonance (MR) and in vivo optical imaging. Molecular-genetic imaging has its roots in both molecular biology and cell biology, as well as in new imaging technologies. The focus of this presentation will be nuclear-based molecular-genetic imaging, but it will comment on the value and utility of combining different imaging modalities. Nuclear-based molecular imaging can be viewed in terms of three different imaging strategies: (1) "indirect" reporter gene imaging; (2) "direct" imaging of endogenous molecules; or (3) "surrogate" or "bio-marker" imaging. Examples of each imaging strategy will be presented and discussed. The rapid growth of in vivo molecular imaging is due to the established base of in vivo imaging technologies, the established programs in molecular and cell biology, and the convergence of these disciplines. The development of versatile and sensitive assays that do not require tissue samples will be of considerable value for monitoring molecular-genetic and cellular processes in animal models of human disease, as well as for studies in human subjects in the future. Noninvasive imaging of molecular-genetic and cellular processes will complement established ex vivo molecular-biological assays that require tissue sampling, and will provide a spatial as well as a temporal dimension to our understanding of various diseases and disease processes.     

A novel method of plasmid isolation using laundry detergent

Since the discovery of plasmid, various methods have been developed to isolate plasmid DNA. All the methods have one common and important target of isolating plasmid DNA of high quality and quantity in less time. These methods are not completely safe because of use of toxic chemicals compounds. The developed protocol for plasmid extraction is based on the alkaline lysis method of plasmid preparation (extraction atpH 8.0) with slight modifications. Cell lysis reagent sodium dodecyl sulfate is replaced by lipase enzyme present in laundry detergent. A good plasmid preparation can be made, which is well suited for subsequent molecular biology applications. By taking safety measures on count, contaminants like, RNA and protein can be completely avoided with maximized plasmid yield. The resultant plasmid quality and quantity can be well comparable to other prevalent methods.