miRNA异构体：miRNA序列多样性

微RNA（microRNA,miRNA)是一类抑制基因表达的调控分子,在多种生物学进程中扮演重要角色。近来,基于新一代测序技术获得的小RNA测序数据发现,对于某一miRNA来说,它并不是单一的序列,而是由一系列长度/序列及表达不同的异构微RNA (isomicroRNA, isomiRNA/isomiR)所组成。这些isomiR表达多样且序列多样,甚至引入多样的5′端及种子区域。特定miRNA位点在疾病组织中可具有异常的表达模式,现已证实,部分isomiR具有重要的生物学功能。所关注的经典miRNA序列,其实仅是多重isomiR序列中的1条特殊序列,仅从miRNA角度的研究已不足以揭示小RNA的奥秘,全面的研究应同时在miRNA和isomiR中开展,由此可进一步拓宽miRNA的研究思路。本文主要从isomiR的生物学特点、表达及功能等方面进行介绍。     

Continuous hidden process model for time series expression experiments

MOTIVATION: When analyzing expression experiments, researchers are often interested in identifying the set of biological processes that are up- or down-regulated under the experimental condition studied. Current approaches, including clustering expression profiles and averaging the expression profiles of genes known to participate in specific processes, fail to provide an accurate estimate of the activity levels of many biological processes. RESULTS: We introduce a probabilistic continuous hidden process Model (CHPM) for time series expression data. CHPM can simultaneously determine the most probable assignment of genes to processes and the level of activation of these processes over time. To estimate model parameters, CHPM uses multiple time series datasets and incorporates prior biological knowledge. Applying CHPM to yeast expression data, we show that our algorithm produces more accurate functional assignments for genes compared to other expression analysis methods. The inferred process activity levels can be used to study the relationships between biological processes. We also report new biological experiments confirming some of the process activity levels predicted by CHPM. AVAILABILITY: A Java implementation is available at http:\\www.cs.cmu.edu\~yanxins\chpm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

生物学基础知识表现方式及学习能力培养

生物学基础知识主要包括植物、动物、微生物等基本生物类群的分类、形态、结构、生理、生化、遗传、进化、生态等方面的知识。研究生物学基础知识的表现方式并据此探讨学习能力培养方法可以指导和帮助师生较快地抓住主要观点、理解确切要义、识记重要内容及提高教学效率。生物学基础知识的表现方式主要有文字、图片、表格、反应式、计算式等,其学习能力的培养要从文字理解能力、图片识别能力、表格分析能力及式子解析能力等四方面入手。     

Gene expression profiling of coronary artery disease and its relation with different severities

Global gene expression profiling is a powerful tool enabling the understanding of pathophysiology and subsequent management of diseases. This study aims to explore functionally annotated differentially expressed genes (DEGs); their biological processes for coronary artery disease (CAD) and its different severities of atherosclerotic lesions. This study also aims to identify the change in expression patterns of DEGs in atherosclerotic lesions of single-vessel disease (SVD) and triple-vessel disease (TVD). The weight of different severities of lesion was estimated using a modified Gensini score. The gene expression profiling was performed using the Affymetrix microarray platform. The functional annotation for CAD was performed using DAVID v6.8. The biological network gene ontology tool (BiNGO) and ClueGO were used to explore the biological processes of functionally annotated genes of CAD. The changes in gene expression from SVD to TVD were determined by evaluating the fold change. Functionally annotated genes were found in an unique set and could be distinguishing two distinct severities of CAD. The biological processes such as cellular migration, locomotion, cell adhesion, cytokine production, positive regulation of cell death etc. enriched the functionally annotated genes in SVD, whereas, wound healing, negative regulation of cell death, blood coagulation, angiogenesis and fibrinolysis were enriched significantly in TVD patients. The genes THBS1 and CAPN10 were functionally annotated for CAD in both SVD and TVD. The 61 DEGs were identified, those have changes their expression with different severities of atherosclerotic lesions, in which 13 genes had more than two-fold change in expression between SVD and TVD. The consistent findings were obtained on validation of microarray gene expression of selected 10 genes in a separate cohort using real-time PCR. This study identified putative candidate genes and their biological processes predisposing toward and affecting the severity of CAD.     

草菇不同菌株木聚糖酶xyn基因的表达及其酶活性分析

木聚糖酶是半纤维素酶系的重要组成部分,能够分解水稻、小麦等农作物秸秆中的半纤维素。研究探讨木聚糖酶相关基因及其功能,为进一步探讨木聚糖酶与草菇生物转化率之间的关系提供理论依据。首先通过生物信息学手段构建了草菇2个木聚糖酶基因xyn1和xynII编码氨基酸序列的系统进化树,然后从生物转化率不同的草菇菌株分别提取各自的RNA,反转录为cDNA后,采用实时荧光定量PCR技术分析了xyn1和xynII基因的转录表达情况;最后应用DNS法对这些菌株中的木聚糖酶活性进行了测定。研究结果表明,xyn1编码的氨基酸序列与草腐菌相似性较高,而xynII编码的氨基酸序列与木腐菌遗传差异较小。在木聚糖酶活性测定和实时荧光定量PCR结果中,不同菌株的木聚糖酶活性趋势与xynII的转录表达趋势相似,均呈现依次递减。草菇的木聚糖酶活性与其生物转化率之间存在正相关性,推测草菇不同菌株木聚糖酶活性差异可能表现在转录水平上的差异。     

STEM: a tool for the analysis of short time series gene expression data

Background  

Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data.     

Gene expression profiling of Leishmania (Leishmania) donovani: overcoming technical variation and exploiting biological variation

Gene expression profiling is increasingly used in the field of infectious diseases for characterization of host, pathogen and the nature of their interaction. The purpose of this study was to develop a robust, standardized method for comparative expression profiling and molecular characterization of Leishmania donovani clinical isolates. The limitations and possibilities associated with expression profiling in intracellular amastigotes and promastigotes were assessed through a series of comparative experiments in which technical and biological parameters were scrutinized. On a technical level, our results show that it is essential to use parasite harvesting procedures that involve minimal disturbance of the parasite's environment in order to 'freeze' gene expression levels instantly; this is particularly a delicate task for intracellular amastigotes and for specific 'sensory' genes. On the biological level, we demonstrate that gene expression levels fluctuate during in vitro development of both intracellular amastigotes and promastigotes. We chose to use expression-curves rather than single, specific, time-point measurements to capture this biological variation. Intracellular amastigote protocols need further refinement, but we describe a first generation tool for high-throughput comparative molecular characterization of patients' isolates, based on the changing expression profiles of promastigotes during in vitro differentiation.     

Phenotypical enrichment strategies for microarray data analysis applied in a type II diabetes study

Combining results from gene microarrays, clinical chemistry, and quantitative tissue histomorphology in an integrated bioinformatics setting enables prioritization of gene families as well as individual genes in a type II diabetes animal study. This new methodology takes advantage of a time-controlled mouse study as the animals progress from a normal phenotype to that of type II diabetes. Profiles from different levels of the biological hierarchy of unpooled entities provide an encompassing, system-wide view of biological changes. Here, phenotypic changes on the tissue-structural and physiological level are used as statistical covariants to enrich the gene expression analysis, suggesting correlative processes between gene expression and phenotype unlocked by multi-sample comparisons. We apply correlative and gene set enrichment procedures and compare the results to differential analysis to identify molecular markers. Evaluation based on ontological classifications proves changes in prioritization of disease-related genes that would have been overlooked by conventional gene expression analyses strategies.     

Studying Gene Expression System Regulation at the Program Level

Understanding how gene expression systems influence biological outcomes is an important goal for diverse areas of research. Gene expression profiling allows for the simultaneous measurement of expression levels for thousands of genes and the opportunity to use this information to increase biological understanding. Yet, the best way to relate this immense amount of information to biological outcomes is far from clear. Here, a novel approach to gene expression systems research is presented that focuses on understanding gene expression systems at the level of gene expression program regulation. It is suggested that such an approach has important advantages over current techniques and may provide novel insights into how gene expression systems are regulated to shape biological outcomes such as the development of disease or response to treatment.     

A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication

We propose a freely accessible web-based pipeline, which processes raw microarray scan data to obtain experimentally consolidated gene expression values. The tool MADSCAN, which stands for MicroArray Data Suites of Computed ANalysis, makes a practical choice among the numerous methods available for filtering, normalizing and scaling of raw microarray expression data in a dynamic and automatic way. Different statistical methods have been adapted to extract reliable information from replicate gene spots as well as from replicate microarrays for each biological situation under study. A carefully constructed experimental design thus allows to detect outlying expression values and to identify statistically significant expression values, together with a list of quality controls with proposed threshold values. The integrated processing procedure described here, based on multiple measurements per gene, is decisive for reliably monitoring subtle gene expression changes typical for most biological events.     

Analysis of Allele-Specific Expression in Mouse Liver by RNA-Seq: A Comparison With Cis-eQTL Identified Using Genetic Linkage

We report an analysis of allele-specific expression (ASE) and parent-of-origin expression in adult mouse liver using next generation sequencing (RNA-Seq) of reciprocal crosses of heterozygous F₁ mice from the parental strains C57BL/6J and DBA/2J. We found a 60% overlap between genes exhibiting ASE and putative cis-acting expression quantitative trait loci (cis-eQTL) identified in an intercross between the same strains. We discuss the various biological and technical factors that contribute to the differences. We also identify genes exhibiting parental imprinting and complex expression patterns. Our study demonstrates the importance of biological replicates to limit the number of false positives with RNA-Seq data.     

RNA expression profiling in cardiac tissue – a successful route to new drug targets?

High-throughput mRNA expression profiling is a widely used first step in the identification of new targets. Proper consideration of this step is essential before initiating the time-consuming and more elaborate biological validation of a target. This review discusses the strengths and limitations of this approach, with a special emphasis on studies in cardiac tissue. Specifically, it focuses on the basis of any such study, the tissue samples and the implications for statistical analysis of data retrieved from heterogeneous biological material. It also discusses studies performed in cardiac tissue and considers strategies to define target candidates based on expression profiles.     

Graph-based analysis and visualization of experimental results with ONDEX

MOTIVATION: Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise. RESULTS: This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function.     

Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives

Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression temporal profiles. This was achieved by focusing on the shapes of the curves rather than on the absolute level of expression. Actually, we combined spline smoothing and first derivative computation with hierarchical and partitioning clustering. A heuristic approach was proposed to tune the spline smoothing parameter using both statistical and biological considerations. Clusters are illustrated a posteriori through principal component analysis and heatmap visualization. Most results were found to be in agreement with the literature on the effects of fasting on the mouse liver and provide promising directions for future biological investigations.     

Differential Expression Analysis for Pathways

Life science technologies generate a deluge of data that hold the keys to unlocking the secrets of important biological functions and disease mechanisms. We present DEAP, Differential Expression Analysis for Pathways, which capitalizes on information about biological pathways to identify important regulatory patterns from differential expression data. DEAP makes significant improvements over existing approaches by including information about pathway structure and discovering the most differentially expressed portion of the pathway. On simulated data, DEAP significantly outperformed traditional methods: with high differential expression, DEAP increased power by two orders of magnitude; with very low differential expression, DEAP doubled the power. DEAP performance was illustrated on two different gene and protein expression studies. DEAP discovered fourteen important pathways related to chronic obstructive pulmonary disease and interferon treatment that existing approaches omitted. On the interferon study, DEAP guided focus towards a four protein path within the 26 protein Notch signalling pathway.     

Improved scoring of functional groups from gene expression data by decorrelating GO graph structure

MOTIVATION: The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. This list is only the starting point for a meaningful biological interpretation. Modern methods identify relevant biological processes or functions from gene expression data by scoring the statistical significance of predefined functional gene groups, e.g. based on Gene Ontology (GO). We develop methods that increase the explanatory power of this approach by integrating knowledge about relationships between the GO terms into the calculation of the statistical significance. RESULTS: We present two novel algorithms that improve GO group scoring using the underlying GO graph topology. The algorithms are evaluated on real and simulated gene expression data. We show that both methods eliminate local dependencies between GO terms and point to relevant areas in the GO graph that remain undetected with state-of-the-art algorithms for scoring functional terms. A simulation study demonstrates that the new methods exhibit a higher level of detecting relevant biological terms than competing methods.