基于序列特征的环状RNA识别

环状RNA是新发现的一类具有重要生物学功能的RNA。现有的环状RNA识别工具依赖高通量测序数据,因数据本身和识别方式的弊端而普遍存在准确性不足、不同方法间重复性低以及假阳性率/假阴性率高等缺点。为了解决该问题,我们搭建模型来实现不依赖于测序数据而根据序列的内在特征的环状RNA从头预测。本文选取了包括剪接位点上下游内含子的长度、A-to-I密度和Alu重复序列等100个与RNA成环相关的序列特征,建立了机器学习模型,并识别了人类基因组中的环状RNA,比较了两种机器学习方法随机森林法(RF)和支持向量机(SVM)的分类效果。结果表明,所选序列特征能有效地鉴别RNA能否成环,同时,不同序列特征对模型的分类预测能力的贡献也不同。相比于SVM方法,RF分类的效果更好。     

福建三明瓦坑的赤枝栲林

本文讨论的赤枝栲1)(Castanopsis kawakamii)林,地处福建省三明市郊瓦坑地区,属我国中亚热带东南缘。据调查31块样地共3100m2群落种类组成中,含维管束植物52科、90属、139种;其中单种属占总数的75.6%。植物区系为热带、泛热带分布的成分、共占科与属总数的61.5%与67.8%。高位芽生活型植物占总数的87.9%。基于对群落外貌的植物生活型与叶特征等分析表明,该群落是从南亚热带雨林到中亚热带常绿阔叶林的过渡类型。对该森林乔木种的年龄结构分析,乔、灌木种多样性指数与均匀度的计算等,显示出赤枝栲林是相对稳定性较大的群落。     

可口革囊星虫肾管纤毛的分布及结构特征

为了解可口革囊星虫(Phascolosoma esculenta)肾管纤毛的结构特点及其功能,采用显微及亚显微技术观察研究了可口革囊星虫肾管纤毛的分布位置及形态结构特征。结果表明,肾管外膜多纤毛细胞表面簇生纤毛、内部柱状上皮细胞与立方上皮细胞游离面着生分散的纤毛,肾口内面也着生纤毛。纤毛结构由纤毛干、过渡区、基体及其纤毛小根组成;纤毛干由"9+2"结构的轴丝外被纤毛膜构成;纤毛干与基体之间为过渡区,中央微管终止于此,外周双联微管通过过渡区和基体的外周轴丝相连;基体呈圆筒状,为"9+0"结构;纤毛小根分长根和短根,均为基体发出的由微细原纤维组成的圆锥形结构,具间隔70 nm的明显横纹。肾管纤毛可能在促进体腔液流动、提高肾管对体腔液的过滤作用以及引导成熟精卵进入肾管等方面发挥作用。     

细菌DeoR家族转录调控因子的研究进展

细菌基因组中存在大量的转录调控家族,这些转录调控家族在细菌的生长、代谢、外界信号感知与传递等方面发挥着至关重要的作用.DeoR家族是一类广泛分布于原核生物中的转录调控因子,主要参与调控细胞中多个生理过程,包括核苷酸类代谢、糖类代谢、致病菌的毒力以及链霉菌的次级代谢等.DeoR蛋白C末端的配体结合结构域,通常能够以相关代...     

融合位置特征与序列进化信息的磷酸化位点预测

磷酸化是蛋白质翻译后的主要修饰,可分为激酶特异性和非激酶特异性两种类型．以非激酶特异性磷酸化位点Dou数据集为基础,本文发展了一种基于位置的卡方差表特征χ²-pos,融合伪氨基酸序列进化信息PsePSSM表征序列,构建正负样本均衡的支持向量机分类器,S, T, Y独立测试Matthew相关系数、ROC曲线下面积分及准确率分别达到了(0.59、0.87、79.74%),(0.55、0.85、77.68%)和(0.50、0.81、75.22%),明显优于文献报道结果． χ²-pos、PsePSSM两种特征的融合在蛋白质磷酸化位点预测中有广泛应用前景．     

长链非编码RNA数据库资源

长链非编码RNA（long non coding RNA, lncRNA)在多个水平参与调节机体的各项基础生物进程,其功能紊乱常伴随疾病的发生。鉴定lncRNA的生物学功能已成为近年来的研究热点。然而,目前从各种真核生物高通量测序中鉴定的几十万个lncRNA中,只有极少数的功能已被实验验证,这对于该领域的深入研究是个巨大的挑战。因此,许多科研机构都建立了lncRNA数据库,并且持续周期性更新,这为研究者共享、注释和分析lncRNA功能提供了十分有效的工具。本文从lncRNA原始资源整合、筛选、鉴定及功能分析和lncRNA与人类疾病等4个方面介绍各lncRNA数据库资源的最新特征和应用范围。这为研究者在选择不同数据库资源进行lncRNA鉴定和分析时提供参考。     

Approaches to dimensionality reduction in proteomic biomarker studies

Mass-spectra based proteomic profiles have received widespreadattention as potential tools for biomarker discovery and earlydisease diagnosis. A major data-analytical problem involvedis the extremely high dimensionality (i.e. number of featuresor variables) of proteomic data, in particular when the samplesize is small. This article reviews dimensionality reductionmethods that have been used in proteomic biomarker studies.It then focuses on the problem of selecting the most appropriatemethod for a specific task or dataset, and proposes method combinationas a potential alternative to single-method selection. Finally,it points out the potential of novel dimension reduction techniques,in particular those that incorporate domain knowledge throughthe use of informative priors or causal inference.      

三种绢蝶翅脉数字化特征的提取及初步分析

利用化学方法去除3种绢蝶翅面的鳞片和斑纹,通过扫描获取翅脉标本图片,并利用DrawWing、TPSDig和BugShape3种软件对其前翅左侧翅脉的数字特征进行了提取,并包括DrawWing提取的小红珠绢蝶右翅翅脉特征值。分析了小红珠绢蝶左右翅脉的对称性以及个体间的差异,对比了3种软件在3种绢蝶分类判别中的优劣。不仅对改进这些工具提出了建议,而且讨论了昆虫鉴定领域中数字化技术和形态测量学的应用前景。     

Colon cancer prediction with genetic profiles using intelligent techniques

Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t‐statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t‐statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques.     

Ranking predicted protein structures with support vector regression

Protein structure prediction is an important problem of both intellectual and practical interest. Most protein structure prediction approaches generate multiple candidate models first, and then use a scoring function to select the best model among these candidates. In this work, we develop a scoring function using support vector regression (SVR). Both consensus-based features and features from individual structures are extracted from a training data set containing native protein structures and predicted structural models submitted to CASP5 and CASP6. The SVR learns a scoring function that is a linear combination of these features. We test this scoring function on two data sets. First, when used to rank server models submitted to CASP7, the SVR score selects predictions that are comparable to the best performing server in CASP7, Zhang-Server, and significantly better than all the other servers. Even if the SVR score is not allowed to select Zhang-Server models, the SVR score still selects predictions that are significantly better than all the other servers. In addition, the SVR is able to select significantly better models and yield significantly better Pearson correlation coefficients than the two best Quality Assessment groups in CASP7, QA556 (LEE), and QA634 (Pcons). Second, this work aims to improve the ability of the Robetta server to select best models, and hence we evaluate the performance of the SVR score on ranking the Robetta server template-based models for the CASP7 targets. The SVR selects significantly better models than the Robetta K*Sync consensus alignment score.