宏蛋白质组学信息分析的基本策略及其挑战

宏蛋白质组学是一门新型科学,它运用质谱技术规模化地采集自然界微生物种群的蛋白质信息,并结合多种组学数据,开展微生物种群的遗传特征及其生物功能的研究.宏蛋白质组学的信息分析与传统蛋白质组学方法有较大的不同,亟需拓展新的分析思路.由于宏蛋白质组的研究对象是复杂度极高的微生物样品,因此,需要构建尽可能囊括样本中所含微生物的基因组信息的物种数据库.面对庞大的数据库,必须考虑到分析过程中所消耗的计算资源和鉴定结果的质控标准,因此,需要高度优化库容量、搜库、假阳性控制等参数.鉴于宏蛋白质组数据中广泛存在复杂的同源蛋白质序列,因此,需要充分利用NCBI数据库中的分类信息进行匹配,并运用LCA算法过滤处理才能将蛋白质有效地归组到物种.本文立足于宏蛋白质组学信息分析,从宏蛋白质组的数据库建立、蛋白质归并、生物学意义发掘等几个方面着手,对该领域的发展现状、面临挑战以及未来研究方向进行了评述.     

Population parameters and reproduction of the piranha Serrasalmus marginatus in the Negro river,Pantanal, Brazil

This paper aimed to define the reproductive period and population parameters of Serrasalmus marginatus relative to local environmental features, such as day length, rainfall and mean river level. The study site was a floodplain in the Negro river, Pantanal, Brazil, and samples were collected bimonthly using gill nets and cast nets with meshes from 1.5 to 8 cm between adjacent knots. The reproduction period, as determined by gonadosomatic index (GSI) and percentage of gonad stages, varied significantly along the year (F_4,116 = 77.5; p < 0.01), but it was well defined from October to December. Reproduction period was positively correlated with rainfall (rs = 0.97; p < 0.01) and photoperiod (rs = 0.92; p = 0.02), but not with the rise of river level (rs = 0.10; p = 0.86). Rainfall and photoperiod may act as predictive factors, providing cues to fish to begin reproduction in order to find the best conditions for offspring in the following months, during the flooding season, when the river level reaches its peak and extends to the plain. We have herein reported the first population parameters described for this species in the Brazilian Pantanal, including growth rate (k = 0.53/year), asymptotic length (L∞ = 32.74), lifespan (A_0.95 = 5.65) and mortality (M = 1.11), showing that this population has better growth performance when compared to data reported for this same species in the Paraná river since the population in the Negro river is characterized by lower growth and mortality rates, and S. marginatus achieved longer lengths and lifespan. This latter parameter was compatible with six cohorts estimated by Electronic Length Frequency Analysis (ELEFAN). The size at first maturity (L50 = 18.26 cm A50 = 1.56 years) was also larger and later than the population in the Paraná river, likely a consequence of the the lower mortality and the maintenance of larger specimens in the Negro river.     

Oreocharis duyunensis (Gesneriaceae), a new species from Guizhou,China

Oreocharis duyunensis Z.Y. Li, X.G. Xiang et Z.Y. Guo (Gesneriaceae), endemic to Guizhou, China, is described as a new species. The new species is easily distinguished from other Oreocharis species by having a white, stellate ring‐like disc. Morphologically, the species is similar to Oreocharis chienii (Chun) Mich. Möller & A. Weber but differs by its densely white pilose leaves, mixed brown‐pilose, pubescent and glandular‐pubescent peduncle and pedicle, white stellate ring‐like disc, and linear‐lanceolate capsule. Based on phylogenetic reconstruction and interspecific genetic distances of nuclear ITS and chloroplast trnL‐F regions, O. duyunensis is a new species with uncertain phylogenetic position.     

The many faces of Peridinium cinctum (Peridiniaceae,Peridiniales): morphological and molecular variability in a common dinophyte

Peridinium cinctum is a common freshwater dinophyte with a long history of research. Erich Lindemann was the first to assess intraspecific variability in this species focusing on plate pattern variation. Since then, this issue has been neglected but with the application of DNA sequence diagnostics, a combination of morphological and molecular characters may enable taxonomic delimitations. Our aim was to identify distinct morphotypes using plate pattern as the main characteristic and then compare them to the geographic occurrence of particular ribotypes (as inferred from sequences of the Internal Transcribed Spacer: ITS) in samples from Central Europe. Approximately 200 observations were carried out under the inverse light microscope for each of a total of 15 strains. We observed two main variations from the abundant plate pattern in P. cinctum, namely an unusual position of the 2a plate and the irregular shape of the 1a plate. In 88 (predominantly clonal) strains, we identified five different ribotypes (submitted as 71 new GenBank entries) which had no clear correlation to the defined morphotypes and/or spatial occurrences. In four cases, we detected two distinct ribotypes at the same locality. However, samples collected south of the Danube River presented a different predominant morphotype from the rest of the samples, thus implying a potential biogeographic signal as inferred from morphology. In general, there is morphological and molecular variability in P. cinctum, which is under-studied and which may uncover geographic or ecological correlations or even the existence of cryptic species.     

ReadTools: A universal toolkit for handling sequence data from different sequencing platforms

Sequencing whole genomes has become a standard research tool in many disciplines including Molecular Ecology, but the rapid technological advances in combination with several competing platforms have resulted in a confusing diversity of formats. This lack of standard formats causes several problems, such as undocumented preprocessing steps or the loss of information in downstream software tools, which do not account for the specifics of the different available formats. ReadTools is an open‐source Java toolkit designed to standardize and preprocess read data from different platforms. It manages FASTQ‐ and SAM‐formatted inputs while dealing with platform‐specific peculiarities and provides a standard SAM compliant output. The code and executable are available at https://github.com/magicDGS/ReadTools .     

mvmapper: Interactive spatial mapping of genetic structures

Characterizing genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata are not always easily integrated into these methods in a user‐friendly fashion. Here, we present a deployable Python‐based web‐tool, mvmapper , for visualizing and exploring results of multivariate analyses in geographic space. This tool can be used to map results of virtually any multivariate analysis of georeferenced data, and routines for exporting results from a number of standard methods have been integrated in the R package adegenet , including principal components analysis (PCA), spatial PCA, discriminant analysis of principal components, principal coordinates analysis, nonmetric dimensional scaling and correspondence analysis. mvmapper 's greatest strength is facilitating dynamic and interactive exploration of the statistical and geographic frameworks side by side, a task that is difficult and time‐consuming with currently available tools. Source code and deployment instructions, as well as a link to a hosted instance of mvmapper , can be found at https://popphylotools.github.io/mvMapper/ .     

ClinMine: Optimizing the Management of Patients in Hospital

Context

A better understanding of “patient pathway” thanks to data analysis can lead to better treatments for patients. The ClinMine project, supported by the French National Research Agency (ANR), aims at proposing, from various case studies, algorithmic and statistical models able to handle this type of pathway data, focusing primarily on hospital data.

Count data in biology—Data transformation or model reformation?

Statistical analyses are an integral component of scientific research, and for decades, biologists have applied transformations to data to meet the normal error assumptions for F and t tests. Over the years, there has been a movement from data transformation toward model reformation—the use of non‐normal error structures within the framework of the generalized linear model (GLM). The principal advantage of model reformation is that parameters are estimated on the original, rather than the transformed scale. However, data transformation has been shown to give better control over type I error, for simulated data with known error structures. We conducted a literature review of statistical textbooks directed toward biologists and of journal articles published in the primary literature to determine temporal trends in both the text recommendations and the practice in the refereed literature over the past 35 years. In this review, a trend of increasing use of reformation in the primary literature was evident, moving from no use of reformation before 1996 to >50% of the articles reviewed applying GLM after 2006. However, no such trend was observed in the recommendations in statistical textbooks. We then undertook 12 analyses based on published datasets in which we compared the type I error estimates, residual plot diagnostics, and coefficients yielded by analyses using square root transformations, log transformations, and the GLM. All analyses yielded acceptable residual versus fit plots and had similar p‐values within each analysis, but as expected, the coefficient estimates differed substantially. Furthermore, no consensus could be found in the literature regarding a procedure to back‐transform the coefficient estimates obtained from linear models performed on transformed datasets. This lack of consistency among coefficient estimates constitutes a major argument for model reformation over data transformation in biology.