Measures of precision for dissimilarity‐based multivariate analysis of ecological communities

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity‐based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity‐based standard error (MultSE) as a useful quantity for assessing sample‐size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided.     

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

Background

RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial.

Results

We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site’s coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone.

Conclusions

These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1456-x) contains supplementary material, which is available to authorized users.     

Prediction model of DnBP degradation based on BP neural network in AAO system

A laboratory-scale anaerobic-anoxic-oxic (AAO) system was established to investigate the fate of DnBP. A removal kinetic model including sorption and biodegradation was formulated, and kinetic parameters were evaluated with batch experiments under anaerobic, anoxic, oxic conditions. However, it is highly complex and is difficult to confirm the kinetic parameters using conventional mathematical modeling. To correlate the experimental data with available models or some modified empirical equations, an artificial neural network model based on multilayered partial recurrent back propagation (BP) algorithm was applied for the biodegradation of DnBP from the water quality characteristic parameters. Compared to the kinetic model, the performance of the network for modeling DnBP is found to be more impressive. The results showed that the biggest relative error of BP network prediction model was 9.95%, while the kinetic model was 14.52%, which illustrates BP model predicting effluent DnBP more accurately than kinetic model forecasting.     

捕获-再捕获抽样中的多元组合估计量

首先介绍了捕获-再捕获抽样的思想方法以及Peterson估计量与性质,然后指出了Peterson估计量存在的问题,在此基础上给出了多元组合估计量并研究了它的性质.     

Phylogenetic relationships of the Cretaceous frog Beelzebufo from Madagascar and the placement of fossil constraints based on temporal and phylogenetic evidence

The placement of fossil calibrations is ideally based on the phylogenetic analysis of extinct taxa. Another source of information is the temporal variance for a given clade implied by a particular constraint when combined with other, well-supported calibrations. For example, the frog Beelzebufo ampinga from the Cretaceous of Madagascar has been hypothesized to be a crown-group member of the New World subfamily Ceratophryinae, which would support a Late Cretaceous connection with South America. However, phylogenetic analyses and molecular divergence time estimates based on other fossils do not support this placement. We derive a metric, Δt, to quantify temporal divergence among chronograms and find that errors resulting from mis-specified calibrations are localized when additional nodes throughout the tree are properly calibrated. The use of temporal information from molecular data can further assist in testing phylogenetic hypotheses regarding the placement of extinct taxa.     

Characterization of plasma triiodophenol binding proteins in vertebrates and tissue distribution of triiodophenol in Rana catesbeiana tadpoles

We investigated the interaction of 2,4,6-triiodophenol (TIP), a potent thyroid hormone disrupting chemical, with serum proteins from rainbow trout (Onchorhynchus mykiss), bullfrog (Rana catesbeiana), chicken (Gallus gallus), pig (Sus scrofa domesticus), and rat (Rattus norvegicus) using a [(125)I]TIP binding assay, gel filtration chromatography, and native polyacrylamide gel electrophoresis. [(125)I]TIP bound non-specifically to proteins in trout serum, specifically but weakly to proteins in bullfrog serum, and specifically and strongly to proteins in chicken, pig, and rat serum samples. Candidate TIP-binding proteins included lipoproteins (220-320kDa) in trout, albumin in bullfrog, albumin and transthyretin (TTR) in chicken and pig, and TTR in rat. TTR in the chicken, pig, and rat serum samples was responsible for the high-affinity, low-capacity binding sites for TIP (dissociation constant 2.2-3.5×10(-10)M). In contrast, a weak interaction of [(125)I]TIP with tadpole serum proteins accelerated [(125)I]TIP cellular uptake in vitro. Intraperitoneal injection of [(125)I]TIP in tadpoles revealed that the radioactivity was predominantly accumulated in the gallbladder and the kidney. The differences in the molecular and binding properties of TIP binding proteins among vertebrates would affect in part the cellular availability, tissue distribution and clearance of TIP.     

The illusion of distribution-free small-sample classification in genomics

Classification has emerged as a major area of investigation in bioinformatics owing to the desire to discriminate phenotypes, in particular, disease conditions, using high-throughput genomic data. While many classification rules have been posed, there is a paucity of error estimation rules and an even greater paucity of theory concerning error estimation accuracy. This is problematic because the worth of a classifier depends mainly on its error rate. It is common place in bio-informatics papers to have a classification rule applied to a small labeled data set and the error of the resulting classifier be estimated on the same data set, most often via cross-validation, without any assumptions being made on the underlying feature-label distribution. Concomitant with a lack of distributional assumptions is the absence of any statement regarding the accuracy of the error estimate. Without such a measure of accuracy, the most common one being the root-mean-square (RMS), the error estimate is essentially meaningless and the worth of the entire paper is questionable. The concomitance of an absence of distributional assumptions and of a measure of error estimation accuracy is assured in small-sample settings because even when distribution-free bounds exist (and that is rare), the sample sizes required under the bounds are so large as to make them useless for small samples. Thus, distributional bounds are necessary and the distributional assumptions need to be stated. Owing to the epistemological dependence of classifiers on the accuracy of their estimated errors, scientifically meaningful distribution-free classification in high-throughput, small-sample biology is an illusion.     

Breaking the rules: bacteria that use several DNA polymerase IIIs

Studies using Escherichia coli DNA polymerase (Pol) III as the prototype for bacterial DNA replication have suggested that--in contrast to eukaryotes--one replicase performs all of the main functions at the replication fork. However, recent studies have revealed that replication in other bacteria requires two forms of Pol III, one of which seems to extend RNA primers by only a few nucleotides before transferring the product to the other polymerase--an arrangement analogous to that in eukaryotes. Yet another group of bacteria encode a second Pol III (ImuC), which apparently replaces a Pol Y-type polymerase (Pol V) that is required for induced mutagenesis in E. coli. A complete understanding of complex bacterial replicases will allow the simultaneous biochemical screening of all their components and, thus, the identification of new antibacterial compounds.     

Testing Equality between Two Diagnostic Procedures in Paired‐Sample Ordinal Data

When a new diagnostic procedure is developed, it is important to assess whether the diagnostic accuracy of the new procedure is different from that of the standard procedure. For paired‐sample ordinal data, this paper develops two test statistics for testing equality of the diagnostic accuracy between two procedures without assuming any parametric models. One is derived on the basis of the probability of correctly identifying the case for a randomly selected pair of a case and a non‐case over all possible cutoff points, and the other is derived on the basis of the sensitivity and specificity directly. To illustrate the practical use of the proposed test procedures, this paper includes an example regarding the use of digitized and plain films for screening breast cancer. This paper also applies Monte Carlo simulation to evaluate the finite sample performance of the two statistics developed here and notes that they can perform well in a variety of situations. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)