Reproducible and reliable microarray results through quality control: good laboratory proficiency and appropriate data analysis practices are essential

Over a few short years, microarray gene expression profiling has permeated most areas of biomedical research. Microarrays are now poised to enter the more demanding realm of clinical applications. The prospect of using microarray data to derive biomarkers of disease or toxicity, predict prognosis, or select treatments raises the validity and reliability bar substantially higher. The potential future payoffs are huge in terms of faster approval of more efficacious and safer medical interventions, and a more personalized implementation of them. Arriving at the future sooner rather than later is the motivation for the FDA-led MicroArray Quality Control (MAQC) project. The widespread collaboration aims to assess achievable technical performance of microarrays and capabilities and limitations of methods for microarray data analysis.     

BiQ Analyzer: visualization and quality control for DNA methylation data from bisulfite sequencing

SUMMARY: Manual processing of DNA methylation data from bisulfite sequencing is a tedious and error-prone task. Here we present an interactive software tool that provides start-to-end support for this process. In an easy-to-use manner, the tool helps the user to import the sequence files from the sequencer, to align them, to exclude or correct critical sequences, to document the experiment, to perform basic statistics and to produce publication-quality diagrams.Emphasis is put on quality control: The program automatically assesses data quality and provides warnings and suggestions for dealing with critical sequences. The BiQ Analyzer program is implemented in the Java programming language and runs on any platform for which a recent Java virtual machine is available. AVAILABILITY: The program is available without charge for non-commercial users and can be downloaded from http://biq-analyzer.bioinf.mpi-inf.mpg.de/     

Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy

Hummingbirds are an important model system in avian biology, but to date the group has been the subject of remarkably few phylogenetic investigations. Here we present partitioned Bayesian and maximum likelihood phylogenetic analyses for 151 of approximately 330 species of hummingbirds and 12 outgroup taxa based on two protein-coding mitochondrial genes (ND2 and ND4), flanking tRNAs, and two nuclear introns (AK1 and BFib). We analyzed these data under several partitioning strategies ranging between unpartitioned and a maximum of nine partitions. In order to select a statistically justified partitioning strategy following partitioned Bayesian analysis, we considered four alternative criteria including Bayes factors, modified versions of the Akaike information criterion for small sample sizes (AIC(c)), Bayesian information criterion (BIC), and a decision-theoretic methodology (DT). Following partitioned maximum likelihood analyses, we selected a best-fitting strategy using hierarchical likelihood ratio tests (hLRTS), the conventional AICc, BIC, and DT, concluding that the most stringent criterion, the performance-based DT, was the most appropriate methodology for selecting amongst partitioning strategies. In the context of our well-resolved and well-supported phylogenetic estimate, we consider the historical biogeography of hummingbirds using ancestral state reconstructions of (1) primary geographic region of occurrence (i.e., South America, Central America, North America, Greater Antilles, Lesser Antilles), (2) Andean or non-Andean geographic distribution, and (3) minimum elevational occurrence. These analyses indicate that the basal hummingbird assemblages originated in the lowlands of South America, that most of the principle clades of hummingbirds (all but Mountain Gems and possibly Bees) originated on this continent, and that there have been many (at least 30) independent invasions of other primary landmasses, especially Central America.     

Identifying functional gene sets from hierarchically clustered expression data: map of abiotic stress regulated genes in Arabidopsis thaliana

We present MultiGO, a web-enabled tool for the identification of biologically relevant gene sets from hierarchically clustered gene expression trees (http://ekhidna.biocenter.helsinki.fi/poxo/multigo). High-throughput gene expression measuring techniques, such as microarrays, are nowadays often used to monitor the expression of thousands of genes. Since these experiments can produce overwhelming amounts of data, computational methods that assist the data analysis and interpretation are essential. MultiGO is a tool that automatically extracts the biological information for multiple clusters and determines their biological relevance, and hence facilitates the interpretation of the data. Since the entire expression tree is analysed, MultiGO is guaranteed to report all clusters that share a common enriched biological function, as defined by Gene Ontology annotations. The tool also identifies a plausible cluster set, which represents the key biological functions affected by the experiment. The performance is demonstrated by analysing drought-, cold- and abscisic acid-related expression data sets from Arabidopsis thaliana. The analysis not only identified known biological functions, but also brought into focus the less established connections to defense-related gene clusters. Thus, in comparison to analyses of manually selected gene lists, the systematic analysis of every cluster can reveal unexpected biological phenomena and produce much more comprehensive biological insights to the experiment of interest.     

Comparing two sets of community data: A method for testing reserve adequacy

Abstract Comparing a new set of samples to what may be considered a reference set is a common problem in ecology. The investigator may be interested in the degree of correspondence or any anomalies. For example, does a set of existing reserves adequately cover the range of communities sampled in a region? A technique for such comparisons is proposed. Being dependent solely on estimates of ecological resemblance, it is simple, efficient and robust. Significant difference is defined by means of a resemblance coefficient. A threshold value denoting significant difference can be defined either by species overlap or other attributes of the data. For presence/absence data the Czekanowski coefficient provides a suitable measure of ecological resemblance. Traditional discriminant analysis does not provide a viable alternative due to its limitations in accommodating ecological data.     

Rapid rescreening of cervical smears: an improved method of quality control

Rapid rescreening of approximately 30% of all negative and inadequate consecutive smears was carried out over a 26-month period. Smears (n = 24012) were rescreened using a × 6.3 objective only. Two minutes were allowed for each slide. Thirty-nine smears were found to have been incorrectly diagnosed as negative, a rate of 0.16%. This can be compared with the previous 26 months during which the traditional 1 in 10 random rescreening of unsatisfactory and negative smears had been carried out at a routine pace and with an objective of × 10. A total of 6866 smears were rescreened. Eleven were found to have been incorrectly diagnosed as negative, a rate of 0.16%. Rapid rescreening is as sensitive as 1 in 10 rescreening, and allows a greater proportion of smears to be rescreened. We propose rapid rescreening should replace the traditional 1 in 10 rescreening methods.     

Quick and simple: quality control of microarray data

MOTIVATION: Microarrays are high-throughput tools for parallel miniaturized detection of biomolecules. In contrast to experiments using ratios of signals in two channels, experiments with only one fluorescent dye cause special problems for data analysis. The present work compares algorithms for quality filtering on spot level as well as array/slide level. RESULTS: Methods for quantitative spot filtering are discussed and new sets of quality scores for data preprocessing are designed. As measures of spot quality also reflect the quality of protocols, they were employed to find the optimal print buffer in an optimization experiment. In order to determine problematic arrays within a set of replicates we tested methods of outlier detection which can suitably replace the visual inspection of slides. CONTACT: Ursula.Sauer@arcs.ac.at.     

CHANCE: comprehensive software for quality control and validation of ChIP-seq data

ChIP-seq is a powerful method for obtaining genome-wide maps of protein-DNA interactions and epigenetic modifications. CHANCE (CHip-seq ANalytics and Confidence Estimation) is a standalone package for ChIP-seq quality control and protocol optimization. Our user-friendly graphical software quickly estimates the strength and quality of immunoprecipitations, identifies biases, compares the user''s data with ENCODE''s large collection of published datasets, performs multi-sample normalization, checks against quantitative PCR-validated control regions, and produces informative graphical reports. CHANCE is available at https://github.com/songlab/chance.     

Strengths and weaknesses of museum and national survey data sets for predicting regional species richness: comparative and combined approaches

We compiled three independent data sets of bird species occurrences in northeastern Colorado to test how predicted species richness compared to a combined analysis using all the data. The first data set was a georeferenced regional museum data set from two major repositories — the Denver Museum of Nature, and the Science and University of Colorado Museum. The two national survey data sets were the Breeding Bird Survey (summer), and the Great Backyard Bird Count (winter). Resulting analyses show that the museum data sets give richness estimates closest to the combined data set while exhibiting a skewed abundance distribution, whereas survey data sets do not accurately estimate overall richness even though they contain far more records. The combined data set allows the strengths of one data set to augment weaknesses in others. It is likely some museum data sets display skewed abundance distributions due to collectors’ potentially self‐selecting under‐represented species over common ones.     

Protocols for the assurance of microarray data quality and process control

Microarrays represent a powerful technology that provides the ability to simultaneously measure the expression of thousands of genes. However, it is a multi-step process with numerous potential sources of variation that can compromise data analysis and interpretation if left uncontrolled, necessitating the development of quality control protocols to ensure assay consistency and high-quality data. In response to emerging standards, such as the minimum information about a microarray experiment standard, tools are required to ascertain the quality and reproducibility of results within and across studies. To this end, an intralaboratory quality control protocol for two color, spotted microarrays was developed using cDNA microarrays from in vivo and in vitro dose-response and time-course studies. The protocol combines: (i) diagnostic plots monitoring the degree of feature saturation, global feature and background intensities, and feature misalignments with (ii) plots monitoring the intensity distributions within arrays with (iii) a support vector machine (SVM) model. The protocol is applicable to any laboratory with sufficient datasets to establish historical high- and low-quality data.     

On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets

In 2012, Karplus and Diederichs demonstrated that the Pearson correlation coefficient CC_1/2 is a far better indicator of the quality and resolution of crystallographic data sets than more traditional measures like merging R‐factor or signal‐to‐noise ratio. More specifically, they proposed that CC_1/2 be computed for data sets in thin shells of increasing resolution so that the resolution dependence of that quantity can be examined. Recently, however, the CC_1/2 values of entire data sets, i.e., cumulative correlation coefficients, have been used as a measure of data quality. Here, we show that the difference in cumulative CC_1/2 value between a data set that has been accurately measured and a data set that has not is likely to be small. Furthermore, structures obtained by molecular replacement from poorly measured data sets are likely to suffer from extreme model bias.     

Codon volatility as an indicator of positive selection: data from eukaryotic genome comparisons

It has been suggested that codon volatility (the proportion of the point-mutation neighbors of a codon that encode different amino acids) can be used as an index of past positive selection. We compared codon volatility with patterns of synonymous and nonsynonymous nucleotide substitution in genome-wide comparisons of orthologous genes between three pairs of related genomes: (1) the protists Plasmodium falciparum and P. yoelii, (2) the fungi Saccharomyces cerevisiae and S. paradoxus, and (3) the mammals mouse and rat. Codon volatility was not consistently associated with an elevated rate of nonsynonymous substitution, as would be expected under positive selection. Rather, the most consistent and powerful correlate of elevated codon volatility was nucleotide content at the second codon position, as expected, given the nature of the genetic code.     

Identifying roles for neurotransmission in circuit assembly: insights gained from multiple model systems and experimental approaches

In the adult nervous system, chemical neurotransmission between neurons is essential for information processing. However, neurotransmission is also important for patterning circuits during development, but its precise roles have yet to be identified, and some remain highly debated. Here, we highlight viewpoints that have come to be widely accepted or still challenged. We discuss how distinct techniques and model systems employed to probe the developmental role of neurotransmission may reconcile disparate ideas. We underscore how the effects of perturbing neurotransmission during development vary with model systems, the stage of development when transmission is altered, the nature of the perturbation, and how connectivity is assessed. Based on findings in circuits with connectivity arranged in layers, we raise the possibility that there exist constraints in neuronal network design that limit the role of neurotransmission. We propose that activity-dependent mechanisms are effective in refining connectivity patterns only when inputs from different cells are close enough, spatially, to influence each other's outcome.