Understanding hospital referral rates: a user's guide.

Detailed referral information from one practice was used to investigate the effect of calculating referral rates in several different ways. Referral rates for individual general practitioners should be related to the number of consultations carried out and not to the number of registered patients; for whole practices list size may be used as the denominator. Most doctors will not need to control for age and sex of patients when comparing referral rates but may need to control for case mix when comparing referral rates to individual specialties. In addition, a method is described for distinguishing systematic variation between the referral rates of individual doctors from the random variation that may arise from data based on fairly small numbers of referrals. The method indicates whether systematic variation is greater than would be expected by chance, and it can be extended to indicate whether variability in referral rates is greater in one specialty than another. Because of random variation with time a year''s data may not be sufficient to allow reliable interpretation of referral rates to individual specialties, except for the largest. At present there is no known relation between high or low referral rates and quality of care. Nevertheless, if doctors are to interpret their own rates of referral they need those rates to be reliable and valid. Use of the 10 guidelines described in this paper will help to prevent unwarranted conclusions being drawn from information on general practitioners'' rates of referral to hospital.     

High-throughput RNAi screening in cultured cells: a user's guide

RNA interference has re-energized the field of functional genomics by enabling genome-scale loss-of-function screens in cultured cells. Looking back on the lessons that have been learned from the first wave of technology developments and applications in this exciting field, we provide both a user's guide for newcomers to the field and a detailed examination of some more complex issues, particularly concerning optimization and quality control, for more advanced users. From a discussion of cell lines, screening paradigms, reagent types and read-out methodologies, we explore in particular the complexities of designing optimal controls and normalization strategies for these challenging but extremely powerful studies.     

The binding database: overview and user's guide

The large and growing body of experimental data on molecular binding is of enormous value in biology, pharmacology, and chemistry. Applications include the assignment of function to biomolecules, drug discovery, molecular modeling, and nanotechnology. However, binding data are difficult to find and access because they are available almost exclusively through scientific journals. BindingDB, a public, web-accessible database of measured binding affinities, is designed to address this problem. BindingDB collects data for natural and modified biomolecules and for synthetic compounds, and provides detailed experimental information. Currently, measurements by isothermal titration calorimetry are fully supported; measurements by enzyme inhibition will soon be included as well. The web site allows data to be searched by a range of criteria, including binding thermodynamics, sequence homology, and chemical structure, substructure, and similarity. Experimentalists are encouraged to publicize their data by entering it into BindingDB via the online forms. Such data can be updated or revised by the depositor, if necessary, and will remain publicly accessible. User involvement and feedback are welcomed.     

Eukaryotic systematics: a user's guide for cell biologists and parasitologists

Single-celled parasites like Entamoeba, Trypanosoma, Phytophthora and Plasmodium wreak untold havoc on human habitat and health. Understanding the position of the various protistan pathogens in the larger context of eukaryotic diversity informs our study of how these parasites operate on a cellular level, as well as how they have evolved. Here, we review the literature that has brought our understanding of eukaryotic relationships from an idea of parasites as primitive cells to a crystallized view of diversity that encompasses 6 major divisions, or supergroups, of eukaryotes. We provide an updated taxonomic scheme (for 2011), based on extensive genomic, ultrastructural and phylogenetic evidence, with three differing levels of taxonomic detail for ease of referencing and accessibility (see supplementary material at Cambridge Journals On-line). Two of the most pressing issues in cellular evolution, the root of the eukaryotic tree and the evolution of photosynthesis in complex algae, are also discussed along with ideas about what the new generation of genome sequencing technologies may contribute to the field of eukaryotic systematics. We hope that, armed with this user's guide, cell biologists and parasitologists will be encouraged about taking an increasingly evolutionary point of view in the battle against parasites representing real dangers to our livelihoods and lives.     

Regulatory sequence analysis tools

The web resource Regulatory Sequence Analysis Tools (RSAT) (http://rsat.ulb.ac.be/rsat) offers a collection of software tools dedicated to the prediction of regulatory sites in non-coding DNA sequences. These tools include sequence retrieval, pattern discovery, pattern matching, genome-scale pattern matching, feature-map drawing, random sequence generation and other utilities. Alternative formats are supported for the representation of regulatory motifs (strings or position-specific scoring matrices) and several algorithms are proposed for pattern discovery. RSAT currently holds >100 fully sequenced genomes and these data are regularly updated from GenBank.     

A user's guide to animal welfare science

Here, I provide a guide for those new to the burgeoning field of animal welfare science as to what this comprehensive, relatively young discipline is all about. Drawing on all branches of biology, including behavioural ecology and neuroscience, the science of animal welfare asks three big questions: Are animals conscious? How can we assess good and bad welfare in animals? How can we use science to improve animal welfare in practice? I also provide guidelines for an evidence-based approach to welfare issues for policy makers and other users of animal welfare research.     

Obtaining and screening compound collections: a user's guide and a call to chemists

Advances in genetics, proteomics and cell biology over the past 20 years have unearthed a multitude of potential macromolecular targets for the selective treatment of disease. The challenge remains to find appropriate small molecule ligands for these proteins (or nucleic acids), and to use these ligands to validate novel disease targets. The advent of low-cost instrumentation has made industrial-style high-throughput screening possible in academic settings. Unfortunately for many, access to large collections of compounds is still limited and limiting. This article is aimed at the user who has an interest in compound screening but does not have ready access to large collections of small molecules. High-throughput screening need not be the exclusive domain of institutions and centers with vast resources and NIH Roadmap-funded compound repositories. As it turns out, many of the most interesting compounds are probably within arm's reach, in our laboratory freezers and in those of our colleagues.     

A user's guide to the tree of life

book reviewed in this article
Classification phylog e en e etique du vivant ("Phylogenetic classification of life").By Guillaume Lecointre and Herv e e Le Guyader, illustrations by Dominique Visset.     

Mass spectrometry-based immuno-precipitation proteomics - the user's guide

Immuno-precipitation (IP) experiments using MS provide a sensitive and accurate way of characterising protein complexes and their response to regulatory mechanisms. Differences in stoichiometry can be determined as well as the reliable identification of specific binding partners. The quality control of IP and protein interaction studies has its basis in the biology that is being observed. Is that unusual protein identification a genuine novelty, or an experimental irregularity? Antibodies and the solid matrices used in these techniques isolate not only the target protein and its specific interaction partners but also many non-specific 'contaminants' requiring a structured analysis strategy. These methodological developments and the speed and accuracy of MS machines, which has been increasing consistently in the last 5 years, have expanded the number of proteins identified and complexity of analysis. The European Science Foundation's Frontiers in Functional Genomics programme 'Quality Control in Proteomics' Workshop provided a forum for disseminating knowledge and experience on this subject. Our aim in this technical brief is to outline clearly, for the scientists wanting to carry out this kind of experiment, and recommend what, in our experience, are the best potential ways to design an IP experiment, to help identify possible pitfalls, discuss important controls and outline how to manage and analyse the large amount of data generated. Detailed experimental methodologies have been referenced but not described in the form of protocols.     

Genomic tools in bioremediation

Bioremediation is a process that uses microorganisms or their enzymes to remove pollutants from the environment. Generally, bioremediation technologies can be classified as in situ or ex situ. In situ bioremediation involves treating the contaminated material at the site while ex situ involves the removal of the contaminated material to be treated elsewhere. Like so much else in biology, the ease and availability of genomic data has created a new level of understanding this system. Bioremediation capabilities of the microbial population can be analyzed; not only by physiological parameters, but also by the use of genomic tools, and efficient remediation strategies can be planned. PCR and DNA- or oligonucleotide-based microarray technology is a powerful functional genomics tool that allows researchers to view the physiology of a living cell from a comprehensive and dynamic molecular perspective. This paper explores the use of such tools in bioremediation process.     

New tools for protein sequence analysis.

Analysis of protein sequence is an important tool in studies of both native and recombinant proteins. Novel techniques and instrumentation which facilitate determination of protein primary structure have recently been developed.     

Recurrence time statistics: versatile tools for genomic DNA sequence analysis

With the completion of the human and a few model organisms' genomes, and with the genomes of many other organisms waiting to be sequenced, it has become increasingly important to develop faster computational tools which are capable of easily identifying the structures and extracting features from DNA sequences. One of the more important structures in a DNA sequence is repeat-related. Often they have to be masked before protein coding regions along a DNA sequence are to be identified or redundant expressed sequence tags (ESTs) are to be sequenced. Here we report a novel recurrence time-based method for sequence analysis. The method can conveniently study all kinds of periodicity and exhaustively find all repeat-related features from a genomic DNA sequence. An efficient codon index is also derived from the recurrence time statistics, which has the salient features of being largely species-independent and working well on very short sequences. Efficient codon indices are key elements of successful gene finding algorithms, and are particularly useful for determining whether a suspected EST belongs to a coding or non-coding region. We illustrate the power of the method by studying the genomes of E. coli, the yeast S. cervisivae, the nematode worm C. elegans, and the human, Homo sapiens. Our method requires approximately 6 . N byte memory and a computational time of N log N to extract all the repeat-related and periodic or quasi-periodic features from a sequence of length N without any prior knowledge on the consensus sequence of those features, hence enables us to carry out sequence analysis on the whole genomic scale by a PC.     

A user's guide for avoiding errors in absorbance image cytometry: a review with original experimental observations

Summary The sources of errors which may occur when cytophotometric analysis is performed with video microscopy using a charged-coupled device (CCD) camera and image analysis are reviewed. The importance of these errors in practice has been tested, and ways of minimizing or avoiding them are described. Many of these sources of error are known from scanning and integrating cytophotometry; they include the use of white instead of monochromatic light, the distribution error, glare, diffraction, shading distortion, and inadequate depth of field. Sources of errors specifically linked with video microscopy or image analysis are highlighted as well; these errors include blooming, limited dynamic range of grey levels, non-linear responses of the camera, contrast transfer, photon noise, dark current, read-out noise, fixed scene noise and spatial calibration. Glare, contrast transfer, fixed scene noise, depth of field and spatial calibration seem to be the most serious sources of errors when measurements are not carried out correctly. We include a table summarizing all the errors discussed in this review and procedures for avoiding them. It can be concluded that if accurate calibration steps are performed and proper guidelines followed, image cytometry can be applied safely for quantifying amounts of chromophore per cell or per unit volume of tissue in sections, even when relatively simple and inexpensive instrumentation is being used.     

Genomic sequence around butterfly wing development genes: annotation and comparative analysis

Background

Analysis of genomic sequence allows characterization of genome content and organization, and access beyond gene-coding regions for identification of functional elements. BAC libraries, where relatively large genomic regions are made readily available, are especially useful for species without a fully sequenced genome and can increase genomic coverage of phylogenetic and biological diversity. For example, no butterfly genome is yet available despite the unique genetic and biological properties of this group, such as diversified wing color patterns. The evolution and development of these patterns is being studied in a few target species, including Bicyclus anynana, where a whole-genome BAC library allows targeted access to large genomic regions.

Methodology/Principal Findings

We characterize ∼1.3 Mb of genomic sequence around 11 selected genes expressed in B. anynana developing wings. Extensive manual curation of in silico predictions, also making use of a large dataset of expressed genes for this species, identified repetitive elements and protein coding sequence, and highlighted an expansion of Alcohol dehydrogenase genes. Comparative analysis with orthologous regions of the lepidopteran reference genome allowed assessment of conservation of fine-scale synteny (with detection of new inversions and translocations) and of DNA sequence (with detection of high levels of conservation of non-coding regions around some, but not all, developmental genes).

Conclusions

The general properties and organization of the available B. anynana genomic sequence are similar to the lepidopteran reference, despite the more than 140 MY divergence. Our results lay the groundwork for further studies of new interesting findings in relation to both coding and non-coding sequence: 1) the Alcohol dehydrogenase expansion with higher similarity between the five tandemly-repeated B. anynana paralogs than with the corresponding B. mori orthologs, and 2) the high conservation of non-coding sequence around the genes wingless and Ecdysone receptor, both involved in multiple developmental processes including wing pattern formation.     

Genomic multiple sequence alignments: refinement using a genetic algorithm

Background  

Genomic sequence data cannot be fully appreciated in isolation. Comparative genomics – the practice of comparing genomic sequences from different species – plays an increasingly important role in understanding the genotypic differences between species that result in phenotypic differences as well as in revealing patterns of evolutionary relationships. One of the major challenges in comparative genomics is producing a high-quality alignment between two or more related genomic sequences. In recent years, a number of tools have been developed for aligning large genomic sequences. Most utilize heuristic strategies to identify a series of strong sequence similarities, which are then used as anchors to align the regions between the anchor points. The resulting alignment is globally correct, but in many cases is suboptimal locally. We describe a new program, GenAlignRefine, which improves the overall quality of global multiple alignments by using a genetic algorithm to improve local regions of alignment. Regions of low quality are identified, realigned using the program T-Coffee, and then refined using a genetic algorithm. Because a better COFFEE (Consistency based Objective Function For alignmEnt Evaluation) score generally reflects greater alignment quality, the algorithm searches for an alignment that yields a better COFFEE score. To improve the intrinsic slowness of the genetic algorithm, GenAlignRefine was implemented as a parallel, cluster-based program.