Comparative bioacoustics: a roadmap for quantifying and comparing animal sounds across diverse taxa

Animals produce a wide array of sounds with highly variable acoustic structures. It is possible to understand the causes and consequences of this variation across taxa with phylogenetic comparative analyses. Acoustic and evolutionary analyses are rapidly increasing in sophistication such that choosing appropriate acoustic and evolutionary approaches is increasingly difficult. However, the correct choice of analysis can have profound effects on output and evolutionary inferences. Here, we identify and address some of the challenges for this growing field by providing a roadmap for quantifying and comparing sound in a phylogenetic context for researchers with a broad range of scientific backgrounds. Sound, as a continuous, multidimensional trait can be particularly challenging to measure because it can be hard to identify variables that can be compared across taxa and it is also no small feat to process and analyse the resulting high-dimensional acoustic data using approaches that are appropriate for subsequent evolutionary analysis. Additionally, terminological inconsistencies and the role of learning in the development of acoustic traits need to be considered. Phylogenetic comparative analyses also have their own sets of caveats to consider. We provide a set of recommendations for delimiting acoustic signals into discrete, comparable acoustic units. We also present a three-stage workflow for extracting relevant acoustic data, including options for multivariate analyses and dimensionality reduction that is compatible with phylogenetic comparative analysis. We then summarize available phylogenetic comparative approaches and how they have been used in comparative bioacoustics, and address the limitations of comparative analyses with behavioural data. Lastly, we recommend how to apply these methods to acoustic data across a range of study systems. In this way, we provide an integrated framework to aid in quantitative analysis of cross-taxa variation in animal sounds for comparative phylogenetic analysis. In addition, we advocate the standardization of acoustic terminology across disciplines and taxa, adoption of automated methods for acoustic feature extraction, and establishment of strong data archival practices for acoustic recordings and data analyses. Combining such practices with our proposed workflow will greatly advance the reproducibility, biological interpretation, and longevity of comparative bioacoustic studies.     

Towards robust and repeatable sampling methods in eDNA‐based studies

DNA‐based techniques are increasingly used for measuring the biodiversity (species presence, identity, abundance and community composition) of terrestrial and aquatic ecosystems. While there are numerous reviews of molecular methods and bioinformatic steps, there has been little consideration of the methods used to collect samples upon which these later steps are based. This represents a critical knowledge gap, as methodologically sound field sampling is the foundation for subsequent analyses. We reviewed field sampling methods used for metabarcoding studies of both terrestrial and freshwater ecosystem biodiversity over a nearly three‐year period (n = 75). We found that 95% (n = 71) of these studies used subjective sampling methods and inappropriate field methods and/or failed to provide critical methodological information. It would be possible for researchers to replicate only 5% of the metabarcoding studies in our sample, a poorer level of reproducibility than for ecological studies in general. Our findings suggest greater attention to field sampling methods, and reporting is necessary in eDNA‐based studies of biodiversity to ensure robust outcomes and future reproducibility. Methods must be fully and accurately reported, and protocols developed that minimize subjectivity. Standardization of sampling protocols would be one way to help to improve reproducibility and have additional benefits in allowing compilation and comparison of data from across studies.     

Robustness of Massively Parallel Sequencing Platforms

The improvements in high throughput sequencing technologies (HTS) made clinical sequencing projects such as ClinSeq and Genomics England feasible. Although there are significant improvements in accuracy and reproducibility of HTS based analyses, the usability of these types of data for diagnostic and prognostic applications necessitates a near perfect data generation. To assess the usability of a widely used HTS platform for accurate and reproducible clinical applications in terms of robustness, we generated whole genome shotgun (WGS) sequence data from the genomes of two human individuals in two different genome sequencing centers. After analyzing the data to characterize SNPs and indels using the same tools (BWA, SAMtools, and GATK), we observed significant number of discrepancies in the call sets. As expected, the most of the disagreements between the call sets were found within genomic regions containing common repeats and segmental duplications, albeit only a small fraction of the discordant variants were within the exons and other functionally relevant regions such as promoters. We conclude that although HTS platforms are sufficiently powerful for providing data for first-pass clinical tests, the variant predictions still need to be confirmed using orthogonal methods before using in clinical applications.     

Reproducible Research Practices and Transparency across the Biomedical Literature

There is a growing movement to encourage reproducibility and transparency practices in the scientific community, including public access to raw data and protocols, the conduct of replication studies, systematic integration of evidence in systematic reviews, and the documentation of funding and potential conflicts of interest. In this survey, we assessed the current status of reproducibility and transparency addressing these indicators in a random sample of 441 biomedical journal articles published in 2000–2014. Only one study provided a full protocol and none made all raw data directly available. Replication studies were rare (n = 4), and only 16 studies had their data included in a subsequent systematic review or meta-analysis. The majority of studies did not mention anything about funding or conflicts of interest. The percentage of articles with no statement of conflict decreased substantially between 2000 and 2014 (94.4% in 2000 to 34.6% in 2014); the percentage of articles reporting statements of conflicts (0% in 2000, 15.4% in 2014) or no conflicts (5.6% in 2000, 50.0% in 2014) increased. Articles published in journals in the clinical medicine category versus other fields were almost twice as likely to not include any information on funding and to have private funding. This study provides baseline data to compare future progress in improving these indicators in the scientific literature.     

Comparison of normalization methods for CodeLink Bioarray data

Background  

The quality of microarray data can seriously affect the accuracy of downstream analyses. In order to reduce variability and enhance signal reproducibility in these data, many normalization methods have been proposed and evaluated, most of which are for data obtained from cDNA microarrays and Affymetrix GeneChips. CodeLink Bioarrays are a newly emerged, single-color oligonucleotide microarray platform. To date, there are no reported studies that evaluate normalization methods for CodeLink Bioarrays.     

Data Quality and the Comparative Method: The Case of Primate Group Size

The comparative method is frequently employed to study primate behavior and evolution. The method is used to infer adaptations, and considerable improvements have been made with respect to its implementation. Despite these advances, scant attention has been given to the nature of the data that are used in comparative analyses. This creates a potential problem as data are often compiled from studies conducted by multiple researchers, whose methods may differ, resulting in variation in data quality. In this article, we investigate the quality of data employed in studies of primate group size. Several issues concerning data quality arise when assembling data on group size. For example, data quality may be compromised if group sizes are estimated from censuses, unhabituated groups, or groups with unrecognized individuals. To mitigate these and other data quality issues, we gathered data from the literature on 23 monkeys and apes using well-defined and biologically relevant criteria for inclusion. We compare our results with those of eight published compilations of group size. Most studies did not provide details regarding the criteria for including data. We found that our group size values were uncorrelated or weakly correlated with those from three other studies and differed in a consistent fashion from those of one other study. Because conclusions derived from comparative analyses are only as accurate as the data that they use, future studies should provide details regarding data collection to ensure their reliability.     

A portable device for measuring donor corneal transparency in eye banks

To develop a portable device for measuring the donor corneal transparency and validate its efficacy for corneal evaluation in the eye-banks and for research. The transparency device (TD) has a light source, a detachable system for corneal insertion and a base for light transmission. The probe detects the transmitted light which is measured by a lux-meter. A contact lens was set as ‘control’ to reduce the light scattering concern, an empty petri-plate as ‘blank’ and the cornea as ‘sample’. Two experts and non-experts (masked) observed the corneas for subjective analysis which was then compared using the TD. The parameters observed were scars, foreign-body, stromal-deformities, folds, thickness and opacity which were then converted to a relative overall percentage by the observer. Twenty corneas were evaluated for correlation, five tissues to obtain standard-deviation and twenty-four pairs for a comparative study. Experts mimicked the eye-banks with long-term experience while non-experts mimicked the emerging eye-banks. Subjective values by the experts closely resembled the measurements by TD. The average correlation between the experts and the non-experts to TD was 0.985 and 0.960 respectively. TD showed higher reproducibility than experts followed by the non-experts. The comparative study showed that increase in thickness reduces the transparency. TD is portable, easy, efficient, maintains sterility and less expensive hence the emerging eye-banks and researchers can use to raise their standards and evaluate the transparency for in vitro tests and comparative studies. The suitable transparency for the cornea deemed for clinical applications was found to be >75 %.     

The search for loci under selection: trends,biases and progress

Detecting genetic variants under selection using F_ST outlier analysis (OA) and environmental association analyses (EAAs) are popular approaches that provide insight into the genetic basis of local adaptation. Despite the frequent use of OA and EAA approaches and their increasing attractiveness for detecting signatures of selection, their application to field‐based empirical data have not been synthesized. Here, we review 66 empirical studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We report trends and biases across biological systems, sequencing methods, approaches, parameters, environmental variables and their influence on detecting signatures of selection. We found striking variability in both the use and reporting of environmental data and statistical parameters. For example, linkage disequilibrium among SNPs and numbers of unique SNP associations identified with EAA were rarely reported. The proportion of putatively adaptive SNPs detected varied widely among studies, and decreased with the number of SNPs analysed. We found that genomic sampling effort had a greater impact than biological sampling effort on the proportion of identified SNPs under selection. OA identified a higher proportion of outliers when more individuals were sampled, but this was not the case for EAA. To facilitate repeatability, interpretation and synthesis of studies detecting selection, we recommend that future studies consistently report geographical coordinates, environmental data, model parameters, linkage disequilibrium, and measures of genetic structure. Identifying standards for how OA and EAA studies are designed and reported will aid future transparency and comparability of SNP‐based selection studies and help to progress landscape and evolutionary genomics.     

Assessment of transparency indicators across the biomedical literature: How open is open?

Recent concerns about the reproducibility of science have led to several calls for more open and transparent research practices and for the monitoring of potential improvements over time. However, with tens of thousands of new biomedical articles published per week, manually mapping and monitoring changes in transparency is unrealistic. We present an open-source, automated approach to identify 5 indicators of transparency (data sharing, code sharing, conflicts of interest disclosures, funding disclosures, and protocol registration) and apply it across the entire open access biomedical literature of 2.75 million articles on PubMed Central (PMC). Our results indicate remarkable improvements in some (e.g., conflict of interest [COI] disclosures and funding disclosures), but not other (e.g., protocol registration and code sharing) areas of transparency over time, and map transparency across fields of science, countries, journals, and publishers. This work has enabled the creation of a large, integrated, and openly available database to expedite further efforts to monitor, understand, and promote transparency and reproducibility in science.

This study uses novel open source automated tools to monitor transparency across all 2.75 million open access articles on PubMed Central, discovering that different disciplines, journals and publishers abide by principles of transparency to varying degrees over time.     

Controlling for non-independence in comparative analysis of patterns across populations within species

How do we quantify patterns (such as responses to local selection) sampled across multiple populations within a single species? Key to this question is the extent to which populations within species represent statistically independent data points in our analysis. Comparative analyses across species and higher taxa have long recognized the need to control for the non-independence of species data that arises through patterns of shared common ancestry among them (phylogenetic non-independence), as have quantitative genetic studies of individuals linked by a pedigree. Analyses across populations lacking pedigree information fall in the middle, and not only have to deal with shared common ancestry, but also the impact of exchange of migrants between populations (gene flow). As a result, phenotypes measured in one population are influenced by processes acting on others, and may not be a good guide to either the strength or direction of local selection. Although many studies examine patterns across populations within species, few consider such non-independence. Here, we discuss the sources of non-independence in comparative analysis, and show why the phylogeny-based approaches widely used in cross-species analyses are unlikely to be useful in analyses across populations within species. We outline the approaches (intraspecific contrasts, generalized least squares, generalized linear mixed models and autoregression) that have been used in this context, and explain their specific assumptions. We highlight the power of ‘mixed models’ in many contexts where problems of non-independence arise, and show that these allow incorporation of both shared common ancestry and gene flow. We suggest what can be done when ideal solutions are inaccessible, highlight the need for incorporation of a wider range of population models in intraspecific comparative methods and call for simulation studies of the error rates associated with alternative approaches.     

Recommendations for utilizing and reporting population genetic analyses: the reproducibility of genetic clustering using the program structure

Reproducibility is the benchmark for results and conclusions drawn from scientific studies, but systematic studies on the reproducibility of scientific results are surprisingly rare. Moreover, many modern statistical methods make use of ‘random walk’ model fitting procedures, and these are inherently stochastic in their output. Does the combination of these statistical procedures and current standards of data archiving and method reporting permit the reproduction of the authors' results? To test this, we reanalysed data sets gathered from papers using the software package structure to identify genetically similar clusters of individuals. We find that reproducing structure results can be difficult despite the straightforward requirements of the program. Our results indicate that 30% of analyses were unable to reproduce the same number of population clusters. To improve this, we make recommendations for future use of the software and for reporting structure analyses and results in published works.     

The Impact of Gene Duplication,Insertion, Deletion,Lateral Gene Transfer and Sequencing Error on Orthology Inference: A Simulation Study

The identification of orthologous genes, a prerequisite for numerous analyses in comparative and functional genomics, is commonly performed computationally from protein sequences. Several previous studies have compared the accuracy of orthology inference methods, but simulated data has not typically been considered in cross-method assessment studies. Yet, while dependent on model assumptions, simulation-based benchmarking offers unique advantages: contrary to empirical data, all aspects of simulated data are known with certainty. Furthermore, the flexibility of simulation makes it possible to investigate performance factors in isolation of one another.Here, we use simulated data to dissect the performance of six methods for orthology inference available as standalone software packages (Inparanoid, OMA, OrthoInspector, OrthoMCL, QuartetS, SPIMAP) as well as two generic approaches (bidirectional best hit and reciprocal smallest distance). We investigate the impact of various evolutionary forces (gene duplication, insertion, deletion, and lateral gene transfer) and technological artefacts (ambiguous sequences) on orthology inference. We show that while gene duplication/loss and insertion/deletion are well handled by most methods (albeit for different trade-offs of precision and recall), lateral gene transfer disrupts all methods. As for ambiguous sequences, which might result from poor sequencing, assembly, or genome annotation, we show that they affect alignment score-based orthology methods more strongly than their distance-based counterparts.     

Inference of the distribution of fitness effects of mutations is affected by single nucleotide polymorphism filtering methods,sample size and population structure

The distribution of fitness effects (DFE) of new mutations has been of interest to evolutionary biologists since the concept of mutations arose. Modern population genomic data enable us to quantify the DFE empirically, but few studies have examined how data processing, sample size and cryptic population structure might affect the accuracy of DFE inference. We used simulated and empirical data (from Arabidopsis lyrata) to show the effects of missing data filtering, sample size, number of single nucleotide polymorphisms (SNPs) and population structure on the accuracy and variance of DFE estimates. Our analyses focus on three filtering methods—downsampling, imputation and subsampling—with sample sizes of 4–100 individuals. We show that (1) the choice of missing-data treatment directly affects the estimated DFE, with downsampling performing better than imputation and subsampling; (2) the estimated DFE is less reliable in small samples (<8 individuals), and becomes unpredictable with too few SNPs (<5000, the sum of 0- and 4-fold SNPs); and (3) population structure may skew the inferred DFE towards more strongly deleterious mutations. We suggest that future studies should consider downsampling for small data sets, and use samples larger than 4 (ideally larger than 8) individuals, with more than 5000 SNPs in order to improve the robustness of DFE inference and enable comparative analyses.     

Are Guinea Pigs Rodents? The Importance of Adequate Models in Molecular Phylogenetics

The monophyly of Rodentia has repeatedly been challenged based on several studies of molecular sequence data. Most recently, D'Erchia et al. (1996) analyzed complete mtDNA sequences of 16 mammals and concluded that rodents are not monophyletic. We have reanalyzed these data using maximum-likelihood methods. We use two methods to test for significance of differences among alternative topologies and show that (1) models that incorporate variation in evolutionary rates across sites fit the data dramatically better than models used in the original analyses, (2) the mtDNA data fail to refute rodent monophyly, and (3) the original interpretation of strong support for nonmonophyly results from systematic error associated with an oversimplified model of sequence evolution. These analyses illustrate the importance of incorporating recent theoretical advances into molecular phylogenetic analyses, especially when results of these analyses conflict with classical hypotheses of relationships.     

Counting on comparative maps

Comparative maps record the history of chromosome rearrangements that have occurred during the evolution of plants and animals. Effective use of these maps in genetic and evolutionary studies relies on quantitative analyses of the patterns of segment conservation. We review the analytical methods that have been developed for characterizing these maps and evaluate their application to existing comparative maps mainly for plants and animals.     

New developments in automated cytogenetic imaging: unattended scoring of dicentric chromosomes, micronuclei, single cell gel electrophoresis, and fluorescence signals

The quantification of DNA damage, both in vivo and in vitro, can be very time consuming, since large amounts of samples need to be scored. Additional uncertainties may arise due to the lack of documentation or by scoring biases. Image analysis automation is a possible strategy to cope with these difficulties and to generate a new quality of reproducibility. In this communication we collected some recent results obtained with the automated scanning platform Metafer, covering applications that are being used in radiation research, biological dosimetry, DNA repair research and environmental mutagenesis studies. We can show that the automated scoring for dicentric chromosomes, for micronuclei, and for Comet assay cells produce reliable and reproducible results, which prove the usability of automated scanning in the above mentioned research fields.     

Skeletal pathology in a prehistoric Pacific Island sample: issues in lesion recording, quantification, and interpretation

This paper presents a profile of evidence of disease in a skeletal sample from Taumako Island, Southeast Solomon Islands, Melanesia, and aims to increase awareness of the prehistoric Pacific Island disease environment. It also addresses issues of lesion recording, quantification, and interpretation. Two methodologies for the determination of lesion prevalence were applied, one based on prevalence in observable individuals and one in skeletal elements. The aim of these methodologies was to provide objective data on skeletal lesions in this sample, with transparency in methods for application in comparative studies. The types of lesions observed were predominantly osteoblastic and affecting multiple bones, particularly in the lower limbs. The individual analysis yielded a prevalence of lesions affecting 56.4% of the postcranial sample from birth to old age. As expected, the skeletal element analysis yielded a lower prevalence, with 15.0% of skeletal elements affected. The skeletal element analysis also revealed a pattern of greater lower limb involvement, with a predilection for the tibia. The pattern of skeletal involvement was similar in both analyses, suggesting the validity of employing either method in paleopathological studies. A differential diagnosis of the lesions included osteomyelitis, treponemal disease, and leprosy. Metabolic disease was also considered for subadult lesions. Based on lesion type, skeletal distribution, and epidemiology of lesions in the sample, an etiology of yaws (Treponema pertenue) was suggested as responsible for nearly half the adult lesions, while multiple causes, including yaws, were suggested for the lesions in subadults.     

Pheno-Pub: a total support system for the publication of mouse phenotypic data on the web

We have developed an open-source database system named “Pheno-Pub” to support a series of data-handling and publication tasks, including statistical analyses, data review, and web site construction, for mouse phenotyping experiments. This system is composed of three applications. “Mou-Stat” provides semiautomatic statistical analyses for a batch of phenotypic data, including a variety of conditions for group comparisons (e.g., different scales of measurement parameters). “Genotype Viewer” and “Strain Viewer” provide representation of genotype-driven and measurement parameter-driven views of phenotypic data; they highlight significant differences in genotypes and between strains, respectively. Direct links from the Strain Viewer web site to the Genotype Viewer web site provide flexible navigation in the exploration of phenotypic data. With these publication tools, phenotypic data can be made available on the Internet by simple operations. This system is expandable for a wide range of uses in phenotypic comparative analyses, including comparisons among different genotypes and strains and comparisons among groups exposed to different environmental conditions. Finally, Pheno-Pub provides advanced usability for both producers of experimental data and consumers of phenotypic information. Therefore, Pheno-Pub contributes significantly to the publication of data in various fields of phenotyping research and to broad data sharing, thereby promoting the understanding of the functions of the entire mouse genome.     

GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations

Modern DNA sequencing technologies enable geneticists to rapidly identify genetic variation among many human genomes. However, isolating the minority of variants underlying disease remains an important, yet formidable challenge for medical genetics. We have developed GEMINI (GEnome MINIng), a flexible software package for exploring all forms of human genetic variation. Unlike existing tools, GEMINI integrates genetic variation with a diverse and adaptable set of genome annotations (e.g., dbSNP, ENCODE, UCSC, ClinVar, KEGG) into a unified database to facilitate interpretation and data exploration. Whereas other methods provide an inflexible set of variant filters or prioritization methods, GEMINI allows researchers to compose complex queries based on sample genotypes, inheritance patterns, and both pre-installed and custom genome annotations. GEMINI also provides methods for ad hoc queries and data exploration, a simple programming interface for custom analyses that leverage the underlying database, and both command line and graphical tools for common analyses. We demonstrate GEMINI''s utility for exploring variation in personal genomes and family based genetic studies, and illustrate its ability to scale to studies involving thousands of human samples. GEMINI is designed for reproducibility and flexibility and our goal is to provide researchers with a standard framework for medical genomics.This is a PLOS Computational Biology Software Article.