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Natural history collections are repositories of biodiversity and are potentially used by molecular ecologists for comparative taxonomic, phylogenetic, biogeographic and forensic purposes. Specimens in fish collections are preserved using a combination of methods with many fixed in formalin and then preserved in ethanol for long-term storage. Formalin fixation damages DNA, thereby limiting genetic analyses. In this study, the authors compared the DNA barcoding and identification success for frozen and formalin-fixed tissues obtained from specimens in the CSIRO Australian National Fish Collection. They studied 230 samples from fishes (consisting of >160 fish species). An optimized formalin-fixed, paraffin-embedded DNA extraction method resulted in usable DNA from degraded tissues. Four mini barcoding assays of the mitochondrial DNA (mtDNA) were characterized with Sanger and Illumina amplicon sequencing. In the good quality DNA (without exposure to formalin), up to 88% of the specimens were correctly matched at the species level using the cytochrome oxidase subunit 1 (COI) mini barcodes, whereas up to 58% of the specimens exposed to formalin for less than 8 weeks were correctly identified to species. In contrast, 16S primers provided higher amplification success with formalin-exposed tissues, although the COI gene was more successful for identification. Importantly, the authors found that DNA of a certain size and quality can be amplified and sequenced despite exposure to formalin, and Illumina sequencing provided them with greater power of resolution for taxa identification even when there was little DNA present. Overall, within parameter constraints, this study highlights the possibilities of recovering DNA barcodes for identification from formalin-fixed fish specimens, and the authors provide guidelines for when successful identification could be expected.

Thanks to sequencing technology, modern molecular bioscience datasets are often compositions of counts, e.g. counts of amplicons, mRNAs, etc. While there is growing appreciation that compositional data need special analysis and interpretation, less well understood is the discrete nature of these count compositions (or, as we call them, lattice compositions) and the impact this has on statistical analysis, particularly log-ratio analysis (LRA) of pairwise association. While LRA methods are scale-invariant, count compositional data are not; consequently, the conclusions we draw from LRA of lattice compositions depend on the scale of counts involved. We know that additive variation affects the relative abundance of small counts more than large counts; here we show that additive (quantization) variation comes from the discrete nature of count data itself, as well as (biological) variation in the system under study and (technical) variation from measurement and analysis processes. Variation due to quantization is inevitable, but its impact on conclusions depends on the underlying scale and distribution of counts. We illustrate the different distributions of real molecular bioscience data from different experimental settings to show why it is vital to understand the distributional characteristics of count data before applying and drawing conclusions from compositional data analysis methods.

We are amidst an ongoing flood of sequence data arising from the application of high-throughput technologies, and a concomitant fundamental revision in our understanding of how genomes evolve individually and within the biosphere. Workflows for phylogenomic inference must accommodate data that are not only much larger than before, but often more error prone and perhaps misassembled, or not assembled in the first place. Moreover, genomes of microbes, viruses and plasmids evolve not only by tree-like descent with modification but also by incorporating stretches of exogenous DNA. Thus, next-generation phylogenomics must address computational scalability while rethinking the nature of orthogroups, the alignment of multiple sequences and the inference and comparison of trees. New phylogenomic workflows have begun to take shape based on so-called alignment-free (AF) approaches. Here, we review the conceptual foundations of AF phylogenetics for the hierarchical (vertical) and reticulate (lateral) components of genome evolution, focusing on methods based on k-mers. We reflect on what seems to be successful, and on where further development is needed.

We sought to explore how knowledge of videogames could inform the design of complex digital interfaces for bioinformatics visualization tools. Aquaria is an open-source web resource for visual exploration of protein structures. We present the results of a series of workshops conducted with participants who have expertise in videogame design and development. We report on emerging themes relating to how videogames might inform improvements in the design of Aquaria and other bioinformatics visualization tools. Future work is described.

Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.