Recent excitement over the development of an initiative to generate DNA sequences for all named species on the planet has in our opinion generated two major areas of contention as to how this ‘DNA barcoding’ initiative should proceed. It is critical that these two issues are clarified and resolved, before the use of DNA as a tool for taxonomy and species delimitation can be universalized. The first issue concerns how DNA data are to be used in the context of this initiative; this is the DNA barcode reader problem (or barcoder problem). Currently, many of the published studies under this initiative have used tree building methods and more precisely distance approaches to the construction of the trees that are used to place certain DNA sequences into a taxonomic context. The second problem involves the reaction of the taxonomic community to the directives of the ‘DNA barcoding’ initiative. This issue is extremely important in that the classical taxonomic approach and the DNA approach will need to be reconciled in order for the ‘DNA barcoding’ initiative to proceed with any kind of community acceptance. In fact, we feel that DNA barcoding is a misnomer. Our preference is for the title of the London meetings—Barcoding Life. In this paper we discuss these two concerns generated around the DNA barcoding initiative and attempt to present a phylogenetic systematic framework for an improved barcoder as well as a taxonomic framework for interweaving classical taxonomy with the goals of ‘DNA barcoding’.

Relationships among representatives of the five major Hawaiian Drosophila species groups were examined using data from eight different gene regions. A simultaneous analysis of these data resulted in a single most-parsimonious tree that (1) places the adiastola picture-winged subgroup as sister taxon to the other picture-winged subgroups, (2) unites the modified-tarsus species group with flies from the Antopocerus species group, and (3) places the white-tip scutellum species group as the most basal taxon. Because of the different gene sources used in this study, numerous process partitions can be erected within this data set. We examined the incongruence among these various partitions and the ramifications of these data for the taxonomic consensus, prior agreement, and simultaneous analysis approaches to phylogenetic reconstruction. Separate analyses and taxonomic consensus appear to be inadequate methods for dealing with the partitions in this study. Although detection of incongruence is possible and helps elucidate particular areas of disagreement among data sets, separation of partitions on the basis of incongruence is problematic for many reasons. First, analyzing all genes separately and then either presenting them all as possible hypotheses or taking their consensus provides virtually no information concerning the relationships among these flies. Second, despite some evidence of incongruence, there are no clear delineations among the various gene partitions that separate only heterogeneous data. Third, to the extent that problematic genes can be identified, these genes have nearly the same information content, within a combined analysis framework, as the remaining nonproblematic genes. Our data suggest that significant incongruence among data partitions may be isolated to specific relationships and the “false” signal creating this incongruence is most likely to be overcome by a simultaneous analysis. We present a new method, partitioned Bremer support, for examining the contribution of a particular data partition to the topological support of the simultaneous analysis tree.

Interpretations and analytical practices surrounding DNA barcoding are examined using a compilation of 3,756 papers (as of December 31, 2018) with “DNA Barcode” in the abstract published since 2004. By examining the rise of DNA barcoding in natural history and biodiversity science over this period, we hope to detect the extent to which its purposes, premises, rationale and application have evolved. The number of studies involving identification, taxonomic decisions and the discovery of cryptic species has grown rapidly and appears to have driven much of the publication activity of DNA barcode studies overall. Forensic studies and papers on biological conservation involving DNA barcodes have loosely tracked the ensemble number of studies but appear to have risen sharply in 2017. Although analytical paradigms have diversified, particularly following the growing availability of tools in BoLD, neighbor-joining and graphic (tree-based) criteria for species delimitation remain preeminent. We conclude that the practices and paradigms of DNA barcoding data are likely to persist and, in groups such as Lepidoptera, remain a widely used tool in taxonomic science.

Background: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) has recently been used to identify the modification patterns for the methylation and acetylation of many different histone tails in genes and enhancers. Results: We have extended the analysis of histone modifications to gene deserts, pericentromeres and subtelomeres. Using data from human CD4⁺ T cells, we have found that each of these non-genic regions has a particular profile of histone modifications that distinguish it from the other non-coding regions. Different methylation states of H4K20, H3K9 and H3K27 were found to be enriched in each region relative to the other regions. These findings indicate that non-genic regions of the genome are variable with respect to histone modification patterns, rather than being monolithic. We furthermore used consensus sequences for unassembled centromeres and telomeres to identify the significant histone modifications in these regions. Finally, we compared the modification patterns in non-genic regions to those at silent genes and genes with higher levels of expression. For all tested methylations with the exception of H3K27me3, the enrichment level of each modification state for silent genes is between that of non-genic regions and expressed genes. For H3K27me3, the highest levels are found in silent genes. Conclusion: In addition to the histone modification pattern difference between euchromatin and heterochromatin regions, as is illustrated by the enrichment of H3K9me2/3 in non-genic regions while H3K9me1 is enriched at active genes; the chromatin modifications within non-genic (heterochromatin-like) regions (e.g. subtelomeres, pericentromeres and gene deserts) are also quite different.