Comparative genomics is the comparison of genetic information within and across organisms to understand the evolution, structure, and function of genes, proteins, and non-coding regions (Sivashankari and Shanmughavel, Bioinformation 1:376-8, 2007). Advances in sequencing technology and assembly algorithms have resulted in the ability to sequence large genomes and provided a wealth of data that are being used in comparative genomic analyses. Comparative analysis can be leveraged to systematically explore and evaluate the biological relationships and evolution between species, aid in understanding the structure and function of genes, and gain a better understanding of disease and potential drug targets. As our knowledge of genetics expands, comparative genomics can help identify emerging model organisms among a broader span of the tree of life, positively impacting human health. This impact includes, but is not limited to, zoonotic disease research, therapeutics development, microbiome research, xenotransplantation, oncology, and toxicology. Despite advancements in comparative genomics, new challenges have arisen around the quantity, quality assurance, annotation, and interoperability of genomic data and metadata. New tools and approaches are required to meet these challenges and fulfill the needs of researchers. This paper focuses on how the National Institutes of Health (NIH) Comparative Genomics Resource (CGR) can address both the opportunities for comparative genomics to further impact human health and confront an increasingly complex set of challenges facing researchers.

Public sequencing databases are invaluable resources to biological researchers, but assessing data veracity as well as the curation and maintenance of such large collections of data can be challenging. Genomes of eukaryotic organelles, such as chloroplasts and other plastids, are particularly susceptible to assembly errors and misrepresentations in these databases due to their close evolutionary relationships with bacteria, which may co-occur within the same environment, as can be the case when sequencing plants. Here, based on sequence similarities with bacterial genomes, we identified several suspicious chloroplast assemblies present in the National Institutes of Health (NIH) Reference Sequence (RefSeq) collection. Investigations into these chloroplast assemblies reveal examples of erroneous integration of bacterial sequences into chloroplast ribosomal RNA (rRNA) loci, often within the rRNA genes, presumably due to the high similarity between plastid and bacterial rRNAs. The bacterial lineages identified within the examined chloroplasts as the most likely source of contamination are either known associates of plants, or co-occur in the same environmental niches as the examined plants. Modifications to the methods used to process untargeted \'raw\' shotgun sequencing data from whole genome sequencing efforts, such as the identification and removal of bacterial reads prior to plastome assembly, could eliminate similar errors in the future.

Recent high-throughput sequencing endeavors have yielded multigene/protein phylogenies that confidently resolve several inter- and intra-class relationships within the phylum Ciliophora. We leverage the massive sequencing efforts from the Marine Microbial Eukaryote Transcriptome Sequencing Project, other SRA submissions, and available genome data with our own sequencing efforts to determine the phylogenetic position of Mesodinium and to generate the most taxonomically rich phylogenomic ciliate tree to date. Regardless of the data mining strategy, the multiprotein data set, or the molecular models of evolution employed, we consistently recovered the same well-supported relationships among ciliate classes, confirming many of the higher-level relationships previously identified. Mesodinium always formed a monophyletic group with members of the Litostomatea, with mixotrophic species of Mesodinium-M. rubrum, M. major, and M. chamaeleon-being more closely related to each other than to the heterotrophic member, M. pulex. The well-supported position of Mesodinium as sister to other litostomes contrasts with previous molecular analyses including those from phylogenomic studies that exploited the same transcriptomic databases. These topological discrepancies illustrate the need for caution when mining mixed-species transcriptomes and indicate that identifying ciliate sequences among prey contamination-particularly for Mesodinium species where expression from stolen prey nuclei appears to dominate-requires thorough and iterative vetting with phylogenies that incorporate sequences from a large outgroup of prey.