Phylogenomics — principles, opportunities and pitfalls of big‐data phylogenetics

Phylogenetics is the science of reconstructing the evolutionary history of life on Earth. Traditionally, phylogenies were constructed using morphological data only, but the introduction of Sanger sequencing and PCR in the late 1970s enabled genetic information to be incorporated into phylogenetic analyses. Early phylogenetic studies employing multilocus analyses contributed greatly to our knowledge of phylogenetic history and challenged some well-established views of the relationships among many groups of plants and animals. Since the publication of these pioneering studies, significant methodological advances in both sequencing and analytical techniques have been made, and molecular phylogenies are now broadly accepted to represent robust hypotheses of organismal relationships. Next-generation sequencing techniques, developed in the mid-2000s, revolutionized DNA sequencing and led to a dramatic reduction in sequencing cost per nucleotide and a sharp increase in data generation speed. As a result, the generation of unprecedented amounts of sequence data for both model and nonmodel organisms has become affordable. This development has transformed the field of molecular phylogenetics into phylogenomics—where genome-scale data are obtained from multiple samples at once at a much reduced cost (Mardis, 2011). The phylogenomic pipeline can be very complex, presenting an overwhelming array of methodologies available for the acquisition, manipulation, analysis and interpretation of massive datasets. Researchers also have to overcome the challenges of sequencing strategy design, identification of orthologous loci, model selection and phylogeny estimation. This can be particularly daunting for researchers new to the field—both students and established scientists—who wish to delve into novel methods and data to reconstruct the evolution of their study group. Here we present an entry-level overview of the theory and tools that are central to phylogenomics, with an emphasis on the appropriate application of techniques useful for phylogenetic analysis of genomic data. We focus on the sequencing technologies and statistical methods for phylogeny estimation, and the software implementing these methods and their application to large molecular datasets. We also discuss the tools and tradeoffs for improving the accuracy of phylogenomic analyses, including the biological and methodological sources of systematic error in phylogeny estimation. Finally, we provide a glossary of commonly encountered terms used in phylogenomics that may be useful for those entering the field and hoping to sort through the multitude of methods, analytical tools and terminology inherent to this relatively new, but rapidly advancing field. The word 'phylogenomics' was first introduced in the context of prediction of gene function for genome-scale data (Eisen, 1998), and soon after in the context of phylogenetic inference (O'Brien and (ii) to use multispecies phylogenetic comparisons to infer putative functions for DNA or protein sequences. Traditional Sanger sequencing studies include relatively few loci and are therefore limited by stochastic or sampling error. As there is a relatively small number of phylogenetically informative characters available in one or a few genes, this random 'noise' influences the inference of backbone nodes, potentially leading to poorly resolved or poorly supported phylogenetic trees. This problem can be addressed successfully by using much larger amounts of sequence data. Modern phylogenomic analyses, which take advantage of hundreds to thousands of loci from across the genome, are, on average, orders of magnitude larger than traditional Sanger sequencing datasets. The size of these datasets therefore significantly reduces the impact of stochastic error and data availability as a limiting factor, offering great promise for resolving historically recalcitrant nodes in the tree of life. High-throughput sequencing technologies also called next-generation sequencing (NGS) (Fig. 1) have yielded genome-scale data in immense quantities. Next-generation sequencing technologies differ fundamentally from the Sanger method in that they allow for massively parallel DNA sequencing, providing extremely high throughput from multiple samples simultaneously and at a much reduced cost (Mardis, 2011). Millions to billions of DNA nucleotides can be sequenced in parallel, yielding orders of magnitude more data and minimizing the need for the fragment-cloning methods that are used with Sanger sequencing (Fig. 1). Recent progress in NGS technology and the rapid development of bioinformatics tools now allow research groups of any size to generate large amounts of genomic sequences for organisms of interest. High-throughput sequencing can be used for whole-genome sequencing (Lam, 2012), whole-transcriptome shotgun sequencing (also called RNA sequencing, RNA-seq, or transcriptomics; Wang, 2009), whole-exome sequencing (Rabbani, 2014), and reduced-representation genome sequencing (also called target enrichment) (e.g., Faircloth, 2012; Lemmon, 2012). Table 1 summarizes the most commonly used sequencing technologies in phylogenomics. For more details on these different technologies see the Beginner's Handbook of Next Generation Sequencing by Genohub (https://genohub.com/next-generation-sequencing-handbook/) (see also Ambardar, 2016; Besser et al., 2018, and references therein). Choosing the appropriate sequencing technology for a phylogenomic study has important effects on downstream workflows, especially in terms of read length, as library preparation in some phylogenomic techniques (e.g. ultraconserved elements and anchored hybrid enrichment, discussed later) requires a read size selection step. Strict experimental reproducibility is an integral—albeit uncommon—aspect of biological sciences, mainly due to varied technical challenges with implementation and curation of experimental methods and procedures. Despite the importance of phylogenetic analyses to most fields of biology, the reproducibility of phylogenetic experiments can be very low, with an estimated 60% of published phylogenetic analyses being 'lost to science' due to the unavailability of the underlying data and methods (Magee, 2014). Published phylogenetic studies can be difficult or impossible to replicate or expand upon, as the utilized analytical software, software versions, software parameters, dependencies and operating system versions can be very challenging to uncover or recreate. The promotion of open science and reproducible research can create a more productive and responsible scientific culture in phylogenomics, enabling researchers to build upon previous studies and continuously address larger and more complex questions. This philosophy encompasses the sharing of data and code used to produce the analysis, as well as open archiving of all raw data (Mork, 2015; Shade Magee, 2014; Debiasse Oakley, 2014; Szitenberg, 2015). To ensure the best practices in phylogenomics and bioinformatics, it is vital that reproducibility checkpoints are enforced—places in a workflow devoted to scrutinizing its integrity, so results are validated across multiple iterations to ensure consistency of results. Additionally, adopting an iterative, branching workflow to systematically explore the methodological space is crucial. Linear methodology, with experimental and computational procedures lined up one after the other, as presented in most published studies, is rarely the reality of phylogenetic analysis. Instead, estimating phylogenetic trees is more often than not a messy enterprise, and a systematic exploration of the methods and data is recommended in order to select the best tools and pipelines to answer the question at hand. Finally, for good provenance of experimental procedures and computational tools utilized in a particular study, it is highly recommended that comprehensive notes are kept throughout the process. In particular, keeping a 'readme' file at every step can be extremely helpful in keeping track of the software versions used, parameter values utilized, goals of each step and how they relate to the software utilized, as well as indication of data format changes. All these can greatly contribute to standardization and ease of downstream efforts (Shade Nabhan Turney, 2015). Although large phylogenomic datasets have become increasingly more accessible and cost-efficient in recent years, it is now widely accepted that simply increasing the amount of sequence data will not unambiguously resolve some of the most difficult nodes in the tree of life, mainly due to systematic error from nonphylogenetic signal or model inadequacy. Appropriate locus selection is therefore crucial in phylogenomics, but knowledge of the best molecular markers for resolving difficult branches at various evolutionary depths is still incipient. Questions still remain about whether to use coding or noncoding sequence data, conserved or highly variable loci, and long or short alignments (Betancur-R. et al., 2014; Edwards et al., 2016; Chen et al., 2017). Therefore, one of the most critical decisions in a phylogenomic project is the sequencing method to be utilized, a decision that must be made a priori as each method will result in different types of genomic data sequenced. Different methods have their own characteristics, advantages, and limitations, including cost-effectiveness, ease of use, sample quality required, and downstream data filtering and analysis workflow. Phylogenomic sequencing methods (Table 2) can be broadly subdivided into shotgun sequencing and target enrichment sequencing. Shotgun sequencing is the process of sequencing from the entire fragmented genome at random, returning part or all of the genome depending on the sequencing depth achieved, whereas target enrichment uses bidirectional probes (analogous to primers in Sanger sequencing) to recover only genomic regions of interest. Popular methods of shotgun sequencing include genome skimming, whole-genome shotgun sequencing and transcriptome sequencing (i.e. RNA-seq). Popular methods of target enrichment for phylogenetics include anchored hybrid enrichment (AHE) (Lemmon, 2012) and UCEs (McCormack et al., 2012; Faircloth et al., 2012) see also Mandel (2014) for an alternative method developed for plants in the family Compositae. These techniques are reviewed briefly in Table 2 and have been covered in more detail elsewhere (e.g. Lemmon McCormack, 2013; Wen et al., 2015; Zhang et al., 2019). Shotgun sequencing (Fig. 3) involves fragmenting template DNA into short pieces, which are then randomly sequenced to obtain reads. Next, various methods and software are used to overlap different reads and assemble them into a longer DNA sequence called a contig. RNA-seq can be considered a special form of shotgun sequencing, where whole mRNA is first extracted and reverse-transcribed into reverse-complement DNA, which is then sequenced. Sequencing depth, or the average number of times an individual base in the genome is sequenced, is a key concept in shotgun sequencing. Because the genome is sequenced at random, multiple-copy regions of the genome (i.e. mitochondrial, ribosomal, and plastid DNA) are sequenced more frequently than single-copy regions. Therefore, when a genome is sequenced at a relatively shallow depth, only fragments from multiple-copy regions of the genome are sequenced in sufficient quantities to be successfully recovered. Shallow-depth whole-genome shotgun sequencing is also called genome skimming (Straub et al., 2012), a time and cost-efficient method of sequencing mostly mitochondrial, ribosomal and plastid DNA. Conversely, when near-complete genomes are desired from whole-genome shotgun sequencing, a much greater sequencing depth is required in order to sequence sufficient numbers of fragments from single-copy regions of the genome. By contrast, in RNA-seq (or transcriptomics) the extracted mRNA is used as a template to generate reverse-complement DNA. This reverse-complement DNA is then sequenced, resulting in data generated from only the genomic regions undergoing active transcription at the time of tissue preservation. This method is therefore not only a genome-reduction strategy, but also facilitates the comparison of transcription activity between individual tissues, life stages, rearing conditions, etc. One of the major drawbacks of transcriptomics is the high tissue quality required—specimens must be flash-frozen in liquid nitrogen or collected directly into RNAlater, thus precluding the utilization of specimens already available in tissue collections or museums. Targeted sequence capture, or target enrichment (Fig. 4), is an umbrella term for multiple efficient, cost-effective methods for generating phylogenomic datasets for nonmodel organisms. These methods effectively reduce genomic DNA complexity through the use of short (60–120 bp), single-stranded nucleotide baits or probes that hybridize with template sequences, thus enabling the of particular sequences of with high As a result, mostly genomic regions of are DNA (including and can be present in the resulting of reads. samples can be and sequenced which the generation of DNA sequence data for hundreds of loci from over samples are two methods of target enrichment commonly used in et al., 2012) and UCEs (McCormack et al., 2012). methods are reduced-representation that on the utilization of a phylogenetically informative of the study genomes. Other target enrichment methods have been with varied locus selection including et al., 2014), loci et al., and 2019), but and UCEs have thus far been the most commonly used methods in phylogenomic studies of animals. In these methods, probes hybridize with sequences, which are the genomic regions by the probes and their regions are sequenced, such that both conserved and more variable thus more phylogenetically regions are sequenced at These reduced-representation methods enable researchers not only to the same of loci across all taxa of but also to or phylogenetically regions of the genome, including and A great of using the same of markers across studies is that it for as more phylogenomic data over By contrast, for RNA-seq datasets to be the must be from the same tissue and it can be challenging to and different analysis. hybrid enrichment sequencing mainly regions of the genome, that loci mostly and in some or other genomic elements (e.g. This that phylogenetic data can be more and both as nucleotides and as target loci are using genomes, or raw genomic reads from two or more sequences for the target loci for each to be in the are and an alignment for each locus is are developed on these for which a amount of quality is such that target loci are and have the appropriate amount of sequence to ensure both phylogenetic accuracy and enrichment (Lemmon, 2012). kits target loci on average, and genes (also called traditional or genes, are often in the target of loci, which facilitates with previous Sanger sequencing phylogenetic studies. in are highly conserved regions of the genome among As genetic markers for particular taxa of data are useful for reconstructing the evolutionary history and relationships of many organisms. In UCEs are by several genomes to each other, with and filtering of areas of very high sequence across all taxa of interest. are a number of different of UCEs for use as genetic markers and baits to target but the most commonly used in phylogenetics was in detail in Faircloth elements have been to well when data from museum samples et al., 2016; et al., which can greatly of taxon sampling as sequencing is longer to Despite specimens and loci in it is still to hundreds of markers from relatively specimens (McCormack et al., 2016). A major advantage of UCEs over data is an and software pipeline developed for the and analysis of data 2016). contains several software and that are extremely helpful and especially to These however, require some with in the of a or For a comprehensive and to on the see the For a comprehensive and informative overview of the theory and of UCEs for phylogenomics, see Zhang et on raw reads obtained from high-throughput sequencing is a step. should be for sequence of sequences and read (i.e. base and small Several have been proposed for quality of NGS data, including for reads and for reads from all other et al., 2010). 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