We previously described CATCH, a computational method that designs probe sets for enriching genomes from diverse taxa prior to sequencing (Metsky et al. 2019). A recent paper in Bioinformatics describes a related method, Syotti, and compares it to CATCH (Alanko et al. 2022). Methodological developments are needed in the field, and we are excited to see a focus on them. Yet, in their benchmarking, Syotti’s authors made five decisions that go counter to a fair comparison and paint a misleading picture of CATCH’s performance. To uphold benchmarking best practices for the field and ensure that future users are not misguided in using CATCH, we describe the five decisions below. Decisions (1)–(3) involve measuring runtime. Decisions (4) and (5) involve analyzing output. The authors in Alanko et al. (2022) emphasize that CATCH’s runtime is impractical and scales poorly with large input. Under different benchmarking decisions—that, instead, are true to how CATCH is typically run—CATCH is orders of magnitude faster than reported (Fig. 1a). In practice, these decisions can make a difference between several months or several hours of runtime. Three decisions allow for the authors’ conclusions about runtime: User-specified arguments affect the runtime of CATCH as well as comparisons of coverage and probe counts. (a) The runtime of CATCH v1.4.0—available 11 months before Syotti was described in a preprint—when run with increasingly large input samplings. The input is the viral dataset used in Syotti’s benchmarking (Alanko et al. 2022), which we had previously constructed and made available; it contains 588 viral species. Colors indicate a manner of running CATCH: purple indicates using no recommended options, and blue and green indicate using options recommended in CATCH’s publication, README, and help messages. Yellow indicates using viral species labels provided in the data, by handling species independently and pooling probes across them (pooling has little effect on the number of probes owing to low whole-genome homology across species; Figure S3). Solid/dashed line types indicate the probe-target divergence (number of mismatches) tolerated in the design; CATCH exhibits lower runtimes at lower values of this parameter, which contrasts with Syotti’s behavior (see (b)). Syotti’s runtime benchmarking (Alanko et al. 2022) runs CATCH as shown by the dashed purple line, which uses no recommended options and 40 mismatches. The jagged pattern of the lines results in part from following the input sampling strategy used in Syotti’s benchmarking (Alanko et al. 2022), in which entire groups of species are successively introduced into the input. ∼1.26 × 109 nt of input is the full viral dataset; lines that end before that input size represent runs of CATCH that were terminated early owing to their expected runtime. “Filter with LSH” refers to the argument ′--filter-with-lsh-minhash 0.6′; “cluster” refers to the arguments ′--cluster-and-design-separately 0.15 --cluster-from-fragments 50000′; and “Pool across species” refers to leveraging taxon labels to design independently on viral species. (b) Runtime of Syotti (top) and CATCH (middle and bottom) when run with different values for the parameter governing the number of mismatches to tolerate between a probe and target, that is, the allowed probe-target divergence. Syotti’s runtime benchmarking (Alanko et al. 2022) sets the value to 40 mismatches for both tools. (c) Left: coverage of targeted genome sequences, as a percentage of all nucleotides, achieved by probe sets designed with Syotti and CATCH, as computed by Syotti’s coverage analyzer. Horizontal axis is the allowed probe-target divergence for probe design, and the divergence is checked when computing coverage. In all cases, coverage is >99%. Right: Number of probes in the output of Syotti and CATCH. Brown indicates a recommended option (′-e 50′), typically set in practice; it models capture by assuming that hybridization captures the portion of a DNA fragment overhanging where the probe hybridizes (i.e. the full fragment), not only the portion of the fragment hybridized to the probe. (Coverage, in the left panel, is not shown using this option because Syotti’s coverage analysis program does not consider fragment overhangs.) In (b) and (c), analyses use an input of 256 viral species (5.22 × 108 nt). The benchmarking disregards CATCH’s options that substantially lower runtime and memory usage on large inputs. Those options are recommended in CATCH’s publication, README, and help messages specifically for that purpose. Turning on those options dramatically decreases CATCH’s runtime and improves its scalability (Fig. 1a and Supplementary Figs S1 and S2). Their trade-off is an increase in the number of probes; since this increase is small (<10% for large input; Supplementary Fig. S3), we often use these recommended options in practice. The benchmarking sets a critical design parameter, the allowed probe-target divergence, to a single unrepresentative value, where Syotti runs relatively fast while CATCH runs relatively slow. The chosen value is higher than the probe-target divergence at which there is a well-established reduction in probe-based enrichment efficiency, and considerably higher than other groups and we have ever allowed in practice, or ever considered allowing. When setting this divergence parameter to lower values that are more relevant in practice, CATCH runs much faster (Fig. 1b); Syotti displays the opposite behavior with this parameter, exhibiting longer runtimes at lower values (Fig. 1b). The benchmarking runs CATCH in a different manner on viruses—the data on which it shows CATCH having the worst performance—than we have described. CATCH benefits from viral species labels that are typically available in datasets, including the one used in Alanko et al. (2022). The benchmarking removes species labels and takes a subroutine intended for one species—which we describe and illustrate in our publication as applied to one species—and applies it to many species at once. That choice dramatically affects CATCH’s runtime (Figure 1 and Supplementary Fig. S1) while making little difference to the output’s optimality (Supplementary Fig. S3). The authors in Alanko et al. (2022) report that Syotti provides more favorable output than CATCH. They conclude, erroneously, that Syotti yields (i) more coverage of targeted genomes and (ii) fewer probes than CATCH. Two decisions allow for those conclusions: The benchmarking selects different design parameters for probes designed with CATCH versus Syotti, but evaluates the probes as if designed with Syotti’s parameters. Specifically, it compares CATCH’s probes, designed using relaxed hybridization criteria, to Syotti’s probes, designed using relatively strict criteria. The evaluation decides which genomic regions are covered according to the criteria given to Syotti. Syotti’s probes always meet that criteria, whereas CATCH’s probes, by design, do not—thus, affording Syotti’s probes more genomic coverage. The evaluation, therefore, concludes mistakenly that CATCH yields designs with low coverage, for example, 84% coverage in one comparison (Alanko et al. 2022). If CATCH were given the same parameter values as those given to Syotti—as it could and should have been in such a comparison—the evaluation would find that the methods yield comparable coverage (both >99% coverage; Fig. 1c, left). The benchmarking, when comparing the number of output probes, does not use an option in CATCH that reduces that number by about 30%–80%. We typically use this option in practice, including in Metsky et al. (2019), as do other users of CATCH. With this option, CATCH designs a probe set with 36% fewer probes than Syotti (Fig. 1c, right). We also emphasize that, even without applying this option, Alanko et al. (2022)’s conclusion that Syotti outputs fewer probes than CATCH is well substantiated by only one of the three datasets analyzed. Supplementary Text provides further details about each of the above decisions and their effects on benchmarking. Informed by this experience, we expanded CATCH to enhance its usability when it is run in a manner that overlooks or runs counter to our public recommendations. We introduced a new command, in CATCH v1.5.0, that makes our publicly recommended options the default settings, thus providing a command for users with limited familiarity with CATCH (Figure S1). We hope that CATCH—with its 5200 lines of code documentation, 308 automated tests, modular implementation, and automated access to NCBI viral genomic data—can continue to grow while providing reliable output. We also look forward to seeing Syotti and other methods continue to develop, and hope that we can all work to fairly compare our results in the service of advancing science. H.C.M., C.B.M., and P.C.S. are co-inventors on a patent (US 11,332,783 B2) related to CATCH. P.C.S. is a co-founder and equity holder in Delve Biosciences, a co-founder and equity holder of Lyra Labs, and an equity holder and former member of the Board of Directors of Danaher Corporation. P.C.S no longer holds a financial interest in Sherlock Biosciences. All potential conflicts are managed in accordance with institutional policy. None declared. No new data were generated.

SUMMARY: In viral genomic research and surveillance, inter-sample contamination can affect variant detection, analysis of within-host evolution, outbreak reconstruction, and detection of superinfections and recombination events. While sample barcoding methods exist to track inter-sample contamination, they are not always used and can only detect contamination in the experimental pipeline from the point they are added. The underlying genomic information in a sample, however, carries information about inter-sample contamination occurring at any stage. Here, we present Polyphonia, a tool for detecting inter-sample contamination directly from deep sequencing data without the need for additional controls, using intrahost variant frequencies. We apply Polyphonia to 1102 SARS-CoV-2 samples sequenced at the Broad Institute and already tracked using molecular barcoding for comparison. AVAILABILITY AND IMPLEMENTATION: Polyphonia is available as a standalone Docker image and is also included as part of viral-ngs, available in Dockstore. Full documentation, source code, and instructions for use are available at https://github.com/broadinstitute/polyphonia.

Given a high-dimensional data set, we often wish to find the strongest relationships within it. A common strategy is to evaluate a measure of dependence on every variable pair and retain the highest-scoring pairs for follow-up. This strategy works well if the statistic used (a) has good power to detect non-trivial relationships, and (b) is equitable, meaning that for some measure of noise it assigns similar scores to equally noisy relationships regardless of relationship type (e.g., linear, exponential, periodic). In this paper, we define and theoretically characterize two new statistics that together yield an efficient approach for obtaining both power and equitability. To do this, we first introduce a new population measure of dependence and show three equivalent ways that it can be viewed, including as a canonical smoothing of mutual information. We then introduce an efficiently computable consistent estimator of our population measure of dependence, and we empirically establish its equitability on a large class of noisy functional relationships. This new statistic has better bias/variance properties and better runtime complexity than a previous heuristic approach. Next, we derive a second, related statistic whose computation is a trivial side-product of our algorithm and whose goal is powerful independence testing rather than equitability. We prove that this statistic yields a consistent independence test and show in simulations that the test has good power against independence. Taken together, our results suggest that these two statistics are a valuable pair of tools for exploratory data analysis.

UNLABELLED: Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains and three-dimensional protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology 2016, Orlando, FL). CONTACT: [email protected] or [email protected].

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research.As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling.Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database.Taken together, such computational efforts could significantly accelerate traditional scientific approaches.In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus.ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology [ISMB] 2016, Orlando, Florida).

MOTIVATION: Efficient simulation of population genetic samples under a given demographic model is a prerequisite for many analyses. Coalescent theory provides an efficient framework for such simulations, but simulating longer regions and higher recombination rates remains challenging. Simulators based on a Markovian approximation to the coalescent scale well, but do not support simulation of selection. Gene conversion is not supported by any published coalescent simulators that support selection. RESULTS: We describe cosi2, an efficient simulator that supports both exact and approximate coalescent simulation with positive selection. cosi2 improves on the speed of existing exact simulators, and permits further speedup in approximate mode while retaining support for selection. cosi2 supports a wide range of demographic scenarios, including recombination hot spots, gene conversion, population size changes, population structure and migration. cosi2 implements coalescent machinery efficiently by tracking only a small subset of the Ancestral Recombination Graph, sampling only relevant recombination events, and using augmented skip lists to represent tracked genetic segments. To preserve support for selection in approximate mode, the Markov approximation is implemented not by moving along the chromosome but by performing a standard backwards-in-time coalescent simulation while restricting coalescence to node pairs with overlapping or near-overlapping genetic material. We describe the algorithms used by cosi2 and present comparisons with existing selection simulators. AVAILABILITY AND IMPLEMENTATION: A free C++ implementation of cosi2 is available at http://broadinstitute.org/mpg/cosi2.