More Like this

1. TaxonWorks as a Tool for Managing Large Biodiversity Projects

   https://doi.org/10.3897/biss.6.93668  

   Dmitriev, Dmitry ; Cao, Yanghui ; Dietrich, Christopher H. ( August 2022 , Biodiversity Information Science and Standards)

   Large systematic revisionary projects incorporating data for hundreds or thousands of taxa require an integrative approach, with a strong biodiversity-informatics core for efficient data management to facilitate research on the group. Our original biodiversity informatics platform, 3i (Internet-accessible Interactive Identification) combined a customized MS Access database backend with ASP-based web interfaces to support revisionary syntheses of several large genera of leafhopers (Hemiptera: Auchenorrhyncha: Cicadellidae). More recently, for our National Science Foundation sponsored project, “GoLife: Collaborative Research: Integrative genealogy, ecology and phenomics of deltocephaline leafhoppers (Hemiptera: Cicadellidae), and their microbial associates”, we selected the new open-source platform TaxonWorks as the cyberinfrastructure. In the scope of the project, the original “3i World Auchenorrhyncha Database” was imported into TaxonWorks. At the present time, TaxonWorks has many tools to automatically import nomenclature, citations, and specimen based collection data. At the time of the initial migration of the 3i database, many of those tools were still under development, and complexity of the data in the database required a custom migration script, which is still probably the most efficient solution for importing datasets with long development history. At the moment, the World Auchenorrhyncha Database comprehensively covers nomenclature of the group and includes data on 70 valid families, 6,816 valid genera, 47,064 valid species as well as synonymy and subsequent combinations (Fig. 1). In addition, many taxon records include the original citation, bibliography, type information, etymology, etc. The bibliography of the group includes 37,579 sources, about 1/3 of which are associated with PDF files. Species have distribution records, either derived from individual specimens or as country and state level asserted distribution, as well as biological associations indicating host plants, predators, and parasitoids. Observation matrices in TaxonWorks are designed to handle morphological data associated with taxa or specimens. The matrices may be used to automatically generate interactive identification keys and taxon descriptions. They can also be downloaded to be imported, for example, into Lucid builder, or to perform phylogenetic analysis using an external application. At the moment there are 36 matrices associated with the project. The observation matrix from GoLife project covers 798 taxa by 210 descriptors (most of which are qualitative multi-state morphological descriptors) (Fig. 2). Illustrations are provided for 9,886 taxa and organized in the specialized image matrix and could be used as a pictorial key for determination of species and taxa of a higher rank. For the phylogenetic analysis, a dataset was constructed for 730 terminal taxa and >160,000 nucleotide positions obtained using anchored hybrid enrichment of genomic DNA for a sample of leafhoppers from the subfamily Deltocephalinae and outgroups. The probe kit targets leafhopper genes, as well as some bacterial genes (endosymbionts and plant pathogens transmitted by leafhoppers). The maximum likelihood analyses of concatenated nucleotide and amino acid sequences as well as coalescent gene tree analysis yielded well-resolved phylogenetic trees (Cao et al. 2022). Raw sequence data have been uploaded to the Sequence Read Archive on GenBank. Occurrence and morphological data, as well as diagnostic images, for voucher specimens have been incorporated into TaxonWorks. Data in TaxonWorks could be exported in raw format, get accessed via Application Programming Interface (API), or be shared with external data aggregators like Catalogue of Life, GBIF, iDigBio. 

   more » « less
2. ClonArch: visualizing the spatial clonal architecture of tumors

   https://doi.org/10.1093/bioinformatics/btaa471  

   Wu, Jiaqi ; El-Kebir, Mohammed ( July 2020 , Bioinformatics)

   null (Ed.)

   Abstract Motivation Cancer is caused by the accumulation of somatic mutations that lead to the formation of distinct populations of cells, called clones. The resulting clonal architecture is the main cause of relapse and resistance to treatment. With decreasing costs in DNA sequencing technology, rich cancer genomics datasets with many spatial sequencing samples are becoming increasingly available, enabling the inference of high-resolution tumor clones and prevalences across different spatial coordinates. While temporal and phylogenetic aspects of tumor evolution, such as clonal evolution over time and clonal response to treatment, are commonly visualized in various clonal evolution diagrams, visual analytics methods that reveal the spatial clonal architecture are missing. Results This article introduces ClonArch, a web-based tool to interactively visualize the phylogenetic tree and spatial distribution of clones in a single tumor mass. ClonArch uses the marching squares algorithm to draw closed boundaries representing the presence of clones in a real or simulated tumor. ClonArch enables researchers to examine the spatial clonal architecture of a subset of relevant mutations at different prevalence thresholds and across multiple phylogenetic trees. In addition to simulated tumors with varying number of biopsies, we demonstrate the use of ClonArch on a hepatocellular carcinoma tumor with ∼280 sequencing biopsies. ClonArch provides an automated way to interactively examine the spatial clonal architecture of a tumor, facilitating clinical and biological interpretations of the spatial aspects of intra-tumor heterogeneity. Availability and implementation https://github.com/elkebir-group/ClonArch. 

   more » « less
3. A weighted distance-based approach for deriving consensus tumor evolutionary trees

   https://doi.org/10.1093/bioinformatics/btad230  

   Guang, Ziyun ; Smith-Erb, Matthew ; Oesper, Layla ( June 2023 , Bioinformatics)

   The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently.

   On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified.

   An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.

    

   more » « less
4. Multiple historical processes obscure phylogenetic relationships in a taxonomically difficult group (Lobariaceae, Ascomycota)

   https://doi.org/10.1038/s41598-019-45455-x  

   Widhelm, Todd J. ; Grewe, Felix ; Huang, Jen-Pan ; Mercado-Díaz, Joel A. ; Goffinet, Bernard ; Lücking, Robert ; Moncada, Bibiana ; Mason-Gamer, Roberta ; Lumbsch, H. Thorsten ( June 2019 , Scientific Reports)

   In the age of next-generation sequencing, the number of loci available for phylogenetic analyses has increased by orders of magnitude. But despite this dramatic increase in the amount of data, some phylogenomic studies have revealed rampant gene-tree discordance that can be caused by many historical processes, such as rapid diversification, gene duplication, or reticulate evolution. We used a target enrichment approach to sample 400 single-copy nuclear genes and estimate the phylogenetic relationships of 13 genera in the lichen-forming family Lobariaceae to address the effect of data type (nucleotides and amino acids) and phylogenetic reconstruction method (concatenation and species tree approaches). Furthermore, we examined datasets for evidence of historical processes, such as rapid diversification and reticulate evolution. We found incongruence associated with sequence data types (nucleotide vs. amino acid sequences) and with different methods of phylogenetic reconstruction (species tree vs. concatenation). The resulting phylogenetic trees provided evidence for rapid and reticulate evolution based on extremely short branches in the backbone of the phylogenies. The observed rapid and reticulate diversifications may explain conflicts among gene trees and the challenges to resolving evolutionary relationships. Based on divergence times, the diversification at the backbone occurred near the Cretaceous-Paleogene (K-Pg) boundary (65 Mya) which is consistent with other rapid diversifications in the tree of life. Although some phylogenetic relationships within the Lobariaceae family remain with low support, even with our powerful phylogenomic dataset of up to 376 genes, our use of target-capturing data allowed for the novel exploration of the mechanisms underlying phylogenetic and systematic incongruence.

    

   more » « less
5. TopHap: rapid inference of key phylogenetic structures from common haplotypes in large genome collections with limited diversity

   https://doi.org/10.1093/bioinformatics/btac186  

   Caraballo-Ortiz, Marcos A. ; Miura, Sayaka ; Sanderford, Maxwell ; Dolker, Tenzin ; Tao, Qiqing ; Weaver, Steven ; Pond, Sergei L. K. ; Kumar, Sudhir ; Schwartz, ed., Russell ( March 2022 , Bioinformatics)

   Building reliable phylogenies from very large collections of sequences with a limited number of phylogenetically informative sites is challenging because sequencing errors and recurrent/backward mutations interfere with the phylogenetic signal, confounding true evolutionary relationships. Massive global efforts of sequencing genomes and reconstructing the phylogeny of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains exemplify these difficulties since there are only hundreds of phylogenetically informative sites but millions of genomes. For such datasets, we set out to develop a method for building the phylogenetic tree of genomic haplotypes consisting of positions harboring common variants to improve the signal-to-noise ratio for more accurate and fast phylogenetic inference of resolvable phylogenetic features.

   We present the TopHap approach that determines spatiotemporally common haplotypes of common variants and builds their phylogeny at a fraction of the computational time of traditional methods. We develop a bootstrap strategy that resamples genomes spatiotemporally to assess topological robustness. The application of TopHap to build a phylogeny of 68 057 SARS-CoV-2 genomes (68KG) from the first year of the pandemic produced an evolutionary tree of major SARS-CoV-2 haplotypes. This phylogeny is concordant with the mutation tree inferred using the co-occurrence pattern of mutations and recovers key phylogenetic relationships from more traditional analyses. We also evaluated alternative roots of the SARS-CoV-2 phylogeny and found that the earliest sampled genomes in 2019 likely evolved by four mutations of the most recent common ancestor of all SARS-CoV-2 genomes. An application of TopHap to more than 1 million SARS-CoV-2 genomes reconstructed the most comprehensive evolutionary relationships of major variants, which confirmed the 68KG phylogeny and provided evolutionary origins of major and recent variants of concern.

   TopHap is available at https://github.com/SayakaMiura/TopHap.

   Supplementary data are available at Bioinformatics online.

    

   more » « less