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Wiens, J.

Publications and source records attributed to Wiens, J..

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SuperCRUNCH: A toolkit for creating and manipulating supermatrices and other large phylogenetic datasets

O_LIPhylogenies with extensive taxon sampling have become indispensable for many types of ecological and evolutionary studies. Many large-scale trees are based on a "supermatrix" approach, which involves amalgamating thousands of published sequences for a group. Constructing up-to-date supermatrices can be challenging, especially as new sequences may become available almost constantly. Additionally, genomic datasets (composed of thousands of loci) are becoming common in phylogenetics and phylogeography, and present novel challenges for constructing such datasets. C_LIO_LIHere we present SuperCRUNCH, a Python toolkit for assembling large phylogenetic datasets. It can be applied to GenBank sequences, unpublished sequences, or combinations of GenBank and unpublished data. SuperCRUNCH constructs local databases and uses them to conduct rapid searches for user-specified sets of taxa and loci. Sequences are parsed into putative loci and passed through rigorous filtering steps. A post-filtering step allows for selection of one sequence per taxon (i.e. species-level supermatrix) or retention of all sequences per taxon (i.e. population-level dataset). Importantly, SuperCRUNCH can generate "vouchered" population-level datasets, in which voucher information is used to generate multi-locus phylogeographic datasets. SuperCRUNCH offers many options for taxonomy resolution, similarity filtering, sequence selection, alignment, and file manipulation. C_LIO_LIWe demonstrate the range of features available in SuperCRUNCH by generating a variety of phylogenetic datasets. Output datasets include traditional species-level supermatrices, large-scale phylogenomic matrices, and phylogeographic datasets. Finally, we briefly compare the ability of SuperCRUNCH to construct species-level supermatrices to alternative approaches. SuperCRUNCH generated a large-scale supermatrix (1,400 taxa and 66 loci) from 16GB of GenBank data in [~]1.5 hours, and generated population-level datasets (<350 samples, <10 loci) in <1 minute. It outperformed alternative methods for supermatrix construction in terms of taxa, loci, and sequences recovered. C_LIO_LISuperCRUNCH is a modular bioinformatics toolkit that can be used to assemble datasets for any taxonomic group and scale (kingdoms to individuals). It allows rapid construction of supermatrices, greatly simplifying the process of updating large phylogenies with new data. It is also designed to produce population-level datasets. SuperCRUNCH streamlines the major tasks required to process phylogenetic data, including filtering, alignment, trimming, and formatting. SuperCRUNCH is open-source, documented, and available at https://github.com/dportik/SuperCRUNCH. C_LI

bioinformatics

Observing the origins of human brain development: Automated processing of fetal fMRI

AO_SCPLOWBSTRACTC_SCPLOWFetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite rapid and widespread growth of this approach, at present we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses (gestational age 24-39 weeks, M=30.9, SD=4.2), we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open source workflow for fetal functional MRI data preprocessing.

neuroscience