RESUMO
Summary: ExRec (Exclusion of Recombined DNA) is a dependency-free Python pipeline that implements the four-gamete test to automatically filter out recombined DNA blocks from thousands of DNA sequence loci. This procedure helps all loci better meet the "no intralocus recombination" assumption common to many coalescent-based analyses in population genomic, phylogeographic, and shallow-scale phylogenomic studies. The user-friendly pipeline contains five standalone applications-four file conversion scripts and one main script that performs the recombination filtering procedures. The pipeline outputs recombination-filtered data in a variety of common formats and a tab-delimited table that displays descriptive statistics for all loci and the analysis results. A novel feature of this software is that the user can select whether to output the longest nonrecombined sequence blocks from recombined loci (current best practice) or randomly select nonrecombined blocks from loci (a newer approach). We tested ExRec with six published phylogenomic datasets that ranged in size from 27 to 2237 loci and came in a variety of input file formats. In all trials the data could be easily analyzed in only seconds for the smaller datasets and <30 min for the largest using a simple laptop computer. Availability and implementation: ExRec was written in Python 3 under the MIT license. The program applications, user manual (including step-by-step tutorials), and sample data are freely available at https://github.com/Sammccarthypotter/ExRec.
RESUMO
Collagen fibers are the primary structural elements that define many soft-tissue structure and mechanical function relationships, so that quantification of collagen organization is essential to many disciplines. Current tissue-level collagen fiber imaging techniques remain limited in their ability to quantify fiber organization at macroscopic spatial scales and multiple time points, especially in a non-contacting manner, requiring no modifications to the tissue, and in near real-time. Our group has previously developed polarized spatial frequency domain imaging (pSFDI), a reflectance imaging technique that rapidly and non-destructively quantifies planar collagen fiber orientation in superficial layers of soft tissues over large fields-of-view. In this current work, we extend the light scattering models and image processing techniques to extract a critical measure of the degree of collagen fiber alignment, the normalized orientation index (NOI), directly from pSFDI data. Electrospun fiber samples with architectures similar to many collagenous soft tissues and known NOI were used for validation. An inverse model was then used to extract NOI from pSFDI measurements of aortic heart valve leaflets and clearly demonstrated changes in degree of fiber alignment between opposing sides of the sample. These results show that our model was capable of extracting absolute measures of degree of fiber alignment in superficial layers of heart valve leaflets with only general a priori knowledge of fiber properties, providing a novel approach to rapid, non-destructive study of microstructure in heart valve leaflets using a reflectance geometry.
