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Matthew L Settles

Publications and source records attributed to Matthew L Settles.

2 recordsLinked to original sources

A targeted subgenomic approach for phylogenomics based on microfluidic PCR and high throughput sequencing

Advances in high-throughput sequencing (HTS) have allowed researchers to obtain large amounts of biological sequence information at speeds and costs unimaginable only a decade ago. Phylogenetics, and the study of evolution in general, is quickly migrating towards using HTS to generate larger and more complex molecular datasets. In this paper, we present a method that utilizes microfluidic PCR and HTS to generate large amounts of sequence data suitable for phylogenetic analyses. The approach uses a Fluidigm microfluidic PCR array and two sets of PCR primers to simultaneously amplify 48 target regions across 48 samples, incorporating sample-specific barcodes and HTS adapters (2,304 unique amplicons per microfluidic array). The final product is a pooled set of amplicons ready to be sequenced, and thus, there is no need to construct separate, costly genomic libraries for each sample. Further, we present a bioinformatics pipeline to process the raw HTS reads to either generate consensus sequences (with or without ambiguities) for every locus in every sample or--more importantly--recover the separate alleles from heterozygous target regions in each sample. This is important because it adds allelic information that is well suited for coalescent-based phylogenetic analyses that are becoming very common in conservation and evolutionary biology. To test our subgenomic method and bioinformatics pipeline, we sequenced 576 samples across 96 target regions belonging to the South American clade of the genus Bartsia L. in the plant family Orobanchaceae. After sequencing cleanup and alignment, the experiment resulted in [~]25,300bp across 486 samples for a set of 48 primer pairs targeting the plastome, and [~]13,500bp for 363 samples for a set of primers targeting regions in the nuclear genome. Finally, we constructed a combined concatenated matrix from all 96 primer combinations, resulting in a combined aligned length of [~]40,500bp for 349 samples.

Evolutionary Biology

Assembly by Reduced Complexity (ARC): a hybrid approach for targeted assembly of homologous sequences.

Analysis of High-throughput sequencing (HTS) data is a difficult problem, especially in the context of non-model organisms where comparison of homologous sequences may be hindered by the lack of a close reference genome. Current mapping-based methods rely on the availability of a highly similar reference sequence, whereas de novo assemblies produce anonymous (unannotated) contigs that are not easily compared across samples. Here, we present Assembly by Reduced Complexity (ARC) a hybrid mapping and assembly approach for targeted assembly of homologous sequences. ARC is an open-source project (http://ibest.github.io/ARC/) implemented in the Python language and consists of the following stages: 1) align sequence reads to reference targets, 2) use alignment results to distribute reads into target specific bins, 3) perform assemblies for each bin (target) to produce contigs, and 4) replace previous reference targets with assembled contigs and iterate. We show that ARC is able to assemble high quality, unbiased mitochondrial genomes seeded from 11 progressively divergent references, and is able to assemble full mitochondrial genomes starting from short, poor quality ancient DNA reads. We also show ARC compares favorably to de novo assembly of a large exome capture dataset for CPU and memory requirements; assembling 7,627 individual targets across 55 samples, completing over 1.3 million assemblies in less than 78 hours, while using under 32 Gb of system memory. ARC breaks the assembly problem down into many smaller problems, solving the anonymous contig and poor scaling inherent in some de novo assembly methods and reference bias inherent in traditional read mapping.

Bioinformatics