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SECAPR—a bioinformatics pipeline for the rapid and user-friendly processing of targeted enriched Illumina sequences, from raw reads to alignments

  • Andermann, Tobias Gothenburg Global Biodiversity Centre, Gothenburg, Sweden
  • Cano, Ángela Department of Botany and Plant Biology, University of Geneva, Geneva, Switzerland
  • Zizka, Alexander Gothenburg Global Biodiversity Centre, Gothenburg, Sweden
  • Bacon, Christine Gothenburg Global Biodiversity Centre, Gothenburg, Sweden
  • Antonelli, Alexandre Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, United States of America
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  • 2018-7-13
Published in:
  • PeerJ. - PeerJ. - 2018, vol. 6, p. e5175
General Biochemistry, Genetics and Molecular Biology
General Neuroscience
General Agricultural and Biological Sciences
General Medicine
English Evolutionary biology has entered an era of unprecedented amounts of DNA sequence data, as new sequencing technologies such as Massive Parallel Sequencing (MPS) can generate billions of nucleotides within less than a day. The current bottleneck is how to efficiently handle, process, and analyze such large amounts of data in an automated and reproducible way. To tackle these challenges we introduce the Sequence Capture Processor (SECAPR) pipeline for processing raw sequencing data into multiple sequence alignments for downstream phylogenetic and phylogeographic analyses. SECAPR is user-friendly and we provide an exhaustive empirical data tutorial intended for users with no prior experience with analyzing MPS output. SECAPR is particularly useful for the processing of sequence capture (synonyms: target or hybrid enrichment) datasets for non-model organisms, as we demonstrate using an empirical sequence capture dataset of the palm genus Geonoma (Arecaceae). Various quality control and plotting functions help the user to decide on the most suitable settings for even challenging datasets. SECAPR is an easy-to-use, free, and versatile pipeline, aimed to enable efficient and reproducible processing of MPS data for many samples in parallel.
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  • English
Open access status
gold
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Persistent URL
https://sonar.ch/global/documents/252125
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