HaTSPiL: A modular pipeline for high-throughput sequencing data analysis

Edoardo Morandi, Matteo Cereda, Danny Incarnato, Caterina Parlato, Giulia Basile, Francesca Anselmi, Andrea Lauria, Lisa Marie Simon, Isabelle Laurence Polignano, Francesca Arruga, Silvia Deaglio, Elisa Tirtei, Franca Fagioli and Salvatore Oliviero

PLOS ONE, 2019, vol. 14, issue 10, 1-9

Abstract: Background: Next generation sequencing methods are widely adopted for a large amount of scientific purposes, from pure research to health-related studies. The decreasing costs per analysis led to big amounts of generated data and to the subsequent improvement of software for the respective analyses. As a consequence, many approaches have been developed to chain different software in order to obtain reliable and reproducible workflows. However, the large range of applications for NGS approaches entails the challenge to manage many different workflows without losing reliability. Methods: We here present a high-throughput sequencing pipeline (HaTSPiL), a Python-powered CLI tool designed to handle different approaches for data analysis with a high level of reliability. The software relies on the barcoding of filenames using a human readable naming convention that contains any information regarding the sample needed by the software to automatically choose different workflows and parameters. HaTSPiL is highly modular and customisable, allowing the users to extend its features for any specific need. Conclusions: HaTSPiL is licensed as Free Software under the MIT license and it is available at https://github.com/dodomorandi/hatspil.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0222512

DOI: 10.1371/journal.pone.0222512

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