1. bcl2fastq Conversion Software, Combines per-cycle BCL files from a run and translates them into demultiplexed FASTQ files for downstream data analysis., \urlhttps://support.illumina.com/sequencing/sequencing_software/bcl2fastq-conversion-software.html, Accessed: 2018-1-26
2. Arteria: An automation system for a sequencing core facility, Dahlberg, Johan and Hermansson, Johan and Sturlaugsson, Steinar and Larsson, Pontus, Arteria is an automation system aimed at sequencing core facilities. It is built on existing open source technologies, with a modular design allowing for a community-driven effort to create plug-and-play micro-services. Herein we describe the Arteria system and elaborate on the underlying conceptual framework. The Arteria system breaks down into three conceptual levels; orchestration, process and execution. At the orchestration level it utilizes an event-based model of automation. It models processes, e.g. the steps involved in processing sequencing data, as workflows and executes these in a micro-service based environment. This creates a system which is both flexible and scalable. The Arteria Project code is available as open source software at http://www.github.com/arteria-project., bioRxiv, 214858, nov, 2017, en, 11
3. FastQC A Quality Control tool for High Throughput Sequence Data, \urlhttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/, Accessed: 2018-1-26
4. DNA sequencing technologies: 2006-2016, Mardis, Elaine R, McDonnell Genome Institute, Washington University, St. Louis, Missouri, USA., Recent advances in the field of genomics have largely been due to the ability to sequence DNA at increasing throughput and decreasing cost. DNA sequencing was first introduced in 1977, and next-generation sequencing technologies have been available only during the past decade, but the diverse experiments and corresponding analyses facilitated by these techniques have transformed biological and biomedical research. Here, I review developments in DNA sequencing technologies over the past 10 years and look to the future for further applications., Nat. Protoc., 12, 2, 213–218, feb, 2017, en, 2
5. Recommendations on e-infrastructures for next-generation sequencing, Spjuth, Ola and Bongcam-Rudloff, Erik and Dahlberg, Johan and Dahlö, Martin and Kallio, Aleksi and Pireddu, Luca and Vezzi, Francesco and Korpelainen, Eija, Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, P.O. Box 591, SE-75124, Sweden. ola.spjuth@farmbio.uu.se. SLU-Global Bioinformatics Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden. National Genomics Infrastructure, Science for Life Laboratory, Uppsala University, Stockholm, P.O. Box 1031, SE-17121, Sweden. Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, P.O. Box 591, SE-75124, Sweden. Science for Life Laboratory, Uppsala University, Husargatan 3, Uppsala, SE-75123, Sweden. CSC - IT Center for Science Ltd., Espoo, P.O. Box 405, FI-02101, Finland. CRS4, Polaris, Loc. Piscina Manna Ed. 1, Pula, 09010, Italy. University of Cagliari, Cagliari, 09124, Italy. Science for Life Laboratory, Stockholm University, Stockholm, SE-17121, Sweden. CSC - IT Center for Science Ltd., Espoo, P.O. Box 405, FI-02101, Finland., With ever-increasing amounts of data being produced by next-generation sequencing (NGS) experiments, the requirements placed on supporting e-infrastructures have grown. In this work, we provide recommendations based on the collective experiences from participants in the EU COST Action SeqAhead for the tasks of data preprocessing, upstream processing, data delivery, and downstream analysis, as well as long-term storage and archiving. We cover demands on computational and storage resources, networks, software stacks, automation of analysis, education, and also discuss emerging trends in the field. E-infrastructures for NGS require substantial effort to set up and maintain over time, and with sequencing technologies and best practices for data analysis evolving rapidly it is important to prioritize both processing capacity and e-infrastructure flexibility when making strategic decisions to support the data analysis demands of tomorrow. Due to increasingly demanding technical requirements we recommend that e-infrastructure development and maintenance be handled by a professional service unit, be it internal or external to the organization, and emphasis should be placed on collaboration between researchers and IT professionals., Gigascience, 5, 26, jun, 2016, Cloud computing; E-infrastructure; High-performance computing; Next-generation sequencing, en, 6