Parallel sequencing lives, or what makes large sequencing projects successful.

Thomas Graf,Yasmina Cuartero,François Serra,Ralph Stadhouders,Enrique Vidal,Guillaume Filion,Javier Quilez,François Le Dily,Miguel Beato,Marc A Marti-Renom

doi:10.1093/gigascience/gix100

Thomas Graf, Yasmina Cuartero + Show 8 more

Open Access

https://doi.org/10.1093/gigascience/gix100

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Abstract

T47D_rep2 and b1913e6c1_51720e9cf were 2 Hi-C samples. They were born and processed at the same time, yet their fates were very different. The life of b1913e6c1_51720e9cf was simple and fruitful, while that of T47D_rep2 was full of accidents and sorrow. At the heart of these differences lies the fact that b1913e6c1_51720e9cf was born under a lab culture of Documentation, Automation, Traceability, and Autonomy and compliance with the FAIR Principles. Their lives are a lesson for those who wish to embark on the journey of managing high-throughput sequencing data.

Highlights

Linda worked hard to produce a Hi-C sample in T47D cells
The life of b1913e6c1 51720e9cf was simple and fruitful, while that of T47D rep2 was full of accidents and sorrow
At the heart of these differences lies the fact that b1913e6c1 51720e9cf was born under a lab culture of Documentation, Automation, Traceability, and Autonomy and compliance with the FAIR Principles

Summary

Introduction

Upon submitting the sample for sequencing, she remembered the motto of the lab: “Make DATA more FAIR (Findable, Interoperable, Accessible, Reusable).” The team had established lab-wide habits of Documentation, Automation, Traceability, and Autonomy of experimenters. Chloe examined the results in the online report she received from Paul and performed some additional analyses with an R Shiny web application to inspect the Hi-C data processed in the lab.

Results

Conclusion