Journal:STATegra EMS: An experiment management system for complex next-generation omics experiments

Full article title	STATegra EMS: An experiment management system for complex next-generation omics experiments
Journal	BMC Systems Biology
Author(s)	Hernández-de-Diego, R.; Boix-Chova, N.; Gómez-Cabrero, D.; Tegner, J.; Abugessaisa, I.; Conesa, A.
Author affiliation(s)	Centro de Investigación Príncipe Felipe and the Karolinska Institute
Primary contact	None given
Year published	2014
Volume and issue	8(Suppl 2)
Page(s)	S9
DOI	10.1186/1752-0509-8-S2-S9
ISSN	1752-0509
Distribution license	Creative Commons Attribution 2.0 Generic
Website	http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S9
Download	http://bmcsystbiol.biomedcentral.com/track/pdf/10.1186/1752-0509-8-S2-S9 (PDF)

Abstract

High-throughput sequencing assays are now routinely used to study different aspects of genome organization. As decreasing costs and widespread availability of sequencing enable more laboratories to use sequencing assays in their research projects, the number of samples and replicates in these experiments can quickly grow to several dozens of samples and thus require standardized annotation, storage and management of preprocessing steps. As a part of the STATegra project, we have developed an Experiment Management System (EMS) for high throughput omics data that supports different types of sequencing-based assays such as RNA-seq, ChIP-seq, Methyl-seq, etc, as well as proteomics and metabolomics data. The STATegra EMS provides metadata annotation of experimental design, samples and processing pipelines, as well as storage of different types of data files, from raw data to ready-to-use measurements. The system has been developed to provide research laboratories with a freely-available, integrated system that offers a simple and effective way for experiment annotation and tracking of analysis procedures.

Background

The widespread availability of high-throughput sequencing techniques have importantly impacted genome research and reshaped the way we study genome function and structure. The rapidly decreasing costs of sequencing make these technologies affordable to small and medium size laboratories. Furthermore, the constant development of novel sequencing based assays, coined with the suffix -seq, expands the scope of cellular properties analyzable by high-throughput sequencing, with sequencing reads forming an underlying common data format. Today, virtually all nucleic acid omics methods traditionally based on microarrays have a -seq counterpart and many more have been made available recently. As a consequence, the possibility of running multiple sequencing-based experiments to measure different aspects of gene regulation and combining these with non-sequencing omics technologies such as proteomics and metabolomics has become practical. For example, the ENCODE project combined ten major types of sequencing-based assays to unravel the complexity of genome architecture. Many records can be found at the SRA archive that integrate multiple -seq technologies measured on the same samples and a PubMed search for NGS plus proteomics or metabolomics results in over hundred entries. Last but not least, one of the advantages of sequenced-based experiments is that they are equally applicable to the study of well-annotated model organisms as well as less-studied non-model organisms since little or no a priori genome knowledge is required.

Moreover, sequencing-based assays come with new challenges for data processing and storage. The memory size requirements of a medium sequencing experiments exceeds the capacity of current regular workstations. At the same time, the analysis steps to go from raw to processed data are more complex and resource intensive. As the number of datasets grows, the need to properly store and track the data and their associated metadata becomes more pressing. For example, a medium-sized RNA-seq experiments ranging between 4 to 20 samples of 20 million reads each can take up to 40 GB of raw data and generate multiple Quality Control and intermediate processing step files of up to 200 to 500 GB. Laboratory Information and Management Systems (LIMS) or Sample Management Systems (SMS) are bioinformatics tools that aid experimentalists to organize samples and experimental procedures in a controlled and annotated fashion. There are several commercial and free dedicated LIMS that have been developed specifically for genotyping labs where thousand of samples are processed by automated pipelines and procedures are tightly standardized. One popular LIMS for genomics is BASE. This software includes a highly structured system for metadata annotation and a flexible architecture for defining experiments and incorporating analysis modules. However, BASE is currently restricted to the annotation of microarray experiments.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.