Multi-omics Data

Here we discuss how to register multi-omics datasets by looking at a few examples.

As you will see, the multi-omics nature of the data implies that a common ancestor sample exists; which must be reflected when importing the datasets into LabID.

The same cell(s) is interogated by different sequencing technologies

In this first use case, cells (a unique sample) are subjected to different sequencing experiment types, using different sequencing technologies (Illumina, Nanopore).

From the same cell, both the total RNA and the chromatin are extracted.

The RNA fraction is then sequenced using the Nanopore technology to gain precise information at the isoform level.

The chromatin fraction is split in halves to prepare two different Illumina libraries:

• one DNA-seq that will be further used to locate SNPs of this particular cell line
• one ChIP-seq library against Histone's K4 tri-methylation to identify active genes

The overall experimental design is depicted in the figure below.

Multi-omics diagram

Here, the multi-omics dimension relies on the proper modeling of the sample inter-connections. LabID allows you to fully replicate this exact lineage.

Correlative microscopy

In this use case, light microscopy is first performed to locate region of interests (ROIs). The sample is then further prepared to operate volume Electron Microscopy.

Correlative Microscopy diagram

As in the first use case, replicating the experiment design is key to anchor the light microscopy and the electron microscopy to a common sample ancestor. In addition, a direct link from the light dataset to the EM dataset is needed to clearly identify the ROI imaged in the volume EM ; precise ROI coordinate in the light microscopy image can even be added as dataset's annotations.