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Webinar: ASHG 2019 MGI Education Session


CoolMPSTM and the non-coding Alzheimer’s transcriptome: A 10-billion read puzzle

Dr. Andreas Keller

Department of Neurology and Neurological Sciences, Stanford University

Chair for Clinical Bioinformatics, Saarland University

Dr. Keller, who is doing research at Saarland and Stanford University is trying to understand how Alzheimer’s and Parkinson’s develop in the human body at small non coding RNA(sncRNA) level. Dr. Keller shows results of his research using alpha version of CoolMPS sequencing chemistry, where he finds CoolMPS provides a higher Q30 value -- the quality for each single base is much higher and the likelihood of wrong base calling is much lower in the new chemistry. High correlation has also been observed between the CoolMPS chemistry and existing data, which enables him to conduct quantitative analysis of new sncRNAs.

Multiplexed Single Cell Genomics with MGI CoolMPSTM Sequencing Chemistry

Ms. Gracie Gordon

PhD candidate, Biological and Medical Informatics Graduate Program,

Department of Medicine and Institute for Human Genetics, UCSF

Multiplexed single cell genomics has been used to increase the throughput of droplet based single cell assays enabling cost effective library preparation of hundreds of thousands to millions of single cells. Multiplexing can be performed leveraging natural genetic variants from genetically distinct individuals or cell hashing based on antibody staining or lipid labels to assign single cells to their donor. The success of these methods hinges on the quality of the next generation sequencing data. To assess the feasibility of performing multiplexed single cell genomics with coolMPS sequencing we prepared primary peripheral mononuclear cells (PBMCs) from 3 healthy individuals, and performed CITE-seq (Abseq, BD), cell hashing (TotalSeq-A, BioLegend), and RNA-sequencing using 10X Genomics’ 3’v3 kit solution. The library was sequenced with both an Illumina Novaseq 6000 system and an early alpha version of the coolMPS sequencing kit in development at MGI. Here, we present comparative data on sequencing quality metrics, cell calling, clustering, and cell classification.

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