SARS-CoV-2 Spike site-specific N-linked glycan analysis 3 Biological Replicates
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Orbitrap Fusion
ORGANISM(S): Homo Sapiens
SUBMITTER: 
Max Crispin
PROVIDER: MSV000087279 | MassIVE | Thu Apr 22 08:16:00 BST 2021
REPOSITORIES: MassIVE
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Project description:BackgroundNext-generation sequencing (NGS) technologies offer fast and inexpensive identification of DNA sequences. Somatic sequencing is among the primary applications of NGS, where acquired (non-inherited) variants are based on comparing diseased and healthy tissues from the same individual. Somatic mutations in genetic diseases such as cancer are tightly associated with genomic instability. Genomic instability increases heterogenity, complicating sequencing efforts further, a task already challenged by the presence of short reads and repetitions in human DNA. This leads to low concordance among studies and limits reproducibility. This limitation is a significant problem since identified mutations in somatic sequencing are major biomarkers for diagnosis and the primary input of targeted therapies. Benchmarking studies were conducted to assess the error rates and increase reproducibility. Unfortunately, the number of somatic benchmarking sets is very limited due to difficulties in validating true somatic variants. Moreover, most NGS benchmarking studies are based on relatively simpler germline (inherited) sequencing. Recently, a comprehensive somatic sequencing benchmarking set was published by Sequencing Quality Control Phase 2 (SEQC2). We chose this dataset for our experiments because it is a well-validated, cancer-focused dataset that includes many tumor/normal biological replicates. Our study has two primary goals. First goal is to determine how replicate-based consensus approaches can improve the accuracy of somatic variant detection systems. Second goal is to develop highly predictive machine learning (ML) models by employing replicate-based consensus variants as labels during the training phase.ResultsEnsemble approaches that combine alternative algorithms are relatively common; here, as an alternative, we study the performance enhancement potential of biological replicates. We first developed replicate-based consensus approaches that utilize the biological replicates available in this study to improve variant calling performance. Subsequently, we trained ML models using these biological replicates and achieved performance comparable to optimal ML models, those trained using high-confidence variants identified in advance.ConclusionsOur replicate-based consensus approach can be used to improve variant calling performance and develop efficient ML models. Given the relative ease of obtaining biological replicates, this strategy allows for the development of efficient ML models tailored to specific datasets or scenarios.
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Project description:Glycosylation is the most common post-translational modification and has myriad of biological functions. However, glycan analysis has always been a challenge. Here, we would like to present new techniques for glycan fingerprinting based on enzymatic fluorescent labeling and gel electrophoresis. The method is illustrated on SARS2 spike (S) glycoproteins. SARS2, a novel coronavirus and the causative agent of the COVID-19 pandemic, has had significant social and economic impacts since the end of 2019. To obtain the N-glycan fingerprint of an S protein, glycans released from the protein are first labeled through enzymatic incorporation of fluorophore-conjugated sialic acid or fucose, then separated by SDS-PAGE, and finally visualized with a fluorescent imager. To identify the labeled glycans of a fingerprint, glycan standards and glycan ladders are enzymatically generated and run alongside the samples as references. By comparing the mobility of a labeled glycan to that of a glycan standard, the identity of glycans maybe determined. O-glycans can also be fingerprinted. Due to the lack of an enzyme for broad O-glycan release, O-glycans on the S protein can be labeled with fluorescent sialic acid and digested with trypsin to obtain labeled glycan peptides that are then separated by gel electrophoresis. Glycan fingerprinting could serve as a quick method for globally assessing the glycosylation of a specific glycoprotein.
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