Technology

Single-cell Technologies: 

single-cell multi-omics technologies allow for the simultaneous analysis of multiple types of biological information from individual cells, providing a more comprehensive understanding of cellular processes. It allows for the identification of unique cell subpopulations, cell-type-specific gene expression, and regulatory networks.

There is still a need for more comprehensive pipelines that can integrate data from different single-cell multi-omics technologies, such as CITE-seq, REAP-seq, and Spatial Transcriptomics.

Long-read sequencing :

Long-read sequencing offers several advantages for biomarker, neoantigen and TCR discovery, including the ability to span complex genomic regions, characterize full-length variable regions, and detect structural variations or gene rearrangements. 

However, it is important to consider the higher error rates associated with long-read sequencing technologies, and additional bioinformatics tools and methods are required to address these errors and enhance the accuracy of antigen and TCR discovery.

Bulk Genomic Technologies:

Bulk genomic technologies have played a crucial role in advancing our understanding of disease by providing valuable insights into the genetic basis of various disorders.

By integrating single-cell with bulk technologies, we can identify and characterize distinct cell types within a heterogeneous population, which is crucial for understanding cell-type-specific functions, interactions, and contributions to disease.

Genomic Data: It provides information about the genetic code and can be used to study genetic variations, mutations, and gene expression.

Transcriptomic Data: It provides insights into gene expression patterns, alternative splicing, and the abundance of different RNA molecules.

Proteomic Data: Proteomic data can provide information about protein expression levels, post-translational modifications, and protein-protein interactions.

Metabolomic Data: Metabolomic data involves the study of small molecules, known as metabolites, present in cells, tissues, or biological fluids. It provides information about the metabolic pathways and processes occurring in an organism. 

Epigenomic Data: Epigenomic data involves the study of chemical modifications, such as DNA methylation and histone modifications, that regulate gene expression without altering the DNA sequence. Epigenetic modifications play a crucial role in development, disease susceptibility, and environmental responses.

Pharmacogenomic Data: Pharmacogenomic data focuses on the study of how genetic variations influence an individual's response to drugs. It involves the analysis of genetic variants that may affect drug efficacy, toxicity, and individualized treatment approaches.

Metagenomic Data: It provides insights into the genetic diversity and functional potential of microbial communities.

Riboseq Data: It is a technique used to study the translation of mRNA into proteins by ribosomes. This data can be used to study translation efficiency, codon usage, alternative translation initiation sites, and ribosome dynamics.

Peptidomics: It focuses on the study of peptides, which are short chains of amino acids. Peptides play important roles in various biological processes, including signalling, immune responses, and cell communication.