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DNA sequencing is critical for genetic research, evolutionary studies, and personalized medicine, where it helps to uncover the genetic basis of diseases, track hereditary conditions, and guide targeted therapies. It provides a detailed understanding of an organism’s complete genetic makeup, offering insights into complex biological processes and evolutionary relationships.

Whole-Genome Sequencing (WGS) involves sequencing the entire genome, including both coding and non-coding regions. WGS provides the most comprehensive view of an organism’s genetic information, as the focus is not only on identifying genetic variants (e.g., single nucleotide variants, insertions, deletions, copy-number variation), but also on identifying rare variants, structural variations, and novel mutations in both coding and non-coding regions. Different algorithms will be applied for germline or somatic samples – the former algorithms are designed to identify inherited variants present in all cells, whereas the latter algorithms focus on detecting mutations acquired in specific tissues which are present only in a subset of cells thus requiring specialized methods to account for tissue purity and heterogeneity.

Exome sequencing is an application of DNA sequencing (see NF62.02.01) that focuses on preferentially sequencing the exons, or protein-coding regions, which make up about 1-2% of the genome and are more likely to harbor disease-causing mutations. It is used to study genetic variations that affect protein function, thus particularly in disease research. The focus is on identifying genetic variants (e.g., single nucleotide variants, insertions, deletions, copy-number variation). Different algorithms will be applied for germline or somatic samples – the former algorithms are designed to identify inherited variants present in all cells, whereas the latter algorithms require specialized methods to account for tissue purity and heterogeneity to detect mutations acquired in specific tissues which are present only in a subset of cells.

The Visium Spatial Gene Expression solution from 10X Genomics enables spatial profiling of gene expression within intact tissue sections. This protocol allows for the analysis of gene expression while preserving the spatial context of cells within a tissue sample. It provides valuable insights into spatially distinct gene expression patterns and cell-type localization, facilitating a deeper understanding of tissue organization, disease progression, and cellular microenvironments.

Single-cell multiome sequencing (scRNA-seq + scATAC-seq) is molecular biology technique used to analyze both gene expression and chromatin accessibility of individual cells/nuclei at a high resolution, allowing the resolution of cellular heterogeneity within a biological sample.

Unlike bulk RNA and ATAC sequencing, which averages gene expression and chromatin accessibility across many cells, multiome sequencing enables the identification of distinct cell types, states, and subpopulations both in terms of transcriptional and epigenetic profiles. This technology is particularly useful in studying various processes and biological mechanisms including developmental processes, tumorigenesis, and immunological memory establishment. It is widely applied in biomedical research to advance personalized medicine, immunology, cancer research, and tissue regeneration studies.

Multiome datasets include two different assays: gene expression (GEX) and chromatin accessibility (ATAC).

Single-cell immune profiling-V(D)J is a powerful molecular biology technique used to profile both 5’ gene expression and T-cell and/or B-cell receptors of individual cells at a high resolution allowing the characterization of cellular heterogeneity and clonal expansion within a biological sample.

Unlike bulk RNA and T/B-cell receptor (TCR/BCR) sequencing, which allow to study gene expression and TCR/BCR repertoires across many cells, single-cell immune profiling-V(D)J enables the identification of distinct cell types, states, and subpopulations both in terms of transcriptional profile (GEX data) and TCR/BCR repertoires (V(D)J data). This approach is crucial for understanding complex tissues, developmental progression, tumorigenesis, and tracking clonal expansion and immune responses. It is widely applied in biomedical research to advance personalized medicine, immunology, cancer immunotherapy, autoimmune disease and infection disease.

The Single-cell Immune profiling-V(D)J datasets include two modalities: gene expression (GEX) and TCR/BCR (V(D)J).

Single-cell RNA sequencing (scRNA-seq) is a technique used to analyse gene expression at the individual cell level, making it possible to resolve cellular heterogeneity within a biological sample. Unlike bulk RNA sequencing, which averages gene expression across many cells, scRNA-seq enables the identification of distinct cell types, states, and subpopulations.

This approach is crucial for understanding complex tissues, developmental processes, and disease progression, as it reveals how gene expression varies from cell to cell, and it is widely applied in biomedical research to advance personalized medicine, immunology, cancer research, and tissue regeneration studies.

Single-cell ATAC sequencing (scATAC-seq) is a powerful molecular biology technique used to profile the chromatin accessibility of individual cells/nuclei at a high resolution. Chromatin accessibility refers to the degree to which DNA within chromatin is accessible by cellular machinery, particularly those parts involved in transcription, such as transcription factors and RNA polymerase.

Unlike bulk ATAC sequencing, which cannot determine the chromatin states of individual subpopulations of cells within a sample, scATAC-seq is widely used to provide valuable insights into chromatin accessibility, transcription factor binding, epigenetic modifications, and gene regulation. This technology is particularly useful in studying various processes and biological mechanisms including developmental processes, tumorigenesis, and immunological memory establishment.

Small RNA sequencing is a specialized technique designed to analyze and profile small RNA molecules present in a biological sample. It is widely used to study the expression profiles of miRNAs and other small RNAs, providing valuable insights into their roles in gene regulation, development, and disease. Small RNAs are polymeric ribonucleic acid molecules with a length lower than 200 nucleotides, comprising microRNA (miRNA), PIWI-interacting RNA (piRNA), small interfering RNA (siRNA), and tRNA-derived small RNA (tsRNA).

miRNAs are the most studied type of small RNAs, constituted by 20 to 25 nucleotides. They participate in several processes and can regulate gene expression at a posttranscriptional level. miRNAs can also act as transcription factors by binding the seed sequence within 3’UTR of target genes, leading to a variety of cell activities at different levels.

Microbiome analysis using 16S and ITS amplicon sequencing is a widely used technique to study the composition and diversity of microbial communities, particularly bacteria and fungi. The 16S ribosomal RNA (rRNA) gene is a molecular marker found in the genomes of bacteria and archaea, and its variable regions are commonly used for taxonomic classification, while ITS is used to profile fungal communities.

Microbiome analysis using 16S and ITS amplicon sequencing is valuable in a range of fields, including environmental science, human health, and agriculture. It provides a cost-effective way to characterize microbial communities and understand their roles in various ecosystems or host-associated environments.

RNA sequencing is a powerful molecular biology technique used to analyse the transcriptome of a biological sample. The transcriptome refers to the complete set of RNA molecules, in particular messenger RNA (for mRNA sequencing) and/or non-coding RNAs (for total RNA sequencing), in a cell or tissue.

RNA sequencing is widely used in genomics research, functional genomics, and clinical studies to understand gene expression patterns, identify novel transcripts, and investigate how gene expression varies under different conditions. In addition, total RNA sequencing provides an insight also on the regulatory mechanisms underlying various biological processes.