Whole Genome Sequencing (WGS)

Whole Genome Sequencing (WGS) is a powerful tool that determines the complete DNA sequence of an organism’s genome, including nuclear and extra-nuclear genomes (organelles: mitochondria, chloroplasts). This provides a comprehensive view of the entire genetic makeup, capturing not only region containing genes coding for proteins, but also non-coding genes and regulatory regions that play critical roles in controlling gene expression, and ultimately cellular functions.

WGS is invaluable to identify variations in the DNA that contribute to diseases, traits, and responses to treatments or environmental factors. These variations include single-nucleotide polymorphisms (SNPs) or somatic variants (SNVs) in cancer, insertions, deletions, copy number variants  and complex structural changes. In personalized medicine, WGS aids in diagnosing genetic diseases as well as cancer by identifying mutations and rearrangements that might go undetected on more targeted tests. By pinpointing relevant genetic changes, WGS enhances diagnostic accuracy and enables the development of personalized therapies.

Beyond healthcare, WGS is essential in diverse fields like evolutionary biology, agriculture, and environmental science. WGS enables to explore genetic diversity, study evolutionary relationships, and understand adaptation mechanisms, driving advances across scientific disciplines.

WGS Libraries: PCR-Based or PCR-Free

WGS libraries can be constructed using either PCR-based or PCR-free methods. While PCR-free WGS requires a higher amount of high-quality starting material, it is preferred due to reduced bias and artifacts. This method provides improved coverage of GC-rich regions, better detection of structural variants, and generally enables more accurate genome characterization by avoiding PCR-induced artifacts that could interfere with sequencing accuracy.

Accessibility and Future Potential

With technological advances and rapidly declining sequencing costs, WGS is now more accessible than ever, transforming our understanding of life at a genetic level and driving innovations across healthcare and various research fields.

Discover our flexible, cost-effective, high-quality WGS services including comprehensive end-to-end solutions, customized options, as well as sequencing of ready-to-load third-party WGS libraries.

Guaranteed quality: We are committed to delivering the highest-quality data through seamless workflows with rigorous quality controls at every step.

Our NGS services are fully scalable, while remaining tailored to individual needs.

WGS Specifications Examples (specific sequence output will be determined according to species genome size, sample type, and application):

	High-Quality WGS
Species	Human, Animal, Plant
Accepted sample type	Cells, Tissues, Blood, High-Quality DNA (for FFPE samples, please contact us)
Recommended  DNA quantity for library preparation	PCR-Based: 200 ng DNA (Input Range 1 ng–200 ng)  PCR-Free : 900 ng DNA  (Input Range 25 ng–900 ng)
Library type	PCR-based or PCR-Free method, depending on sample type, customer’s specifications and pre-analytics
Sequencing Technology	MGI DNBSEQTM PE 150 bp (G400 or T7 sequencer)  Q40 quality available for more accurate variant calling
Typical output for e.g Standard Human WGS	99 Gb (30x) / 165 Gb (50x) – Flexible Output at customer’s request
Typical output for e.g Low-Pass Human WGS	1-5 x – Flexible Output at customer’s request
Regular TAT (Express service on demand)	Two weeks from sample reception to FASTQ

Transcriptomics with RNA-seq 

RNA sequencing (RNA-seq) is a cost-effective and widely used technology for analyzing the transcriptome—the complete set of RNA molecules expressed within a cell or tissue at a given time. RNA-seq provides insights into the functional expression of the genome, which is essential for understanding gene function, cellular responses, and molecular mechanisms involved in development, disease, and treatment responses.

Bulk RNA-seq

Bulk RNA sequencing analyzes the RNA content of a group of cells, tissue samples, or whole organisms. In bulk RNA-seq, RNA is extracted from a mixed population of cells, and the RNA is sequenced to determine the overall gene expression profile of that entire cell population. This approach provides an average measure of gene expression across all cells in the sample, making it ideal for studying gene expression patterns in tissues, organs, or larger cell populations.

Benefits of bulk RNA-seq over targeted methods:

• Unbiased Detection of Transcripts: RNA-seq does not require pre-designed probes, enabling the detection of known and novel transcripts, as well as non-polyadenylated transcripts, without any prior assumptions.
• Detection of non-coding RNAs: non-coding RNAs are important RNA molecules playing key roles in gene regulation.
• Detection of virus RNAs: bulk RNA-seq captures virus RNAs expressed in a tissue sample.
• Quantification of Gene Expression Levels: RNA-seq accurately measures the abundance of each RNA molecule.
• Detection of Alternative Splicing: RNA-seq captures all RNA isoforms, enabling the analysis of alternative splicing patterns
• Identification of Novel Transcripts and Fusion Genes: RNA-seq can detect previously unknown genes, novel transcripts, and gene fusions.

RNA-seq has a broad range of applications in biological and medical research, for instance in developmental biology, cancer research, infectious diseases,personalized medicine and precision oncology. RNA-Seq contributes to personalized medicine by providing data on gene expression profiles unique to an individual or disease state. This enables more precise diagnosis, prognosis, and treatment recommendations based on a patient’s specific gene expression patterns.

Bulk RNA-Seq enables various levels of analysis, including for instance:

• Gene Expression Profiling: Provides insights into gene expression patterns in specific tissues or conditions.
• Differential Expression Analysis: By comparing gene expression between different samples or conditions (e.g., treated vs. untreated, diseased vs. healthy), bulk RNA-seq can identify genes involved in response to treatment or disease progression.
• Understanding Gene Regulation Networks: RNA-seq helps decipher gene regulation networks and pathways involved in development and disease, enhancing understanding of underlying biological mechanisms.
• Biomarker Discovery: Bulk RNA-seq can identify genes associated with specific diseases or physiological states, aiding in the discovery of diagnostic or prognostic biomarkers.

 

Bulk RNA-Seq vs. Single-Cell or Single-Nucleus RNA-Seq

Bulk RNA-seq provides a broad overview of gene expression across heterogeneous cell populations, making it well-suited for studies focused on global gene expression patterns and cost-effective large-scale experiments. However, bulk RNA-seq lacks single-cell resolution and may dilute signals from rare cell types, making it difficult to detect gene expression unique to smaller cell populations.

In contrast, single-cell RNA-seq offers much higher resolution, allowing for gene expression analysis at the level of individual cells. This method is ideal for understanding cellular heterogeneity within complex tissues and for analyzing gene expression differences between cell subtypes. Known limitations of single-cell RNA-seq include the potential loss of fragile cell types during sample preparation, limited dynamic range, dropout of low-expressed genes, and the high costs associated with this technology.

Bulk RNA-Seq Technical Considerations:

• Sample Quality and Quantity:
  The quality and quantity of starting RNA are critical for successful sequencing. Degraded or low-quality RNA may negatively impact the accuracy of results.
• Sequencing Depth:
  The number of reads generated per sample determines the sensitivity of RNA-seq. Higher sequencing depth allows for the detection of low-expressed genes, transcript isoforms, long non-coding RNAs, and gene fusions.
• Choice of RNA-Seq Library Kits:
  Numerous RNA-seq library kits are commercially available. For bulk RNA-seq, the most popular kits involve depletion of highly abundant RNA molecules, such as ribosomal and globin RNAs, to enable better representation of less abundant and rare RNA molecules in the sequencing data. For low-quality, partially degraded RNA, libraries based on exome-enrichment may be preferred.
• Alacris’s Extensive RNA-Seq Quality Controls:
  With over a decade of RNA-seq expertise, Alacris has developed rigorous quality controls throughout the RNA-seq workflow, from pre-analytics to processing and data analysis.

 

Data Analysis Requirements for RNA-Seq

RNA-seq generates large, complex datasets that require advanced bioinformatics tools for alignment, quantification, and interpretation. Alacris offers dedicated RNA-seq data analysis to extract meaningful biological insights.

Discover our flexible, cost-effective, high-quality RNA-Seq services including comprehensive end-to-end solutions, customized options, as well as sequencing of ready-to-load third-party libraries.

Guaranteed quality: We are committed to delivering the highest-quality data through seamless workflows with rigorous quality controls at every step.

Our NGS services are fully scalable, while remaining tailored to individual needs.

RNA-Seq Specifications Examples (specific sequence output will be determined according to the application and customer’s needs):

	Bulk RNAseq	Exome-Targeted RNAseq
Species	Human, Animals	Human
Accepted sample type	Cells, Tissues, High Quality RNA	FFPE Tissues, Low Quality RNA
RNA Quality	RIN ≥ 7.0	DV200 ≥ 30%
Enrichment method	rRNA depletion, rRNA and Globin depletion, Poly A enrichment	Whole Exome panel
Recommended input for library preparation	200 ng Total RNA (Input Range 10 ng- 1 µg)	DV200% > 70 %: 20 ng Total RNA DV200% 50–70 %: 40 ng (Input Range 20–40 ng) DV200% 30–50 %: 100 ng (Input Range 40–100 ng)
Regular library Preparation Kit (other kits tested, customer’s preferences)	MGIEasy Fast RNA Library Prep Set V1.0	Illumina TruSeq RNA Exome
Sequencing Technology	MGI DNBSEQ (G400 or T7) PE 100 or PE 150	MGI DNBSEQ (G400 or T7) PE 100
Typical output	10Gb—20 Gb (50M—100 Mio fragments)	20-24 Gb, > 100 Mio fragments
TAT (express service on demand)	Two weeks from sample reception to FASTQ	Two weeks from sample reception to FASTQ