A need for reliable analytical tools to alleviate genetic QC challenges in genetic research
To date, a range of gene editing techniques are exploited to create transgenic models for research purposes or to engineer cell lines for the production of pharmaceutical biologics. However, many of these techniques (used for random or targeted transgene integrations) can result in undesired (off-target) integrations, multiple integration sites, unexpected integration of backbone sequences or even, in undesired sequence or structural variants in the integrated transgene sequence and surrounding host genome sequence.1-4
With a growing interest for rigorous genetic characterization, many researchers actively seek to adopt the best possible analytical tools, or at least identify the most suitable one(s) for their own research needs. Amidst the increasing demand for proper genetic quality control (QC), and in response to the frequently asked question "which sequencing platform should I choose?", we set out to unravel some of the main advantages and shortcomings of whole genome sequence (WGS) versus TLA-based targeted sequencing method.
Should I pick TLA or WGS?
If the intention is to sequence and to scrutinize the entire genome for genetic mutations that might be linked to the trait(s) that you are interested in, then WGS is unarguably the best option.5
However, if you (1) have narrowed down the genes that are most relevant to your study or (2) already know the transgene sequences that you are working with, then WGS can be a rather inefficient way to go about specifically generating sequence information across those regions of interest(s) only. Indeed, with WGS, you would be generating such large volumes of data, most of which will essentially be “junk”, thereby rendering the method rather superfluous.6 As a result, WGS would prove to not be time-efficient nor cost-effective (due to the untargeted nature of the method). Moreover, the sheer mass of information generated via WGS will only render the analysis and interpretation of the data even more complex.7-10
Finally, because WGS will generate sequence information across the entire genome, this will result in lower coverage. In other words, fewer reads will span the region of interest, thereby affecting the sensitivity. Therefore, the sensitivity with which you will be able to detect rare mutations and rare sequence changes will be lower. For this reason, low-coverage WGS typically results in lower confidence for variant calling.
| TLA | WGS | ||
| Pros | Cons | Pros | Cons |
|
Complete targeted sequencing of any (trans)genes of interest |
Relies on a reference genome |
Sequences the entire genome |
The sheer volume of generated information renders the analysis and interpretation of the data complex Most of the generated data would essentially be useless, if you are only interested in specific (trans)genes |
|
Generates high NGS coverage across the locus of interest and adjacent sequences (extending into >100 kb) |
Coupled with NGS, TLA will struggle sequencing across highly repetitive regions (i.e. rich in AT or CG repeats), thereby leading to a drop in coverage) |
Allows detection of coding and non-coding variants |
Low coverage |
|
Enables the robust detection of all genetic variation (including structural variants) in and around (trans)genes of interest |
Provides an estimation of copy number, based on the:
TLA only provides information on cells with integrated transgene |
Assesses copy numbers in population of cells. It enables the assessment of transgene sequencing coverage vs. the coverage across the host genome |
Cannot reliably detect structural variations in and around (trans)genes of interest |
|
Cost-effective method if you are interested in a robust assay, capable of extracting all relevant genetic characteristics in a single experiment |
|
Cost will possibly continue to plummet in the future11 |
|
A one-stop shop solution for complete and unbiased genetic characterization
It is unquestionable that WGS and TLA are both invaluable screening tools, and that they both greatly improve the quality of genetic research as a whole. Ultimately, whether TLA or WGS is the better choice, will largely depend on your own specific research need, since both present their own set of strengths and weaknesses.
Our customers typically place heavy emphasis on the in-depth genetic characterization of their (genetic) manipulation. As such, they often turn to our TLA-based solutions to help them troubleshoot some of the most pressing genetic QC challenges, e.g. to identify the exact genomic position(s) of inserted transgene(s) as well as reliably detect sequence variants and potential structural variations (i.e. large genomic rearrangements) that may have accompanied their genetic engineering.
Despite the wealth of genomic technologies currently available, the robust detection of all genetic variation (including structural variants) in and around genes of interest is still limited with conventional technologies.12 To this end, our unique TLA-based genetic QC method truly represents an innovative and unmatched solution for the complete genetic characterization of genetically engineered (animal) models, pharmaceutical cell lines and immunotherapy products (ATMPs).
To learn more about the “how and why” of our proprietary TLA technology, check out our TLA animation video below.
References
[1] Leslie O. Goodwin et al. (2019). Large-scale discovery of mouse transgenic integration sites reveals frequent structural variation and insertional mutagenesis Genome Res. 29: 494-505
[2] Carol Cain-Hom et al. (2017). Efficient mapping of transgene integration sites and local structural changes in Cre transgenic mice using targeted locus amplification Nucleic Acids Research 45(8): e62
[3] Justin Eyquem et al. (2017). Targeting a CAR to the TRAC locus with CRISPR/Cas9 enhances tumour rejection Nature 543: 113-117
[4] Kosicki, M., Tomberg, K. & Bradley, A. Repair of double-strand breaks induced by CRISPR–Cas9 leads to large deletions and complex rearrangements. Nat Biotechnol 36, 765–771 (2018). https://doi.org/10.1038/nbt.4192
[5] Wrzeszczynski KO, Felice V, Shah M, et al (2018) Whole genome sequencing-based discovery of structural variants in glioblastoma. In: Placantonakis D. (eds) Glioblastoma. Methods in molecular biology, vol 1741. Humana Press, New York, NY, pp 1–29.
[6] Botkin JR, Rothwell E. Whole genome sequencing and newborn screening. Curr Genet Med Rep, 2016; 4(1):1–6.
[7] Meienberg J, Bruggmann R, Oexle K, et al. Clinical sequencing: is WGS the better WES? Hum Genet, 2016; 135(3):359-36213.
[8] Gong J, Pan K, Fakih M, Pal S, Salgia R. Value-based genomics. Oncotarget, 2018; 9(21):15792-15815.
[9] Gonzaga-Jauregui C, Lupski JR, Gibbs RA. Human genome sequencing in health and disease. Annu Rev Med, 2012; 63:35-61.
[10] Ferguson, E. (2020). The strenghts and weaknesses of whole-genome sequencing. Inspire Student Health Sciences Research Journal, Autumn 2020. https://inspirestudentjournal.co.uk/wp-content/uploads/2020/10/Inspire-Student-Journal-Emily-Ferguson.pdf
[11] Radford C, Prince A, Lewis K, et al. Factors which impact the delivery of genetic risk assessment services focused on inherited cancer genomics: expanding the role and reach of certified genetics professionals. Journal of Genetic Counseling, 2013; 23(4):522-530.
[12] de Vree, P., de Wit, E., Yilmaz, M. et al. Targeted sequencing by proximity ligation for comprehensive variant detection and local haplotyping. Nat Biotechnol 32, 1019–1025 (2014). https://doi.org/10.1038/nbt.2959
