5/n

The following paper provides an excellent overview of sequence-to-graph mapping algorithms—covering their principles, trade-offs, and performance benchmarks. It’s a great starting point if you’re interested on the topic

genomebiology.biomedcentral.com/articles/10....

#bioinformatics #bioinfo

4/n

From equitable precision medicine to large-scale population studies and evolutionary research, pangenomics graphs and graph mapping excel in capturing diversity—especially in highly variable or repetitive genome regions where linear mapping fails.

3/n

Unlike linear references, which favour the “reference” allele (known as reference bias), the integration of multiple haplotypes in pangenomic graphs and respective mapping reduces bias, improves alignment quality, and makes analyses fairer across diverse populations.

2/n
Pangenome graphs represent variants as alternative paths, so reads can follow the path matching their true sequence.
These graphs can represent SNPs, indels, structural variants, and complex rearrangements, all in one model—boosting variant calling accuracy for common and rare alleles.

12/12

"It is an ethical imperative that research be designed and analysed to avoid wasting investment of animals, research dollars and effort".

#biostatistics #stats

11/n

Ultimately, understanding the C&N concepts, design experiments, and analytical approaches accounting for that are fundamental for rigorous and reproducible preclinical research.

10/n

In a collaborative setting, another key element is a good and clear communication between the experimentalists and the data analysts. Only then can we have sound and robust experiments, with efficient analysis, that can really provide insight into the questions being asked.

9/n

The analysis must reflect the data hierarchy (e.g., measurements nested within animals, animals within cages). Reporting statistical model details, including random effects and degrees of freedom adjustments, is also highly recommended.

8/n

For the analysis, researchers can either aggregate data to a single summary per cluster (e.g., mean per cage) - which implies losing some information (the variation within a group can still be relevant) or use mixed or hierarchical models.

7/n

Additionally, increasing the number of independent groups is generally more beneficial than having more animals per group.

6/n

For experimental design, when a condition or treatment is applied, the concepts of blocking (e.g. ensuring that each cage have both treatments) and randomisation (e.g. in each cage, the animals that have each treatment is random) are essential.

5/n

Researchers must account for these effects by considering the group (e.g. cage, litter, etc.) as the experimental unit for power calculations. Experimental design and downstream analyses should take into consideration this group effect as well.

4/n

Repeated sampling data (repeated measures from the same individual) is also composed of nested observations - This is probably the scenario most easily recognised and accounted for in research.

3/n

Examples of C&N include "cage effects" (e.g. mice housed in the same cage) , "litter effects" (animals from the same litter, thus sharing genetics or maternal environment), and "in vitro plate effects" (cells from the same plate).

2/n

The interdependency of the obtained data points violates the assumptions of standard statistical tests like t-tests or ANOVA. Therefore, ignoring C&N can inflate Type I error rates, leading to false positives, thus undermining the validity of any conclusion obtained.