From our News&Views with @rayanchikhi and @yoanndufresne
"[I]t is a good time to reinforce the standard that the output produced by a bioinformatics tool should be well defined...[W]hen a user has a pangenome reference in their hands, they should know what properties it has .... [P]angenome tool developers should ... minimize ad hoc heuristics and arbitrary parameters..."
"On the one hand, a population pangenome graph faithfully represents the complete variation of haplotypes present in some population of interest. On the other hand, an application-specific pangenome graph reflects only some of the population variability and is instead tailored toward maximizing the performance of application-specific downstream tools."
Gasarch on what counts as "closed form" and what does not, using the Ordered Bell numbers as an example: https://blog.computationalcomplexity.org/2024/05/what-is-closed-form-horse-numbers-are.html
He asks, in the form of a Platonic dialogue: is \(\tbinom{n}{i}\) or \(\tfrac{n!}{i!(n-i)!}\) closed form? What, if anything, makes that sort of formula different from the formula \(H(n)\), denoting the \(n\)th ordered Bell number?
@themaklin @EricPelletier @Pashadag
This is now published at PLOS CB!
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011920
A collaboration work, during my time at Penn State, is published: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05223-1
Triggers many wonderful memories! 😀
Background Epigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation. Results To reduce the complexity, we developed a package, called Snapshot, for clustering and visualizing candidate cis-regulatory elements (cCREs) based on their epigenetic signals during cell differentiation. This package first introduces a binarized indexing strategy for clustering the cCREs. It then provides a series of easily interpretable figures for visualizing the signal and epigenetic state patterns of the cCREs clusters during the cell differentiation. It can also use different hierarchies of cell types to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from a consortium project for ValIdated Systematic IntegratiON (VISION) of epigenomic data in hematopoiesis. Conclusion The package Snapshot can identify all distinct clusters of genomic locations with unique epigenetic signal patterns during cell differentiation. It outperforms other methods in terms of interpreting and reproducing the identified cCREs clusters. The package of Snapshot is available at GitHub: https://github.com/guanjue/Snapshot .
Rob
0xDE
Pierre Lindenbaum
Luis Pedro Coelho
bioRxiv Bioinformatics
Chen Sun