mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters
Clicks: 1
ID: 292108
2013
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #598 of 825 articles by views in BMC Bioinformatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 825 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Ancient DNA (aDNA) molecules in fossilized bones and teeth, coprolites, sediments, mummified specimens and museum collections represent fantastic sources of information for evolutionary biologists, revealing the agents of past epidemics and the dynamics of past populations. However, the analysis of aDNA generally faces two major issues. Firstly, sequences consist of a mixture of endogenous and various exogenous backgrounds, mostly microbial. Secondly, high nucleotide misincorporation rates can be observed as a result of severe post-mortem DNA damage. Such misincorporation patterns are instrumental to authenticate ancient sequences versus modern contaminants. We recently developed the user-friendly mapDamage package that identifies such patterns from next-generation sequencing (NGS) sequence datasets. The absence of formal statistical modeling of the DNA damage process, however, precluded rigorous quantitative comparisons across samples.
| Reference Key |
openalex_W2150163641
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Hákon Jónsson, Aurélien Ginolhac, Mikkel Schubert, Philip L. Johnson, Ludovic Orlando |
| Journal | BMC Bioinformatics |
| Year | 2013 |
| DOI |
10.1093/bioinformatics/btt193
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.