Tag: joint inference

hiHMM – Bayesian non-parametric joint inference of Chromatin State Maps

hiHMM

:: DESCRIPTION

hiHMM (hierarchically-linked infinite hidden Markov model) is a new Bayesian non-parametric method to jointly infer chromatin state maps in multiple genomes (different cell types, developmental stages, even multiple species) using genome-wide histone modification data.

::DEVELOPER

SNUBI (Snubi’s Not Unics, Biomedical Informatics.)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux
  • MatLab/ R

:: DOWNLOAD

 hiHMM

:: MORE INFORMATION

Citation:

hiHMM: Bayesian non-parametric joint inference of chromatin state maps.
Sohn KA, Ho JW, Djordjevic D, Jeong HH, Park PJ, Kim JH.
Bioinformatics. 2015 Feb 27. pii: btv117.

ROHan v1.0 – Joint inference of Runs of Homozygosity and rates of Heterozygosity

ROHan v1.0

:: DESCRIPTION

ROHan is a Bayesian framework to estimate local rates of heterozygosity, infer runs of homozygosity (ROH) and compute global rates of heterozygosity outside of ROHs. ROHan can work on modern and ancient samples with signs of ancient DNA damage.

::DEVELOPER

The Centre for Anthropobiology and Genomics of Toulouse

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

ROHan

:: MORE INFORMATION

Citation

Renaud G, Hanghoj K, Korneliussen TS, Willerslev E, Orlando L.
Joint Estimates of Heterozygosity and Runs of Homozygosity for Modern and Ancient Samples.
Genetics. 2019 Jul;212(3):587-614. doi: 10.1534/genetics.119.302057. Epub 2019 May 14. PMID: 31088861; PMCID: PMC6614887.