Probabilistic Mechanistic Models for Genomics

We develop methods for efficient Bayesian inference in complex modelling problems. Our main applications are in developing statistical methods for modelling molecular biology time series based on Gaussian processes, as well as methods for RNA-sequencing and metagenomics data analysis. We are a subgroup of Statistical Machine Learning and Bioinformatics group at HIIT.

Group members

Selected publications

P. Glaus, A. Honkela, and M. Rattray.
Identifying differentially expressed transcripts from RNA-seq data with biological variation.
Bioinformatics 28(13):1721-1728 (2012), doi:10.1093/bioinformatics/bts260.

A. Honkela, T. Raiko, M. Kuusela, M. Tornio, and J. Karhunen.
Approximate Riemannian conjugate gradient learning for fixed-form variational Bayes.
Journal of Machine Learning Research 11(Nov):3235-3268 (2010).

A. Honkela, C. Girardot, E. H. Gustafson, Y.-H. Liu, E. E. M. Furlong, N. D. Lawrence and M. Rattray.
Model-based method for transcription factor target identification with limited data.
Proc. Natl. Acad. Sci. U S A 107(17):7793-7798 (2010), doi:10.1073/pnas.0914285107.

Free software packages based on our research

  • BitSeq: Transcript isoform expression and differential expression estimation from RNA-seq data (also available through Bioconductor)
  • tigre: Ranking transcription factor candidate target genes based on time series gene expression data
  • tigreBrowser: Web-based browser for genomic time course modelling results

Last updated on 28 Feb 2014 by Antti Honkela - Page created on 28 Feb 2014 by Antti Honkela