Genetic Interactions

RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The package provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis.
Download: RNAinteract available from bioconductor

Vignette: RNAinteract.pdf

Publication: Thomas Horn, Thomas Sandmann, Bernd Fischer, Elin Axelsson, Wolfgang Huber, Michael Boutros, Mapping of signaling networks through synthetic genetic interaction analysis by RNAi, Nature methods 8 (4), 341-346, 2011. [pdf]

RNAinteractMAPK. This package includes all data used in the paper Mapping of Signalling Networks through Synthetic Genetic Interaction Analysis by RNAi, Horn, Sandmann, Fischer et al.., Nat. Methods, 2011. The package vignette shows the R code to reproduce all figures in the paper.
Download: RNAinteractMAPK available from bioconductor

Vignette: RNAinteractMAPK.pdf

Publication: Thomas Horn, Thomas Sandmann, Bernd Fischer, Elin Axelsson, Wolfgang Huber, Michael Boutros, Mapping of signaling networks through synthetic genetic interaction analysis by RNAi, Nature methods 8 (4), 341-346, 2011. [pdf]

Mass Spectrometry and Proteomics

mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a wrapper for the ISB random access parser for mass spectrometry mzXML, mzData and mzML files. The package contains the original code written by the ISB, and a subset of the proteowizard library for mzML. The netCDF reading code has previously been used in XCMS.
Download: mzR available from bioconductor

Vignette: mzR.pdf


NovoHMM. De novo sequencing of peptides poses one of the most challenging tasks in data analysis for proteome research. In this paper, a generative hidden Markov model (HMM) of mass spectra for de novo peptide sequencing which constitutes a novel view on how to solve this problem in a Bayesian framework is proposed. Further extensions of the model structure to a graphical model and a factorial HMM to substantially improve the peptide identification results are demonstrated. Inference with the graphical model for de novo peptide sequencing estimates posterior probabilities for amino acids rather than scores for single symbols in the sequence. Our model outperforms state-of-the-art methods for de novo peptide sequencing on a large test set of spectra.
Download: NovoHMM.zip

Publication: Bernd Fischer, Volker Roth, Franz Roos, Jonas Grossmann, Sacha Baginsky, Peter Widmayer, Wilhelm Gruissem, and Joachim M. Buhmann, NovoHMM: A Hidden Markov Model for de Novo Peptide Sequencing, Analytical Chemistry, vol. 77, no. 22, pp. 7265 - 7273, 2005. [pdf]

HDF5 file format

rhdf5. An HDF5 interface to R.
Download: rhdf5 available from bioconductor

Vignette: rhdf5.pdf

Clustering

path-based clustering. Fast implementation for path-based clustering as described in the paper "clustering with the connectivity kernel".
Download: path-based clustering

Publication: B Fischer, V Roth, JM Buhmann, Clustering with the connectivity kernel, Advances in Neural Information Processing Systems 16, 89-96, 2004.
Bernd Fischer, Joachim M Buhmann, Bagging for path-based clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (11), 1411-1415, 2003.
Bernd Fischer, Joachim M. Buhmann, Path-based clustering for grouping of smooth curves and texture segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (4), 513-518, 2003.

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