Supplementary material for the paper "ARTS: accurate recognition of transcription starts in human" by S. Sonnenburg, A. Zien, and G. Raetsch (Bioinformatics doi:10.1093/bioinformatics/btl250) Errata * There is a typo in the definition of the positive predictive value. It should be PPV=TP/(FP+TP)
Supplementary material for the paper "RASE: recognition of alternatively spliced exons in C. elegans" by G. Rätsch, S. Sonnenburg and B. Schölkopf (Bioinformatics doi:10.1093/bioinformatics/bti1053)
Supplementary material for the paper "Large Scale Genomic Sequence SVM Classifiers" by S. Sonnenburg, G. Rätsch and B. Schölkopf (ICML 2005)
Supplementary material for the paper "Large Scale Multiple Kernel Learning" by S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf (JMLR 7:1531-1565, 2006)
sequence motif kernel, multiclass multiple kernel learning.
Supplementary Webpage for our NIPS*05 submission "A General and Efficient Multiple Kernel Learning Algorithm" by Sören Sonnenburg, Gunnar Rätsch and Christin Schäfer.
Shogun, a kernel based machine learning toolbox including multiple kernel learning and many sequence kernels.
Supplementary material for the paper "PALMA: mRNA to Genome Alignments using Large Margin Algorithms" by Uta Schulze, Bettina Hepp, Cheng Soon Ong and Gunnar Rätsch (Bioinformatics 2007 23(15):1892-1900; doi:10.1093/bioinformatics/btm275)
First using homology and cross species alternative splicing prediction, then using active learning.
Supplementary Website for our paper on "Transcript Normalization and Segmentation of Tiling Array Data" presented at the Pacific Symposium on Biocomputing 2008.
Positional Oligomer Importance Matrices
Supplementary material for the submission "Optimal Spliced Alignments of Short Sequence Reads" by De Bona, F. et al. (ECCB08/Bioinformatics, 24 (16):i174, 2008)
KIRMES: Kernel-based Identification of Regulatory Modules in Euchromatic Sequences
High-throughput sequencing technologies open exciting new approaches to transcriptome profiling. For the important task of inferring transcript abundances from RNA-seq data, we developed a new technique, called rQuant, based on quadratic programming. Our method estimates biases introduced by experimental settings and is thus a powerful tool to reveal and quantify novel (alternative) transcripts.
Supplementary material for the submission "PALMapper: Fast and Accurate Spliced Alignments of Sequence Read"
Web Service Study
Recent advances in high-throughput cDNA sequencing (RNA-seq) technology have made it a powerful tool for transcriptome studies. A pivotal step in the analysis of RNA-seq data is the accurate reconstruction of expressed transcripts. Our machine learning-based transcript reconstruction method, which we call mTiM (Margin-based TranscrIpt Mapping), exploits features derived from RNA-seq read alignments and from computational splice sites predictions to infer the exon-intron structure of the corresponding transcripts.
This page contains supplementary material for the manuscript "Multiple insert size paired-end sequencing for deconvolution of complex transcriptomes" by L.M. Smith, L. Hartmann, P. Drewe, R. Bohnert, C. Lanz, and G. Rätsch.
Simple Alignment Filter Tool
This page contains supplementary information to this publication: Schultheiss SJ, Münch MC, Andreeva GD, Rätsch G (2011) Persistence and availability of web services in computational biology. PLoS ONE doi:10.1371/journal.pone.0024914.