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Grau, J.

Publications and source records attributed to Grau, J..

2 recordsLinked to original sources
bioRxiv2017

Learning from mistakes: Accurate prediction of cell type-specific transcription factor binding

Computational prediction of cell type-specific, in-vivo transcription factor binding sites is still one of the central challenges in regulatory genomics, and a variety of approaches has been proposed for this purpose.\n\nHere, we present our approach that earned a shared first rank in the \"ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge\" in 2017. This approach employs features derived from chromatin accessibility, binding motifs, gene expression, genomic sequence and annotation to train classifiers using a supervised, discriminative learning principle. Two further key aspects of this approach are learning classifier parameters in an iterative training procedure that successively adds additional negative examples to the training set, and creating an ensemble prediction by averaging over classifiers obtained for different training cell types.\n\nIn post-challenge analyses, we benchmark the influence of different feature sets and find that chromatin accessiblity and binding motifs are sufficient to yield state-of-the-art performance for in-vivo binding site predictions. We also show that the iterative training procedure and the ensemble prediction are pivotal for the final prediction performance.\n\nTo make predictions of this approach readily accessible, we predict 682 peak lists for a total of 31 transcription factors in 22 primary cell types and tissues, which are available for download at https://www.synapse.org/#!Synapse:syn11526239, and we demonstrate that these may help to yield biological conclusions. Finally, we provide a user-friendly version of our approach as open source software at http://jstacs.de/index.php/Catchitt.\n\nContactgrau@informatik.uni-halle.de

bioinformatics↗
bioRxiv2017

Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi

MotivationGenome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction.\n\nResultsHere, we present an extension of the gene prediction tool GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions.\n\nAvailabilityGeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa.\n\nContactjens.keilwagen@julius-kuehn.de

bioinformatics↗