Removes the biases inherent in raw Unique Molecular Identifier (UMI) counts and produces unbiased and low-noise measurements of transcript abundance. In these conditions, TRUmiCount can realize comparisons between different genes, exons, and other genomic feature. This algorithm exploits the tree-step bias-correction and phantom-removal in expected read counts. In addition, TRUmiCount can thus help to increase the accuracy of many quantitative applications of Next Generation Sequencing (NGS).
Max F. Perutz Laboratories (MFPL), Center for Integrative Bioinformatics Vienna (CIBIV), University of Vienna, Medical University of Vienna, Vienna, Austria; Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria