Accurate transcript structure and abundance inference from RNA sequencing (RNA-seq) data is foundational for molecular discovery. Here we present TACO, a computational method to reconstruct a consensus transcriptome from multiple RNA-seq data sets. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to improved reconstruction accuracy compared with other tools in its class. The tool is available at http://tacorna.github.io and can be readily incorporated into RNA-seq analysis workflows.
This work was supported in part by the NIH Prostate Specialized Program of Research Excellence grant P50CA186786 (A.M.C.), F30 CA 200328 (Y.S.N.), U01CA214170 (A.M.C.), and U24 CA210967 (A.M.C.). A.M.C. is supported by the Prostate Cancer Foundation and the Howard Hughes Medical Institute. A.M.C. is an American Cancer Society Research Professor and a Taubman Scholar of the University of Michigan.
Integrated supplementary information
Batch size statistics.
Isoform fraction statistics.
GTEX Samples used.
High expression statistics.
Changepoint parameter statistics.