MOTIVATION: Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. RESULTS: Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: (i) infer the presence/absence of each protein isoform (via a posterior probability), (ii) estimate its abundance (and credible interval), and (iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. AVAILABILITY AND IMPLEMENTATION: IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

) Weiping Cai, Anne E. Condon, Robert M. Corn, Elton Glaser, Zhengdong Fei, Tony Frutos, Zhen Guo, Max G. Lagally, Qinghua Liu, Lloyd M. Smith, Andrew Thiel University of Wisconsin Madison, WI 57306 USA Abstract A new model of DNA computation that is based on surface chemistry is studied. Such computations involve the manipulation of DNA strands that are immobilized on a surface, rather than in solution as in the work of Adleman. Surface-based chemistry has been a critical technology in many recent advances in biochemistry and offers several advantages over solution-based chemistry, including simplified handling of samples and elimination of loss of strands, which reduce error in the computation. The main contribution of this paper is in showing that in principle, surface-based DNA chemistry can efficiently support general circuit computation on many inputs in parallel. To do this, an abstract model of computation that allows parallel manipulation of binary inputs is described. It is...