Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships 1,2 . Here, we present a human "all-by-all" reference interactome map of human binary protein interactions, or "HuRI". With ~53,000 high-quality protein-protein interactions (PPIs), HuRI has approximately four times more such interactions than high-quality curated interactions from smallscale studies. Integrating HuRI with genome 3 , transcriptome 4 , and proteome 5 data enables the study of cellular function within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying specific subcellular roles of PPIs. Inferred tissuespecific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms underlying tissue-specific phenotypes of Mendelian Reprints and permissions information is available at http://www.nature.com/reprints.Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms

Noncoding mutations in cancer genomes are frequent but challenging to interpret. PVT1 encodes an oncogenic lncRNA, but recurrent translocations and deletions in human cancers suggest alternative mechanisms. Here, we show that the PVT1 promoter has a tumor-suppressor function that is independent of PVT1 lncRNA. CRISPR interference of PVT1 promoter enhances breast cancer cell competition and growth in vivo. The promoters of the PVT1 and the MYC oncogenes, located 55 kb apart on chromosome 8q24, compete for engagement with four intragenic enhancers in the PVT1 locus, thereby allowing the PVT1 promoter to regulate pause release of MYC transcription. PVT1 undergoes developmentally regulated monoallelic expression, and the PVT1 promoter inhibits MYC expression only from the same chromosome via promoter competition. Cancer genome sequencing identifies recurrent mutations encompassing the human PVT1 promoter, and genome editing verified that PVT1 promoter mutation promotes cancer cell growth. These results highlight regulatory sequences of lncRNA genes as potential disease-associated DNA elements.

BackgroundCancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring genomic copy numbers, including various types of array-CGH, SNP arrays and high-throughput sequencing, calls for a coherent framework offering unified and consistent handling of single- and multi-track segmentation problems. In addition, there is a demand for highly computationally efficient segmentation algorithms, due to the emergence of very high density scans of copy number.ResultsA comprehensive Bioconductor package for copy number analysis is presented. The package offers a unified framework for single sample, multi-sample and multi-track segmentation and is based on statistically sound penalized least squares principles. Conditional on the number of breakpoints, the estimates are optimal in the least squares sense. A novel and computationally highly efficient algorithm is proposed that utilizes vector-based operations in R. Three case studies are presented.ConclusionsThe R package is a software suite for segmentation of single- and multi-track copy number data using algorithms based on coherent least squares principles.