The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

DNA methylation is a dynamic epigenetic mark that undergoes extensive changes during differentiation of self-renewing stem cells. However, whether these changes are the cause or consequence of stem cell fate remains unknown. Here, we show that alternative functional programs of hematopoietic stem cells (HSCs) are governed by gradual differences in methylation levels. Constitutive methylation is essential for HSC self-renewal but dispensable for homing, cell cycle control and suppression of apoptosis. Notably, HSCs from mice with reduced DNA methyltransferase 1 activity cannot suppress key myeloerythroid regulators and thus can differentiate into myeloerythroid, but not lymphoid, progeny. A similar methylation dosage effect controls stem cell function in leukemia. These data identify DNA methylation as an essential epigenetic mechanism to protect stem cells from premature activation of predominant differentiation programs and suggest that methylation dynamics determine stem cell functions in tissue homeostasis and cancer.

PCAWG working groups focused on unified analyses of copynumber variation 6 , structural variants 7,8 , germline variants 5 , mutational signatures 9 and identification of driver genes 8 , among others 5. Here, we report the joint analysis of available matched transcriptome and genome profiling for 1,188 samples from 27 tumour types by the PCAWG Transcriptome Working Group 5 , providing the largest, to our knowledge, resource of RNA phenotypes and their underlying genetic changes in cancer so far (Extended Data Fig. 1, Methods, Supplementary Results, Supplementary Table 23). We demonstrate the importance of transcriptomics data in understanding how different dimensions of specific DNA alterations contribute to carcinogenesis and map out the landscape of cancer-related RNA alterations.

The regulation of intragenic miRNAs by their own intronic promoters is one of the open problems of miRNA biogenesis. Here, we describe PROmiRNA, a new approach for miRNA promoter annotation based on a semi-supervised statistical model trained on deepCAGE data and sequence features. We validate our results with existing annotation, PolII occupancy data and read coverage from RNA-seq data. Compared to previous methods PROmiRNA increases the detection rate of intronic promoters by 30%, allowing us to perform a large-scale analysis of their genomic features, as well as elucidate their contribution to tissue-specific regulation. PROmiRNA can be downloaded from http://promirna.molgen.mpg.de.

The biomedical literature is represented by millions of abstracts available in the Medline database. These abstracts can be queried with the PubMed interface, which provides a keyword-based Boolean search engine. This approach shows limitations in the retrieval of abstracts related to very specific topics, as it is difficult for a non-expert user to find all of the most relevant keywords related to a biomedical topic. Additionally, when searching for more general topics, the same approach may return hundreds of unranked references. To address these issues, text mining tools have been developed to help scientists focus on relevant abstracts. We have implemented the MedlineRanker webserver, which allows a flexible ranking of Medline for a topic of interest without expert knowledge. Given some abstracts related to a topic, the program deduces automatically the most discriminative words in comparison to a random selection. These words are used to score other abstracts, including those from not yet annotated recent publications, which can be then ranked by relevance. We show that our tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time. MedlineRanker is free for use and is available at http://cbdm.mdc-berlin.de/tools/medlineranker.