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Outsourcing computation for genomic data processing offers the ability to allocate massive computing power and storage on demand. Here, Popic and Batzoglou develop a hybrid cloud aligner for sequence read mapping that preserves privacy with competitive accuracy and speed.

A chromatin immunoprecipitation and sequencing (ChIP-Seq) data analysis package, QuEST, facilitates transcription factor binding site discovery at about 20-base-pair resolution.

Synthetic long-read sequencing technology (TruSeq) identifies substrain diversity in microbiome samples.

The expectation maximization algorithm arises in many computational biology applications that involve probabilistic models. What is it good for, and how does it work?

Using common variants in six non-human primate species, the authors train a deep neural network that identifies pathogenic mutations in patients with rare disease with 88% accuracy and enables the discovery of 14 new candidate genes in intellectual disability.

The SIMLR software identifies similarities between cells across a range of single-cell RNA-seq data, enabling effective dimension reduction, clustering and visualization.

Genetic association studies with affinity proteomics face challenges when dealing with protein altering variants. Suhre et al. show that nanoparticle enrichment mass-spectrometry can distinguish between epitope effects and bona fide protein quantitative traits.

Technical noise in experiments is unavoidable, but it introduces inaccuracies into the biological networks we infer from the data. Here, the authors introduce a diffusion-based method for denoising undirected, weighted networks, and show that it improves the performances of downstream analyses.

Individual microbes in metagenomic samples are sequenced by assembly of barcoded short reads

A description is given of the ENCODE consortium’s efforts to examine the principles of human transcriptional regulatory networks; the results are integrated with other genomic information to form a hierarchical meta-network where different levels have distinct properties.

Most databases of genotype-phenotype associations are manually curated. Here, Kuleshov et al. describe a machine curation system that extracts such relationships from the GWAS literature and synthesizes them into a structured knowledge base called GWASkb that can complement manually curated databases.

Whole-genome bisulfite sequencing along with whole-genome and transcriptome sequencing of 100 prostate cancer metastases identifies genomic regions that are differentially methylated during disease progression and a novel epigenomic subtype.

The next step after sequencing a genome is to figure out how the cell actually uses it as an instruction manual. A large international consortium has examined 1% of the genome for what part is transcribed, where proteins are bound, what the chromatin structure looks like, and how the sequence compares to that of other organisms.

Identifying molecular subtypes of cancer can improve personalized treatment. Here the authors present CIMLR, an algorithm that integrates multi-omic data to reveal cancer subtypes; subtypes discovered by CIMLR differ in activity of cancer-associated pathways and are significantly predictive of patient outcomes.

GROC-SVs enables the accurate detection and reconstruction of large and complex structural variants from read clouds generated by the 10x Genomics platform and subsequent Illumina sequencing.

Mammalian genomes are scattered with repetitive sequences, but their biology remains largely elusive. Here, the authors show that transcription can initiate from short tandem repetitive sequences, and that genetic variants linked to human diseases are preferentially found at repeats with high transcription initiation level.

This overview of the ENCODE project outlines the data accumulated so far, revealing that 80% of the human genome now has at least one biochemical function assigned to it; the newly identified functional elements should aid the interpretation of results of genome-wide association studies, as many correspond to sites of association with human disease.

An international consortium reports the genomic sequence for ten Drosophila species, and compares them to two other previously published Drosophila species. These data are invaluable for drawing evolutionary conclusions across an entire phylogeny of species at once.

In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.

Machine learning methods are becoming increasingly important in the analysis of large-scale genomic, epigenomic, proteomic and metabolic data sets. In this Review, the authors consider the applications of supervised, semi-supervised and unsupervised machine learning methods to genetic and genomic studies. They provide general guidelines for the selection and application of algorithms that are best suited to particular study designs.