It has proven exceedingly difficult to ascertain rare copy number alterations (CNAs) that may have strong effects in individual tumors. We show that a regulatory network inferred from gene expression and gene copy number data of 768 human cancer cell lines can be used to quantify the impact of patient-specific CNAs on survival signature genes. A focused analysis of tumors from six tissues reveals that rare patient-specific gene CNAs often have stronger effects on signature genes than frequent gene CNAs. Further comparison to a related network-based approach shows that the integration of indirectly acting gene CNAs significantly improves the survival analysis.

Adaptation of a complex trait often requires the accumulation of many modifications to finely tune its underpinning molecular components to novel environmental requirements. The investigation of cis-acting regulatory modifications can be used to pinpoint molecular systems partaking in such complex adaptations. Here, we identify cis-acting modifications with the help of an interspecific crossing scheme designed to distinguish modifications derived in each of the two sister species, Arabidopsis halleri and A. lyrata. Allele-specific expression levels were assessed in three environmental conditions chosen to reflect interspecific ecological differences: cold exposure, dehydration, and standard conditions. The functions described by Gene Ontology categories enriched in cis-acting mutations are markedly different in A. halleri and A. lyrata, suggesting that polygenic adaptation reshaped distinct polygenic molecular functions in the two species. In the A. halleri lineage, an excess of cis-acting changes affecting metal transport and homeostasis was observed, confirming that the well-known heavy metal tolerance of this species is the result of polygenic selection. In A. lyrata, we find a marked excess of cis-acting changes among genes showing a transcriptional response to cold stress in the outgroup species A. thaliana. The adaptive relevance of these changes will have to be validated. We finally observed that polygenic molecular functions enriched in derived cis-acting changes are more constrained at the amino acid level. Using the distribution of cis-acting variation to tackle the polygenic basis of adaptation thus reveals the contribution of mutations of small effect to Darwinian adaptation.

The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.

High-throughput methods based on mass spectrometry (proteomics, metabolomics, lipidomics, etc.) produce a wealth of data that cannot be analyzed without computational methods. The impact of the choice of method on the overall result of a biological study is often underappreciated, but different methods can result in very different biological findings. It is thus essential to evaluate and compare the correctness and relative performance of computational methods. The volume of the data as well as the complexity of the algorithms render unbiased comparisons challenging. This paper discusses some problems and challenges in testing and validation of computational methods. We discuss the different types of data (simulated and experimental validation data) as well as different metrics to compare methods. We also introduce a new public repository for mass spectrometric reference data sets (http://compms.org/RefData) that contains a collection of publicly available data sets for performance evaluation for a wide range of different methods.

The accurate mapping of causal variants in genome-wide association studies requires the consideration of both, confounding factors (for example, population structure) and nonlinear interactions between individual genetic variants. Here, we propose a method termed ‘mixed random forest’ that simultaneously accounts for population structure and captures nonlinear genetic effects. We test the model in simulation experiments and show that the mixed random forest approach improves detection power compared with established approaches. In an application to data from an outbred mouse population, we find that mixed random forest identifies associations that are more consistent with prior knowledge than competing methods. Further, our approach allows predicting phenotypes from genotypes with greater accuracy than any of the other methods that we tested. Our results show that approaches that simultaneously account for both, confounding due to population structure and epistatic interactions, are important to fully explain the heritable component of complex quantitative traits.

Lifespan is a remarkably diverse trait ranging from a few days to several hundred years in nature, but the mechanisms underlying the evolution of lifespan differences remain elusive. Here we de novo assemble a reference genome for the naturally short-lived African turquoise killifish, providing a unique resource for comparative and experimental genomics. The identification of genes under positive selection in this fish reveals potential candidates to explain its compressed lifespan. Several aging genes are under positive selection in this short-lived fish and long-lived species, raising the intriguing possibility that the same gene could underlie evolution of both compressed and extended lifespans. Comparative genomics and linkage analysis identify candidate genes associated with lifespan differences between various turquoise killifish strains. Remarkably, these genes are clustered on the sex chromosome, suggesting that short lifespan might have co-evolved with sex determination. Our study provides insights into the evolutionary forces that shape lifespan in nature.

Our current understanding of how natural genetic variation affects gene expression beyond well-annotated coding genes is still limited. The use of deep sequencing technologies for the study of expression quantitative trait loci (eQTLs) has the potential to close this gap. Here, we generated the first recombinant strain library for fission yeast and conducted an RNA-seq-based QTL study of the coding, non-coding, and antisense transcriptomes. We show that the frequency of distal effects (trans-eQTLs) greatly exceeds the number of local effects (cis-eQTLs) and that non-coding RNAs are as likely to be affected by eQTLs as protein-coding RNAs. We identified a genetic variation of swc5 that modifies the levels of 871 RNAs, with effects on both sense and antisense transcription, and show that this effect most likely goes through a compromised deposition of the histone variant H2A.Z. The strains, methods, and datasets generated here provide a rich resource for future studies.

Repurposing existing proteins for new cellular functions is recognized as a main mechanism of evolutionary innovation, but its role in organelle evolution is unclear. Here, we explore the mechanisms that led to the evolution of the centrosome, an ancestral eukaryotic organelle that expanded its functional repertoire through the course of evolution. We developed a refined sequence alignment technique that is more sensitive to coiled coil proteins, which are abundant in the centrosome. For proteins with high coiled-coil content, our algorithm identified 17% more reciprocal best hits than BLAST. Analyzing 108 eukaryotic genomes, we traced the evolutionary history of centrosome proteins. In order to assess how these proteins formed the centrosome and adopted new functions, we computationally emulated evolution by iteratively removing the most recently evolved proteins from the centrosomal protein interaction network. Coiled-coil proteins that first appeared in the animal–fungi ancestor act as scaffolds and recruit ancestral eukaryotic proteins such as kinases and phosphatases to the centrosome. This process created a signaling hub that is crucial for multicellular development. Our results demonstrate how ancient proteins can be co-opted to different cellular localizations, thereby becoming involved in novel functions.