In the past decade, several studies have estimated the human per-generation germline mutation rate using large pedigrees. More recently, estimates for various non-human species have been published. However, methodological differences among studies in detecting germline mutations and estimating mutation rates make direct comparisons difficult. Here, we describe the many different steps involved in estimating pedigree-based mutation rates, including sampling, sequencing, mapping, variant calling, filtering, and how to appropriately account for false-positive and false-negative rates. For each step, we review the different methods and parameter choices that have been used in the recent literature. Additionally, we present the results from a 'Mutationathon', a competition organized among five research labs to compare germline mutation rate estimates for a single pedigree of rhesus macaques. We report almost a two-fold variation in the final estimated rate among groups using different post-alignment processing, calling, and filtering criteria and provide details into the sources of variation across studies. Though the difference among estimates is not statistically significant, this discrepancy emphasizes the need for standardized methods in mutation rate estimations and the difficulty in comparing rates from different studies. Finally, this work aims to provide guidelines for computational and statistical benchmarks for future studies interested in identifying germline mutations from pedigrees.

During protein synthesis genetic instructions are passed from DNA via mRNA to the ribosome to assemble a protein chain. Occasionally, stop codons in the mRNA are bypassed and translation continues into the untranslated region (3′-UTR). This process, called translational readthrough (TR), yields a protein chain that becomes longer than would be predicted from the DNA sequence alone. Protein sequences vary in propensity for translational errors, which may yield evolutionary constraints by limiting evolutionary paths. Here we investigated TR in Saccharomyces cerevisiae by analysing ribosome profiling data. We clustered proteins as either prone or non-prone to TR, and conducted comparative analyses. We find that a relatively high frequency (5%) of genes undergo TR, including ribosomal subunit proteins. Our main finding is that proteins undergoing TR are highly expressed and have a higher proportion of intrinsically disordered C-termini. We suggest that highly expressed proteins may compensate for the deleterious effects of TR by having intrinsically disordered C-termini, which may provide conformational flexibility but without distorting native function. Moreover, we discuss whether minimizing deleterious effects of TR is also enabling exploration of the phenotypic landscape of protein isoforms.

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The mutation rate of a specific position in the human genome depends on the sequence context surrounding it. Modeling the mutation rate by estimating a rate for each possible k-mer, however, only works for small values of k since the data becomes too sparse for larger values of k. Here we propose a new method that solves this problem by grouping similar k-mers. We refer to the method as k-mer pattern partition and have implemented it in a software package called kmerPaPa. We use a large set of human de novo mutations to show that this new method leads to improved prediction of mutation rates and makes it possible to create models using wider sequence contexts than previous studies. As the first method of its kind, it does not only predict rates for point mutations but also insertions and deletions. We have additionally created a software package called Genovo that, given a k-mer pattern partition model, predicts the expected number of synonymous, missense, and other functional mutation types for each gene. Using this software, we show that the created mutation rate models increase the statistical power to detect genes containing disease-causing variants and to identify genes under strong selective constraint.