MOTIVATION: Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry. RESULTS: Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions. AVAILABILITY AND IMPLEMENTATION: deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.

Summary: AnaCoDa is an R package for estimating biologically relevant parameters of mixture models, such as selection against translation inefficiency, non-sense errors and ribosome pausing time, from genomic and high throughput datasets. AnaCoDa provides an adaptive Bayesian MCMC algorithm, fully implemented in C++ for high performance with an ergonomic R interface to improve usability. AnaCoDa employs a generic object-oriented design to allow users to extend the framework and implement their own models. Current models implemented in AnaCoDa can accurately estimate biologically relevant parameters given either protein coding sequences or ribosome foot-printing data. Optionally, AnaCoDa can utilize additional data sources, such as gene expression measurements, to aid model fitting and parameter estimation. By utilizing a hierarchical object structure, some parameters can vary between sets of genes while others can be shared. Genes may be assigned to clusters or membership may be estimated by AnaCoDa. This flexibility allows users to estimate the same model parameter under different biological conditions and categorize genes into different sets based on shared model properties embedded within the data. AnaCoDa also allows users to generate simulated data which can be used to aid model development and model analysis as well as evaluate model adequacy. Finally, AnaCoDa contains a set of visualization routines and the ability to revisit or re-initiate previous model fitting, providing researchers with a well rounded easy to use framework to analyze genome scale data. Availability and implementation: AnaCoDa is freely available under the Mozilla Public License 2.0 on CRAN (https://cran.r-project.org/web/packages/AnaCoDa/).

BACKGROUND: Any method that de novo predicts protein function should do better than random. More challenging, it also ought to outperform simple homology-based inference. METHODS: Here, we describe a few methods that predict protein function exclusively through homology. Together, they set the bar or lower limit for future improvements. RESULTS AND CONCLUSIONS: During the development of these methods, we faced two surprises. Firstly, our most successful implementation for the baseline ranked very high at CAFA1. In fact, our best combination of homology-based methods fared only slightly worse than the top-of-the-line prediction method from the Jones group. Secondly, although the concept of homology-based inference is simple, this work revealed that the precise details of the implementation are crucial: not only did the methods span from top to bottom performers at CAFA, but also the reasons for these differences were unexpected. In this work, we also propose a new rigorous measure to compare predicted and experimental annotations. It puts more emphasis on the details of protein function than the other measures employed by CAFA and may best reflect the expectations of users. Clearly, the definition of proper goals remains one major objective for CAFA.