Package: deTELpy
detecting Translation Error Landscape: deTELdeTEL Python packageempirical Translation Error Landscape: eTELCommand line optionsExamplemultinomial Translation Error Landscape: mTELCommand line optionsExamplesNormal runBootstrapping datasetsdetecting Translation Error Landscape: deTELdeTEL is a simple pipeline that allows for the exploration of translation errors in mass-spectrometry data.
deTEL consists of two components:eTEL detects translation errors in mass-spectrometry data and explores the empirical translation error landscape.mTEL is a model fitted to the translation errors detected by eTEL and describes the multinomial translation error landscape and extends the empirical translation error landscape.
deTELpy packages these components into an easy to use python package.deTEL Python packagepipinstalldeTELpy

python-mdeTEL# To run eTEL/mTEL with GUI# To run eTEL with command linepython-mdeTELeTEL-ftests/resources/s228c_orf_cds.fasta-psmtests/resources/psm.tsv-otests/output-decoyrev_-psubstitutions-tol0.005# To run mTEL with command linepython-mdeTELmTEL-ftests/resources/results_ionquant2-rtests/resources/tRNA_count/yeast_tRNA_count.csv-otests/output-s250-p100-c4.2e-17-t10-b100-nb-1-anempirical Translation Error Landscape: eTELeTEL detects the empirical translation error landscape by first performing an open search using MSFragger(see: Perform open search).
The second step is to extract translation errors using custom pythons scripts packaged(see: detect_substitutions).
The output of eTEL can directly be used to fit the mTEL model(see: multinomial Translation Error Landscape: mTEL).Command line options-f: Fasta file of coding sequences located, matching the amino acid sequenes used for the open search.-psm: path to psm.tsv file created by the open search (by philosopher).-s: Folder to which output files are writen.-p: Prefix used for output files-decoy: identification prefix of decoy sequences (default: rev_)-tol: m/z tolerance, used to filter DP–BP couples that resemble substitutions and exclude pairs that resemble known PTM (default: 0.005)ExampleThe below command will detect substitutions in the specifiedpsm.tsvcreated by the open search.
Since we may have one experimenet and want to collect all files in a common folder, we specify a prefix (Experiment1) to identify the output belonging to each individual experiment.python-mdeTELeTEL-fproject/fasta/s228c_orf_cds.fasta-psmproject/open_search_experiment1/psm.tsv-oproject/results-pExperiment1multinomial Translation Error Landscape: mTELmTEL uses observed translation errors to estimate a multinomial translation error landscape.
mTEL is based on the competition of tRNAs and estimates affinity parameters between codon/anticodon pairs.Command line options-f: Folder with codon_count and error files.-r: tRNA count file.-o: Output folder.-s: Number of samples of the chain.-p: Number of posterior samples.-c: Cell volume assumed (in cubic micrometers), Default: 4.2e-17 (approximate size of a yeast cell).-t: Number to thin out chain by.-b: Number of burn-in steps.-nb: Number of sub-samplings performed.-os: suffix added to output files (default: date).-a: aggregate all datasets by summation (y,n) Default: No (n).ExamplesNormal runWe assume that the folderecolicontains all needed pairs of*_codon_counts.csvand*_substitution_errors.csvfiles.
A cell volume of 0.6e-18 is assumed for E. coli. We will collect 1000 samples after disgarding 1000 burn-in samples.
In total, this run will perform (1000 + 1000) * 10 = 20000 steps. The last 200 samples will be used to estimate the posterior distributions of the indivisual parameters.$python-mdeTELmTEL-fecoli-rtRNA_count/ecoli_tRNA_count.csv-c0.6e-18-ooutput/ecoli/-s1000-p200-t10-b1000Bootstrapping datasetsWe can bootstrap datasets as they can show a high variability. This allows us to explore parameter sensitivity and robustness.
This run perfomes 20 resamplings with replacement of the datasets found in the folderyeast, keeping the number of datasets constant.
For each resampling, the model will collect 250 samples after 10 burn-in steps and perform a total of (250 + 10) * 20 = 5200. The last 100 samples of each run will be used to estimate the posterior mean.$python-mdeTELmTEL-fyeast-rtRNA_count/yeast_tRNA_count.csv-c4.2e-17-ooutput/yeast/-s250-p100-t20-b10-nb20