Program for converting nucleotide or amino acid sequences into an "optimized" nucleotide sequence using nucleotide triplet information.
Automatically downloads codon translation tables from NCBI.
Can parse several formats of codon usage tables (see included examples).
- Python >= 3.6 (source, binaries, documentation)
- requests module (source, whls, documentation)
This will print out command line parameter descriptions and examples.
$ python3 simple-codom-optimizer.py --helpIt returns the following output.
usage: simple-codon-optimizer.py [-h] [--deterministic] [--samples N] [--display N] [--suppress] USAGE/EXPRESSION_TABLE TRANSLATION_TABLE SEQUENCE description: Simple program for optimizing a protein-coding sequence. Several formats for codon usage table are supported (See included example files). Additionally, a gene expression table can be provided to base codon optimality from. Output is in the format (FREQUENCY, SEQUENCE). The program will first check to see if the input SEQUENCE is composed exclusively of 'nt' characters. If it is not, then it will check to see if it is made of 'aa' characters. Space (' ') characters are allowed in SEQUENCE. positional arguments: USAGE/EXPRESSION_TABLE File containing either the codon usage table (counts), or a gene expression table. TRANSLATION_TABLE The translation table id. SEQUENCE 'nt' or 'aa' sequence to optimize. optional arguments: -h, --help Show this help message and exit. --deterministic Instead of calculating a distribution of sequences, just find the single most-optimal sequence. (default: False) --samples N Number of sequences to generate. (default: 100000) --display N Number of output sequences to display. (default: 10) --suppress Suppress STDERR messages. (default: False) examples: python3 simple-codon-optimizer.py examples/5501_codons.txt 1 ASRWLAQC python3 simple-codon-optimizer.py "examples/Codon usage table 5501.html" 1 "GCA TCA AGA TGG CTG GCG CAA TGT" python3 simple-codon-optimizer.py examples/C_albicans_codon_usage.tab 12 EGRGSLLTCGDVEENPGP --deterministic
A basic program usage using DNA triplets as input would look like this.
$ python3 simple-codon-optimizer.py "examples/Codon usage table 5501.html" 1 "GCA TCA AGA TGG CTG GCG CAA TGT" --suppressWhich returns the following.
0.0009 GCCTCCCGCTGGCTCGCCCAGTGC 0.00073 GCCAGCCGCTGGCTCGCCCAGTGC 0.00072 GCCTCCCGCTGGCTCGCCCAGTGT 0.00066 GCCTCCCGCTGGCTCGCTCAGTGC 0.00065 GCCTCTCGCTGGCTCGCCCAGTGC 0.00065 GCCTCTCGTTGGCTCGCCCAGTGC 0.00062 GCCTCCCGTTGGCTCGCCCAGTGC 0.00061 GCCAGCCGCTGGCTCGCTCAGTGC 0.0006 GCCTCTCGCTGGCTCGCTCAGTGC 0.00059 GCTAGCCGCTGGCTCGCCCAGTGC
Here's a simple example using an amino acid sequence as input without the --suppress command line option.
$ python3 simple-codon-optimizer.py examples/5501_codons.txt 1 ASRWLAQCThis creates the following (verbose) output.
Loading translation tabels. Parsing codon usage table. Usage table: TTT 291368 0.016864017795171243 TTC 349064 0.02020339058391332 TTA 140536 0.008134049054330558 TTG 281524 0.016294259307019953 CTT 337452 0.01953130245262392 CTC 343944 0.019907051345866324 CTA 163988 0.00949142167360363 CTG 278148 0.016098860621932717 ATT 329656 0.019080079659691426 ATC 377884 0.021871456373076283 ATA 168296 0.009740763360616607 ATG 368400 0.02132253423760017 GTT 313632 0.018152630450616224 GTC 314600 0.018208657087809485 GTA 121956 0.007058661741261581 GTG 258528 0.014963279401135442 TAT 237604 0.013752224280648075 TAC 222076 0.012853482935258674 TAA 13436 0.0007776589848436371 TAG 9092 0.00052623366256314 CAT 232416 0.013451949287095769 CAC 190644 0.011034237831685795 CAA 326356 0.01888907976016895 CAG 370060 0.02141861297493572 AAT 331276 0.019173843246729733 AAC 319832 0.018511478746688757 AAA 402628 0.023303608346950274 AAG 481472 0.02786700109784427 GAT 531948 0.030788489257934135 GAC 419976 0.024307689030864194 GAA 560712 0.032453313836680965 GAG 524856 0.030378013110233103 TCT 287384 0.016633428825565927 TCC 298932 0.017301812716379733 TCA 230612 0.013347536008690148 TCG 229684 0.01329382452179413 CCT 283644 0.016416962272773786 CCC 248512 0.014383565766706009 CCA 306240 0.017724790675685876 CCG 240160 0.013900162384641849 ACT 235828 0.013649431607450524 ACC 289932 0.016780903899500252 ACA 244556 0.01415459740230876 ACG 206384 0.01194524947365058 GCT 381676 0.02209093262125484 GCC 381452 0.02207796777959028 GCA 338976 0.01961950967894885 GCG 282004 0.016322041110586858 TGT 94896 0.005492462565177269 TGC 131700 0.007622632353669768 TGA 17112 0.0009904212971601903 TGG 234756 0.013587385579484435 CGT 168068 0.009727567003922327 CGC 215888 0.012495329184275313 CGA 210676 0.012193665100544662 CGG 167304 0.009683347633245003 AGT 172268 0.009970657785132753 AGC 264248 0.01529434589364107 AGA 208276 0.012054756082710134 AGG 157392 0.009109653389588399 GGT 272268 0.015758533528238118 GGC 347092 0.020089253674259278 GGA 315272 0.018247551612803153 GGG 200944 0.011630389033225648 Treating input sequence as 'aa'. Confusion matrix with potential codons for each aa in sequence. aa A S R W L A Q C high GCT TCC CGC TGG CTC GCT CAG TGC GCC TCT CGA CTT GCC CAA TGT GCA AGC AGA TTG GCA GCG TCA CGT CTG GCG TCG CGG CTA low AGT AGG TTA 0.00033 GCTTCTAGATGGCTTGCCCAGTGC 0.00031 GCATCTCGCTGGCTTGCTCAGTGC 0.0003 GCCTCTAGATGGCTTGCTCAATGC 0.00029 GCTTCCCGATGGCTCGCCCAGTGC 0.00028 GCCTCTCGATGGCTTGCCCAGTGC 0.00028 GCTTCTCGCTGGTTGGCCCAGTGC 0.00028 GCTTCTAGATGGCTCGCTCAGTGC 0.00027 GCATCCCGATGGCTCGCCCAGTGC 0.00026 GCTTCTCGATGGCTTGCTCAGTGC 0.00026 GCATCTCGCTGGCTCGCTCAATGC Simple Codon Optimizer finished.
An experimental feature allows you to codon-optimize using gene expression data instead of just the codon usage frequencies.
The example file examples/expressions.txt contains some simple test data:
# gene expression sequence gene_0001 10 GCA TAC GCA TAT AGG GCT CCT CAG ACT CAG CCA TAT GGC TCT TAA gene_0002 100 GCA AAA TAT AAT GCC TAG AAT GGC ATA GGC CAT GGT AAG AAT GAC TCT CGC TAT TAA gene_0003 1000 GTG ATG AAT GGT TTC GTT GAA TTA GCT ATA GGT AAG ATG AGA TAC GCT AAG CGC TAC GAT CAT AGC CAT TAG gene_0004 125 GCT CCG CTA GCT CAG ATA TAG AAT CGC CTC GCT CGC TGA
We specify an amino acid input with multiple alanine residues:
$ python3 simple-codon-optimizer.py examples/expressions.txt 1 ASRWLAQCAnd we see the alanine codon associated with the highest expression changes depending on its location in the sequence.
Loading translation tables. Treating input sequence as 'aa'. Parsing input file. No valid codon usage table discovered. Assuming input is an expression table. Confusion matrix with potential codons for each aa in sequence. aa A S R W L A Q C high GCA AGC AGA TGG TTA GCT CAA TGT GCG TCG CGA CTA GCG CAG TGC GCT TCC CGG CTT GCC GCC TCA CGT CTG GCA AGT AGG TTG low TCT CGC CTC Simple Codon Optimizer finished.
Thaddeus D. Seher (@tdseher)
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