Generate putative blocking oligos based on barcoding sequences for use in target enrichment.
Takes an excel file as an input containing two columns 'barcode_name' and 'barcode_sequence', for each of the sequences in this file it prepends sets of random bases until an oligo is found that melts within a set range. The results are then written to another excel file along with the estimated melting temperature.
Default melting temperatures are based on the blockers published in Scheunert et al. "Nano-Strainer: A workflow for the identification of single-copy nuclear loci for plant systematic studies, using target capture kits and Oxford Nanopore long reads"
- Python 3.X
- pandas
- openpyxl
- Biopython (Bio.SeqUtils)
-m, --minMelt # Minimum oligo melting temperature. Defaults to 63.4
-M, --maxMelt # Maximim oligo melting temperature. Defaults to 65.6
-n, --nbases # Number of bases to prepend. Defaults to 8
-b, --barcodes # Directory and name for input excel file containing barcode sequences and names.
-o, --output # Directory and name for output excel file. Defaults to ./output.xlsx
Based on the barcoding sequences and 5' flanking regions from the Oxford Nanopore PCR Barcoding Expansion (EXP-PBC0001)
| barcode_name | barcode_sequence |
|---|---|
| BC01 | GGTGCTGAAGAAAGTTGTCGGTGTCTTTGTG |
| BC02 | GGTGCTGTCGATTCCGTTTGTAGTCGTCTGT |
| BC03 | GGTGCTGGAGTCTTGTGTCCCAGTTACCAGG |
| BC04 | GGTGCTGTTCGGATTCTATCGTGTTTCCCTA |
| BC05 | GGTGCTGCTTGTCCAGGGTTTGTGTAACCTT |
| BC06 | GGTGCTGTTCTCGCAAAGGCAGAAAGTAGTC |
| BC07 | GGTGCTGGTGTTACCGTGGGAATGAATCCTT |
| BC08 | GGTGCTGTTCAGGGAACAAACCAAGTTACGT |
| BC09 | GGTGCTGAACTAGGCACAGCGAGTCTTGGTT |
| BC10 | GGTGCTGAAGCGTTGAAACCTTTGTCCTCTC |
| BC11 | GGTGCTGGTTTCATCTATCGGAGGGAATGGA |
| BC12 | GGTGCTGCAGGTAGAAAGAAGCAGAATCGGA |