Analyze genomic phage display NGS data.
Teixeira AAR, et al. A refined genome phage display methodology delineates the human antibody response in patients with Chagas disease. iScience 2021; 24:102540.
Process fastq files to extract insert sequences and compare to reference genomes and proteomes.
Customize variables to adjust to you pipeline:
| Variable | Description |
|---|---|
| BLASTN_EXE | path/command to BlastN executable |
| BLASTN_DB | path to BlastN database |
| BLASTP_EXE | path/command to BlastP executable |
| BLASTP_DB | path to BlastP database |
| dict_sample_fastq | Sample names and path to respective FastQ to be processes. To add a sample, add a new line with the following code: dict_sample_fastq["Sample Name"] = "path_to_fastq.fastq" |
| cutoff_dna_identity | Minimum relative identity to reference genome for a DNA insert to be considered a hit (default is 0.9) |
| cutoff_peptide_similarity | Minimum relative similarity to reference proteome for a peptide to be considered a hit (default is 0.6) |
| flank_upstream | sequence found upstream of the DNA insert (default set for pG8SAET sequence) |
| flank_downstream | sequence found downstream of the DNA insert (default set for pG8SAET sequence) |
The script will generate the following output:
| Output File | Description |
|---|---|
| dna_dataframe.xlsx | table containing all DNA inserts identified in the all samples, respective frequencies in each sample, including the frequency in each sample and BlastN results against reference genomes. |
| dna_above_cutoff.xlsx | same as dna_dataframe.xlsx, but containing only the inserts that are above the BlastN identity cutoff (match reference genomes) |
| dna_below_cutoff.xlsx | same as dna_dataframe.xlsx, but containing only the inserts that are below the BlastN identity cutoff (do not match reference genomes) |
| peptide_dataframe.xlsx | table containing all peptides identified in the all samples peptide, including the frequency in each sample and BlastP results against reference proteomes. |
| norm_peptide_df.xlsx | same as peptide_dataframe.xlsx, but with counts normalized by total DNA (relative frequency) |
| peptide_above_cutoff.xlsx | same as peptide_dataframe.xlsx, but containing only the peptides that are above the BlastP similarity cutoff (match reference proteomes) |
| peptide_below_cutoff.xlsx | same as peptide_dataframe.xlsx, but containing only the peptides that are below the BlastP similarity cutoff (do not match reference proteomes) |
| statistics.xlsx | general processing statistics |
| peptides.txt | A list of all peptides identified by the script. This file can used as input for the clustering.py script |
- Pandas (v1.3.0)
- Biopython (v1.79)
- Numpy (v1.21.0)
Cluster peptide sequences to identify epitopes.
Customize variables to adjust to you pipeline:
| Variable | Description |
|---|---|
| PEPTIDES_TXT | Path to the file containing the peptides to be clustered (text file containing one peptide sequence per line) |
| MAFFT | path/command to MAFFT executable |
| HMMBUILD | path/command to hmmbuild executable |
| HMMEMIT | path/command to hmmemit executable |
| K | K-mer length for peptide search. Only peptides that share a K-mer are compared. Default is 4 amino acids. |
| min_len | Minimum length of the peptides used for clustering. Peptides with length below this threshold are excluded. Default is 6 amino acids. |
| score_cutoff | Minimum score (% partial identity) for peptides to be clustered together. Default is 80. |
The script will generate the following output:
| Output File | Description |
|---|---|
| cluster_consensus.xlsx | table containing cluster IDs, number of different peptides forming the clusters, and consensus sequence. |
| peptide_cluster.xlsx | table containing all peptide sequences and corresponding cluster. |
| align folder | Folder containing the multiple sequence alignments, consensus sequences, and HMM profiles for each cluster. |
- Pandas (v1.3.0)
- Biopython (v1.79)
- Fuzzywuzzy (v0.18.0)