These scripts were applied to study the evolution of FNS I in the Apiaceae. Please cite the corresponding publication when using these scripts.
This script performs an automatic collection of sequences with some similarity to the bait sequences. Since BLAST does not allow the reliable identification of orthologs, a following validation through a phylogenetic analysis is necessary for most applications.
Usage:
python collect_best_BLAST_hits.py --baits <FILE> --out <DIR> [--subject <FILE>|--subjectdir <DIR>]
Mandatory (option1):
--baits STR A multiple FASTA file.
--out STR Directory for temporary and output files.
--subject STR Subject sequence file.
Mandatory (option2):
--baits STR A multiple FASTA file.
--out STR Directory for temporary and output files.
--subjectdir STR Folder containing subject sequence files.
Optional:
--number STR Number of BLAST hits to consider[10]
--simcut FLOAT Minimal alignment similarity[30.0]
--lencut INT Minimal alignment length[30]
--cpu INT Number of CPUs[4]
--baits FASTA file containing the bait sequences.
--out is the output folder. The folder will be created if it does not exist already.
--subject is the subject file for the screen via BLAST. Candidates will be identified in this collection of sequences.
--subjectdir is a folder containig the subject files. This option allows the automatic collection of sequences from multiple different species. This option prevents the need to run the analysis multiple times with different subject files.
--number defines how many different BLAST hits will be considered per query. A non-redundant set of sequences will be collected per subject sequence. Increasing this number make the search more sensitive, but also computationally more expensive. Default: 10.
--simcut specifies the minimal similarity cutoff. Default: 30.0.
--lencut specifies the minimal alignment length. Default: 30.
--cpus specifies the numbers of cores to use. Default: 4.
Extract the sequences belonging to IDs that are highlighted in a phylogenetic tree. This script enables the selection of specific clades after inspecting a tree in FigTree. It is important that the "Clade" is selected and colored in red (R=255, G=0,B=0; default) or any other color of choice. The color needs to be specified if it is not red.
Usage:
python extract_red.py --tree <FILE> --out <FILE> --seq <FILE>
Mandatory:
--tree STR Tree file.
--out STR Sequence ouptut file.
--seq STR Sequence input file.
Optional:
--taxon STR Taxon file.
--color STR Color of highlighted clade.[#ff0000]
--tree phylogenetic tree file. One clade is highlighted by a color ('clade' needs to be highlighted via FigTree). The color string attached to the sequence names is used for the extraction of the IDs of interest and used for the collection of these sequences in a new file.
--out output FASTA file contains the sequences of the highlighted IDs.
--seq FASTA input file contains all sequences that were used for the phylogenetic tree construction. A subset of these sequences will be written into the output file.
--taxon this taxon file allows to connect IDs in the tree file to different IDs in the FASTA input file. This is necessary if sequence IDs were replaced by sequence names after constructing the tree. If possible, the need for this option should be avoided.
--color color that was used to highlight one clade in the tree file. Default color is red (#ff0000). FigTree provides an option to set the color. The default red is R=255, G=0, and B=0.
This scripts performs a search for genes that were missed in the annotation process. The results of a TBLASTN are processed and hits are compared against the annotated genes in a GFF3 file. If a gene is bit by BLAST, the ID is returned in the output file. The genomic position of a BLAST hit is returned if no gene is hit. This enables a phylogenetic evaluation of the candidates.
Usage:
python3 TBLASTN_check.py --in <FILE> --out <FILE> --ref <FILE> --gff <FILE>
Mandatory:
--in STR FASTA file.
--out STR Ouptut folder.
--ref STR Reference FASTA file.
--gff STR Annotation file.
--in FASTA input file containing the bait sequence(s).
--out output folder for temporary and final output files. This folder will be created if it does not exist already.
--ref genome sequence FASTA file.
--gff annotation file (GFF3) corresponding to the genome sequence FASTA file.
This script generate a gene expression plot for a selection of genes based on a table of TPM values.
Usage:
python3 exp_plots.py --genes <FILE> --out <FILE> --exp <FILE>
Mandatory:
--genes STR Input file.
--out STR Ouptut figure file.
--exp STR Expression table (TPMs).
Optional:
--cutfac FLOAT Outliers defining number of IQRs.
--logscale - Activates log scale [off]
--genes text file with one gene ID per line. It is possible to provide an additional gene symbol in the second column if both columns are tab separated. The gene ID needs to match an entry in the TPM table.
--out specifies the output figure file. The extension of the file determines the file format. Only formats supported by matplotlib and the local system are possible.
--exp specifies a table of expression values (TPMs).
--cutfac sets the outlier definition. Outliers are identified by the number of IQRs between them and the median. This arguments defines how many IQRs are the cutoff. Default: 3.
--logscale setting this flag actives a log scale in the figure. Default: linear scale.
This script generate a gene expression plot for a selection of genes based on a table of TPM values and a selection of samples. One figure per specified organ/tissue will be generated.
Usage:
python3 exp_plot_tissue.py --genes <FILE> --out <FILE> --exp <FILE>
Mandatory:
--genes STR Input file.
--out STR Ouptut figure file.
--exp STR Expression table (TPMs).
--samples STR Sample input file.
Optional:
--cutfac FLOAT Outliers defining number of IQRs.
--logscale - Activates log scale [off]
--genes text file with one gene ID per line. It is possible to provide an additional gene symbol in the second column if both columns are tab separated. The gene ID needs to match an entry in the TPM table.
--out specifies the output figure file. The extension of the file determines the file format. Only formats supported by matplotlib and the local system are possible.
--exp specifies a table of expression values (TPMs).
--samples specifies a table with organ/tissue name in the first column and a comma-separated list of run IDs in the second column. Both columns are TAB-separated. The list of run IDs may contain a single ID.
--cutfac sets the outlier definition. Outliers are identified by the number of IQRs between them and the median. This arguments defines how many IQRs are the cutoff. Default: 3.
--logscale setting this flag actives a log scale in the figure. Default: linear scale.
Usage:
python3 coexp3.py --genes <FILE> --out <FILE> --exp <FILE>
Mandatory:
--in STR Genes input file.
--out STR Ouptut file.
--exp STR Expression table (TPMs).
Optional:
--ann STR Annotation file name.[off]
--rcut FLOAT Minimal correlation fector.[0.65]
--pcut FLOAT Maximal p-value cutoff[0.05]
--expcut FLOAT Minimal expression cutoff[5]
--in text file with one gene ID per line.
--out output file.
--exp text file with gene expression values. Genes are in idividual rows and samples are in columns. Gene IDs need to be matching the gene IDs of interest.
--ann annotation text file. Gene ID is given in first column and annotation string in second column if available.
--rcut minimal correlation coefficient to consider candidate genes. Default: 0.65.
--pcut maximal adjusted p-value to consider candidate genes. Default: 0.05.
--expcut minimal cumulative gene expression across all samples of a species. Default: 5.
This script calculates the percentage of identical amino acids between all pairs of sequences in a given FASTA file.
Usage:
python3 pairwise_comp3.py --in <FILE> --out <FOLDER>
Mandatory:
--in STR FASTA input file.
--out STR Ouptut folder.
--in specifies a multiple FASTA file that contains all sequences for the comparison. All pairs of sequences will be analyzed in global alignments constructed by MAFFT. The percentage of identical amino acid residues will be counted in the alignment.
--out specifies the output folder. This folder will be created if it does not exist already. Temporary alignment files and the final summary file will be placed in this folder.
This script constructs a functional annotation file for the co-expression analysis. Sequence similarity to Arabidopsis thaliana is exploited to transfer functional annotation to uncharacterized sequences.
Usage:
python3 construct_anno.py --in <FILE> --out <FOLDER> --ref <FILE> --anno <FILE>
Mandatory:
--in STR FASTA input file.
--out STR Ouptut folder.
--ref STR A. thaliana reference sequence file
--anno STR A. thaliana annotation file
--in specifies a multiple FASTA input file with peptide sequences that need to be functionally annotated.
--out specifies the output folder. This folder will be created if it does not exist already. Temporary files and final result files will be stored in this folder.
--ref specifies a FASTA file containing the representative peptide sequences of Arabidopsis thaliana. This file is used in a sequence similarity analysis against the input sequences.
--anno specifies an annotation file that matches the reference sequence file.
Please find all details about the co-expression analysis here: https://github.com/bpucker/CoExp.
This script performs the trimming of a multiple sequence alignment in FASTA format and returns a FASTA file.
Usage:
python3 algntrim.py --in <FILE> --out <FILE>
Mandatory:
--in STR FASTA input file.
--out STR Ouptut folder.
Optional:
--occ FLOAT Minimal column occupancy
--sort Activates alphanumerical sorting
--in specifies the input file that contains a multiple sequence alignment in FASTA format.
--out specifies the output file that will contain the trimmed multiple sequence alignment in FASTA format.
--occ specifies the minimal occupancy of an alignment column. Columns with more gaps than allowed by this argument will be removed from the alignment. Default: 0.1 (10% gaps allowed).
--sort activates the alphanumerical sorting of sequences in the output file.
Pucker, B. & Iorizzo M. (2022). Apiaceae FNS I originated from F3H through tandem gene duplication. bioRxiv 2022.02.16.480750; doi:10.1101/2022.02.16.480750