# HG changeset patch # User vimalkumarvelayudhan # Date 1519759014 18000 # Node ID 231e4c6696756d9bff40e20fb6bd3fe4c35f8a00 Initial commit - v0.10.3 git commit deeded0 diff -r 000000000000 -r 231e4c669675 LICENSE --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/LICENSE Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. 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Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff -r 000000000000 -r 231e4c669675 README.md --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.md Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,270 @@ +# VIGA +De novo Viral Genome Annotator + +VIGA is a script written in Python 2.7 that annotates viral genomes automatically (using a de novo algorithm) and predict the function of their proteins using BLAST and HMMER. + +## REQUIREMENTS: + +Before using this script, the following Python modules and programs should be installed: + +* Python modules: + - BCBio (https://github.com/chapmanb/bcbio-nextgen) + - Biopython (Bio module; Cock et al. 2009) + - Numpy (https://github.com/numpy/numpy) + - Scipy (https://github.com/scipy/scipy) + +* Programs: + - GNU Parallel (Tange 2011): it is used to parallelize HMMER. The program is publicly available at https://www.gnu.org/software/parallel/ under the GPLv3 licence. + - LASTZ (Harris 2007): it is used to predict the circularity of the contigs. The program is publicly available at https://github.com/lastz/lastz under the MIT licence. + - Prodigal (Hyatt et al. 2010): it is used to predict the ORFs. When the contig is smaller than 20,000 bp, MetaProdigal (Hyatt et al. 2012) is automatically activated instead of normal Prodigal. This program is publicly available at https://github.com/hyattpd/prodigal/releases/ under the GPLv3 licence. + - BLAST+ (Camacho et al. 2008): it is used to predict the function of the predicted proteins according to homology. This suite is publicly available at ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/ under the GPLv2 licence. Databases are available at ftp://ftp.ncbi.nlm.nih.gov/blast/db/ + - DIAMOND (Buchfink et al. 2015): it is used to predict the function of proteins according to homology when "--noblast" parameter is used. This program is publicly available at https://github.com/bbuchfink/diamond under the GPLv3 licence. Databases must be created from FASTA files according to their instructions before running. + - HMMER (Finn et al. 2011): it is used to predict the function of the predicted proteins according to Hidden Markov Models. This suite is publicly available at http://hmmer.org/ under the GPLv3 licence. Databases must be in FASTA format and examples of potential databases are UniProtKB (ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_trembl.fasta.gz) or PFAM (http://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.fasta.gz). + - INFERNAL (Nawrocki and Eddy 2013): it is used to predict ribosomal RNA in the contigs when using the RFAM database (Nawrocki et al. 2015). This program is publicly available at http://eddylab.org/infernal/ under the BSD licence and RFAM database is available at ftp://ftp.ebi.ac.uk/pub/databases/Rfam/ + - ARAGORN (Laslett and Canback 2004): it is used to predict tRNA sequences in the contig. This program is publicly available at http://mbio-serv2.mbioekol.lu.se/ARAGORN/ under the GPLv2 licence. + - PILERCR (Edgar 2007): it is used to predict CRISPR repeats in your contig. This program is freely available at http://drive5.com/pilercr/ under a public licence. + - Tandem Repeats Finder (TRF; Benson 1999): it is used to predict the tandem repeats in your contig. This program is freely available at https://tandem.bu.edu/trf/trf.html under a custom licence. + - Inverted Repeats Finder (IRF; Warburton et al. 2004): it is used to predict the inverted repeats in your contig. This program is freely available at https://tandem.bu.edu/irf/irf.download.html under a custom licence. + +Although you can install the programs manually, we strongly recommend the use of the Docker image to create an environment for VIGA. The link to the Docker image is https://hub.docker.com/r/vimalkvn/viga/ + +However, you will need to download the databases for BLAST, HMMER, and INFERNAL: +* BLAST DBs: https://ftp.ncbi.nlm.nih.gov/blast/db/ +* BLAST FASTA (DIAMOND): https://ftp.ncbi.nlm.nih.gov/blast/db/FASTA +* RFAM (INFERNAL): http://ftp.ebi.ac.uk/pub/databases/Rfam/ +* UniProtKB (HMMER): ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/complete/uniprot_trembl.fasta.gz +* PFAM (HMMER): http://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.fasta.gz + +Note that this bioinformatic pipeline only takes protein databases (i.e. "nr", "swissprot"...)! +Additionally, before running the "ultrafast" mode, you need to convert the FASTA file to the DIAMOND DB format using the following command: + + diamond makedb --in nr -d nr + +When using this program, you must cite their use: + + VIGA v. 0.10.3 (https://github.com/EGTortuero/viga) + +## PARAMETERS: + +The program has the following two types of arguments: + +### Mandatory parameters: + + + + + +
--input FASTAFILEInput file as a nucleotidic FASTA file. It can contains multiple sequences (e.g. metagenomic contigs)
--rfamdb RFAMDBRFAM database that will be used for the ribosomal RNA prediction.
--modifiers TEXTFILEInput file as a plain text file with the modifiers per every FASTA header according to SeqIn (https://www.ncbi.nlm.nih.gov/Sequin/modifiers.html). All modifiers must be written in a single line and are separated by a single space character. No space should be placed besides the = sign. For example: [organism=Serratia marcescens subsp. marcescens] [sub-species=marcescens] [strain=AH0650_Sm1] [moltype=DNA] [tech=wgs] [gcode=11] [country=Australia] [isolation-source=sputum]. This line will be copied and printed along with the record name as the definition line of every contig sequence.
+ +### Advanced parameters: + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
--readlength INTRead length for the circularity prediction (default: 101 bp)
--windowsize INTWindow length used to determine the origin of replication in circular contigs according to the cumulative GC skew(default: 100 bp)
--slidingsize INTSliding window length used to determine the origin of replication in circular contigs according to the cumulative GC skew(default: 10 bp)
--out OUTPUTNAMEName of the outputs files without extensions, as the program will add them automatically. By default, the program will use the input name as the output.
--locus STRINGName of the contigs. If the input is a multiFASTA file, please put a general name as the program will add the number of the contig at the end of the name. By default, the name of the contigs will be "LOC".
--threads INTNumber of threads/CPUs. By default, the program will use 1 CPU.
--gffPrinting the output as a General Feature Format (GFF) version 3. It is a flat table file with contains 9 columns of data (see http://www.ensembl.org/info/website/upload/gff3.html for more information). By default, the program will not print the GFF3 file (--gff False).
--blastdb BLASTDBBLAST database that will be used for the protein function prediction. The database MUST be for amino acids. This is only mandatory if the "ultrafast" mode is not active
--diamonddb DIAMONDDBDIAMOND database that will be used for the protein function prediction. The database MUST be for amino acids. This is only mandatory when "ultrafast" mode is active
--blastevalue FLOATBLAST e-value threshold. By default, the threshold will be 1e-05.
--nohmmerRunning the program without using PHMMER to predict protein function. In this case, the program will be as fast as Prokka (Seemann 2014) but the annotations will not be accurate. By default, this program had this flag disabled.
--noblastRunning the program replacing BLAST by DIAMOND. In this case, the program will be fast but the annotations will not be accurate. By default, this program had this flag disabled.
--hmmdb HMMDBPHMMER Database that will be used for the protein function prediction according to Hidden Markov Models. In this case, HMMDB must be in FASTA format (e.g. UniProt). This parameter is mandatory if the "--fast" option is disabled. "
--hmmerevalue FLOATPHMMER e-value threshold. By default, the threshold is 1e-03.
--typedata BCT|CON|VRL|PHGGenBank Division: One of the following codes: + + + + + +
BCTProkaryotic chromosome
VRLEukaryotic/Archaea virus
PHGPhages
CONContig
+By default, the program will consider every sequence as a contig (CON)
--gcode NUMBERNumber of GenBank translation table. At this moment, the available options are: + + + + + + + + + + + + + + + + + + + + + + + + + +
1Standard genetic code [Eukaryotic]
2Vertebrate mitochondrial code
3Yeast mitochondrial code
4Mycoplasma/Spiroplasma and Protozoan/mold/coelenterate mitochondrial code
5Invertebrate mitochondrial code
6Ciliate/dasycladacean/Hexamita nuclear code
9Echinoderm/flatworm mitochondrial code
10Euplotid nuclear code
11Bacteria/Archaea/Phages/Plant plastid
12Alternative yeast nuclear code
13Ascidian mitochondrial code
14Alternative flatworm mitochondrial code
16Chlorophycean mitochondrial code
21Trematode mitochondrial code
22Scedenesmus obliquus mitochondrial code
23Thraustochytrium mitochondrial code
24Pterobranquia mitochondrial code
25Gracilibacteria and Candidate division SR1
26Pachysolen tannophilus nuclear code
27Karyorelict nuclear code
28Condylostoma nuclear code
29Mesodinium nuclear code
30Peritrich nuclear code
31Blastocrithidia nuclear code
+By default, the program will use the translation table no. 11
--mincontigsize INTMinimum contig length to be considered in the final files. By default, the program only consider from 200 bp.
--idthr FLOATIdentity threshold to consider that a protein belong to a specific hit. By default, the threshold is 50.0 %
--coverthr FLOATCoverage threshold to consider that a protein belong to a specific hit. By default, the threshold is 50.0 %
--diffid FLOAT (>0.01)Max allowed difference between the ID percentages of BLAST and HMMER. By default, the allowed difference is 5.00 % and we do not recommended to change such value.
--minrepeat INTMinimum repeat length for CRISPR detection (Default: 16)
--maxrepeat INTMaximum repeat length for CRISPR detection (Default: 64)
--minspacer INTMinimum spacer length for CRISPR detection (Default: 8)
--maxspacer INTMaximum spacer length for CRISPR detection (Default: 64)
--blastexhUse of exhaustive BLAST to predict the proteins by homology according to Fozo et al. (2010). In this case, the search will be done using a word size of 2, a gap open penalty of 8, a gap extension penalty of 2, the PAM70 matrix instead of the BLOSUM62 and no compositional based statistics. This method is more accurate to predict the functions of the proteins but it is slower than BLAST default parameters. By default, exhaustive BLAST is disabled.
+ +## Examples +An example of execution (using BLAST and HMMER) is: + + python VIGA.py --input eukarya.fasta --blastdb databases/blast/nr/nr --hmmdb databases/UniProt/uniprot_trembl.fasta --rfamdb databases/rfam/Rfam.cm --gcode 1 --out eukarya_BENCHMARK --modifiers ../modifiers.txt --threads 10 + +Another example (but this time using BLAST but not HMMER - "fast mode") is: + + python VIGA.py --input bacteria.fasta --blastdb databases/blast/nr/nr --nohmmer --rfamdb databases/rfam/Rfam.cm --out bacteria_BENCHMARK --modifiers ../modifiers.txt --threads 10 + +Finally, an example using DIAMOND and not HMMER ("ultrafast mode") is: + + python VIGA.py --input archaea.fasta --noblast --diamonddb databases/diamond/nr --nohmmer --rfamdb databases/rfam/Rfam.cm --out archaea_BENCHMARK --modifiers ../modifiers.txt --threads 10 + +## Galaxy wrapper +VIGA can be integrated into [Galaxy](https://galaxyproject.org) using the wrapper included in this repository. + +### Requirements + +[Docker](https://www.docker.com) should first be installed and working on the +server where this Galaxy instance is setup. The user running Galaxy should be +part of the **docker** user group. + +#### Manual installation of the wrapper from Github + +1. Download or clone this repository (as a submodule) in the **tools** + directory of the Galaxy installation. + +2. Update **config/tool_conf.xml** to add the VIGA wrapper in a relevant section of the tool panel. For example, "Annotation". + +
+ +
+ +3. Copy (or update the file if it is already present) the included + **tool_data_table_conf.xml.sample** file to + **config/tool_data_table_conf.xml**. + + + + + value, dbkey, name, path + +
+ + value, dbkey, name, path + +
+ + value, dbkey, name, path + +
+ + value, dbkey, name, path + +
+ + +4. Copy the **.loc.sample** files from **viga/tool-data** to **galaxy/tool-data** + and rename them as **.loc**. For example: + + viga_blastdb.loc.sample -> viga_blastdb.loc + +#### Update database paths in .loc files + +Edit the following files in the **tool-data** directory and add paths to +corresponding databases + +* viga_blastdb.loc +* viga_diamonddb.loc +* viga_rfamdb.loc +* viga_hmmdb.loc + +#### Create or update the Galaxy job configuration file + +If the file **config/job_conf.xml** does not exist, create it by copying the +template **config/job_conf.xml.sample_basic** in the Galaxy directory. Then +add a Docker destination for viga. Change ``/data/databases`` under +``docker_volumes`` to the location where your databases are stored. Here is +an example: + + + + + + + + + + + + + + true + false + true + $defaults,/data/databases:ro + + + + + + + + +**Restart Galaxy**. The tool will now be ready to use. + + +## HISTORY OF THE SOURCE CODE: + +* v 0.10.3 - New output: all protein sequences per contig. +* v 0.10.1 - Fixed error when the start coordinate of a gene is equal to one. In these cases, genes were annotated as if they started in the position zero (which it has no biological logic). Now, the program should be able to deal with these genes, annotate them from the position 1. Moreover, added new terms to reduce all non-informative protein descriptions before running the decision tree. +* v 0.10.0 - Added the prediction of the origin and terminus of replication for circular contigs based on the cumulative GC skew (based on the iRep software - Brown et al (2016)). After detecting the origin coordinate, the chromosome is realigned from the origin. As a consequence of that, two new parameters ("--windowsize" and "--slidingsize") were added to determine the window size and the sliding window size respectively. Moreover, fixed error with the start position of the genes in the GenBank files, which were not related to the amino acid sequences and made that the sequence length was not multiple of three. Finally, added "/locus_tag" in the putative genes in the GenBank files +* v 0.9.1 - Fixed a bug in creation of logfile +* v 0.9.0 - Improved the BLAST/DIAMOND and HMMER parsers to reduce all non-informative protein descriptions (e.g. "hypothetical protein", "ORF") before running the decision tree algorithm. Additionally, a new output file (logfile.txt) is generated to harbour the information about the old contig names and the new ones (generated by the program). +* v 0.8.2 - Fixed issue with DIAMOND when there is no protein sequence as input. +* v 0.8.1 - The program is able to deal with tmRNA sequences in a proper way. There were an error due to the "(Permuted)" flag in ARAGORN files in some cases. Additionally, the name of the "--fast" and "--ultrafast" parameters were changed to "--nohmmer" and "--noblast" as their descriptions are more accurate. +* v 0.8.0 - Added the "--ultrafast" parameter. In this case, DIAMOND (Buchfink et al. 2015) will be launch to predict protein function according to homology instead of BLAST. It is faster than the "fast" mode but the sensitivity of the annotations will not be the highest. +* v 0.7.1 - Fixed error on the "--fast" parameter. All proteins that had no hits in BLAST analyses were not parsed properly. By now, these are identified as "Hypothetical proteins" in all files. +* v 0.7.0 - Added the "--fast" parameter. In this case, the program will launch BLAST (but not PHMMER) to annotate protein function. In this case, the program will be as fast as Prokka (Seemann 2014) but the annotations will not be accurate. As a consequence of this new parameter, the "--hmmdb" parameter is only mandatory when this flag is NOT used (as by default). +* v 0.6.2 - Removed the "--noparallel" parameter. After doing time benchmarks to test the speed of BLAST and HMMER when they are run using the multithreading option and as a parallel program, we found that BLAST tends to be faster using multithreading option while HMMER had the opposite behavior. For that, we decided to consider only the parallelization of HMMER and to run BLAST using multiple threads. +* v 0.6.1 - Fixed issue with parallel HMMER (the program tend to take all available CPUs independently of the parsed arguments) and with the BLAST/HMMER decision trees (typos). +* v 0.6.0 - Replaced HHSUITE by HMMER 3.1 to predict protein function according to Hidden Markov Models. In a recent benchmark (as well as internal ones), we found that HHPred tends to be the slowest program to predict protein function (compared with PHMMER and BLASTP). Additionally, HMMER had a high accuracy when proteins are annotated (Saripella et al. 2016). Moreover, it has the advantage that the databases must be in FASTA format (such UniProt and, even, PFAM), which it is a standard format. For all these reasons, we replaced HHSUITE by HMMER 3.1. Additionally, fixed small issues related to the GenBank file (omission of the contig topology as well as the name of the locus). +* v 0.5.0 - Implemented PILER-CR to predict CRISPR repeats regions. Additionally, fixed errors in the rRNA prediction and inverted and tandem repeats. +* v 0.4.0 - Replaced RNAmmer v 1.2. by INFERNAL 1.1 + RFAM to predict rRNA in the contigs. In this case, you must specify where you have downloaded the RFAM database using the "--rfamdb" option. +* v 0.3.0 - Implemented RNAmmer v 1.2 to predict rRNA in the contigs. If such program is able to predict ribosomal genes, a warning is printed (as viral sequences do not have ribosomal genes). +* v 0.2.0 - Added parallelization of BLAST and HHSUITE. To do that, GNU Parallel (Tange 2011) is required. To disable this option, run the program with the "--noparallel" option. +* v 0.1.0 - Original version of the program. + +## REFERENCES: + + - Benson G (2008) Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Research 27: 573–80. + - Brown CT, Olm MR, Thomas BC, Banfield JF (2016) Measurement of bacterial replication rates in microbial communities. Nature Biotechnology 34: 1256-63. + - Buchfink B, Xie C, Huson DH (2015) Fast and sensitive protein alignment using DIAMOND. Nature Methods 12: 59-60. + - Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL (2008) BLAST+: architecture and applications. BMC Bioinformatics 10: 421. + - Edgar RC (2007) PILER-CR: fast and accurate identification of CRISPR repeats. BMC Bioinformatics 8:18. + - Finn RD, Clements J, Eddy SR (2011) HMMER web server: interactive sequence similarity searching. Nucleic Acids Research 39: W29-37. + - Fozo EM, Makarova KS, Shabalina SA, Yutin N, Koonin EV, Storz G (2010) Abundance of type I toxin-antitoxin systems in bacteria: searches for new candidates and discovery of novel families. Nucleic Acids Research 38: 3743-59. + - Harris RS (2007) Improved pairwise alignment of genomic DNA. Ph.D. Thesis, The Pennsylvania State University. + - Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ (2010) Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11: 119. + - Hyatt D, Locascio PF, Hauser LJ, Uberbacher EC (2012) Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics 28: 2223-30. + - Laslett D, Canback B (2004) ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Research 32, 11–16. + - Nawrocki EP, Eddy SR (2013) Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29: 2933-35. + - Nawrocki EP, Burge SW, Bateman A, Daub J, Eberhardt RY, Eddy SR, Floden EW, Gardner PP, Jones TA, Tate J, Finn RD (2013) Rfam 12.0: updates to the RNA families database. Nucleic Acids Research 43: D130-7. + - Saripella GV, Sonnhammer EL, Forslund K (2016) Benchmarking the next generation of homology inference tools. Bioinformatics 32: 2636-41. + - Seemann T (2014) Prokka: rapid prokaryote genome annotation. Bioinformatics 30: 2068-9. + - Tange O (2011) GNU Parallel - The Command-Line Power Tool. ;login: The USENIX Magazine 36:42-7. + - Warburton PE, Giordano J, Cheung F, Gelfand Y, Benson G (2004) Inverted repeat structure of the human genome: The X-chromosome contains a preponderance of large, highly homologous inverted repeats that contain testes genes. Genome Research 14: 1861-9. diff -r 000000000000 -r 231e4c669675 VIGA.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/VIGA.py Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,997 @@ +#!/usr/bin/env python + +# -*- coding: utf-8 -*- + +# VIGA - De-novo VIral Genome Annotator +# +# Copyright (C) 2017 - Enrique Gonzalez-Tortuero +# Vimalkumar Velayudhan +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation, either version 3 of the License, or +# (at your option) any later version. +# +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with this program. If not, see . + +# Importing python libraries +from __future__ import print_function +import argparse +import csv +import fileinput +import fractions +import glob +import numpy +import os +import os.path +import re +import sys +import shutil +import subprocess +from BCBio import GFF +from Bio import SeqIO +from Bio import SeqFeature +from Bio.Alphabet import IUPAC +from Bio.Seq import Seq +from Bio.SeqFeature import FeatureLocation +from Bio.SeqRecord import SeqRecord +from Bio.SeqUtils.ProtParam import ProteinAnalysis +from collections import OrderedDict, defaultdict +from itertools import product +from scipy import signal +from time import strftime + +# Preparing functions +def batch_iterator(iterator, batch_size): + entry = True + while entry: + batch = [] + while len(batch) < batch_size: + try: + entry = iterator.next() + except StopIteration: + entry = None + if entry is None: + break + batch.append(entry) + if batch: + yield batch + +def check_peaks(peaks, length): + # Checking if origin/terminus peaks are too close or too far apart. In that case, they are probably wrong + closest, farthest = int(length * float(0.45)), int(length * float(0.55)) + pairs = [] + for pair in list(product(*peaks)): + ### added this to make sure gets origin and ter right + tr, pk = sorted(list(pair), key = lambda x: x[1], reverse = False) # trough and peak + a = (tr[0] - pk[0]) % length + b = (pk[0] - tr[0]) % length + pt = abs(tr[1] - pk[1]) # distance between values + if (a <= farthest and a >= closest) or (b <=farthest and b >= closest): + pairs.append([pt, tr, pk]) + if len(pairs) == 0: + return [False, False] + pt, tr, pk = sorted(pairs, reverse = True)[0] + return [tr[0], pk[0]] + +def cmd_exists(cmd): + return subprocess.call("type " + cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) == 0 + +def GCskew(name, length, seq, window, slide): + replacements = {'G':1, 'C':-1, 'A':0, 'T':0, 'N':0} + gmc = [] # G - C + for base in seq: + try: + gmc.append(replacements[base]) + except: + gmc.append(0) + # convert to G + C + gpc = [abs(i) for i in gmc] # G + C + # calculate sliding windows for (G - C) and (G + C) + weights = numpy.ones(window)/window + gmc = [[i, c] for i, c in enumerate(signal.fftconvolve(gmc, weights, 'same').tolist())] + gpc = [[i, c] for i, c in enumerate(signal.fftconvolve(gpc, weights, 'same').tolist())] + # calculate gc skew and cummulative gc skew sum + skew = [[], []] # x and y for gc skew + c_skew = [[], []] # x and y for gc skew cummulative sums + cs = 0 # cummulative sum + # select windows to use based on slide + for i, m in gmc[0::slide]: + p = gpc[i][1] + if p == 0: + gcs = 0 + else: + gcs = m/p + cs += gcs + skew[0].append(i) + c_skew[0].append(i) + skew[1].append(gcs) + c_skew[1].append(cs) + ori, ter = find_ori_ter(c_skew, length) + return ori, ter, skew, c_skew + +def eprint(*args, **kwargs): + print(*args, file=sys.stderr, **kwargs) + +def find_ori_ter(c_skew, length): + # Find origin and terminus of replication based on cumulative GC skew min and max peaks + c_skew_min = signal.argrelextrema(numpy.asarray(c_skew[1]), numpy.less, order = 1)[0].tolist() + c_skew_max = signal.argrelextrema(numpy.asarray(c_skew[1]), numpy.greater, order = 1)[0].tolist() + # return False if no peaks were detected + if len(c_skew_min) == 0 or len(c_skew_min) == 0: + return [False, False] + else: + c_skew_min = [[c_skew[0][i], c_skew[1][i]] for i in c_skew_min] + c_skew_max = [[c_skew[0][i], c_skew[1][i]] for i in c_skew_max] + ori, ter = check_peaks([c_skew_min, c_skew_max], length) + return ori, ter + +def stringSplitByNumbers(x): + r = re.compile('(\d+)') + l = r.split(x) + return [int(y) if y.isdigit() else y for y in l] + +# Defining the program version +version = "0.10.3" + +# Processing the parameters +parser = argparse.ArgumentParser(description='VIGA is an automatic de novo VIral Genome Annotator.') +basic_group = parser.add_argument_group('Basic options for VIGA [REQUIRED]') + +basic_group.add_argument("--input", dest="inputfile", type=str, required=True, help='Input file as a FASTA file', metavar="FASTAFILE") +basic_group.add_argument("--rfamdb", dest="rfamdatabase", type=str, required=True, help='RFAM Database that will be used for the ribosomal RNA prediction. RFAMDB should be in the format "/full/path/to/rfamdb/Rfam.cm" and must be compressed accordingly (see INFERNAL manual) before running the script.', metavar="RFAMDB") +basic_group.add_argument("--modifiers", dest="modifiers", type=str, required=True, help='Input file as a plain text file with the modifiers per every FASTA header according to SeqIn (https://www.ncbi.nlm.nih.gov/Sequin/modifiers.html). All modifiers must be written in a single line and are separated by a single space character. No space should be placed besides the = sign. For example: [organism=Serratia marcescens subsp. marcescens] [sub-species=marcescens] [strain=AH0650_Sm1] [topology=linear] [moltype=DNA] [tech=wgs] [gcode=11] [country=Australia] [isolation-source=sputum]. This line will be copied and printed along with the record name as the definition line of every contig sequence.', metavar="TEXTFILE") + +advanced_group = parser.add_argument_group('Advanced options for VIGA [OPTIONAL]') +advanced_group.add_argument("--readlength", dest="read_length", type=int, default=101, help='Read length for the circularity prediction (default: 101 bp)', metavar="INT") +advanced_group.add_argument("--windowsize", dest="window", type=int, default=100, help='Window size used to determine the origin of replication in circular contigs according to the cumulative GC skew (default: 100 bp)', metavar="INT") +advanced_group.add_argument("--slidingsize", dest="slide", type=int, default=10, help='Window size used to determine the origin of replication in circular contigs according to the cumulative GC skew (default: 10 bp)', metavar="INT") +advanced_group.add_argument("--out", dest="rootoutput", type=str, help='Name of the outputs files (without extension)', metavar="OUTPUTNAME") +advanced_group.add_argument("--locus", dest="locus", type=str, default='LOC', help='Name of the sequences. If the input is a multiFASTA file, please put a general name as the program will add the number of the contig at the end of the name (Default: %(default)s)', metavar="STRING") +advanced_group.add_argument("--gff", dest="gffprint", action='store_true', default=False, help='Printing the output as GFF3 file (Default: False)') +advanced_group.add_argument("--threads", dest="ncpus", default=1, help='Number of threads/cpus (Default: %(default)s cpu)', metavar="INT") +advanced_group.add_argument("--nohmmer", dest="nohmmer", action='store_true', default=False, help='Running only BLAST to predict protein function. (Default: False)') +advanced_group.add_argument("--noblast", dest="noblast", action='store_true', default=False, help='Running DIAMOND instead of BLAST to predict protein function according to homology. This will be less sensitive but faster than BLAST. (Default: False)') +advanced_group.add_argument("--blastdb", dest="blastdatabase", type=str, help='BLAST Database that will be used for the protein function prediction. The database must be an amino acid one, not nucleotidic. This argument is mandatory if --noblast option is disabled', metavar="BLASTDB") +advanced_group.add_argument("--diamonddb", dest="diamonddatabase", type=str, help='DIAMOND Database that will be used for the protein function prediction. The database must be created from a amino acid FASTA file as indicated in https://github.com/bbuchfink/diamond. This argument is mandatory when --noblast option is enabled', metavar="DIAMONDDB") +advanced_group.add_argument("--blastevalue", dest="blastevalue", default=0.00001, help='BLAST/DIAMOND e-value threshold (Default: 0.00001)', metavar="FLOAT") +advanced_group.add_argument("--hmmdb", dest="hmmdatabase", type=str, help='PHMMER Database that will be used for the protein function prediction according to Hidden Markov Models. In this case, HMMDB must be in FASTA format (e.g. UniProt: "', metavar="HMMDB") +advanced_group.add_argument("--hmmerevalue", dest="hmmerevalue", default=0.001, help='PHMMER e-value threshold (Default: 0.001)', metavar="FLOAT") + +type_choices = {'BCT': 'Prokaryotic chromosome', 'CON': 'Contig', 'PHG': 'Phages', 'VRL': 'Eukaryotic/Archaea virus'} +type_help = ('GenBank Division: One of the following codes - {0}. (Default: %(default)s)'.format(', '.join('{0} ({1})'.format(k, v) for k, v in type_choices.items()))) +advanced_group.add_argument("--typedata", dest="typedata", type=str, default='CON', help=type_help, metavar="BCT|CON|VRL|PHG") + +gcode_choices = {'1': 'Standard genetic code [Eukaryotic]', '2': 'Vertebrate mitochondrial code', '3': 'Yeast mitochondrial code', '4': 'Mycoplasma/Spiroplasma and Protozoan/mold/coelenterate mitochondrial code', '5': 'Invertebrate mitochondrial code', '6': 'Ciliate, dasycladacean and hexamita nuclear code', '9': 'Echinoderm/flatworm mitochondrial code', '10': 'Euplotid nuclear code', '11': 'Bacteria/Archaea/Phages/Plant plastid', '12': 'Alternative yeast nuclear code', '13': 'Ascidian mitochondrial code', '14': 'Alternative flatworm mitochondrial code', '16': 'Chlorophycean mitochondrial code', '21': 'Trematode mitochondrial code', '22': 'Scedenesmus obliquus mitochondrial code', '23': 'Thraustochytrium mitochondrial code', '24': 'Pterobranquia mitochondrial code', '25': 'Gracilibacteria & Candidate division SR1', '26': 'Pachysolen tannophilus nuclear code', '27': 'Karyorelict nuclear code', '28': 'Condylostoma nuclear code', '29': 'Mesodinium nuclear code', '30': 'Peritrich nuclear code', '31': 'Blastocrithidia nuclear code'} +gcode_help = ('Number of GenBank translation table. At this moment, the available options are {0}. (Default: %(default)s)'.format('{}'.format(', '.join('{0} ({1})'.format(k, v) for k, v in gcode_choices.items())))) +advanced_group.add_argument("--gcode", dest="gcode", type=str, default='11', help=gcode_help, metavar="NUMBER") + +advanced_group.add_argument('--mincontigsize', dest="mincontigsize", type=int, default = 200, help = 'Minimum contig length to be considered in the final files (Default: 200 bp)', metavar="INT") +advanced_group.add_argument("--idthr", dest="idthreshold", default=50.00, help='ID threshold (Default: 50.0)', metavar="FLOAT") +advanced_group.add_argument("--coverthr", dest="covthreshold", default=50.00, help='Coverage threshold (Default: 50.0)', metavar="FLOAT") +advanced_group.add_argument("--diffid", dest="diffid", default=5.00, help='Max allowed difference between the ID percentages of BLAST and HMMER. (Default = 5.00; Not recommended to change such value)', metavar="FLOAT (>0.01)") +advanced_group.add_argument("--minrepeat", dest="minrepeat", type=int, default=16, help="Minimum repeat length for CRISPR detection (Default: 16)", metavar="INT") +advanced_group.add_argument("--maxrepeat", dest="maxrepeat", type=int, default=64, help="Maximum repeat length for CRISPR detection (Default: 64)") +advanced_group.add_argument("--minspacer", dest="minspacer", type=int, default=8, help="Minimum spacer length for CRISPR detection (Default: 8)") +advanced_group.add_argument("--maxspacer", dest="maxspacer", type=int, default=64, help="Maximum spacer length for CRISPR detection (Default: 64)") +advanced_group.add_argument("--blastexh", dest="blastexh", action='store_true', default=False, help='Use of exhaustive BLAST to predict the proteins by homology according to Fozo et al. (2010) Nucleic Acids Res (Default=FALSE)') + +args = parser.parse_args() + +root_output = args.rootoutput +if not root_output: + root_output = '{}_annotated'.format(os.path.splitext(args.inputfile)[0]) + +if args.noblast == False and args.blastdatabase == None: + sys.exit('You MUST specify BLAST database using the parameter --blastdb if you are not using --noblast option') + +if args.noblast == True and args.diamonddatabase == None: + sys.exit('You MUST specify DIAMOND database using the parameter --diamonddb if you are using --noblast option') + +if args.nohmmer == False and args.hmmdatabase == None: + sys.exit('You MUST specify HMMER database using the parameter --hmmdb if you are not using --nohmmer option') + +# Printing the header of the program +eprint("This is VIGA %s" % str(version)) +eprint("Written by Enrique Gonzalez Tortuero & Vimalkumar Velayudhan") +eprint("Homepage is https://github.com/EGTortuero/virannot") +eprint("Local time: ", strftime("%a, %d %b %Y %H:%M:%S")) +eprint("\n\n") + +# checking the presence of the programs in the system + +if not cmd_exists("lastz")==True: + sys.exit("You must install LASTZ before using this script") +elif not cmd_exists("cmscan")==True: + sys.exit("You must install INFERNAL before using this script") +elif not cmd_exists("prodigal")==True: + sys.exit("You must install prodigal before using this script") +elif not cmd_exists("parallel")==True: + sys.exit("You must install GNU Parallel before using this script") +elif not cmd_exists("blastp")==True: + sys.exit("You must install BLAST before using this script") +elif not cmd_exists("diamond")==True: + sys.exit("You must install DIAMOND before using this script") +elif not cmd_exists("phmmer")==True: + sys.exit("You must install HMMER 3 before using this script") +elif not cmd_exists("aragorn")==True: + sys.exit("You must install ARAGORN before using this script") +elif not cmd_exists("pilercr")==True: + sys.exit("You must install PILERCR before using this script") +elif not cmd_exists("trf")==True: + sys.exit("You must install Tandem Repeats Finder before using this script") +elif not cmd_exists("irf")==True: + sys.exit("You must install Inverted Repeats Finder before using this script") + +eprint("Data type is {0} and GenBank translation table no is {1}\n".format(args.typedata, args.gcode)) + +# Correcting the original file (long headers problem + multiple FASTA files) +record_iter = SeqIO.parse(open(args.inputfile, "rU"),"fasta") +counter = 1 +newnamessequences = {} +for i, batch in enumerate(batch_iterator(record_iter, 1)): + seq_index = "CONTIG_%i" % (i + 1) + filename = "%s.temp.fna" % seq_index + newfilename = "%s.fna" % seq_index + with open(filename, "w") as handle: + count = SeqIO.write(batch, filename, "fasta") + + with open(filename, "rU") as original, open(newfilename, "w") as corrected: + sequences = SeqIO.parse(original, "fasta", IUPAC.ambiguous_dna) + for record in sequences: + original_name = record.id + record.id = "%s_%i" % (args.locus, counter) + record.description = record.description + counter += 1 + newnamessequences[record.id] = original_name + eprint("WARNING: The name of the sequence %s was corrected as %s" % (original_name, record.id)) + SeqIO.write(record, corrected, "fasta") + + with open("logfile.txt", "w") as logfile: + logfile.write("#Original\tNew\n") + for oldname in sorted(newnamessequences, key = stringSplitByNumbers): + logfile.write("%s\t%s\n" % (oldname, newnamessequences[oldname])) + os.remove(filename) + +for newfile in sorted(glob.glob("CONTIG_*.fna")): + + # Predicting the shape of the contig (code based on Alex Crits-Christoph's find_circular.py script [https://github.com/alexcritschristoph/VICA/blob/master/find_circular.py]) + eprint("Predicting the shape of the contig using LASTZ") + genomeshape = {} + with open(newfile, "rU") as targetfasta: + Sequence = SeqIO.parse(newfile, "fasta") + for record in Sequence: + seq_beginning = str(record.seq[0:args.read_length]) + seq_ending = str(record.seq[len(record.seq)-args.read_length:len(record.seq)]) + combined_seqs = SeqRecord(Seq(seq_beginning + seq_ending, IUPAC.ambiguous_dna), id = record.description) + SeqIO.write(combined_seqs, "temporal_circular.fasta", "fasta") + outputlastz = subprocess.check_output(["lastz", "temporal_circular.fasta", "--self", "--notrivial", "--nomirror", "--format=general-:start1,end1,start2,end2,score,strand1,strand2,identity,length1"]) + resultslastz = outputlastz.split("\n") + for resultlastz in resultslastz: + if resultlastz != '': + start1 = resultlastz.split()[0] + end1 = resultlastz.split()[1] + start2 = resultlastz.split()[2] + end2 = resultlastz.split()[3] + strand1 = resultlastz.split()[5] + strand2 = resultlastz.split()[6] + identity = resultlastz.split()[7] + length = int(resultlastz.split()[9]) + if strand1 == strand2 and length > 0.4 * args.read_length and float(fractions.Fraction(identity)) > 0.95 and int(start1) < 5 and int(start2) > args.read_length and int(end1) < args.read_length and int(end2) > args.read_length * 2 * 0.9: + genomeshape['genomeshape'] = "circular" + try: + if genomeshape['identity'] >= float(fractions.Fraction(identity)): + genomeshape['identity'] = float(fractions.Fraction(identity)) + genomeshape['length'] = length + except KeyError: + genomeshape['identity'] = float(fractions.Fraction(identity)) + genomeshape['length'] = length + else: + continue + if strand1 == strand2 and length > 0.4 * args.read_length and float(fractions.Fraction(identity)) > 0.95 and int(start1) < 5 and int(start2) > args.read_length and int(end1) < args.read_length and int(end2) > args.read_length * 2 * 0.9: + genomeshape['genomeshape'] = "circular" + try: + if genomeshape['identity'] >= float(fractions.Fraction(identity)): + genomeshape['identity'] = float(fractions.Fraction(identity)) + genomeshape['length'] = length + except KeyError: + genomeshape['identity'] = float(fractions.Fraction(identity)) + genomeshape['length'] = length + try: + if genomeshape['genomeshape'] == "": + genomeshape['genomeshape'] = "linear" + except KeyError: + genomeshape['genomeshape'] = "linear" + else: + genomeshape['genomeshape'] = "circular" + with open("temp.fasta", "w") as correctedcircular: + Corrseq = str(record.seq[int(genomeshape['length'])//2:-int(genomeshape['length'])//2]) + Newseq = SeqRecord(Seq(Corrseq, IUPAC.ambiguous_dna), id = record.description) + SeqIO.write(Newseq, correctedcircular, "fasta") + os.rename("temp.fasta", "temp2.fasta") + eprint("LASTZ predicted that %s is %s\n" % (newfile, genomeshape['genomeshape'])) + os.remove("temporal_circular.fasta") + + # Calculate the cumulative GCskew in circular contigs to determine the origin of replication (Based on iRep -- Brown CT, Olm MR, Thomas BC, Banfield JF (2016) Measurement of bacterial replication rates in microbial communities. Nature Biotechnology 34: 1256-63.) + if genomeshape['genomeshape'] == "circular": + for record in SeqIO.parse("temp2.fasta", "fasta"): + length_contig = len(record.seq) + #if length < min_len: + # print('%s: Too Short' % (name), file=sys.stderr) + # continue + oric, term, gcskew, cgcskew = GCskew(record.id, length_contig, record.seq, args.window, args.slide) + valoric = oric + if oric == False: + oric, term = 'n/a', 'n/a' + else: + oric, term = '{:,}'.format(oric), '{:,}'.format(term) + eprint('%s -> Origin: %s Terminus: %s' % (record.id, oric, term)) + #print('\t'.join(['# Name', 'Position', 'GC Skew', 'Cumulative GC Skew'])) + #for i, pos in enumerate(gcskew[0]): + # out = [record.id, pos, gcskew[1][i], cgcskew[1][i]] + # print('\t'.join([str(i) for i in out])) + if valoric != False: + firstpartseq = str(record.seq[valoric:-1]) + secondpartseq = str(record.seq[0:(valoric-1)]) + combinedcorrectedseq = SeqRecord(Seq(firstpartseq + secondpartseq, IUPAC.ambiguous_dna), id = record.description) + SeqIO.write(combinedcorrectedseq, newfile, "fasta") + else: + eprint("As the program was unable to predict the origin of replication, %s was considered as is without correcting!" % record.id) + os.rename("temp2.fasta", newfile) + if os.path.isfile("temp2.fasta"): + os.remove("temp2.fasta") + + # Predicting genes using PRODIGAL + eprint("\nRunning Prodigal to predict the genes in %s" % newfile) + for record in SeqIO.parse(newfile, "fasta"): + length_contig = len(record.seq) + if (length_contig >= 100000): + if genomeshape['genomeshape'] == 'linear': + subprocess.call(["prodigal", "-a", "pretemp.faa", "-i", newfile, "-o", "/dev/null", "-g", args.gcode, "-c", "-q"]) + else: + subprocess.call(["prodigal", "-a", "pretemp.faa", "-i", newfile, "-o", "/dev/null", "-g", args.gcode, "-q"]) + else: + if genomeshape['genomeshape'] == 'linear': + subprocess.call(["prodigal", "-a", "pretemp.faa", "-i", newfile, "-o", "/dev/null", "-p", "meta", "-g", args.gcode, "-c", "-q"]) + else: + subprocess.call(["prodigal", "-a", "pretemp.faa", "-i", newfile, "-o", "/dev/null", "-p", "meta", "-g", args.gcode, "-q"]) + num_seqs = len(list(SeqIO.parse("pretemp.faa", "fasta"))) + eprint("PRODIGAL was able to predict %i genes in %s\n" % (num_seqs, newfile)) + + with open("pretemp.faa", "rU") as originalfaa, open("temp.faa", "w") as correctedfaa: + sequences = SeqIO.parse(originalfaa, "fasta") + for record in sequences: + record.seq = record.seq.rstrip("*") + SeqIO.write(record, correctedfaa, "fasta") + + faa_file = "%s.faa" % newfile # TO DEBUG + shutil.copyfile("temp.faa", faa_file) # TO DEBUG + os.remove("pretemp.faa") + + # Predicting the function of the proteins based on homology using BLAST + equivalence = {} + record_iter = SeqIO.parse(open("temp.faa"),"fasta") + for i, batch in enumerate(batch_iterator(record_iter, 1)): + seq_index = "SEQ_%i" % (i + 1) + filename = "%s.faa" % seq_index + with open(filename, "w") as handle: + count = SeqIO.write(batch, handle, "fasta") + equivalence[seq_index] = batch[0].id + + if args.blastexh==True: + eprint("Running BLAST to predict the genes according to homology inference in %s using exhaustive mode (see Fozo et al. (2010) Nucleic Acids Res for details)" % newfile) + subprocess.call(['blastp', '-query', "temp.faa", '-db', args.blastdatabase, '-evalue', str(args.blastevalue), '-outfmt', '6 qseqid sseqid pident length qlen slen qstart qend evalue bitscore stitle', '-out', 'temp_blast.csv', '-max_target_seqs', '10', '-word_size', '2', '-gapopen', '8', '-gapextend', '2', '-matrix', '"PAM70"', '-comp_based_stats', '"0"', "-num_threads", str(args.ncpus)]) + eprint("Done. BLAST was done to predict the genes by homology\n") + blast_log = "%s.blast.log" % newfile # TO DEBUG + shutil.copyfile("temp_blast.csv", blast_log) # TO DEBUG + elif args.noblast==True: + eprint("Running DIAMOND to predict the genes according to homology inference in %s using default parameters" % newfile) + with open("temp.faa", "r") as tempfile: + first_line = tempfile.readline() + if first_line.startswith(">"): + subprocess.call(['diamond', 'blastp', '-q', 'temp.faa', '-d', args.diamonddatabase, '-e', str(args.blastevalue), '-f', '6', 'qseqid', 'sseqid', 'pident', 'length', 'qlen', 'slen', 'qstart', 'qend', 'evalue', 'bitscore', 'stitle', '-o', 'temp_blast.csv', '-k', '10', "-p", str(args.ncpus), '--quiet']) + else: + open("temp_blast.csv", 'a').close() + blast_log = "%s.blast.log" % newfile # TO DEBUG + shutil.copyfile("temp_blast.csv", blast_log) # TO DEBUG + eprint("Done. DIAMOND was done to predict the genes by homology\n") + else: + eprint("Running BLAST to predict the genes according to homology inference in %s using default parameters" % newfile) + subprocess.call(['blastp', '-query', "temp.faa", '-db', args.blastdatabase, '-evalue', str(args.blastevalue), '-outfmt', '6 qseqid sseqid pident length qlen slen qstart qend evalue bitscore stitle', '-out', 'temp_blast.csv', '-max_target_seqs', '10', "-num_threads", str(args.ncpus)]) + blast_log = "%s.blast.log" % newfile + shutil.copyfile("temp_blast.csv", blast_log) # TO DEBUG + eprint("Done. BLAST was done to predict the genes by homology\n") # TO DEBUG + + # Parsing the results from BLAST + with open("temp_blast.csv", "rU") as blastresults: + hypotheticalpat = re.compile(r'(((((?i)hypothetical)|(?i)uncharacteri(z|s)ed|(?i)predicted)) protein)|((?i)ORF|((?i)unnamed protein product|(?i)gp\d+))') + reader = csv.DictReader(blastresults, delimiter='\t', fieldnames=['qseqid','sseqid','pident','length','qlen','slen','qstart','qend','evalue','bitscore','stitle']) + information_proteins_blast = {} + for row in reader: + perc_cover = round(100.00*(float(row['length'])/float(row['qlen'])),2) + perc_id = float(row['pident']) + infoprot_blast = {} + infoprot_blast['sseqid'] = row['sseqid'] + infoprot_blast['pident'] = perc_id + infoprot_blast['pcover'] = perc_cover + infoprot_blast['evalue'] = row['evalue'] + infoprot_blast['descr'] = row['stitle'] + try: + data = information_proteins_blast[row['qseqid']] + except KeyError: + if not re.search(hypotheticalpat, infoprot_blast['descr']) and float(perc_id) >= float(args.idthreshold) and float(perc_cover) >= float(args.covthreshold) and float(row['evalue']) <= float(args.blastevalue): + information_proteins_blast[row['qseqid']] = infoprot_blast + else: + continue + else: + if not re.search(hypotheticalpat, infoprot_blast['descr']) and float(perc_id) >= float(args.idthreshold) and float(perc_id) >= float(infoprot_blast['pident']) and float(perc_cover) >= float(args.covthreshold) and float(perc_cover) >= float(infoprot_blast['pcover']) and float(row['evalue']) <= float(args.blastevalue): + information_proteins_blast[row['qseqid']] = infoprot_blast + + ## Predicting the function of the proteins based on HMM predictions using phmmer + if args.nohmmer == False: + with open("commands.sh", "w") as commands: + for singleprot in sorted(glob.glob("SEQ_*.faa")): + hhmtable = "%s.tbl" % singleprot + eprint("Creating file to run parallel PHMMER") + eprint("Adding %s to run PHMMER." % singleprot) + line2write = ' '.join(["phmmer", "--cpu", "1", "--domtblout", hhmtable, "-E", str(args.hmmerevalue), "-o", "/dev/null", singleprot, args.hmmdatabase, '\n']) + commands.write(line2write) + + eprint("Running parallel PHMMER") + subprocess.call(['parallel', '-j', str(args.ncpus)], stdin=open('commands.sh')) + eprint("Done. PHMMER was done to predict the function of the genes according to Hidden Markov Models\n") + os.remove("commands.sh") + + # Parsing the results from HMMER + information_proteins_hmmer = {} + hypotheticalpat = re.compile(r'(((((?i)hypothetical)|(?i)uncharacteri(z|s)ed|(?i)predicted)) protein)|((?i)ORF|((?i)unnamed protein product|(?i)gp\d+))') + for singletbl in sorted(glob.glob("*.faa.tbl")): + rootname = singletbl.replace(".faa.tbl", "") + with open(singletbl) as tblfile: + infoprot_hmmer = {} + for line in tblfile: + if line.startswith("#"): + continue + else: + try: + pat = re.compile("^(\S+)\s+\S\s+\d+\s+(\S+)\s+\S\s+(\d+)\s+((?:0|[1-9]\d*)(?:\.\d*)?(?:[eE][+\-]?\d+)?)\s+\S+\s+\S+\s+\S+\s+\S+\s+(?:0|[1-9]\d*)(?:\.\d*)?(?:[eE][+\-]?\d+)?\s+\S+\s+\S+\s+\S+\s+(\d+)\s+(\d+)\s+\d+\s+\d+\s+\S+\s+\S+\s+(\S+)\s+(.*)") + matchname, lociname, length, evaluehh, start, end, pident, description = re.match(pat, line).groups() + except AttributeError: + continue + else: + length = float(length) + pident = 100.00*float(pident) + protarea = 100.00*(((float(end)-1.00) - (float(start)-1.00))/length) + try: + data2 = infoprot_hmmer['lociname'] + except KeyError: + if not re.search(hypotheticalpat, description) and float(protarea) >= float(args.covthreshold) and float(evaluehh) <= float(args.hmmerevalue) and float(pident) >= 50.00: + infoprot_hmmer['lociname'] = lociname + infoprot_hmmer['name'] = matchname + infoprot_hmmer['evalue'] = float(evaluehh) + infoprot_hmmer['pcover'] = float(protarea) + infoprot_hmmer['pident'] = float(pident) + infoprot_hmmer['descr'] = description + else: + try: + if not re.search(hypotheticalpat, description) and float(protarea) >= float(args.covthreshold) and float(evaluehh) <= float(args.hmmerevalue) and float(pident) >= 50.00 and float(pident) >= infoprot_hmmer['pident']: + infoprot_hmmer['lociname'] = lociname + infoprot_hmmer['name'] = matchname + infoprot_hmmer['evalue'] = float(evaluehh) + infoprot_hmmer['pcover'] = float(protarea) + infoprot_hmmer['pident'] = float(pident) + infoprot_hmmer['descr'] = description + except KeyError: + continue + else: + if not re.search(hypotheticalpat, description) and float(protarea) >= float(args.covthreshold) and float(evaluehh) <= float(args.hmmerevalue) and float(pident) >= 50.00 and float(pident) >= infoprot_hmmer['pident']: + infoprot_hmmer['lociname'] = lociname + infoprot_hmmer['name'] = matchname + infoprot_hmmer['evalue'] = float(evaluehh) + infoprot_hmmer['pcover'] = float(protarea) + infoprot_hmmer['pident'] = float(pident) + infoprot_hmmer['descr'] = description + information_proteins_hmmer[rootname] = infoprot_hmmer + + #Storing protein information in memory + with open("temp.faa", "rU") as protsfile: + tempprotsdict = {} + for protseq in SeqIO.parse(protsfile, "fasta"): + tempindprot = {} + dataprot = protseq.description.split(' # ') + modseq = str(protseq.seq).replace("X","") # Removing all ambiguous amino acids to avoid problems with ProteinAnalysis module + analysed_seq = ProteinAnalysis(modseq) + tempindprot['length'] = len(protseq.seq) + tempindprot['isoelectricpoint'] = analysed_seq.isoelectric_point() + tempindprot['molweightkda'] = analysed_seq.molecular_weight()/1000.00 + tempindprot['instability'] = analysed_seq.instability_index() + tempindprot['protein_id'] = dataprot[0] + tempindprot['strand'] = int(dataprot[3]) + tempindprot['begin'] = int(dataprot[1])-1 + tempindprot['end'] = int(dataprot[2]) + tempprotsdict[dataprot[0]] = tempindprot + + # Creation of table + debugtable = "%s.csv" % newfile + with open(debugtable, "w") as tablefile: + if args.nohmmer == False: + print("\t".join(["Identifier", "Start", "Stop", "Strand", "size_aa", "pI", "Mol_weight_kDa", "Instability_index", "ID_BLAST", "Descr_BLAST", "evalue_BLAST", "%ID_BLAST", "%Cover_BLAST", "ID_HMMER", "Descr_HMMER", "evalue_HMMER", "%ID_HMMER", "%Cover_HMMER"]), file=tablefile) + keylist = information_proteins_hmmer.keys() + keylist.sort() + for keyB in keylist: + keyB = keyB.replace(".faa.tbl", "") + try: + print("\t".join([equivalence[keyB], str(tempprotsdict[equivalence[keyB]]['begin']), str(tempprotsdict[equivalence[keyB]]['end']), str(tempprotsdict[equivalence[keyB]]['strand']), str(tempprotsdict[equivalence[keyB]]['length']), str(tempprotsdict[equivalence[keyB]]['isoelectricpoint']), str(tempprotsdict[equivalence[keyB]]['molweightkda']), str(tempprotsdict[equivalence[keyB]]['instability']), information_proteins_blast[equivalence[keyB]]['sseqid'], information_proteins_blast[equivalence[keyB]]['descr'], str(information_proteins_blast[equivalence[keyB]]['evalue']), str(information_proteins_blast[equivalence[keyB]]['pident']), str(information_proteins_blast[equivalence[keyB]]['pcover']), information_proteins_hmmer[keyB]['name'], information_proteins_hmmer[keyB]['descr'], str(information_proteins_hmmer[keyB]['evalue']), str(information_proteins_hmmer[keyB]['pident']), str(information_proteins_hmmer[keyB]['pcover'])]), file=tablefile) + except KeyError: + try: + print("\t".join([equivalence[keyB], str(tempprotsdict[equivalence[keyB]]['begin']), str(tempprotsdict[equivalence[keyB]]['end']), str(tempprotsdict[equivalence[keyB]]['strand']), str(tempprotsdict[equivalence[keyB]]['length']), str(tempprotsdict[equivalence[keyB]]['isoelectricpoint']), str(tempprotsdict[equivalence[keyB]]['molweightkda']), str(tempprotsdict[equivalence[keyB]]['instability']), information_proteins_blast[equivalence[keyB]]['sseqid'], information_proteins_blast[equivalence[keyB]]['descr'], str(information_proteins_blast[equivalence[keyB]]['evalue']), str(information_proteins_blast[equivalence[keyB]]['pident']), str(information_proteins_blast[equivalence[keyB]]['pcover']), "None", "None", "NA", "NA", "NA"]), file=tablefile) + except KeyError: + try: + print("\t".join([equivalence[keyB], str(tempprotsdict[equivalence[keyB]]['begin']), str(tempprotsdict[equivalence[keyB]]['end']), str(tempprotsdict[equivalence[keyB]]['strand']), str(tempprotsdict[equivalence[keyB]]['length']), str(tempprotsdict[equivalence[keyB]]['isoelectricpoint']), str(tempprotsdict[equivalence[keyB]]['molweightkda']), str(tempprotsdict[equivalence[keyB]]['instability']), "None", "None", "NA", "NA", "NA", information_proteins_hmmer[keyB]['name'], information_proteins_hmmer[keyB]['descr'], str(information_proteins_hmmer[keyB]['evalue']), str(information_proteins_hmmer[keyB]['pident']), str(information_proteins_hmmer[keyB]['pcover'])]), file=tablefile) + except KeyError: + print("\t".join([equivalence[keyB], str(tempprotsdict[equivalence[keyB]]['begin']), str(tempprotsdict[equivalence[keyB]]['end']), str(tempprotsdict[equivalence[keyB]]['strand']), str(tempprotsdict[equivalence[keyB]]['length']), str(tempprotsdict[equivalence[keyB]]['isoelectricpoint']), str(tempprotsdict[equivalence[keyB]]['molweightkda']), str(tempprotsdict[equivalence[keyB]]['instability']), "None", "None", "NA", "NA", "NA", "None", "None", "NA", "NA", "NA"]), file=tablefile) + else: + print("\t".join(["Identifier", "Start", "Stop", "Strand", "size_aa", "pI", "Mol_weight_kDa", "Instability_index", "ID_BLAST", "Descr_BLAST", "evalue_BLAST", "%ID_BLAST", "%Cover_BLAST"]), file=tablefile) + keylist = equivalence.values() + keylist.sort() + for keyB in keylist: + try: + print("\t".join([keyB, str(tempprotsdict[keyB]['begin']), str(tempprotsdict[keyB]['end']), str(tempprotsdict[keyB]['strand']), str(tempprotsdict[keyB]['length']), str(tempprotsdict[keyB]['isoelectricpoint']), str(tempprotsdict[keyB]['molweightkda']), str(tempprotsdict[keyB]['instability']), information_proteins_blast[keyB]['sseqid'], information_proteins_blast[keyB]['descr'], str(information_proteins_blast[keyB]['evalue']), str(information_proteins_blast[keyB]['pident']), str(information_proteins_blast[keyB]['pcover'])]), file=tablefile) + except KeyError: + print("\t".join([keyB, str(tempprotsdict[keyB]['begin']), str(tempprotsdict[keyB]['end']), str(tempprotsdict[keyB]['strand']), str(tempprotsdict[keyB]['length']), str(tempprotsdict[keyB]['isoelectricpoint']), str(tempprotsdict[keyB]['molweightkda']), str(tempprotsdict[keyB]['instability']), "None", "None", "NA", "NA", "NA"]), file=tablefile) + + # Algorithm of decisions (which one: BLAST/HMMER?) + multipleprots = {} + Hypotheticalpat = re.compile(r'(((((?i)hypothetical)|(?i)uncharacteri(z|s)ed|(?i)predicted)) protein)|((?i)ORF|((?i)unnamed protein product|(?i)gp\d+))') + if args.nohmmer == False: + keylist = information_proteins_hmmer.keys() + keylist.sort() + for keyB in keylist: + keyB = keyB.replace(".faa.tbl", "") + singleprot = {} + singleprot['name'] = equivalence[keyB] + if (equivalence[keyB] in information_proteins_blast) and (keyB in information_proteins_hmmer): + if re.search(Hypotheticalpat, information_proteins_blast[equivalence[keyB]]['descr']): + try: + if re.search(Hypotheticalpat, information_proteins_hmmer[keyB]['descr']): + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + else: + singleprot['descr'] = information_proteins_hmmer[keyB]['descr'] + except KeyError: + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + else: + try: + if (float(information_proteins_blast[equivalence[keyB]]['pident'])>float(information_proteins_hmmer[keyB]['pident'])) and (float(information_proteins_blast[equivalence[keyB]]['pcover'])>float(information_proteins_hmmer[keyB]['pcover'])): + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + elif (float(information_proteins_blast[equivalence[keyB]]['pident'])float(information_proteins_hmmer[keyB]['pident'])) and (float(information_proteins_blast[equivalence[keyB]]['pcover'])= args.diffid): + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + else: + singleprot['descr'] = information_proteins_hmmer[keyB]['descr'] + else: + if (float(information_proteins_hmmer[keyB]['pident'])-float(information_proteins_blast[equivalence[keyB]]['pident']) >= args.diffid): + singleprot['descr'] = information_proteins_hmmer[keyB]['descr'] + else: + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + except KeyError: + try: + if (float(information_proteins_blast[equivalence[keyB]]['pcover'])>float(information_proteins_hmmer[keyB]['pcover'])): + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + else: + singleprot['descr'] = information_proteins_hmmer[keyB]['descr'] + except KeyError: + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + elif equivalence[keyB] in information_proteins_blast: + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + elif keyB in information_proteins_hmmer: + try: + singleprot['descr'] = information_proteins_hmmer[keyB]['descr'] + except KeyError: + singleprot['descr'] = 'Hypothetical protein' + else: + singleprot['descr'] = information_proteins_blast[equivalence[keyB]]['descr'] + multipleprots[keyB] = singleprot + else: + keylist = equivalence.values() + keylist.sort() + for keyB in keylist: + singleprot = {} + singleprot['name'] = keyB + try: + if information_proteins_blast[keyB]['descr'] == None: + singleprot['descr'] = 'Hypothetical protein' + elif re.search(Hypotheticalpat, information_proteins_blast[keyB]['descr']): + singleprot['descr'] = 'Conserved hypothetical protein' + else: + singleprot['descr'] = information_proteins_blast[keyB]['descr'] + except KeyError: + singleprot['descr'] = 'Hypothetical protein' + multipleprots[keyB] = singleprot + + #Storing protein information in memory + with open("temp.faa", "rU") as protsfile: + protsdict = {} + for protseq in SeqIO.parse(protsfile, "fasta"): + indprot = {} + dataprot = protseq.description.split(' # ') + indprot['translation'] = protseq.seq + indprot['protein_id'] = dataprot[0] + indprot['strand'] = int(dataprot[3]) + indprot['begin'] = int(dataprot[1])-1 + indprot['end'] = int(dataprot[2]) + for keyOmega in sorted(multipleprots): + if multipleprots[keyOmega]['name'] == dataprot[0]: + indprot['product'] = multipleprots[keyOmega]['descr'] + protsdict[dataprot[0]] = indprot + + # Predicting the rRNA sequences + with open(newfile, "rU") as targetfasta, open("/dev/null", "w") as apocalypse: + eprint("Running INFERNAL+RFAM to predict rRNA-like sequences in %s" % newfile) + subprocess.call(["cmscan", "--rfam", "--cut_ga", "--nohmmonly", "--tblout", "rrnafile.csv", "--cpu", args.ncpus, args.rfamdatabase, newfile], stdout=apocalypse) + + #Storing rRNA information in memory + with open("rrnafile.csv", "rU") as rrnafile: + namedict = {"SSU_rRNA_archaea": "16s_rRNA", "SSU_rRNA_bacteria": "16s_rRNA", "SSU_rRNA_eukarya": "18s_rRNA", "SSU_rRNA_microsporidia": "16s_rRNA", "LSU_rRNA_archaea": "23s_rRNA", "LSU_rRNA_bacteria": "23s_rRNA", "LSU_rRNA_eukarya": "28s_rRNA", "5S_rRNA": "5s_rRNA"} + rRNAdict = defaultdict(list) + for line in rrnafile: + if not line.startswith("#"): + InfoLINE = re.sub("\s+", ",", line) + line_splitted = InfoLINE.split(",") + item_type = line_splitted[0] + if item_type.startswith(('LSU', 'SSU', '5S')): + strand = 0 + if line_splitted[9] == "+": + strand = 1 + else: + strand = -1 + rRNAdict[item_type].append({'score': float(line_splitted[14]), 'product': namedict[line_splitted[0]], 'begin': int(line_splitted[7]), 'end': int(line_splitted[8]), 'strand': strand}) + + subunits = {'LSU': {'max_score': 0 }, 'SSU': {'max_score': 0 }, '5S': {'max_score': 0 }} + for type_rRNA, rRNA_data in rRNAdict.items(): + max_score = max([item['score'] for item in rRNAdict[type_rRNA]]) + for item in ('LSU', 'SSU'): + if type_rRNA.startswith(item): + if max_score > subunits[item]['max_score']: + subunits[item]['listdata'] = rRNA_data + subunits[item]['max_score'] = max_score + if type_rRNA.startswith('5S'): + subunits['5S']['listdata'] = rRNA_data + subunits['5S']['max_score'] = max_score + + for rRNA in subunits: + i = 0 + try: + lengthlist = len(subunits[rRNA]['listdata']) + except KeyError: + continue + else: + while i < lengthlist: + eprint("%s harbours a %s from %i to %i" % (newfile, subunits[rRNA]['listdata'][i]['product'], int(subunits[rRNA]['listdata'][i]['begin']), int(subunits[rRNA]['listdata'][i]['end']))) + i += 1 + + # Predicting the tRNA sequences + eprint("Running ARAGORN to predict tRNA-like sequences in %s" % newfile) + genetictable = "-gc%s" % str(args.gcode) + with open("trnafile.fasta", "w") as trnafile: + if genomeshape['genomeshape'] == "circular": + subprocess.call(["aragorn", "-c", "-fon", genetictable, newfile], stdout=trnafile) + else: + subprocess.call(["aragorn", "-l", "-fon", genetictable, newfile], stdout=trnafile) + num_tRNA = len(list(SeqIO.parse("trnafile.fasta", "fasta"))) + eprint("ARAGORN was able to predict %i tRNAs in %s\n" % (num_tRNA, newfile)) + + #Storing tRNA and tmRNA information in memory + with open("trnafile.fasta", "rU") as trnafile: + tRNAdict = {} + tmRNAdict = {} + for tRNAseq in SeqIO.parse(trnafile, "fasta"): + indtRNA = {} + indtmRNA = {} + tRNA_information = tRNAseq.description.split(" ") + tRNApat = re.compile("^tRNA-") + if tRNA_information[1] == "tmRNA": + if str(tRNA_information[2]) == "(Permuted)": + indtmRNA['product'] = "tmRNA" + tmRNA_coords = str(tRNA_information[3]) + Beginningrevcomppat = re.compile("^c") + if re.match(Beginningrevcomppat, tmRNA_coords): + indtmRNA['strand'] = -1 + tmRNA_coords = tmRNA_coords.replace("c[","").replace("]","").split(",") + else: + indtmRNA['strand'] = 1 + tmRNA_coords = tmRNA_coords.replace("[","").replace("]","").split(",") + indtmRNA['begin'] = int(tmRNA_coords[0]) + indtmRNA['end'] = int(tmRNA_coords[1]) + tmRNAdict[tRNAseq.id] = indtmRNA + else: + indtmRNA['product'] = "tmRNA" + tmRNA_coords = str(tRNA_information[2]) + Beginningrevcomppat = re.compile("^c") + if re.match(Beginningrevcomppat, tmRNA_coords): + indtmRNA['strand'] = -1 + tmRNA_coords = tmRNA_coords.replace("c[","").replace("]","").split(",") + else: + indtmRNA['strand'] = 1 + tmRNA_coords = tmRNA_coords.replace("[","").replace("]","").split(",") + indtmRNA['begin'] = int(tmRNA_coords[0]) + indtmRNA['end'] = int(tmRNA_coords[1]) + tmRNAdict[tRNAseq.id] = indtmRNA + elif re.match(tRNApat, tRNA_information[1]): + indtRNA['product'] = re.sub("\(\w{3}\)", "", tRNA_information[1]) + tRNA_coords = tRNA_information[2] + Beginningrevcomppat = re.compile("^c") + if re.match(Beginningrevcomppat, tRNA_coords): + indtRNA['strand'] = -1 + tRNA_coords = tRNA_coords.replace("c[","").replace("]","").split(",") + else: + indtRNA['strand'] = 1 + tRNA_coords = tRNA_coords.replace("[","").replace("]","").split(",") + indtRNA['begin'] = int(tRNA_coords[0]) + indtRNA['end'] = int(tRNA_coords[1]) + tRNAdict[tRNAseq.id] = indtRNA + + #Predicting CRISPR repeats and others + eprint("Running PILERCR to predict CRISPR repeats in %s" % newfile) + subprocess.call(["pilercr", "-in", newfile, "-out", "crisprfile.txt", "-noinfo", "-minrepeat", str(args.minrepeat), "-maxrepeat", str(args.maxrepeat), "-minspacer", str(args.minspacer), "-maxspacer", str(args.maxspacer)]) + eprint("Predicting repeats in the sequences using TRF and IRF") + with open("/dev/null", "w") as stderr: + subprocess.call(["trf", newfile, "2", "7", "7", "80", "10", "50", "500", "-h"], stderr=stderr) + os.rename("%s.2.7.7.80.10.50.500.dat" % newfile, "trf_temp.dat") + with open("/dev/null", "w") as stderr: + subprocess.call(["irf", newfile, "2", "3", "5", "80", "10", "40", "500000", "10000", "-d", "-h"], stderr=stderr) + os.rename("%s.2.3.5.80.10.40.500000.10000.dat" % newfile, "irf_temp.dat") + + # Storing CRISPR repeats information + information_CRISPR = {} + with open("crisprfile.txt", "rU") as crisprfile: + for line in crisprfile: + if "SUMMARY BY POSITION" in line: + for line in crisprfile: + information_crispr_repeat = {} + try: + patC = re.compile('^\s+(\d+)\s+.{16}\s+(\d+)\s+(\d+)\s+\d+\s+\d+\s+\d+\s+\d?\s+(\w+)') + key, start, length, seq = re.match(patC, line).groups() + except AttributeError: + continue + else: + information_crispr_repeat['start'] = start + information_crispr_repeat['end'] = int(start) + int(length) + information_crispr_repeat['repeatseq'] = seq + information_crispr_repeat['repeatend'] = int(start) + len(seq) + information_CRISPR[key] = information_crispr_repeat + + # Storing tandem repeats information + information_TRF = {} + count = 1 + with open("trf_temp.dat", "rU") as trfile: + for line in trfile: + information_tandem_repeat = {} + try: + patT = re.compile('^(\d+)\s(\d+)\s\d+\s\d+\.\d+\s') + start, end = re.match(patT, line).groups() + except AttributeError: + continue + else: + information_tandem_repeat['start'] = start + information_tandem_repeat['end'] = end + information_TRF[count] = information_tandem_repeat + count += 1 + + # Storing inverted repeats information + information_IRF = {} + count = 1 + with open("irf_temp.dat", "rU") as irfile: + for line in irfile: + information_inverted_repeat = {} + try: + patI = re.compile('^(\d+)\s(\d+)\s\d+\s\d+\s\d+') + start, end = re.match(patI, line).groups() + except AttributeError: + continue + else: + information_inverted_repeat['start'] = start + information_inverted_repeat['end'] = end + information_IRF[count] = information_inverted_repeat + count += 1 + + # Creating a new Genbank and GFF file + eprint("Creating the output files") + newtempgbk = "%s.temp.gbk" % newfile + with open(newfile, "rU") as basefile, open(newtempgbk, "w"): + for record in SeqIO.parse(basefile, "fasta", IUPAC.ambiguous_dna): + whole_sequence = SeqRecord(record.seq) + whole_sequence.id = str(record.id) + whole_sequence.annotations['data_file_division'] = args.typedata.upper() + whole_sequence.annotations['date'] = strftime("%d-%b-%Y").upper() + for protein in sorted(protsdict, key = stringSplitByNumbers): + putative_start = int(protsdict[protein]['begin']) + start_pos = SeqFeature.ExactPosition(putative_start) + end_pos = SeqFeature.ExactPosition(protsdict[protein]['end']) + feature_location = SeqFeature.FeatureLocation(start_pos, end_pos, strand=protsdict[protein]['strand']) + qualifiersgene = OrderedDict([('locus_tag', protsdict[protein]['protein_id'])]) + new_data_gene = SeqFeature.SeqFeature(feature_location, type = "gene", strand = protsdict[protein]['strand'], qualifiers = qualifiersgene) + whole_sequence.features.append(new_data_gene) + qualifiers = [('locus_tag', protsdict[protein]['protein_id']), ('product', protsdict[protein]['product']), ('protein_id', protsdict[protein]['protein_id']), ('translation', protsdict[protein]['translation'])] + feature_qualifiers = OrderedDict(qualifiers) + new_data_cds = SeqFeature.SeqFeature(feature_location, type = "CDS", strand = protsdict[protein]['strand'], qualifiers = feature_qualifiers) + whole_sequence.features.append(new_data_cds) + for rRNA in sorted(subunits, key = stringSplitByNumbers): + i = 0 + try: + lengthlist = len(subunits[rRNA]['listdata']) + except KeyError: + continue + else: + while i < lengthlist: + putative_start = int(subunits[rRNA]['listdata'][i]['begin']) + start_pos = SeqFeature.ExactPosition(putative_start) + end_pos = SeqFeature.ExactPosition(subunits[rRNA]['listdata'][i]['end']) + feature_location = SeqFeature.FeatureLocation(start_pos, end_pos, strand=subunits[rRNA]['listdata'][i]['strand']) + new_data_gene = SeqFeature.SeqFeature(feature_location, type = "gene", strand = subunits[rRNA]['listdata'][i]['strand']) + whole_sequence.features.append(new_data_gene) + qualifiers = [('product', subunits[rRNA]['listdata'][i]['product'])] + feature_qualifiers = OrderedDict(qualifiers) + new_data_rRNA = SeqFeature.SeqFeature(feature_location, type = "rRNA", strand = subunits[rRNA]['listdata'][i]['strand'], qualifiers = feature_qualifiers) + whole_sequence.features.append(new_data_rRNA) + i += 1 + for tRNA in sorted(tRNAdict, key = stringSplitByNumbers): + putative_start = int(tRNAdict[tRNA]['begin']) + start_pos = SeqFeature.ExactPosition(putative_start) + end_pos = SeqFeature.ExactPosition(tRNAdict[tRNA]['end']) + feature_location = SeqFeature.FeatureLocation(start_pos, end_pos, strand=tRNAdict[tRNA]['strand']) + new_data_gene = SeqFeature.SeqFeature(feature_location, type = "gene", strand = tRNAdict[tRNA]['strand']) + whole_sequence.features.append(new_data_gene) + qualifiers = [('product', tRNAdict[tRNA]['product'])] + feature_qualifiers = OrderedDict(qualifiers) + new_data_tRNA = SeqFeature.SeqFeature(feature_location, type = "tRNA", strand = tRNAdict[tRNA]['strand'], qualifiers = feature_qualifiers) + whole_sequence.features.append(new_data_tRNA) + for tmRNA in sorted(tmRNAdict, key = stringSplitByNumbers): + putative_start = int(tmRNAdict[tmRNA]['begin']) + start_pos = SeqFeature.ExactPosition(putative_start) + end_pos = SeqFeature.ExactPosition(tmRNAdict[tmRNA]['end']) + feature_location = SeqFeature.FeatureLocation(start_pos, end_pos, strand=tmRNAdict[tmRNA]['strand']) + new_data_gene = SeqFeature.SeqFeature(feature_location, type = "gene", strand = tmRNAdict[tmRNA]['strand']) + whole_sequence.features.append(new_data_gene) + qualifiers = [('product', tmRNAdict[tmRNA]['product'])] + feature_qualifiers = OrderedDict(qualifiers) + new_data_tmRNA = SeqFeature.SeqFeature(feature_location, type = "tmRNA", strand = tmRNAdict[tmRNA]['strand'], qualifiers = feature_qualifiers) + whole_sequence.features.append(new_data_tmRNA) + for CRISPR in sorted(information_CRISPR, key = stringSplitByNumbers): + putative_start = int(information_CRISPR[CRISPR]['start']) + start_pos = SeqFeature.ExactPosition(putative_start) + end_pos = SeqFeature.ExactPosition(information_CRISPR[CRISPR]['end']) + feature_location = SeqFeature.FeatureLocation(start_pos, end_pos) + qualifiers = [('rpt_family', 'CRISPR'), ('rpt_type', 'direct'), ('rpt_unit_range', "%i..%i" % (int(information_CRISPR[CRISPR]['start']), int(information_CRISPR[CRISPR]['repeatend']))), ('rpt_unit_seq', information_CRISPR[CRISPR]['repeatseq'])] + feature_qualifiers = OrderedDict(qualifiers) + new_data_CRISPRrepeat = SeqFeature.SeqFeature(feature_location, type = "repeat_region", qualifiers = feature_qualifiers) + whole_sequence.features.append(new_data_CRISPRrepeat) + for tandem in sorted(information_TRF): + putative_start = int(information_TRF[tandem]['start']) + start_pos = SeqFeature.ExactPosition(putative_start) + end_pos = SeqFeature.ExactPosition(information_TRF[tandem]['end']) + feature_location = SeqFeature.FeatureLocation(start_pos, end_pos) + qualifiers = [('rpt_type', 'direct')] + feature_qualifiers = OrderedDict(qualifiers) + new_data_tandemrepeat = SeqFeature.SeqFeature(feature_location, type = "repeat_region", qualifiers = feature_qualifiers) + whole_sequence.features.append(new_data_tandemrepeat) + for inverted in sorted(information_IRF): + putative_start = int(information_IRF[inverted]['start']) + start_pos = SeqFeature.ExactPosition(putative_start) + end_pos = SeqFeature.ExactPosition(information_IRF[inverted]['end']) + feature_location = SeqFeature.FeatureLocation(start_pos, end_pos) + qualifiers = [('rpt_type', 'inverted')] + feature_qualifiers = OrderedDict(qualifiers) + new_data_invertedrepeat = SeqFeature.SeqFeature(feature_location, type = "repeat_region", qualifiers = feature_qualifiers) + whole_sequence.features.append(new_data_invertedrepeat) + SeqIO.write(whole_sequence, newtempgbk, "genbank") + + newgbk = "%s.gbk" % newfile + with open(newtempgbk, "rU") as gbktempfile, open(newgbk, "w") as gbkrealfile: + newpat = re.compile("D|RNA\s+(CON|PHG|VRL|BCT)") + for line in gbktempfile: + if line.startswith("LOCUS ") and re.search(newpat, line): + if genomeshape['genomeshape'] == "linear": + newline = re.sub("bp DNA\s+", "bp DNA linear ", line) + else: + newline = re.sub("bp DNA\s+", "bp DNA circular ", line) + gbkrealfile.write(newline) + else: + gbkrealfile.write(line) + + for f in glob.glob("*.temp.gbk"): + os.remove(f) + + if args.gffprint==True: + newgff = "%s.gff" % newfile + with open(newgff, "w") as outgff, open(newgbk, "rU") as ingbk: + GFF.write(SeqIO.parse(ingbk, "genbank"), outgff) + + # Removing intermediate files + os.remove(newfile) + os.remove("temp.faa") + os.remove("temp_blast.csv") + os.remove("crisprfile.txt") + os.remove("trnafile.fasta") + os.remove("rrnafile.csv") + os.remove("trf_temp.dat") + os.remove("irf_temp.dat") + for f in glob.glob("SEQ*"): + os.remove(f) + +# Joining all GENBANK files into one +listgbk = sorted(glob.glob('CONTIG_*.gbk')) +gbkoutputfile = "%s.gbk" % root_output +with open(gbkoutputfile, 'w') as finalgbk: + for fname in listgbk: + with open(fname) as infile: + for line in infile: + finalgbk.write(line) + +for tempgbk in glob.glob("CONTIG_*.gbk"): + os.remove(tempgbk) + +# Joining all GFF files into one +if args.gffprint==True: + listgff = sorted(glob.glob('CONTIG_*.gff')) + gffoutputfile = "%s.gff" % root_output + with open(gffoutputfile, 'w') as finalgff: + for fname in listgff: + with open(fname) as infile: + for line in infile: + finalgff.write(line) + for tempgff in glob.glob("CONTIG_*.gff"): + os.remove(tempgff) + +# Joining all TABLE files into one +listcsv = sorted(glob.glob('CONTIG_*.csv')) +tbloutputfile = "%s.csv" % root_output +with open(tbloutputfile, 'w') as finaltable: + for fname in listcsv: + with open(fname) as infile: + for line in infile: + finaltable.write(line) + +for temptbl in glob.glob("CONTIG_*.csv"): + os.remove(temptbl) + +# Preparing sequences for GenBank submission (Original code from Wan Yu's gbk2tbl.py script [https://github.com/wanyuac/BINF_toolkit/blob/master/gbk2tbl.py]) +allowed_qualifiers = ['locus_tag', 'gene', 'product', 'pseudo', 'protein_id', 'gene_desc', 'old_locus_tag', 'note', 'inference', 'organism', 'mol_type', 'strain', 'sub_species', 'isolation-source', 'country'] +newfastafile = "%s.fasta" % root_output +newtablefile = "%s.tbl" % root_output +with open(args.modifiers, "rU") as modifiers, open(gbkoutputfile, "r") as genbank_fh, open(newfastafile, "w") as fasta_fh, open(newtablefile, "w") as feature_fh: + info = modifiers.readline() + wholelist = list(SeqIO.parse(genbank_fh, 'genbank')) + for record in wholelist: + if len(record) <= args.mincontigsize: + eprint("WARNING: Skipping small contig %s" % record.id) + continue + record.description = "%s %s" % (record.id, info) + SeqIO.write([record], fasta_fh, 'fasta') + print('>Feature %s' % (record.name), file=feature_fh) + for line in record.features: + if line.strand == 1: + print('%d\t%d\t%s' % (line.location.nofuzzy_start + 1, line.location.nofuzzy_end, line.type), file=feature_fh) + else: + print('%d\t%d\t%s' % (line.location.nofuzzy_end, line.location.nofuzzy_start + 1, line.type), file=feature_fh) + for (key, values) in line.qualifiers.iteritems(): + if key not in allowed_qualifiers: + continue + for v in values: + print('\t\t\t%s\t%s' % (key, v), file=feature_fh) + +# Final statement +eprint("Genome annotation done!") +eprint("The GenBank file is %s" % gbkoutputfile) +if args.gffprint==True: + eprint("The GFF3 file is %s" % gffoutputfile) +eprint("The table file for GenBank submission is %s" % tbloutputfile) +eprint("The FASTA file for GenBank submission is %s" % newfastafile) +eprint("The table file with all protein information is %s" % newtablefile) diff -r 000000000000 -r 231e4c669675 tool_data/viga_blastdb.loc.sample --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_data/viga_blastdb.loc.sample Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,31 @@ +# virannot_blastdb.loc +# This is a *.loc.sample file distributed with Galaxy that enables tools +# to use a directory of indexed data files. This one is for Bowtie2 and Tophat2. +# See the wiki: http://wiki.galaxyproject.org/Admin/NGS%20Local%20Setup +# First create these data files and save them in your own data directory structure. +# Then, create a bowtie_indices.loc file to use those indexes with tools. +# Copy this file, save it with the same name (minus the .sample), +# follow the format examples, and store the result in this directory. +# The file should include an one line entry for each index set. +# The path points to the "basename" for the set, not a specific file. +# It has four text columns seperated by TABS. +# +# +# +# So, for example, if you had hg18 indexes stored in: +# +# /data/databases/blast/nr/nr +# +# containing hg19 genome and hg19.*.bt2 files, such as: +# -rw-rw-r-- 1 james james 914M Feb 10 18:56 hg19canon.fa +# -rw-rw-r-- 1 james james 914M Feb 10 18:56 hg19canon.1.bt2 +# -rw-rw-r-- 1 james james 683M Feb 10 18:56 hg19canon.2.bt2 +# -rw-rw-r-- 1 james james 3.3K Feb 10 16:54 hg19canon.3.bt2 +# -rw-rw-r-- 1 james james 683M Feb 10 16:54 hg19canon.4.bt2 +# -rw-rw-r-- 1 james james 914M Feb 10 20:45 hg19canon.rev.1.bt2 +# -rw-rw-r-- 1 james james 683M Feb 10 20:45 hg19canon.rev.2.bt2 +# +# then the virannot_blastdb.loc entry could look like this: + +nr nr Non_redundant (nr) /data/databases/blast/nr/nr +swissprot swissprot Swissprot (swissprot) /data/databases/blast/swissprot/swissprot diff -r 000000000000 -r 231e4c669675 tool_data/viga_diamonddb.loc.sample --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_data/viga_diamonddb.loc.sample Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,5 @@ +# +# + +nr nr Non_redundant (nr) /data/databases/diamonddb/nr +swissprot swissprot Swissprot (swissprot) /data/databases/diamonddb/swissprot diff -r 000000000000 -r 231e4c669675 tool_data/viga_hmmdb.loc.sample --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_data/viga_hmmdb.loc.sample Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,5 @@ +# +# +uniprot_trembl uniprot_trembl UniProt TrEMBL /data/databases/SwissProt_UniProt/uniprot_trembl.fasta +uniprot_sprot uniprot_sprot UniProt Swiss-Prot /data/databases/SwissProt_UniProt/uniprot_sprot.fasta + diff -r 000000000000 -r 231e4c669675 tool_data/viga_rfamdb.loc.sample --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_data/viga_rfamdb.loc.sample Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,4 @@ +# +# +Rfam Rfam Rfam /data/databases/rfam/Rfam.cm + diff -r 000000000000 -r 231e4c669675 tool_data_table_conf.xml.sample --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/tool_data_table_conf.xml.sample Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,19 @@ + + + + value, dbkey, name, path + +
+ + value, dbkey, name, path + +
+ + value, dbkey, name, path + +
+ + value, dbkey, name, path + +
+ diff -r 000000000000 -r 231e4c669675 wrapper.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/wrapper.xml Tue Feb 27 14:16:54 2018 -0500 @@ -0,0 +1,304 @@ + + de novo VIral Genome Annotator + + vimalkvn/viga + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + gffprint + + + + + + + + + + + + + + + + + + + + + + + + `_ should first be installed and working on the +server where this Galaxy instance is setup. The user running Galaxy should be +part of the **docker** user group. + +#. Download or clone the VIGA_ Github repository (as a submodule) + in to the ``tools`` directory. + +**Configuration** + +**Update database paths in .loc files** + +Edit the following files in the **tool-data** directory and add paths to +corresponding databases: + +* viga_blastdb.loc +* viga_diamonddb.loc +* viga_rfamdb.loc +* viga_hmmdb.loc + +**Create or update the Galaxy job configuration file** + +If the file **config/job_conf.xml** does not exist, create it by copying the +template **config/job_conf.xml.sample_basic** in the Galaxy directory. Then +add a Docker destination for viga. Change ``/data/databases`` under +``docker_volumes`` to the location where your databases are stored. Here is +an example:: + + + + + + + + + + + + + + true + false + true + $defaults,/data/databases:ro + + + + + + + + +**Restart Galaxy**. The tool will now be ready to use. + +---- + +**Output files** + +VIGA creates the following output files: + +* tbl - Table file with all protein information. +* gbk - GenBank format file with annotations. +* fasta - FASTA format file for GenBank submission +* csv - Table file for GenBank submission. +* gff - GFF3 format file (if option is selected) + +---- + +**License and citation** + +VIGA_ and this Galaxy wrapper - `GPLv3 `_. + + +Galaxy + +- Goecks, J, Nekrutenko, A, Taylor, J and The Galaxy Team. "Galaxy: a + comprehensive approach for supporting accessible, reproducible, and + transparent computational research in the life sciences." + Genome Biol. 2010 Aug 25;11(8):R86. + +- Blankenberg D, Von Kuster G, Coraor N, Ananda G, Lazarus R, Mangan M, + Nekrutenko A, Taylor J. "Galaxy: a web-based genome analysis tool for + experimentalists". Current Protocols in Molecular Biology. + 2010 Jan; Chapter 19:Unit 19.10.1-21. + +- Giardine B, Riemer C, Hardison RC, Burhans R, Elnitski L, Shah P, Zhang Y, + Blankenberg D, Albert I, Taylor J, Miller W, Kent WJ, Nekrutenko A. "Galaxy: + a platform for interactive large-scale genome analysis." + Genome Research. 2005 Oct; 15(10):1451-5. + +You can use this tool only if you agree to the license terms of: `VIGA`_. + +.. _VIGA: https://github.com/EGTortuero/viga + +]]> + +