Preparared by D. E. Salas-Leiva

1. ncbi blast and hmmer for homology searches. These are available at perun's environmental path
2. maftt, trimal, fastree for tree reconstruction. These are available at perun's environmental path or at /home/dsalas/Shared/
3. the following python scripts written by Dayana. These are available at /home/dsalas/Shared/RNAdecayCarp/Scripts
   • Blastp_search.sh
   • BlastParser.by.Pident.py uses python36-generic
   • ShellMaker2.py uses python36-generic
   • Tsv_parser.py uses python36-generic
   • SeqCollector.py uses python36-generic
   • ETE_standAlone1.4.py uses python27-generic

These MUST belong to proteins with experimental evidence from MODEL ORGANISMS. This information should be gathered through literature searches, as well as download the information from specialized databases. Query sequences from model organisms in which the pathway of interest has been very well studied.

Panther data
For the current workflow 103 proteomes were gathered and reparsed from Uniprot (it contains Archaea, Bacteria, Eukaryota). This data is organized as:

• A full proteome blast-able database at: /scratch3/rogerlab_databases/other_dbs/UniProt103.fasta
• A full panther classification file for each protein in Uniprot103.fasta at: /scratch3/rogerlab_databases/other_dbs/PTHR_103classification.tsv
• A full panther database containing hmm profiles by panther superfamily at: /db1/extra-data-sets/panther/PANTHER13.1/books/
• A full panther database containing fasta files by panther superfamily at: /scratch3/rogerlab_databases/other_dbs/UniProt103_byPTHRfam

Pfam-A
hmmr database at: /scratch3/rogerlab_databases/other_dbs/Pfam-A.hmm

Predicted proteomes
The directory containing the predicted proteomes is called Pred_Proteomes_playground.
For Carperdiemonas membranifera paper these are the predicted proteomes:

Carp_sept19.aa.fasta
Cfrisia.masked.aa.fasta
GinA_50803.aa.fasta
GinB_50581.aa.fasta
Gmuris.aa.fasta
Kipferlia.aa.fasta
Monoc.aa.fasta
SSAL.aa.fasta
Trepo.aa.fasta
Trich.aa.fasta

1. Blast queries (experimentally characterized proteins of interest from model organisms) against Uniprot103 database to get the best matching sequence
2. Obtain the PANTHER classification for that sequence (PANTHER superfamily and subfamily)
3. Scan the predicted proteomes using the hmm-profile corresponding to the subfamily
4. Parse the hmm output: empty files and files with results
5. Redo the scan on the predicted proteomes but this time using the hmm-profile corresponding to the superfamily
6. Parse the hmm output: empty files and files with results
7. Concatenate the parsed results from step 4
8. Retrieve the sequences mentioned from each predicted proteome by PANTHER superfamily
9. Create a working fasta file with each of the candidates and PANTHER files from UniProt103_byPTHRfam (see above)
10. Create shells for sequence aligment, trimming and tree search
11. Tree search
12. Map protein domain architecture to each tree and build a pdf file by PANTHER superfamily
13. Creating a tabulated file to keep track of the findings
14. Move the pdf to your desktop for tree inspection and orthology assigment.

NOTE: The pathway of interest for this example is the RNA decay pathway, query file name is RNAreduced.seqs and Metadata associated is RNAreduced.METADATA

BEFORE YOU START: Make a working directory, copy the predicted proteomes, queries and metadata, and the scripts for this workflow:

mkdir Pred_Proteomes_playground
cp Queries/* Pred_Proteomes_playground/
cp Comparative_Genomics/* Pred_Proteomes_playground/
cp *.py Blastp*.sh Pred_Proteomes_playground/
cd Pred_Proteomes_playground

Run blast search against UniProt103.fasta to find the closest Panther superfamily for each of the queries from model organisms and then parse the blast output by identity and e-value:
The query file name is RNAreduced.seqs, the blast output will be called RNA.reduced.blastout and the shell is Blastp_search.sh

Parse the blast output by identity and e-value: Given the queries you are using (see Initial information required) it is highly likely that these sequences will find themselves during the blast search against UniProt103.fasta For more information on species contained in Panther see the file PTHR_103classification.tsv or visit http://www.pantherdb.org/.

source activate python36-generic
python BlastParser.by.Pident.py RNA.reduced.blastout
source deactivate

The resulting file RNA.reduced.blastout_pparsed.tab should contain one result by query. To check this, use the following commands:

To figure out how many sequences you started with:

 grep ‘>’ RNAreduced.seqs | wc -l
 # outputs is -> 97 RNAreduced.seqs

To figure out how many blast hits output you have:

 wc -l RNA.reduced.blastout
 # outputs is -> 99 RNA.reduced.blastout
 
 wc -l RNA.reduced.blastout_pparsed.tab
 # outputs is -> 98 RNA.reduced.blastout_pparsed.tab

NOTE: Remember that this file has a header, so the total of blast results is 97, so everything is fine so far. If your number of queries and blast output results differ: 1) double check your input files for errors in format 2) go online to panther to check if the panther family for the query of your interest has been curated and/or exists.

Obtain the codes for each PANTHER superfamily and subfamily for each query by using the command commands below and the PTHR_103classification.tsv to create a customized file for your queries.

Post-processing the blast output to get panther information:

1) Separate the first column that correspond to the queries accession numbers:

 cut -d $'\t' RNA.reduced.blastout_pparsed.tab -f1 > queries_acc

2) The uniprot accession numbers (these numbers will be used to grep PTHR_103classification.tsv):

 cut -d $'\t' RNA.reduced.blastout_pparsed.tab -f2|cut -d '|' -f2 > hits_acc  

3) Create a file containing both columns for future crosschecking:

 paste -d $'\t' queries_acc hits_acc > query_hits_columns

4) Remove the header of query_hits_columns:

  sed -i '/query ID\tsubject ID/d' query_hits_columns

Getting the information from PANTHER classification: Now, create a file containing the panther information only for 97 queries, by grepping the hits_acc information from the PTHR_103classification.tsv:

5) create a file containing the panther information 97 queries:

 grep -w -F -f hits_acc /scratch3/rogerlab_databases/other_dbs/PTHR_103classification.tsv > Panther97queries_hit_info.tsv  

6) Create a tsv file containing identify the panther families by hits_acc:

 cut -d $'\t' -f1,2,3 Panther97queries_hit_info.tsv |cut -d '|' -f3,4|cut -d '=' -f2 > Pantherby97Uniprotaccession.tsv

7) eliminate extra tabulations in the file:

 sed -i.bak 's/\t\t/\t/g' Pantherby97Uniprotaccession.tsv
 sed -i 's/:/_/g' Pantherby97Uniprotaccession.tsv

8) Creating a cheat sheet for you. Sort and Merge the headerless file ‘query_hits_columns’ with ‘Pantherby97Uniprotaccession.tsv’:

 sort -u -k 2 query_hits_columns > sorted_query_hits_columns
 sort -u -k 1 Pantherby97Uniprotaccession.tsv > sorted_Panther97byUniprotaccession
 join -1 1 -2 2 sorted_Panther97byUniprotaccession sorted_query_hits_columns > hit_panther_query.tsv
 sed -i 's/ /\t/g' hit_panther_query.tsv
 

9) Creating input files to create shells for the hmmr searches:

• Creating a master shells for the search of panther hmmrs:

Make a security copy of sorted_Panther97byUniprotaccession:

 cp sorted_Panther97byUniprotaccession sorted_Panther97byUniprotaccession_copy
 

Create a list of predicted proteomes:

 ls -1 *.aa.fasta| sort > Prot_Inlist
 for f in sorted_Panther97byUniprotaccession; do for x in `cat Prot_Inlist`; do sed -i "s/$/\t$x/g" $f; done; done
 

if you head -2 in sorted_Panther97byUniprotaccession after the command line above, this is how it should look like:

 A5YKK6	PTHR13162_SF8	Carp_sept19.aa.fasta	Cfrisia.masked.aa.fasta	GinA_50803.aa.fasta	GinB_50581.aa.fasta	Gmuris.aa.fasta	Kipferlia.aa.fasta	Monoc.aa.fasta	SSAL.aa.fasta	Trepo.aa.fasta	Trich.aa.fasta
 A8BCK6	PTHR22891_SF0	Carp_sept19.aa.fasta	Cfrisia.masked.aa.fasta	GinA_50803.aa.fasta	GinB_50581.aa.fasta	Gmuris.aa.fasta	Kipferlia.aa.fasta	Monoc.aa.fasta	SSAL.aa.fasta	Trepo.aa.fasta	Trich.aa.fasta

1) You are ready to create the master shell for the hmmr search:

 source activate python36-generic
 python ShellMaker2.py sorted_Panther97byUniprotaccession hmmrsearch 1 1
 source deactivate
 where hmmrsearch is the task for which the shell be created, 1 is the number of threads and 1 specifies a narrow search.
 

To double check that the number of lines coincide with number of queries * number of proteomes (97 * 10):

 wc -l Panther.HmmrSearch.sh1
 remember that the shell has 6 lines before the command lines for HMM search.
 NOTE: Do **NOT** use more than **1** thread when using hmmr in this protocol, other wise it'll be a waste of resources as the won't we allocated.

 The script will produce a file called ‘Panther.HmmrSearch1.sh’ that contains all the command lines required to search the 97 queries/panther hmmr in all of the proteomes you specified.\\

2) Qsub the Shell. Please avoid lower ram nodes.

 qsub Panther.HmmrSearch.sh1
 NOTE: The search will produce one OUT file by query by predicted proteome (1 x 97 x 10= 970 tsv, however, keep in mind that different queries may hit the same panther super and subfamily, so this number could be smaller). \\
 

Parsing the HMM outputs

 source activate python36-generic
 python Tsv_parser.py Panther.HmmrSearch.sh1 5 1
 source deactivate
 where 5 corresponds to the maximum of candidates to keep. The value can increase or decrease depending on what is needed. the number 1 refers to the search by Subfamily.
 
 The program will check for failed searches and empty outputs, and create 3 outputs:
 1) A large file called 'BestHMM_Candidates_1.tsv' with up to 5 candidates by each query from each predicted proteome
 2) A Input file called 'Input4Step2' to run a broader query by panther subfamily
 3) A file called 'Missingfrom_Step_1' that corresponds to searches that couldn't be carried out by different reasons.

1) You are ready to create the master shell for the hmmr search by PANTHER SUPERFAMILY:

 python ShellMaker2.py Input4Step2 hmmrsearch 1 2
 where hmmrsearch is the task for which the shell be created, 1 is the number of threads and 2 specifies a broad search
 NOTE: Do **NOT** use more than **1** thread when using hmmr in this protocol, other wise it'll be a waste of resources as the won't we allocated.

The script will produce a file called ‘Panther.HmmrSearch.sh2’ that contains all the command lines required to search the queries that did not have hits during the search carried out in the step 3. 2) Qsub the Shell. please avoid lower ram nodes.

 qsub Panther.HmmrSearch.sh2

Parsing the HMM outputs

 source activate python36-generic
 python Tsv_parser.py Panther.HmmrSearch.sh2 10 2
 source deactivate
 where 10 corresponds to the maximum of candidates to keep and 2 means that the search was by super family. The value to keep of candidates can increase or decrease depending on what is needed.
 The program will:
 1)  Create a file called 'BestHMM_Candidates_2.tsv' with up to 10 candidates by each query from each proteome and merge it with 'BestHMM_Candidates_1.tsv' into a new file called 'BestHMM_Candidates_All.tsv'
 2) Create a file containing all the information from candidates above the threshold.
 3) Create a Final summary file with all queries that were not found ('NotFound_inproteomes')
 4) Retrieve the sequences mentioned in 'BestHMM_Candidates_All.tsv' from each predicted proteome by Panther super-family
 5) Retrieve the Panther superfamilies of interest and will create a merged fasta file ('PTHR*.REC.fasta') that includes the panther sequences and the sequences retrieved in (4)
 6) Create the Shells for alignment, trimming and tree reconstruction. 
 7) Create an input file (Input4ETE) to be later applied with the ETE_standAlone1.4.py script
 

 ls -1 *Reconstruction.sh > list_of_shells
 for i in `cat list_of_shells`; do qsub $i; done
 

  1) Create an input file separated by tabs containing a list records by line following this format: fastafile treefile
  source activate python27-generic
  xvfb-run -a python ETE_standAlone1.4.py Input4ETE
  source deactivate
  

NOTE: you will need a metadata file. it may consist of accession numbers and a fasta header. please see the format of the metadata provided for this example 'RNAreduced.METADATA'

 source activate python36-generic
 pyton PrepMetatable.py sorted_Panther97byUniprotaccession query_hits_column BestHMM_Candidates_All.tsv RNAreduced.METADATA 
 source deactivate

DON'T FORGET to Check how many searches were not done due to errors:

grep 'Error:' Panther.HmmrSearch.sh2.e* |sort -u \\
Error: File existence/permissions problem in trying to open HMM file /db1/extra-data-sets/panther/PANTHER13.1/books/PTHR44316/hmmer.hmm.