View source for Task 2 lab journal (MSUD)

== Sequence searches ==
=== Configure parameters===
* We have used <tt>blastall</tt> for blastp and psiblast
* BLAST and PSI-BLAST
** '''big_80''' database was used for sequence search: <tt>-d /mnt/project/pracstrucfunc13/data/big/big_80</tt>
** output put format was set to xml and tab separated values:
*** XML output: <tt>-m 7</tt>
*** TSV output: <tt>-m 9</tt>
** the number of hits to be shown was set to 200000: <tt>-b 200000</tt>
** For PSI-Blast number of iterations and cutoff of E-values were set with following parameters
*** iteration: -j <number_of_iterations>
*** E-value cutoff: -h <threshold>
* HHBlits
** '''uniprot20''' database was used for sequence search: <tt>-d /mnt/project/pracstrucfunc13/data/hhblits/uniprot20_current</tt>
** Resulted a3m, hhm and hhr files are stored: <tt>-o result.hhr -oa3m result.a3m -ohhm result.hhm</tt>
** Output size was also set to 200000: <tt>-Z 200000 -B 200000</tt>

=== Data creation ===
For each of blast, psi-blast and hhblits, a shell script was written to perform sequence search.
* Blast: [[all-blast.sh]] [[run-blast.pl]]
* Psi-Blast: [[all-psiblast.sh]]
* HHBlits: [[all-hhblits.sh]]

==== Convert result of hhblits ====
We find result of hhblits in hhr format is not parser-friendly. So the program '''<tt>[[hhr2tsv]]</tt>''' was written. It finds out all hits and their statistics information in hhr file and write the data out to a tsv (tab-separated values) file.

==== SCOP classification ====
In order to check the quality of sequence search programs, we have used the parsable file from Structural Classification of Proteins('''SCOP''').
The classification of domains in PDB files can be observed in file <tt>[http://scop.mrc-lmb.cam.ac.uk/scop/parse/ dir.cla.scop.txt_1.75]</tt>.

==== Id mapping from Uniprot to Gene Ontology ====
Quality check of sequence search programs was also performed using Gene Ontology(GO) annotations. The Idmapping data was used to assign GO annotations to each Uniprot proteins (genes).
* Idmapping data in TSV format was downloaded from FTP site of Uniprot: [http://www.uniprot.org/downloads idmapping_selected.tab.gz] (large file! 1.2 GB) [ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping/README README of idmapping]
* GO term IDs are stored in the 7th column
* Uniprot accession codes are in the 1st column

Following command was used for extraction of all Uniprot entries with GO annotation:
 <nowiki>zcat idmapping_selected.tab.gz | cut -f1,7 | grep -vP '\s$' > uniprot_go_mapping.tsv</nowiki>

For efficient searching of GO terms, the mapping data was stored into a SQLite3 database. Following is the schema of the database :
<source lang='sql'>
CREATE TABLE uniprot_go_mapping ( 
    uniprot_ac CHAR( 6 )   NOT NULL,
    go_term    CHAR( 10 )  NOT NULL,
    PRIMARY KEY ( uniprot_ac, go_term ) 
);
CREATE INDEX idx_uniprot_ac ON uniprot_go_mapping (uniprot_ac ASC);
</source>

Following is the python script for transporting mapping data into SQLite.
<source lang='python'>
#!/usr/bin/env python
import sqlite3
import math
conn = sqlite3.connect('idmapping.sqlite3')
c = conn.cursor()

'''callback function for map procedure
pack key and value into one tuple'''
def generate_key_val_pair(key, val):
	return (key, val)

tsv  = file('/mnt/datentank/uniprot_go_reduced.map')

entriesBuffer = []

# add uniprot ac and GO term pairs into sqlite db
for lineId, line in enumerate(tsv):
	line = line.strip()
	keyval = line.split('\t')
	key = keyval[0]
	vals = keyval[1].split('; ')
	keys = [ key ] * len(vals)
	entries = map(generate_key_val_pair, keys, vals)
	if (len(entriesBuffer) < 1000000):
		entriesBuffer.extend(entries)
	else:
		c.executemany("INSERT OR IGNORE INTO uniprot_go_mapping \
		(uniprot_ac,go_term) VALUES (?,?);", entriesBuffer)
		conn.commit()
		entriesBuffer[:] = []
		print 'Processed %d lines' % (lineId)

tsv.close()
</source>

== Multiple sequence alignments ==

=== Dataset creation ===
The datasets were created from the Blast output.

For creating datasets with low, high and whole range sequence identity, the following Python script was used:



<source lang="python">
'''
Use blast output to create datasets with different sequence identities.

Call with:
python create_dataset.py <query fasta file> <blast xml output> <database fasta file>

@author: Laura Schiller
'''

import sys
from Bio import SeqIO, pairwise2
from Bio.Blast import NCBIXML
from Bio.SubsMat import MatrixInfo

def get_sequences(query, blast_xml, db_path, out_file):
    '''
    Fetch full length sequences for a BLAST result and write them in a FASTA file.
    
    @param query:     fasta file with query sequence.
    @param blast_xml: xml file with BLAST search result.
    @param db_path:   path of db fasta file.
    @param out_file:  file to store the sequences.
    @return:          name of the file where the sequences are stored.
    '''
    
    print("get all sequences for blast result in %s" % blast_xml)
    query_sequence = SeqIO.read(query, "fasta")
    hit_seqs = [query_sequence]
    
    blast_output = open(blast_xml)
    blast_result = NCBIXML.read(blast_output)
    blast_output.close()
    
    hit_list = []
    for alignment in blast_result.alignments:
        hit_list.append(alignment.title.split(" ")[1]) # the id of the sequence    
    
    # get all blast hits
    seqs_db = SeqIO.parse(db_path, "fasta")
    counter = 0
    number = len(blast_result.alignments)
    for seq in seqs_db:
        if seq.id in hit_list:
            hit_seqs.append(seq)
            counter = counter + 1
            if (counter % 100) == 0:
                print("%d of %d sequences found" % (counter, number))
            if counter == number:
                break
    print("%d of %d sequences found" % (counter, len(blast_result.alignments)))        
    
    SeqIO.write(hit_seqs, out_file, "fasta")
    
    print("sequences saved in %s" % out_file)
    return out_file

def filter_seqs(seq_file, name):
    '''
    Filter sequences according to sequence identity limits.
    
    @param seq_file: fasta file with sequences.
    @param name:     string used for output file names.
    '''
    
    sequences = SeqIO.parse(seq_file, "fasta")
    seqs = []
    for seq in sequences:
        seqs.append(seq)
    query = seqs.pop(0) # always keep query (first sequence)
    # lists for low / high / whole range sequence identity
    filtered = [[query], [query], [query]]
    
    # identify hits with pdb structures -> these are preferentially taken
    hits_pdb = [hit for hit in seqs if (hit.id.split("|")[0] == "pdb")]
    seqs = [seq for seq in seqs if not (seq.id in [pdb_seq.id for pdb_seq in hits_pdb])]
    hits_pdb.extend(seqs) # now pdb hits are at the beginning
    
    print("filter sequences")
    for seq in hits_pdb:           
        try: 
            ident = identity(query, seq) 
        except KeyError: # raises if there is a non amino acid letter
            continue
        if (len(filtered[0]) < 10):
            keep = True            
            for seq2 in filtered[0]:
                try: 
                    ident2 = identity(seq, seq2) 
                except KeyError:
                    keep = False
                    continue
                if ident2 >= 0.3:
                    keep = False
                    break
            if keep:
                filtered[0].append(seq)
        if (len(filtered[1]) < 10) and (ident > 0.6):
            filtered[1].append(seq)
        if (len(filtered[2]) < 8) and (ident >= 0.3) and (ident <= 0.6):
            filtered[2].append(seq)
        if (len(filtered[0]) == 10) and (len(filtered[1]) == 10) and (len(filtered[2]) == 8):
            break
    
    # for whole range take a part of low and a part of high sequence identity
    # plus the rest middle sequence identity
    filtered[2].extend(filtered[0][1:min(len(filtered[0]), 7)])
    filtered[2].extend(filtered[1][1:min(len(filtered[1]), 7)])
    
    SeqIO.write(filtered[0], name + "_low_seq_ident.fasta", "fasta")
    SeqIO.write(filtered[1], name + "_high_seq_ident.fasta", "fasta")
    SeqIO.write(filtered[2], name + "_whole_range_seq_ident.fasta", "fasta")
    
    print("sequences with low / high / whole range sequence identity saved in:")
    print(name + "_low_seq_ident.fasta")
    print(name + "_high_seq_ident.fasta")
    print(name + "_whole_range_seq_ident.fasta") 

def identity(seq1, seq2):
    '''
    Calculate relative sequence identity of two sequences.
    
    @return: number of identical residues divided by mean length.
    '''

    #pairwise alignment
    matrix = MatrixInfo.blosum62
    gap_open = -10
    gap_extend = -0.5     
    alignment = pairwise2.align.globalds(seq1, seq2, matrix, gap_open, gap_extend)
    seq1_aligned = alignment[0][0]
    seq2_aligned = alignment[0][1]

    #sequence identity
    ident = sum(c1 == c2 for c1, c2 in zip(seq1_aligned, seq2_aligned))
    ref_length = (len(seq1) + len(seq2)) / 2 # mean length
    return float(ident) / ref_length


if __name__ == '__main__':
    namestring = sys.argv[1].split("/")[-1].split(".")[0] # used as beginning of the output files
    print("-----------------------------------------------------------")
    all_seqs = get_sequences(sys.argv[1], sys.argv[2], sys.argv[3], namestring + "_all_sequences.fasta")
    filter_seqs(all_seqs, namestring)
</source>



Note: for DBT there were some problems aligning very long sequences to calculate mutual sequence identities. So there, for the low sequence identity group, only sequences that have less than 6 times the length of the query sequence were taken.

=== Calling MSA programs ===

Call of T-Coffee:

<pre>
#!/bin/bash

proteins=( BCKDHA BCKDHB DBT DLD )
identities=( low high whole_range )

for protein in ${proteins[*]}
do
	for identity in ${identities[*]}
	do
		t_coffee -output fasta -infile ${protein}_${identity}_seq_ident.fasta -outfile ${protein}_${identity}_seq_ident_tcoffee.fasta 
	done
done
</pre>

Muscle:

<pre>
#!/bin/bash

proteins=( BCKDHA BCKDHB DBT DLD )
identities=( low high whole_range )

for protein in ${proteins[*]}
do
	for identity in ${identities[*]}
	do
		muscle -in ${protein}_${identity}_seq_ident.fasta -out ${protein}_${identity}_seq_ident_muscle.fasta 
	done
done
</pre>

Mafft:

<pre>
#!/bin/bash

proteins=( BCKDHA BCKDHB DBT DLD )
identities=( low high whole_range )

for protein in ${proteins[*]}
do
	for identity in ${identities[*]}
	do
		mafft ${protein}_${identity}_seq_ident.fasta > ${protein}_${identity}_seq_ident_mafft.fasta  
	done
done
</pre>