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: [File:all-blast.sh] [File:run-blast.pl]
* Psi-Blast: [File:all-psiblast.sh]
* HHBlits: [File:all-hhblits.sh]

== 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>