>>> for alignment in blast_record.alignments:
... for hsp in alignment.hsps:
... if hsp.expect < E_VALUE_THRESH:
... print '****Alignment****'
... print 'sequence:', alignment.title
... print 'length:', alignment.length
... print 'e value:', hsp.expect
... print hsp.query[0:75] + '...'
... print hsp.match[0:75] + '...'
... print hsp.sbjct[0:75] + '...'

>>> from Bio import SearchIO
>>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
>>> blast_hsp = blast_qresult[0][0] # first hit, first hsp
>>> print blast_hsp
Query: 42291 mystery_seq
Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520...
Query range: [0:61] (1)
Hit range: [0:61] (1)
Quick stats: evalue 4.9e-23; bitscore 111.29
Fragments: 1 (61 columns)
Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
>>> blast_hsp.query_range
(0, 61)
>>> blast_hsp.evalue
4.91307e-23
>>> blast_hsp.hit_start # start coordinate of the hit sequence
0
>>> blast_hsp.query_span # how many residues in the query sequence
61
>>> blast_hsp.aln_span # how long the alignment is
61
>>> blast_hsp.gap_num # number of gaps
0
>>> blast_hsp.ident_num # number of identical residues
61
>>> blast_hsp.query
SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='42291', name='aligned query sequence', description='mystery_seq', dbxrefs=[])
>>> blast_hsp.hit
SeqRecord(seq=Seq('CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTT...GGG', DNAAlphabet()), id='gi|262205317|ref|NR_030195.1|', name='aligned hit sequence', description='Homo sapiens microRNA 520b (MIR520B), microRNA', dbxrefs=[])
HSP 对象有个 alignment 属性
>>> print blast_hsp.aln
DNAAlphabet() alignment with 2 rows and 61 columns
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG 42291
CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAG...GGG gi|262205317|ref|NR_030195.1|

>>> from Bio import SearchIO
>>> blast_qresult = SearchIO.read('my_blast.xml', 'blast-xml')
>>> blast_frag = blast_qresult[0][0][0] # first hit, first hsp, first fragment
>>> print blast_frag
Query: 42291 mystery_seq
Hit: gi|262205317|ref|NR_030195.1| Homo sapiens microRNA 520b (MIR520...
Query range: [0:61] (1)
Hit range: [0:61] (1)
Fragments: 1 (61 columns)
Query - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Hit - CCCTCTACAGGGAAGCGCTTTCTGTTGTCTGAAAGAAAAGAAAGTGCTTCCTTTTAGAGGG

有时，你得到了一个包含成百上千个query的搜索输出文件要分析，你当然可以使用 Bio.SearchIO.parse 来处理，但是如果你仅仅需要访问少数query的话，效率 是及其低下的。这是因为 parse 会分析所有的query，直到找到你感兴趣。
在这种情况下，理想的选择是用 Bio.SearchIO.index 或 Bio.SearchIO.index_db 来索引文件。

>>> from Bio import SearchIO
>>> qresults = SearchIO.parse('mirna.xml', 'blast-xml') # read XML file
>>> SearchIO.write(qresults, 'results.tab', 'blast-tab') # write to tabular file
(3, 239, 277, 277)

>>> from Bio import SearchIO
>>> SearchIO.convert('mirna.xml', 'blast-xml', 'results.tab', 'blast-tab')
(3, 239, 277, 277)

刚开始接触，愿越来越好

不错