A BLAST to SAM converter.

Some times ago, I've received a set of Ion-Torrent /mate-reads with a poor quality. I wasn't able to align much things using bwa. I've always wondered if I could get better alignments using NCBI-BLASTN (short answer: no) . That's why I asked guyduche, my intership student to write a C program to convert the output of blastn to SAM. His code is available on github at :

https://github.com/guyduche/Blast2Bam

The input for blast2sam is

• the XML output of NCBI blastn (or stdin)
• The single or pair of fastq file(s)
• The reference sequence indexed with picard

.

Example:

fastq2fasta in.R1.fq.gz in.R2.fq.gz |\
blastn -db REFERENCE   -outfmt 5 | \
blast2bam -o result.bam -W 40 -R '@RG   ID:foo  SM:sample' - REFERENCE.dict  in.R1.fq.gz in.R2.fq.gz

Output:

@SQ SN:gi|9629357|ref|NC_001802.1|  LN:9181 
@RG ID:foo  SM:sample
@PG ID:Blast2Bam    PN:Blast2Bam    VN:0.1  CL:../../bin/blast2bam -o results.sam -W 40 -R @RG  ID:foo  SM:sample - db.dict test_1.fastq.gz test_2.fastq.gz
(...)
ERR656485.2 83  gi|9629357|ref|NC_001802.1| 715 60  180S7=1X8=1X11=1X2=2X4=1X14=1X8=1X33=1X4=1X2=1X5=1X2=1X6=1S =   715 -119    CCTAGTGTTGCTTGCTTTTCTTCTTTTTTTTTTCAAGCAGAAGACGGCATACGAGATCCTCTATCGAGATCGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCTAAGATAGAGGAAGAACAAAACAAATGTCAGCAAAGTCAGCAAAAGACACAGCAGGAAAAAGGGGCTGACGGGAAGGTCAGTCAAAATTATCCTATAGTGCAAAATCTCCAAGGGCAAATGGTACACCAGGCCATGTCACCTAGAACTTTAAATGCATGGGTAAAAGTAATAGAGGAAAAGGCCTTTAGCCCAN    (),.((((,(((((,((.((.-(>69>20E>6/=>5EC@9-52?BEE::2951.)74B64=B==FFAF=A??59:>FFFDF:55GGFGF?DFGGFE868>GGGFGGGGED;FGFFGGGGGGGGGGGEFFGE9GGGGFGGGGGGGGDGECGGFGGGGGGGGGGFGGGGGEGGFGGGGGGFFGGGGGFF?EGGFFFEGGGGGGGGFEGGGEGGGFEGGGGGGGGGGDGFFCEGFGGGGGGGGGGGFFECFGGGGFGGGGGGGGGGGFCGGGGGGGGGGGGGGGGGGFGGGGGGGGF@CCA8!    NM:i:13 RG:Z:foo    AS:i:80 XB:f:148.852    XE:Z:4.07e-39
ERR656485.2 163 gi|9629357|ref|NC_001802.1| 715 60  73S7=1X8=1X11=1X2=2X4=1X14=1X8=1X33=1X4=1X2=1X5=1X2=1X8=106S    =   715 119 NAGATAGAGGAAGAACAAAACAAATGTCAGCAAAGTCAGCAAAAGACACAGCAGGAAAAAGGGGCTGACGGGAAGGTCAGTCAAAATTATCCTATAGTGCAAAATCTCCAAGGGCAAATGGTACACCAGGCCATGTCACCTAGAACTTTAAATGCATGGGTAAAAGTAATAGAGGAAAAGGCCTTTAGCCCAGAGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCCGTCTCATTACAAAAAAAACATACACAATAAATGATATAAGCGGAATCAACAGCATGA    !8A@CGGEFGFGCDFGGGGGGGGGGGGGGFGGGGGFGFGGGGGGGGGGGGGGGGGGGGGGGGEGGGGGGGGGGGGGGFGGFGGGGGGGGGEGFGFGGGFFGGGGGGGGFGGGGGGGGGGGGFFFFGGGGGG=FFGGFFDGGGGGGGG8FGFGGGGGGGGGFGGGGGGGGGGFDGGFGGFGGGFFFGFF8DFDFDFFFFFFFFFBCDB<@EAFB@ABAC@CDFF?4>EEFE<*>BDAFB@FFBFF>((6<5CC.;C;=D9106(.))).)-46<<))))))))))((,(-)))()((()))    NM:i:13 RG:Z:foo    AS:i:82 XB:f:152.546    XE:Z:3.15e-40
(...)

Now, I would be interested in finding another dataset where this tool could be successfully used.
That's it,
Pierre