Trace formatting

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Articles

Cleaning

  • run dust on Ecoli & UniVector to mask low complexity seqs
  • screen against Ecoli & UniVector database

Technologies

Latest Technology Summary

 Technology            454                       Illumina                              Solid
                       seq-by-synthesis            seq-by-synthesis                        ABI
 Company               454(Roche)                  Illumina
 Location              Brandford,CT                SanDiego,CA          
 Latest                GS FLX, Titanium reagents   Genome Analyzer II                      SOLID 3
 Throughput            500M/run                    1G/run                                  20G/run
 RunTime               10hr                        3days
 ReadLen               500bp                       36                                      35
 InsertLen             3K                          100-200bp                               600-10K 
 Accuracy                                                                                  99.94%
 Q20(99%accuracy)      400bp                       34bp
 Cost                                              $3K/run                                 60K/3G                             
                                                   $400/4M bacterial genome(25-30X)
 Problems              homopolimers

Sanger

454

Anomalies:

 * homopolymer lengths can be shorter than real
 * substitutions less likely than in traditional methodssingle base insertions
 * carry forward events usually near but not adjacent to homopolymers

GS20

 * 1.6M total wells
 * 450K detactable wells
 * 200K usable wells
 Accuracy:
 * published per-base accuracy of a Roche GS20 is only 96%.
 * Mitch Sogin paper
   * 99.5% accuracy rate in unassembled sequences
   * identified several factors that can be used to remove a small percentage of low-quality reads, improving the accuracy to 99.75% or better => better quality than Sanger sequencing
   * The error rate, defined as the number of errors (miscalled bases plus inserted and deleted bases) divided by the total number of expected bases, was 0.49%
  * 36% insertions, 27% delitions, 21% N's, 16% substitutions
  * A to G and T to C, were more frequent than other mismatches
  * reverse transitions, G to A and C to T, were not that frequent 
  * Nearly 70% of the homopolymer extensions were A/T
  * errors were evenly distributed along the length of the reference sequences, they were not evenly distributed

among reads: 82% had no errors, 93% had no more than a single error, and 96% had no more than 2 errors.

  * A small number of reads, fewer than 2%, contained a disproportionate number of errors that account for nearly 50% of the miscalls for the entire dataset  
  * Avg quality is 25; in homopolymers can drop as low as 5
  * Reads much longer than avg length had more errors
  * strong correlation between the presence of ambiguous base calls and other errors in a read
  * The presence of even a single ambiguous base in a read correlates strongly with the presence of other errors 
  * Primer errors also correlated with errors

GS FLX

GS FLX with Titanium reagents

 * up to 500M/run
 * reads up to 500bp

Get info from .sff files:

 $ sffinfo -h
 Usage:  sffinfo [options...] [- | sfffile] [accno...]
 Options:
      -a or -accno      Output just the accessions
      -s or -seq      Output just the sequences
      -q or -qual     Output just the quality scores
      -f or -flow     Output just the flowgrams
      -t or -tab      Output the seq/qual/flow as tab-delimited lines
      -n or -notrim   Output the untrimmed sequence or quality scores
      -m or -mft      Output the manifest text

un-paired reads

paired ends

Features:

 * approximately 84-nucleotide DNA fragments 
 * have a ~ 44-mer linker sequence in the middle 
 * flanked by a ~ 20-mer sequence on each side. 
 * The two flanking 20-mers are segments of DNA that were originally located approximately 2.5 (3?) kb apart in the genome of interest.  
 * The ordering and orienting of contigs generates scaffolds which provide a high-quality draft sequence of the genome.
 Linker(palindrome) : GTTGGAACCGAAAGGGTTTGAATTCAAACCCTTTCGGTTCCAAC
 Check for linker   : sffinfo -s *.sff | ~/bin/fasta2tab.pl | grep GTTGGAACCGAAAGGGTTTGAATTCAAACCCTTTCGGTTCCAAC
 
 12345678901234567890123456789012345678901234
 GTTGGAACCGAAAGGGTTTGAATTCAAACCCTTTCGGTTCCAAC 
 GTTGGAACCGA
 AAGGGTTTGAA
 TTCAAACCCTT
 TCGGTTCCAAC

Anomalies:

 * the linker can appear (tandem,completely/partially) more than once
 * some reads end up in linker (partial)
 * some reads don't contain the linker at all
 * some reads are cloning vector

NCBI bacterial data sets:

 Accession          Center  Instrument     
 SRR001351,2,3      JCVI    454 GS FLX      454 Paired End Sequencing                       Porphyromonas gingivalis W83
 SRR001355          JCVI    454 GS FLX      454 Paired End Sequencing                       Escherichia coli str. K-12 MG1655
 SRR004895,9        BI      454 GS FLX      45X on 454 (15X fragment and 30X paired)        Brucella pinnipedialis
 SRR004900,1        BI      454 GS FLX      45X on 454 (15X fragment and 30X paired)        Brucella suis bv. 3
 SRR005309,10,11    BI      454 GS FLX      45X on 454 (15X fragment and 30X paired)        Brucella ceti
 SRR005481,2        BI      454 GS FLX      45X on 454 (15X fragment and 30X paired)        Brucella ceti BROAD:SEQUENCING_SAMPLE:24613.2
 SRR005486,7,8      BI      454 GS FLX      45X on 454 (15X fragment and 30X paired)        Brucella pinnipedialis BROAD:SEQUENCING_SAMPLE:246
 SRR006465          GSC     454 GS FLX      454 Paired-End Library.                         Acinetobacter sp. ADP1

File location:

 /fs/szdata/454p/
 /fs/szasmg2/Bacteria/Pseudomonas_syringae/Data/DC3000.format.454Reads.fna :  Pseudomonas_syringae

Illumina

  • Sequencing by Synthesis
  • Platforms:
 * Genome Analyzer (GA)
 * Genome Analyzer II : faster, higher tput
 * Future: 10GB/run  50bp reads
 * Future: 20GB/run 100bp reads

Data sets:

 Strep suis Solexa data set for download at Sanger
 Staphylococcus aureus strain MW2 (edena paper)
 NCBI Solexa example data set
 Pseudomonas aeruginosa
 Pseudomonas syringae
 NCBI SRA

Applications:

 * Gene Expression
 * ChIPSeq (hight throughput)
 * Re-sequencing
 * mRNA sequencing

Software:

 Staden & Io_lib
 * IO_LIB package /fs/sz-user-supported/common/packages/io_lib-1.11-x86_64/bin/
 * STADEN package /fs/sz-user-supported/common/packages/staden-src-1-7-0/distrib/unix-rel-1-7-0/linux-bin
 
 MAQ Sanger assembler
 FASTQ sequence format

Illumina 1G :

 * ~40 Million DNA sequencing reactions
 * about 36 hours for a run
 * each sequence is up to 36 bases long
 * insert len=~200bp

Illumina Genome Analyzer II:

 * up to 51 bp
 * mate-pairs: opposite directions, slight overlap (insert size is less than 200bp "advertised")
 * on the SRA mate-pairs are joined; when downloaded only one read is shown. What about the mate pair?

SRA: set of 4 files

 *_seq.txt  : lane,run, well(x,y) sequence
 *_prb.txt  : max quality from each group of 4 values is taken as quality
 *_sig2.txt : lane,run, well(x,y); max signal from each group of 4 values corresponds to max quality
 *_qhg.txt  : lane,run, well(x,y); some encoded info?
 # *_seq.txt 
 5       1       1269    1795    AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA  
 # _prb.txt 
 40  -40  -40  -40       40  -40  -40  -40  ...
 # _sig2.txt <==
 5       1       1269    1795    2594.0 2367.0  -10.0  -96.0 ...

Qualities:

 Range : -5..40
 Avg   : ~25, depending on the data set

Fastq format

Maq help

Example:

 1 lane of Solexa reads: 10,959 READS; all are 36 bp
 $ /fs/sz-user-supported/common/packages/io_lib-x86_64/bin/solexa2srf s_8_0100_seq.txt  ; mv traces.srf  s_8_0100.srf
 $ /fs/sz-user-supported/common/packages/io_lib-x86_64/bin/srf2fastq s_8_0100.srf > s_8_0100.fastq

   @s_8_100_293_551
   CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCACC
   +
   IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
   @s_8_100_35_698
   TATATGATTGACAATATAAAAATATGAGTATAAAAT
   +
   IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII4/:I
   @s_8_100_880_947
   TTATTATCTTTATTGACGTACCTCTAGAAGACCCAA
   +
   IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII;>1
   ...
 Edge effect: 
 N's have quality -14
 $ cat s_8_0100_seq.txt | sort -nk3 -nk4 
 8       100     0       37      ......AT.AT...TAATCAATA..GA.GAAG....
 ...
 8       100     1003    959     AGTC.......T.C.........GT.........AA
 $ more traces.qual
 ...
 >s_8_100_0_37
 -14 -14 -14 -14 -14 -14 25 13 -14 25 25 -14 -14 -14 25 25 25 25 22 25 25 25 25 -14 -14 25 25 -14 25 -11 25 14 -14 -14 -14 -14 
 ...
 >s_8_100_1003_959
 25 25 25 25 -14 -14 -14 -14 -14 -14 -14 25 -14 25 -14 -14 -14 -14 -14 -14 -14 -14 -14 25 -10 -14 -14 -14 -14 -14 -14 -14 -14 -14 8 25
 ...
 # bioperl script to convrt seq formats
 $ seqconvert.PLS --from fastq --to fasta < s_8_0100.fastq
 
 # get fastq qualities
 $ more *fastq | grep -A 1 "^+" | grep -v ^+ | grep -v -- ^-- | perl -ane '@F=split //,$F[0]; foreach (@F) { $n=ord($_)-33; print $n," ";} print "\n";'
 # convert Solexa format (maq fq_all2std.pl script)
 $ fq_all2std.pl seqprb2std s_5_0001_seq.txt s_5_0001_prb.txt > s_5_001.fastq
 $ fq_all2std.pl fq2fa s_5_001.fastq > s_5_001.seq
 # convert fastq to seq & qual
 fastq2seqqual.pl test.fastq     
 fastq2seqqual.pl -solexa test.fastq
 => test.seq , test.qual

SOLiD

Example:

 Ecoli_F3.csfasta
 >600_50_31_F3
 T2222002113300322132112231
 >600_50_63_F3
 T2330133212130133221033110
 Ecoli_F3_QV.qual
 >600_50_31_F3
 15 20 14 11 25 20 17 21 16 9 15 12 21 8 2 10 15 5 3 5 10 6 2 4 4
 >600_50_63_F3
 5 13 9 23 5 9 8 4 6 4 5 4 7 6 10 7 7 5 13 11 5 8 2 7 6
 Ecoli_R3.csfasta
 >600_51_85_R3
 G0331332123123330101312331
 >600_51_178_R3
 G1111033111110111101111111
 Ecoli_R3_QV.qual
 >600_51_85_R3
 10 13 16 15 11 17 6 13 10 9 15 8 13 10 12 11 8 5 3 6 12 8 11 6 14
 >600_51_178_R3
 2 2 2 5 8 2 2 2 2 3 2 3 2 3 7 4 3 4 5 4 2 2 5 6 17
  • low error rate (higher accuracy than Illumina)
  • 2008: 4G run, read_len=35bp; insert=3Kbp (old)
  • 2009: 9G run, read_len=50bp;
  • SOLiD™ 3 System generates (Oct 1 2008)
    • over 20 gigabases
    • mate-paired libraries with insert sizes ranging from 600 bp up to 10 kbp
    • human genome for less than $60,000.
  • uniform bases quality
  • accuracy greater than 99.94%
  • because of double base interogation & high cvg, qualities can be "discarded"

Alignment matrix

   A C G T
 A 0 1 2 3
 C 1 0 3 2
 G 2 3 0 1
 T 3 2 1 0

Examples:

  AA is encoded as 0
  CG is encoded as 3
  AACG is encoded as 0 1 3

Features of Color space:

 * Color space data are self-complementary
   Example:
       Base        A G C T C G T C G T G C A G
       Color space   2 3 2 2 3 1 2 3 1 1 3 1 2
   
       Complemented
       Base        T C G A G C A G C A C G T C
       Color space   2 3 2 2 3 1 2 3 1 1 3 1 2
 * Two-Base Encoding and Error Recognition
   1 change: measuring error 
   multiple changes starting at a certain point: SNP
   Example:
      Reference 2 3 2 2 3 1 2 3 1 1 3 1 2
      Observed  2 3 2 2 0 1 2 3 1 1 3 1 2

Data Sets:

Pos-vs-quality.png

Helicos

Pacific Biosciences

  • Company website
  • BioIT article
  • Science paper
  • A closer look at the first PacBio sequence dataset BLOG
  • InSequenec article May 2009
    • strobe reads: 3kbp+
    • Instead of generating a single, uninterrupted read of approximately 3.2 kilobases in length, by turning the lasers off and on during the run, one could double the size of the read, which consists of around 20 short "strobed" sequence reads that are interspersed with gaps.
    • Uninterrupted reads are limited to approximately 3,000 bases due to laser-induced photochemistry that damages the polymerases, he explained. However, when the lights are turned off, the polymerases keep incorporating nucleotides without incurring damage, and the available read length is "essentially put on hold, so you can take whatever read length you have and distribute it" over a longer stretch of DNA
    • Researchers could, for example, create the equivalent of a mate-pair read, with two sequences of more than a kilobase at each end and a long stretch of unidentified bases in between. Alternatively, they could cover a long piece of DNA with lots of short stitches of sequence. The length of these stitches and their distance can be varied without a need for different DNA libraries, Turner noted.

Megachile rotundata PacBio sequence

    • HDF5 (http://www.hdfgroup.org/HDF5/) This is PacBio's native data format, used to feed their primary and secondary pipelines. There is almost no support for this format outside of PacBio's analysis pipeline. Subsets of this data may be generated, however, as indicated below (e.g., FASTA and FASTQ format). According to the documentation (attached) for PacBio's hybrid assembly protocol (HybridAssembly) you may use HDF5 or FASTA in conjunction with Illumina-based scaffolds (or assembly-rpoduced contigs.)
    • High Quality (HQ) FASTA/FASTQ (post-primary analysis) We can derive from the HDF5 file FASTA and FASTQ sequences for reads passing primary analysis in the PacBio pipeline. These reads have essentially been "okayed" by the pipeline as high quality reads to carry forward in secondary analysis--such as attempted alignment to a reference genome, etc. These reads have not had PacBio's proprietary SMRT bell adaptor (45 nt x 2) trimmed... as trimming is part of the downstream PacBio secondary analysis pipeline. These are single pass representations of the sequence reads and therefore contain uncorrected errors, insertions, and deletions incidental to the current state of the PacBio technology. The FASTQ files have PacBio's "Phred-like" quality values corresponding to the nucleotide representation of the sequence. In the instances where a SMRT bell molecule is sequenced multiple times (i.e., multiple passes) each pass is termed a "sub-read". In this representation of the data, sub-reads have not been broken out from the parent sequence.
    • High Quality (HQ) FASTA (edited) These FASTA files are similar to the ones described above, but have been filtered and edited. Here, the SMRT bell adaptors have been excised from the parent reads, and entries have been broken into corresponding sub-reads. Only reads with a quality filter of 75% or better have been retained; sub-reads less than 50 nt in length have been removed. These are still single pass representations of the sequence reads and therefore contain uncorrected errors, insertions, and deletions incidental to the current state of the PacBio technology.

Visigen

Polonator

RainDance Technologies

Whole genome profiling (Wgp)

In our view getting to a good quality assembly especially in large genomes is a real challenge and therefore I would like to get your attention to our Whole Genome Profiling technology that generated a lot of interest at the last PAG meeting in San Diego, where we presented the technology in several workshops. WGP has now been used to help the assembly of many of the plant genomes for Melon, Tomato, Potato, Lettuce, Brassica napus, Brassica rapa, Brassica oleraceae, Strawberry, Tobacco, Wheat, and Sunflower. Also at the moment we are working on a number of genomes both for academic labs/consortia as well as for industry. The power of the WGP technology lies in the creation of a sequence-based scaffold of the genome based on the creating of sequence tags in BAC libraries (average 30 tags pet BAC). This provides ordered BAC clones covering the genome on which shot-gun sequence information can be placed. WGP solves the problem of genome assembly that most laboratories are struggling with because it simplifies the bio-informatics effort required to assemble shot-gun sequence information considerably. For instance, shot-gun sequencing of the melon genome (450 Mb) left us with 13,486 contigs, while the WGP map for Melon gave us BAC-based 1085 contigs. By putting these two methods together, we ended up with a very high quality reference amp of only 249 supercontigs. In addition, the BAC clones linked to the physical map allows you to go directly to clones of those regions of the genome you are interested in. I have included three relevant abstracts of PAG presentations in the attachment for your reference. In short: The assembly of the tetraploid tobacco genome (4.5 Gb) was presented at the PAG and worked perfectly well, showing that genome size does not influence the quality of the WGP maps (see abstract below) and in addition showed that the WGP assemblies are very accurate as concluded from the fact that 97% of the WGP contigs can be assigned to one of the ancestral genomes that are part of the tetraploid tobacco genome. Also the progress on the Sunflower genome (3.6 Gb) was presented and my colleague Michiel van Eijk showed the power of the technology to deal with repetitivegenomes as well a new approach on genome sequence assembly that takes as a starting oint a good quality WGP physical map. Taking all this together and the fact that the technical paper on WGP was just accepted forpublication in Genome Research (preprint included in attachment), I think this technology may be of interest for you to improve your genome assembly as well.

Ion Torrent

  • SImilar to 454; indel & homopolimer issues

Download

Genbank

  • Bioperl scripts:
 bp_fetch.pl net::genbank:NC_005810.1 > NC_005810.1
 bp_fetch.pl net::genbank:NC_005810 > NC_005810
 bp_fetch.pl net::genbank:45439865 > 45439865
  • NCBI utilities:
 wget "http://eutils.ncbi.nlm.nih.gov/entrez/eutils/epost.fcgi?dbfrom=nucleotide&id=257481614" -O - | grep WebEnv | p 'next unless /<WebEnv>(.+)<\/WebEnv>/; echo "setenv WebEnv $1\n";'
 wget "http://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=nucleotide&query_key=1&WebEnv=NCID_1_35952872_130.14.18.46_9001_1260305427" -O -

TA

 query_tracedb "query text species_code='BACILLUS ANTHRACIS'" > ids.bacillus_anthracis.001
 ( echo -n "retrieve_tgz fasta quality xml " ; cat ids.bacillus_anthracis.001 | head -40000 | paste -s -d ","  ) \
         | query_tracedb > bacillus_anthracis.001.1.tgz
 ( echo -n "retrieve_tgz fasta quality xml " ; cat ids.bacillus_anthracis.001 | head -80000 | tail -40000 | paste -s -d ","  ) \ 
         | query_tracedb > bacillus_anthracis.001.2.tgz
 ...

SRA

Format

FASTA

Utilities:

  • Indexing large files
 #NCBI utilies: expect a certain id format Ex: ">lcl|12345"
 formatdb -i prefix.fasta -p F -o T
 fastacmd -d prefix.fasta -p F -s id1,id2,... 
 fastacmd -d prefix.fasta -p F -i prefix.filter
 ~/bin/formatdb.pl <  prefix.fasta > prefix.fasta.offset
 ~/bin/fastacmd.pl -s 1,2 -o prefix.fasta.offset < prefix.fasta > subset.fasta

TA

  • Sanger:
 tarchive2amos -o Ba Ba.seq                                              # TA FTP
 tarchive2amos -o Ba -tracedir traces/                                   # TA querytrace_db 
 tarchive2amos -o Ba -assembly assembly/ASSEMBLY.xml -tracedir traces/   # AA

SRA

  • Solexa mated:
 seq2amos.pl -n solexap -m 100 -s 20 -fs solexa_f.fasta  -rs solexa_f.fasta  > solexap.afg
 seq2amos.pl -n solexap -m 100 -s 20 -fs solexa_f.fasta -fq solexa_f.qual -rs solexa_r.fasta -rq solexa_r.qual > solexap.afg
 seq2amos.pl -n solexap -m 100 -s 20 -fs solexa_f*.fasta -rs solexa_r*.fasta > solexap.afg

  • Solexa unmated:
 seq2amos.pl -n solexa               -fs solexa.fasta                        > solexa.afg
  • 454 mated:
 seq2amos.pl -n 454p    -m 3000 -s 300 -fs 454.seq                           > 454p.afg
  • 454 unmated:
 seq2amos.pl -n 454                    -fs 454.seq                           > 454.afg

Convestion

  • Readseq
 ~/bin//readseq.sh -f Fasta -o prefix.fasta prefix.embl
  • Bioperl
 bp_sreformat.pl -i prefix.embl -o prefix.fasta -if EMBL -of Fasta
  • AMOS:
 amos2frg [-i infile] [-o outfile] 
 amos2sq [-i] infile [-o outprefix] =>  outprefix.seq outprefix.qual

Read Simulators

  • Metasim

Read Mapping Software

BFAST

  • need to e-mail to author to get the code

BLAT

 blat -noHead -t=dna  -q=dna  -tileSize=10 -stepSize=3 Pa.1con    Pa.seq    Pa.blat

BLASTALL

  • Env vars
 echo $BLASTMAT $BLOSUM62 $BLASTDB
 /fs/sz-user-supported/Linux-x86_64/packages/blast-2.2.18/data/ 
 /fs/sz-user-supported/Linux-x86_64/packages/blast-2.2.18/data/BLOSUM62 
 /fs/szdata/ncbi/ftp.ncbi.nih.gov/blast/db/
  • Example
 blastall -p blastn -d refseq_genomic -i acc.keep.fna

BOWTIE

  • build index:
 bowtie-build prefix.1con prefix
 =>
 prefix.rev.2.ebwt
 prefix.rev.1.ebwt
 prefix.2.ebwt
 prefix.1.ebwt
 prefix.3.ebwt
 prefix.4.ebwt
  • run alignments
 bowtie prefix qry.fastq  -a --best --strata -n 2 -l 28 -e 70

MAQ/BWA

RMAP

  • RMAP : designed for Illumina-Solexa
  • Command: rmap
 rmap         -m 3 -w 33                            -c Pa.1con    Pa.seq -o Pa.rmap

SHRiMP

  • Web site
  • Commands: rmapper-cs , rmapper-ls, ...

SeqMap

  • SeqMap developed at Stanford
  • allows up to five mixed substitutions and inserted/deleted nucleotides in the mapping
  • allows sequences to contain N’s, and to have unequal lengths
 ./seqmap
 Usage: seqmap <number of mismatches> <probe FASTA file name> <transcript FASTA file name> <output file name> [options]
 
 Parameters:
 <number of mismatches>                          maximum edit distance allowed
 <probe FASTA file name>                         probe/tag/read sequences
 <transcript FASTA file name>                    reference sequences
 <output file name>                              name of the output file
 ...

SHORE

SOAP

 soap -v <max_mismatches> -g <max_gap> -r <repeat_behavior> -p <processors> -d ref.fasta -a qry.fasta -o ref-qry.soap

SOAPaligner/soap2

  • ~10 times faster than SOAP
/nfshomes/dpuiu/szdevel/soap2.20release/2bwt-builder ref.fasta                      # => ref.fasta.index 
 soap2 -D ref.fasta.index                                 -a qry.fasta           -o qry.soap            -r <repeat_behavior> -l <seed_len> -p <processors>
 soap2 -D /scratch1/Bombus_impatiens/Data/mito.1con.index -a s_3_1_sequence.seq -o s_3_1_sequence.soap2 -r 2                 -l 36         -p 16            # up to 2 mismatches in the first 36bp

SOCS

 socs socs.pref
 more socs.pref
 Req.fa
 Seq_F3.csfasta
 Seq_F3_QV.qual
 out_prefix
 2
 1000
 2
 false
 true
 0

SOLiD

SSAHA

  • Web site(Sanger)
  • Focused on exact, nearly exact matches
  • Does not find all the exact matches???
  • Example: Solexa 33bp ~30% of reads are not found

ZOOM

Whole genomes alignments

Genome Resequencing

Data sets

Solexa

 /fs/szdata/Solexa/Streptococcus_suis/suisp17/strip3.fna
 
 Velvet (0.7.55) assembly:
 .      elem     min  q1  q2   q3    max    mean  n50   sum       misassembled
 seq    2659250  36   36  36   36    36     36    36    95733000  .
 ctg    669      45   69  383  4314  32213  2944  8437  1969901   8

 time   153.166u 4.673s 2:20.85 112.0%  0+0k 0+0io 2pf+0w
 SOAPdenovo (1.03) assembly:
 .      elem     min  q1  q2  q3  max    mean  n50   sum        misassembled
 seq    2659250  36   36  36  36  36     36    36    95733000
 ctg    2219     24   25  32  93  24421  907   7209  2014315    1
 ctg45  866      45   65  214 3307 24421 2282  7275  1976767    1
 
 time   89.614u 1.599s 1:36.51 94.4%    0+0k 0+0io 0pf+0w
 SOAPdenovo vs Velvet:
 * slightly shorted ctgs
 * fewer errors (1 vs 8)
 * shorter run time (~half)
  • velvet example: 142,858 35bp reads that should assemble into a ~100Kbp contig
 /nfshomes/dpuiu/szdevel/velvet/data/test_reads.fa
 Accession       #Runs   Instrument                      Center  Study                          [Individual]
 SRA000303       41      Solexa 1G Genome Analyzer       BI      1000Genomes Project Pilot 2     NA12878
 SRA000304       49      Solexa 1G Genome Analyzer       BI      1000Genomes Project Pilot 2     NA12891
 SRA000305       56      Solexa 1G Genome Analyzer       BI      1000Genomes Project Pilot 2     NA12892
 SRA000307       1       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA10851
 SRA000308       2       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA11993
 SRA000309       3       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA11995
 SRA000310       1       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA12006
 SRA000311       1       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA12044
 SRA000312       2       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA12156
 SRA000313       1       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA12414
 SRA000314       1       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA12776
 SRA000315       1       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA12828
 SRA000316       12      Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 2     NA12878
 SRA000317       8       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 2     NA12891
 SRA000318       14      Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 2     NA12892
 SRA000319       1       Solexa 1G Genome Analyzer       SC      1000Genomes Project Pilot 1     NA12004

June 14th 2008: Sept 19th 2008

 SRA001100       23      Illumina Genome Analyzer        BGI     1000Genomes Project Pilot 2     NA19240
 ...
 SRA002029       1       Illumina Genome Analyzer II     WUGSC   1000Genomes Project Pilot 2     NA19239
 /fs/szdata/Solexa/1000genomes
  • Example SRR001113.seq :
 7,058,926 47 bp sequences
 2,402,398 contain at least 1 '.'

454

  • 1000 Genomes

June 14th 2008

 Accession       #Runs   Instrument      Center  Study                           [Individual]
 SRA000302       121     454 GS FLX      BCM     1000Genomes Project Pilot 2     NA12878
 SRA001032       2       454 GS FLX      BCM     1000Genomes Project Pilot 2     NA12878
 SRA001036       1       454 GS FLX      BCM     1000Genomes Project Pilot 1     NA12812
 SRA001094       1       454 GS FLX      BCM     1000Genomes Project Pilot 2     NA12878

June 14th 2008: Sept 19th 2008

 SRA001037       2       454 GS FLX      BCM     1000Genomes Project Pilot 1     NA12812
 ...
 SRA001819       1       454 GS FLX      BCM     1000Genomes Project Pilot 2     NA12878
  • Cryptosporidium_muris_RN66: SRA001029 (not paired)
  • EcoliK12: SRR001355 (paired)
  • Porphyromonas_gingivalis_W83: E8YURXS01 (paired)

Refseq

  • /fs/szdata/genomes/human_ncbi_build36/ NCBI build36.1 May 2006 (Current build is 36.3 March 2008)
  • /fs/szdata/genomes/human_celera_2001_Orig/

Links