Difference between revisions of "Programs"

Revision as of 14:35, 20 June 2011

OLC Assemblers

CABOG (now wgs assmebler)

• sffToCA -libraryname GWLSDZG07 -clear all -trim chop -output timshel.frg GWLSDZG07.sff
  • These settings because I think they used a 454 FLX titanium reader
    • sffToCA -libraryname GWLSDZG07 -clear all -trim chop -output timshelTRIM.frg -insertsize 3200 900 -linker titanium GWLSDZG07.sff
  • For 454 FLX reader
    • sffToCA -libraryname GWLSDZG07 -clear 454 -trim chop -output timshel.frg GWLSDZG07.sff
    • sffToCA -libraryname GWLSDZG07 -clear all -trim chop -output timshelTRIM.frg -insertsize 3200 900 -linker flx GWLSDZG07.sff
• runCA -p timshel -d /Users/letaylor/Desktop/Timshel/cabog/timshelTemp timshel.frg
  • -s allows the user to specify a specification file that formats the assembly [1]
• From here, use bambus
• Parameters and Thoughts
  • Automatic trimming of 454 reads
  • Need to know insert size and linker sequence for scaffolding part to work
    • Include scaffolding software. (can choose not to use it)
    • output placed in 9-terminator folder - xxx.scf.fasta = scaffolds (.ctg. is contig)
• Another tutoiral http://www.cbcb.umd.edu/research/CeleraAssembler.shtml

De Novo Assemblers

ABySS

• • Designed to address memory limitations for mammalian size genome assemblies

AllPaths

• • Assembler intended for large genomes

• Celera Assembler
  • Uses Poisson statistics to estimate the likelihood a particular genomic region represents a collapsed repeat

Euler+ (Euler 2.0)

• • Developed in 2004
  • Uses A-Bruijn graphs
  • Deals with errors in reads by inducing vertices with ungapped alignments (2.1 pg 2)
    • Allows mismatches rather than exact l-tuples in Bruijn graphs
  • Does not scale well with next gen because graph grows with coverage - run out of memory (2.1 pg 2)
    • However, focus for this project is phages. May not be an issue.

Euler-SR (short read) (introduced in 2.1)

• • Developed in 2007
  • Modified version of Euler+
    • "Substitutes the maximum spanning tree optimization of the A-Bruijn graph by the maximum branching optimization on de Bruijin graphs" (2.1 pg 2)
  • General Walkthrough of all Euler implementations (3.1 pg 320)
    1. Filter reads with spectral alignment process
       • Detect erroneous base calls by noting low frequency K-mers
         • Most true K-mers = repeated in several reads
         • Sequence errors are random, so for any K where 4^K exceeds twice the genome size, most erroneous K-mers should be unique.
         • List K-mers and their observed frequencies
           • Either exclude or correct low frequency k-mers
           • Correction - reduces total number of k-mers (and the size of the graph), can also mask polymorphism
             • Only corrects substitution errors (not indels)
         • Step 1. Determine threshold to trust or not trust reads
           • Threshold determined via distribution of k-mer frequencies present in the reads
             • Usually binomial distribution... 1st peak = k-mers that occur once or twice (due to seq error or low coverage) 2nd peak = redundant k-mers (due to read coverage or repeats)
             • Threshold = between the peaks
         • Step 2. Determine if read is good or bad and proceed accordingly
           • Bad reads - "Euler executes a greedy exploration for base call substitutions that reduce bad k-mer count" (3.1 pg 320)
           • If fully corrected, then accept the read. Otherwise reject
    2. Build K-mer graph from processed reads
       • Processes k-mers, not reads, so discards long-range continuity info (within reads)
  • Output: both contigs and the repeat graph
    • Connects the contigs by repeats and can help direct the finishing efforts
  • Only uses read and qual files
  • Bulk of the time this program takes is in error correction
    • Error correction: can be done based on qual values or based on spectral alignment technique
  • Steps: error correction, graph construction, graph correction, assembly by transforming paths into contigs
• Command Steps
  1. Clean Data {EUSRC}/assembly/${MACHTYPE}/sff2fasta reads.sff reads.fasta
     • /Users/letaylor/Documents/euler/assembly/x86_64/sff2fasta timshel.sff timshel.fasta
     • Newbler can take advantage of the flow values, but euler-sr does not (Euler tutorial)
     • EITHER OUTPUT IN FASTQ FORMAT OR NO LONGER CORRECTLY READING .SFF FILES
     • OPTIONAL: quality filter ${EUSRC}/assembly/${MACHTYPE}/qualityTrimmer -fasta reads.fasta -qual reads.fasta.qual -outFasta reads.trimmed
  2. Pair paired end reads
     • ${EUSRC}/assembly/${MACHTYPE}/splitLinkedClones reads.454.fasta linker.fasta reads.454.fasta.split
     • Can also do separate file for unpaired sequences
       • ${EUSRC}/assembly/${MACHTYPE}/splitLinkedClones reads.454.fasta linker.fasta reads.454.fasta.split -singletons reads.454.fasta.singletons
         • Need to have linker sequence in linker.fasta. According to wgs-Assembler documentation, the default linker sequence for 454 FLX titanium are...
           • CGTATAACTTCGTATAATGTATGCTATACGAAGTTATTACG and the reverse-complement CGTAATAACTTCGTATAGCATACATTATACGAAGTTATACGA (SffToCA)
       • I think fasta files given to me already have this info cut out. Don't know if I can get it or what.
  3. Run Assembly
     • Basic Assembly - ${EUSRC}/assembly/Assemble.pl reads.fa 25
       • Choose kmer - 25 <= k <= 28
     • Mate Pair Assembly
       • ${EUSRC}/assembly/Assemble.pl reads.fa 25 -ruleFile readtitle.rules
       • You will have to generate a "rule file" that describes the mates you are using. This is a file with a regular expression in quotes, followed by two required keywords, CloneLength and CloneVar. The mate-pairs are used in the order that they are specified in rule file. For optimal results, the clone rules should be specified in order of increasing clone size.
       • Rule File - pretty confusing
         • Example = ([^/]*)/([12])" CloneLength=200 CloneVar=50
         • The Regex format http://opengroup.org/onlinepubs/007908775/xbd/re.html#tag_007_004
  4. Refining the Assembly
     • Assembly in transformed directory, however if mate pairs, in matetransformed directory
     • Show Dead Ends: ${EUSRC}/assembly/${MACHTYPE}/printGraphSummary transformed/reads.fasta -sources
       • If you have many, may be due to low coverage. Should want to regroup.
     • You can try to fix dead ends by joining them when the overlap is less than the vertex size, but greater than some minimal length, say 10.
       • ${EUSRC}/assembly/${MACHTYPE}/joinsas transformed/reads.fasta 10 transformed/reads.j
       • ${EUSRC}/assembly/${MACHTYPE}/printContigs transformed/reads.j
       • cp transformed/reads.j.contig

MIRA 3 (also http://sourceforge.net/apps/mediawiki/mira-assembler/index.php?title=Main_Page)

• • Good for < 1.2 mil bases (http://seqanswers.com/forums/showthread.php?t=4136&highlight=virus+assembly)
  • Can assemble de novo or to a scaffold
  • Installation
    • Basic installation for single environment
      1. PATH=/Users/letaylor/Documents/mira/bin:$PATH
      2. export PATH
      3. This works for this environment only... must redo it every time
    • Write bash script to do this... (http://prashanthmadi.blogspot.com/2010/10/installing-mira.html)
      1. $ vim ~/.bashrc
      2. export PATH=$PATH:/path/to/mira/bin
      3. export PATH=/Users/letaylor/Documents/mira/scripts:$PATH
      4. 'esc' to stop editing in vim

         wq to write and quit vim (http://www.tuxfiles.org/linuxhelp/vimcheat.html)

      5. Now when want to run type $ source ~/.bashrc then $ mira
    • For 454 data
      1. Download sff_extract and save as sff_extract (no file type)
      2. Make executable chmod +x /path/to/script
      3. sff_extract -s bchoc_in.454.fasta -q bchoc_in.454.fasta.qual -x bchoc_traceinfo_in.454.xml EV10YMP01.sff EV5RTWS01.sff EVX95GF02.sff

Newbler (aka gsAssembler)

• • An Overlap Layout Consensus assembler.
  • Good for reads > 250nt (http://seqanswers.com/forums/showthread.php?t=5092&highlight=Brujin).
  • Assembler provided by 454 company.
    • Specific to 454 data... so uses 454 dependent error model and has access to flowgrams (2.1 pg 3)
  • Good blog: http://contig.wordpress.com/
  • For pure 454 data, probably the best assembler

Velvet

• • Does extensive compression of de Bruijn graph
    • PreProcessing to reduce graph size
      • Spurr removal algorithm = similar to Euler erosion procedure.
      • Note: No spectral alignment filter (because authors thought it naive)
    • Graph processing to reduce graph size
      • Bounded search for bubbles
        • Breadth-first-search - starts at nodes with multiple out edges
        • Bounded because can have bubbles within bubbles, so finding all of them would be impractical
        • Limits bubbles to those with a sequence similarity requirement on alternate paths
        • Removes bubble path with fewer reads
        • Step here about realignment I do not understand
        • This operation risks ignoring polymorphisms or the collapse of near identical repeats
        • Whole process similar to Euler's bulge remover and the bubble smoothing in OLC assemblers
    • Read Threading
      • Removes path with fewer reads than a threshold
      • Risks removing low coverage sequence... but supposed to remove spurious connections induced by convergent sequencing errors
    • Mate Pairs reduction
      • Uses pebble algorithm (see Pebble and Rock Band)
  • Variables
    • Length of k-mers (note this is constrained to be an odd number)
    • The min frequency of expected k-mers in reads - determines which k-mers are pruned a-priori
    • The expected coverage of genome - controls spurious connection breaking
  • Uses Poisson statistics to estimate the likelihood a particular genomic region represents a collapsed repeat
  • SUMMARY: does not have error-correction pre-processor, but has error avoidance filter. (not good for large genomes)
  • velveth ./ 21,31,2 -long 7.GAC.454Reads.fna
    • m <= k <= M with a step of s
  • velvetg ./ -cov_cutoff auto -exp_cov auto

Veiwers

Consed

• Aligned Reads View
  • Low quality base = darker and lower case
  • High quality base = ligher (whiter) and upper case
  • Black = unaligned
  • Asterisk = spacer... like dash in blast
  • Red letters = disagree with consensus read. Alot of red in high quality region
  • Middle click = open up trace window
  • Navigate menue for low quality reads
  • Can also export consensus sequences
• Assembly View
  • Dark green line = read coverage
  • Can help identify contaminations... (if have area of high quality reads that drops suddenly to lower, or two contigs with different quality reads)
• Programs Compatible with Consed: Newbler, Phrap

Other

Jellyfish

• Used to identify k-mers
• PATH=/Users/letaylor/Documents/jellyfish1.1/bin:$PATH
• jellyfish count -m 22 -o outputfilename -c 3 -s 10000000 timshelRaw.fasta
  • -c For sequencing reads, use a value for -c large enough to counts up to twice the coverage
• jellyfish stats -v /Users/letaylor/Desktop/Timshel/mer_counts_0
  • Read stats output file from above
• jellyfish dump -o kmerlist -c /Users/letaylor/Desktop/Timshel/mer_counts_0
• jellyfish histo -o kmerhisto /Users/letaylor/Desktop/Timshel/mer_counts_0

Shustring

• Used to calculate the SHortest Unique SubSTRINGs (in C implementation)
• Global shustring
• Local shustring - local shustring is the shortest string that starts at a given sequence position and is unique.

[http://www.sanger.ac.uk/resources/software/ssaha2/ SSAHA2

• USe to clean up tagged sequences