Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • KevinLam
    Senior Member
    • Nov 2009
    • 204

    #1

    Conversion from base space to colorspace

    Hi is there a script that can do this?

    Hypothetically, I wish to convert known ESTs to colorspace so that I may assemble short reads of a transcriptome with known ESTs in a colorspace aware software.

    Thanks!
    http://kevin-gattaca.blogspot.com/
  • DNAjunk
    Member
    • Jun 2009
    • 62

    #2
    How about the corona_lite script:

    encodeFasta.py myRead.fasta

    output is in color space

    Ex.

    >myRead.fasta
    CGCCTTGGCCGTACAGCAGGGGCTTACGAA

    >myRead.csfasta
    C33020103031311231200032031320

    Comment

    • aguffanti
      Member
      • Dec 2008
      • 29

      #3
      Why not doing the opposite, ie convert color space in sequence space usign the usual rules and after using a standard assembler? I would also ty the SAET tool which has been just posted on the SOLiD software web page on your color space reads and then convert in color space. Quick note: I have many problems in getting encodeFasta.py working correctly

      Comment

      • SOLiD J
        Junior Member
        • Jul 2009
        • 4

        #4
        Originally posted by aguffanti View Post
        Why not doing the opposite, ie convert color space in sequence space usign the usual rules and after using a standard assembler? I would also ty the SAET tool which has been just posted on the SOLiD software web page on your color space reads and then convert in color space. Quick note: I have many problems in getting encodeFasta.py working correctly
        There's a thread on this topic, but in short you get the most out of color space by aligning in color space. I believe its especially important for SNPs and catching sequencing errors.

        Comment

        • aguffanti
          Member
          • Dec 2008
          • 29

          #5
          color space to base space

          Indeed working in CS does have advantages in various context, from miRNA analysis to, correctly, variation detection. However, the question here was specifically on assembly I understood.

          I would definitively go, in that case, for SAET + EncodeFasta.py (used with -d option i think) + a good sequence space assembler. This is also more or less the way in which the ABI port of Velvet works.

          You may also try, if you have a good reference sequence, with the Mosaik assembler from Marth lab which handles both CS and Sequence Sapce together

          HTH, Regards,

          Alessandro

          Comment

          • flobpf
            Member
            • Apr 2010
            • 76

            #6
            A very basic script

            I couldn't find any code for doing this, so wrote a quick python script. I've validated it using a couple of test sequences in the Data Formats file from ABI, but I must warn you...use it at your own risk!

            This script requires sequence in one continuous line after the name. Interleaved sequence, split across multiple lines needs to be first merged into one single line.
            Code:
            import sys
            '''
            Combinations (FROM CMS_058717.PDF):
            AA:0, AC:1 ,AG:2 ,AT:3
            CA:1 ,CC:0 ,CG:3 ,CT:2
            GA:2 ,GC:3 ,GG:0 ,GT:1  
            TA:3 ,TC:2 ,TG:1 ,TT:0
            '''
            print "INP1: FASTA sequence file"
            
            #########
            def base2color(basepair):
                bdict={'AA':0, 'AC':1 ,'AG':2 ,'AT':3, 'CA':1 ,'CC':0 ,'CG':3 ,'CT':2, \
                       'GA':2 ,'GC':3 ,'GG':0 ,'GT':1, 'TA':3 ,'TC':2 ,'TG':1 ,'TT':0}
                return str(bdict[basepair])
            #########
            
            file1=open(sys.argv[1],'r')
            out1=open(sys.argv[1]+".color",'w')
            line1=file1.readline()
            while line1:
                if line1.startswith('#'):
                    pass
                else:
                    if line1.startswith('>'):
                        name=line1.strip()[1:]
                        line1=file1.readline()
                        seq=line1.strip()
                        clist=[]
                        for i in range(0,len(seq)-1):                                
                            b1=seq[i].upper(); b2=seq[i+1].upper(); bp='%s%s'%(b1,b2)
                            #print name,i,b1,i+1,b2,len(seq)
                            color=base2color(bp)
                            clist.append(color)
                        out1.write('>%s\n%s\n'%(name,''.join(clist)))
                line1=file1.readline()
            file1.close(); out1.close()
            print "Done!"

            Comment

            Latest Articles

            Collapse

            • SEQadmin2
              Beyond CRISPR/Cas9: Understand, Choose, and Use the Right Genome Editing Tool
              by SEQadmin2



              CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).

              Despite this, “CRISPR helped turn genome editing from a specialized technique into
              ...
              07-31-2026, 11:01 AM
            • SEQadmin2
              Proteomic Platforms: How to Choose the Right Analytical Strategy to Improve Detection and Clinical Applications
              by SEQadmin2


              Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.

              The systematic characterization of the human proteome has
              ...
              07-20-2026, 11:48 AM
            • SEQadmin2
              Advanced Sequencing Platforms Tackle Neuroscience’s Toughest Genomics Problems
              by SEQadmin2



              Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
              ...
              07-09-2026, 11:10 AM

            ad_right_rmr

            Collapse

            News

            Collapse

            Topics Statistics Last Post
            Started by SEQadmin2, 08-03-2026, 10:13 AM
            0 responses
            15 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, 07-31-2026, 02:55 AM
            0 responses
            32 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, 07-24-2026, 12:17 PM
            0 responses
            23 views
            0 reactions
            Last Post SEQadmin2  
            Started by SEQadmin2, 07-23-2026, 11:41 AM
            0 responses
            21 views
            0 reactions
            Last Post SEQadmin2  
            Working...