Header Leaderboard Ad

Collapse

SOLiD seq process: Covert colorspace to basespace

Collapse

Announcement

Collapse
No announcement yet.
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • SOLiD seq process: Covert colorspace to basespace

    I have a collection of colorspace solid sequences to be translated into basespace sequences. After view the post http://seqanswers.com/forums/showthread.php?t=1425 , people are recommending that the official accessory python script "encodeFasta.py" which is shipped with the Corona Lite, is ideal to accomplish the task. However, I was unable to find the package Corona Lite both by following the link provided in the post and by searching Corona Lite on SOLiD's official website http://solidsoftwaretools.com/gf/. After viewing the post http://seqanswers.com/forums/showthr...ht=corona+lite , I realized that in 2009 Bioscope has replaced Corona Lite. By following the link in this post, I again failed to find what I want.
    Now I really want to ask help from people who have been dealing with SOLiD data everyday. What is the right place to go or right software package to download at this moment?

    Any suggestions are welcome and will be appreciated.


    Zhigang
    Last edited by zhigangwu; 12-08-2011, 11:03 AM.

  • #2
    Hi Zhigang,
    It is a very bad idea to convert raw SOLiD reads into base space. Instead you should map them (converting your reference into color space) in color space. There are a wide assortment of mappers that will do this.
    --
    Phillip

    Comment


    • #3
      Hi Phillip,
      Can you explain a bit to me why that's a bad idea?
      By borrowing idea from the script posted by roedel in http://seqanswers.com/forums/showthread.php?t=1425, I made myself one parser, which has been attached along with this post.

      Originally posted by pmiguel View Post
      Hi Zhigang,
      It is a very bad idea to convert raw SOLiD reads into base space. Instead you should map them (converting your reference into color space) in color space. There are a wide assortment of mappers that will do this.
      --
      Phillip
      Attached Files

      Comment


      • #4
        Sequencer reads have a chance of read error (e.g. spot misidentification), combined with a chance of sequence error (e.g. polymerase misread in the PCR step). For sequencers that output in base space, both these errors have a similar effect on the base-space mapping. For sequencers that output in color-space, the read errors result in a somewhat unexpected base-space translation even if the underlying sequence has a perfect match to the reference. The issues relating to color-space to base-space translation were discussed in the thread you linked to, but here's my take on it (dumped from an email I recently sent to someone else):

        A color-space sequence is an encoding of adjacent dimers such that unchanging bases are encoded with '0', complementary changes are encoded with '3', the colour '1' is used for a non-complementary base change on the same side of the alphabet (AC, CA, GT, or TG), and the colour '2' is used for a non-complementary base change on a different side of the alphabet (AG, GA, CT, or TC). A table of these changes can be found here:

        http://www.ploscompbiol.org/article/...i.1000386.g002

        This has a few nice properties (e.g. the reverse-complement of a color-space sequence is the same as the reverse of the color-space sequence, a SNP will have two transitions), but many annoying and nasty properties. The first is that a color-space sequence in itself is meaningless without a base reference (usually the starting base).

        Here's an example color-space sequence:

        Code:
        2112322311010133121320003202203201302321 [40 chars]
        That color-space sequence can describe four different base-space sequences:

        Code:
        0: AGTGATCTACAACCATACTGCTTTTAGGAGGCTTGCCTAGT [41 chars]
        1: CTGTCGAGCACCAACGCAGTAGGGGCTTCTTAGGTAAGCTG
        2: GACAGCTCGTGGTTGCGTCATCCCCGAAGAATCCATTCGAC
        3: TCACTAGATGTTGGTATGACGAAAATCCTCCGAACGGATCA
        Note that these sequences are one character longer than their color-space equivalent, so by adding a starting base the sequence length does not change from the base-space representation.

        Code:
        A2112322311010133121320003202203201302321 [41 chars, decoded sequence 0]
        Here's another annoying property. It was pointed out before that the reverse complement of a color-space sequence is the reverse of the sequence. This is not entirely true if you include the starting base, because that base has now shifted to the end, and is its complementary partner:
        Code:
        rc(A2112322311010133121320003202203201302321)
        =1232031023022023000231213310101132232112<rc(A)> [easy]
        =1232031023022023000231213310101132232112T [easy]
        =A1232031023022023000231213310101132232112
        The last step is computationally hard, because it requires stepping through the sequence to work in reverse to find the first base.

        And for the last trick, errors are fairly common in color-space sequence reads:
        Code:
        A2112322311010133121320003202203201302321 [original]
        A2112322311010133121310003202203201302321 [error at position 21, before the 000]
        The base-space representation of these sequences:
        Code:
        AGTGATCTACAACCATACTGCTTTTAGGAGGCTTGCCTAGT [original]
        AGTGATCTACAACCATACTGCAAAATCCTCCGAACGGATCA [after a single error]
        This error has caused the base-space representation to switch from decoded sequence 0 above to decoded sequence 3 at position 22. A match of the base-space representation of this sequence would have 20 nucleotide differences, while there is only a single difference in color-space. The great differences between base-space representations are why color-space sequences should be assembled and/or mapped in color-space.

        Comment


        • #5
          Great Gringer, a very comprehensive explanation.


          Originally posted by gringer View Post
          Sequencer reads have a chance of read error (e.g. spot misidentification), combined with a chance of sequence error (e.g. polymerase misread in the PCR step). For sequencers that output in base space, both these errors have a similar effect on the base-space mapping. For sequencers that output in color-space, the read errors result in a somewhat unexpected base-space translation even if the underlying sequence has a perfect match to the reference. The issues relating to color-space to base-space translation were discussed in the thread you linked to, but here's my take on it (dumped from an email I recently sent to someone else):

          A color-space sequence is an encoding of adjacent dimers such that unchanging bases are encoded with '0', complementary changes are encoded with '3', the colour '1' is used for a non-complementary base change on the same side of the alphabet (AC, CA, GT, or TG), and the colour '2' is used for a non-complementary base change on a different side of the alphabet (AG, GA, CT, or TC). A table of these changes can be found here:

          http://www.ploscompbiol.org/article/...i.1000386.g002

          This has a few nice properties (e.g. the reverse-complement of a color-space sequence is the same as the reverse of the color-space sequence, a SNP will have two transitions), but many annoying and nasty properties. The first is that a color-space sequence in itself is meaningless without a base reference (usually the starting base).

          Here's an example color-space sequence:

          Code:
          2112322311010133121320003202203201302321 [40 chars]
          That color-space sequence can describe four different base-space sequences:

          Code:
          0: AGTGATCTACAACCATACTGCTTTTAGGAGGCTTGCCTAGT [41 chars]
          1: CTGTCGAGCACCAACGCAGTAGGGGCTTCTTAGGTAAGCTG
          2: GACAGCTCGTGGTTGCGTCATCCCCGAAGAATCCATTCGAC
          3: TCACTAGATGTTGGTATGACGAAAATCCTCCGAACGGATCA
          Note that these sequences are one character longer than their color-space equivalent, so by adding a starting base the sequence length does not change from the base-space representation.

          Code:
          A2112322311010133121320003202203201302321 [41 chars, decoded sequence 0]
          Here's another annoying property. It was pointed out before that the reverse complement of a color-space sequence is the reverse of the sequence. This is not entirely true if you include the starting base, because that base has now shifted to the end, and is its complementary partner:
          Code:
          rc(A2112322311010133121320003202203201302321)
          =1232031023022023000231213310101132232112<rc(A)> [easy]
          =1232031023022023000231213310101132232112T [easy]
          =A1232031023022023000231213310101132232112
          The last step is computationally hard, because it requires stepping through the sequence to work in reverse to find the first base.

          And for the last trick, errors are fairly common in color-space sequence reads:
          Code:
          A2112322311010133121320003202203201302321 [original]
          A2112322311010133121310003202203201302321 [error at position 21, before the 000]
          The base-space representation of these sequences:
          Code:
          AGTGATCTACAACCATACTGCTTTTAGGAGGCTTGCCTAGT [original]
          AGTGATCTACAACCATACTGCAAAATCCTCCGAACGGATCA [after a single error]
          This error has caused the base-space representation to switch from decoded sequence 0 above to decoded sequence 3 at position 22. A match of the base-space representation of this sequence would have 20 nucleotide differences, while there is only a single difference in color-space. The great differences between base-space representations are why color-space sequences should be assembled and/or mapped in color-space.

          Comment

          Latest Articles

          Collapse

          • seqadmin
            A Brief Overview and Common Challenges in Single-cell Sequencing Analysis
            by seqadmin


            ​​​​​​The introduction of single-cell sequencing has advanced the ability to study cell-to-cell heterogeneity. Its use has improved our understanding of somatic mutations1, cell lineages2, cellular diversity and regulation3, and development in multicellular organisms4. Single-cell sequencing encompasses hundreds of techniques with different approaches to studying the genomes, transcriptomes, epigenomes, and other omics of individual cells. The analysis of single-cell sequencing data i...

            01-24-2023, 01:19 PM
          • seqadmin
            Introduction to Single-Cell Sequencing
            by seqadmin
            Single-cell sequencing is a technique used to investigate the genome, transcriptome, epigenome, and other omics of individual cells using high-throughput sequencing. This technology has provided many scientific breakthroughs and continues to be applied across many fields, including microbiology, oncology, immunology, neurobiology, precision medicine, and stem cell research.

            The advancement of single-cell sequencing began in 2009 when Tang et al. investigated the single-cell transcriptomes
            ...
            01-09-2023, 03:10 PM

          ad_right_rmr

          Collapse
          Working...
          X