Difference between revisions of "Program to find probes"
From Ucsbgalaxy
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| − | project in | + | project on oakley-dev in PIA user |
Program flow: | Program flow: | ||
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*3. Reads through the sequence.fasta file again, this time running blastn on each sequence | *3. Reads through the sequence.fasta file again, this time running blastn on each sequence | ||
**3b. For each hit, set the hash with the display_id as key to true | **3b. For each hit, set the hash with the display_id as key to true | ||
| + | |||
| + | Verbal description of program: | ||
| + | * Stage 1. | ||
| + | **After finding 'all' opsins (>9000 genes), I aligned them with MAFFT and calculated a NJ tree using Clearcut. | ||
| + | **A new program uses a phylogeny and unaligned sequences as input. | ||
| + | **Using the tree, the most closely related genes are compared and aligned using blast2seq | ||
| + | **If the sequences we compare have a match, the matching sequence is propagated down to the ancestral node of the 2 sequences. | ||
| + | **More distant relatives are compared using blast2seqs, and any time there is sufficient similarity, the consensus match is propagated "down" the tree toward the root. | ||
| + | **As such, a sequence that matches all sequences in a clade is propagated down the tree, until the next distant relative no longer has a match. | ||
| + | **These results are currently written to a file called output.txt . | ||
| + | **Tests of output.txt indicate that all known opsin sequences will have blast similarity with some sequence(s) in the output.txt file | ||
| + | **The remaining challenge is that many of the sequences in output.txt are too long to be probes. So, we need to find the sub-sequence of each result that has the most coverage across genes. This will require the Stage 2 algorithm. | ||
| + | |||
| + | *Stage 2 (Not written yet) | ||
| + | ** Use a 'sliding window' approach to test sub-sequences (putative-probe) of each full sequence in the output file. | ||
| + | ** Use blast to find all full sequences the putative-probe hits, with particular similarity and length parameters. | ||
| + | ** Find the PD (phylogenetic diversity=sum of branch lengths) of the full sequences that were hit | ||
| + | ** Use 1 or 2 putative probes from each sequence that hit the maximum PD | ||
Revision as of 11:51, 27 February 2014
project on oakley-dev in PIA user
Program flow:
- 1. Creates a BLAST+ database using sequence.fasta
- 2. Reads in sequence.fasta sequence by sequence
- 2b. creates a hash of all sequence ids to false
- 3. Reads through the sequence.fasta file again, this time running blastn on each sequence
- 3b. For each hit, set the hash with the display_id as key to true
Verbal description of program:
- Stage 1.
- After finding 'all' opsins (>9000 genes), I aligned them with MAFFT and calculated a NJ tree using Clearcut.
- A new program uses a phylogeny and unaligned sequences as input.
- Using the tree, the most closely related genes are compared and aligned using blast2seq
- If the sequences we compare have a match, the matching sequence is propagated down to the ancestral node of the 2 sequences.
- More distant relatives are compared using blast2seqs, and any time there is sufficient similarity, the consensus match is propagated "down" the tree toward the root.
- As such, a sequence that matches all sequences in a clade is propagated down the tree, until the next distant relative no longer has a match.
- These results are currently written to a file called output.txt .
- Tests of output.txt indicate that all known opsin sequences will have blast similarity with some sequence(s) in the output.txt file
- The remaining challenge is that many of the sequences in output.txt are too long to be probes. So, we need to find the sub-sequence of each result that has the most coverage across genes. This will require the Stage 2 algorithm.
- Stage 2 (Not written yet)
- Use a 'sliding window' approach to test sub-sequences (putative-probe) of each full sequence in the output file.
- Use blast to find all full sequences the putative-probe hits, with particular similarity and length parameters.
- Find the PD (phylogenetic diversity=sum of branch lengths) of the full sequences that were hit
- Use 1 or 2 putative probes from each sequence that hit the maximum PD
