/MetaFinisherSC

This tool is for users to upgrade their metagenomics assemblies using long reads. This includes fixing mis-assemblies and scaffolding/gap-filling.
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  2. Perl 3.8%
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srcRefactor
README.md

README.md

MetaFinisherSC

Mixed read type and mixed contig type###

Assume you have short reads (SR.fasta), long reads(LR.fasta) , contigs formed from short reads(SC.fasta), contigs formed from long reads(LC.fasta).

The final output is abun.fasta after running through the M-Fixer(mis-assembly fixing), FinisherSC and A-Splitter(join contigs based on abundance information).

In order to do that, here are the steps.

  1. Run M-Fixer which merge contigs with information from reads. It also fixes misassembly

     python -m srcRefactor.misassemblyFixerLib.mFixer -par 20 destinedFolder mummerPath

  2. Run FinisherSC to join the contigs together based on overlap information

     python -m srcRefactor.repeatPhaserLib.finisherSCCoreLib.finisherSC -par 20 destinedFolder mummerPath

  3. Run A-Splitter to join contigs based on abundance information (to run faster, you can use the -ar True -rs 0 -rd True )

     python -m srcRefactor.repeatPhaserLib.aSplitter -par 20 -rp improved2.fasta  destinedFolder mummerPath

Only long reads and contigs formed from long reads

Assume you have your long reads (LR.fasta) and contigs formed from long reads (LC.fasta).

The final output is abun.fasta after running through the M-Fixer(mis-assembly fixing), FinisherSC and A-Splitter(join contigs based on abundance information).

In order to do that, here are the steps.

  1. Run M-Fixer which merge contigs with information from reads(key intermediate output here is noEmbed.fasta). It also fixes misassembly

     python -m srcRefactor.misassemblyFixerLib.mFixer -par 20 -t LR destinedFolder mummerPath

  2. Run FinisherSC to join the contigs together based on overlap information(key intermediate output here is improved2.fasta)

     python -m srcRefactor.repeatPhaserLib.finisherSCCoreLib.finisherSC -par 20 destinedFolder mummerPath

  3. Run A-Splitter to join contigs based on abundance information (to run faster, you can use the -ar True -rs 0 -rd True ) (key intermediate output here is abun.fasta)

     python -m srcRefactor.repeatPhaserLib.aSplitter -par 20 -rp improved2.fasta destinedFolder mummerPath

Example

Below is a step by step example on running MetaFinisherSC on the testset provided(for simplicity we use the only long read mode here).

  1. Clone MetaFinisherSC

     git clone https://github.com/kakitone/MetaFinisherSC.git

  2. Get into the MetaFinisherSC folder, create directory to hold the data, download data from Dropbox and check to see if you get everything(LC.fasta, LR.fasta, reference.fasta).

     cd MetaFinisherSC/        

 mkdir dataFolder
 
 cd dataFolder
 
 wget -O testset.zip  https://www.dropbox.com/sh/slahcgv55037aiv/AAAzkaP6HILdBlH2G_xapKYHa?dl=1
 
 unzip testset.zip
 
 ls -lt

  3. Go back to the main directory containing MetaFinisherSC and run the following two commands. Here we assume that /usr/bin/ is the path to your MUMmer. This step can take a few minutes.

     cd ../

 python -m srcRefactor.misassemblyFixerLib.mFixer -par 20 -t LR dataFolder/ /usr/bin/
 
 python -m srcRefactor.repeatPhaserLib.finisherSCCoreLib.finisherSC -par 20  dataFolder/ /usr/bin/
 
 python -m srcRefactor.repeatPhaserLib.aSplitter -par 20 -rp improved2.fasta -ar True -rs 0 -rd True dataFolder/ /usr/bin/

  4. Now verify that the original LC.fasta contain 4 contigs whereas the final abun.fasta contains 2 contigs

     fgrep -o ">" dataFolder/LC.fasta  | wc -lc
 
 fgrep -o ">" dataFolder/abun.fasta  | wc -lc

  5. Align the abun.fasta with reference.fasta.

     nucmer  -maxmatch dataFolder/abun.fasta dataFolder/reference.fasta         

 show-coords out.delta

  6. You should now see

     NUCMER
 
 |[S1]     [E1]  |     [S2]     [E2]  |  [LEN 1]  [LEN 2]  |  [% IDY]  | [TAGS] |
 |---------------|--------------------|--------------------|-----------|--------|
 |       1  4999997  |  4999999        1  |  4999997  4999999  |    99.99  | Segkk0	Segkk0|
 | 2490000  2500003  |  2510000  2500001  |    10004    10000  |    99.59  | Segkk1	Segkk0|
 |       1  5000004  |  4999999        1  |  5000004  4999999  |    99.99  | Segkk1	Segkk1|
 | 2490000  2499997  |  2510000  2500001  |     9998    10000  |    99.29  | Segkk0	Segkk1|

  7. As a check, you may also want to see that there is really a 10K long repeat in the reference across the species. This can be seen by

     nucmer  -maxmatch dataFolder/reference.fasta dataFolder/reference.fasta         

 show-coords out.delta



 NUCMER
 
 |[S1]     [E1]  |     [S2]     [E2]  |  [LEN 1]  [LEN 2]  |  [% IDY]  | [TAGS] |
 |---------------|--------------------|--------------------|-----------|--------|
 |       1  5000000  |        1  5000000  |  5000000  5000000  |   100.00  | Segkk0	Segkk0|
 | 2500001  2510000  |  2500001  2510000  |    10000    10000  |   100.00  | Segkk1	Segkk0|
 |       1  5000000  |        1  5000000  |  5000000  5000000  |   100.00  | Segkk1	Segkk1|
 | 2500001  2510000  |  2500001  2510000  |    10000    10000  |   100.00  | Segkk0	Segkk1|