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ALLMAPS orders scaffolds by maximizing colinearity to a collection of genetic/genomic maps into the final chromosome build. More info: http://goo.gl/KI61Ow
The ordering and orientation of genomic scaffolds to reconstruct chromosomes is an essential step during de novo genome assembly. This process is often assisted by various mapping techniques. Because each map provides a unique line of evidence, a combination of multiple maps can greatly improve the accuracy of the resulting chromosomal assemblies. ALLMAPS is capable of computing a scaffold ordering that maximizes the colinearity to a collection of maps, including genetic, physical or comparative maps into the final chromosome build. We highlight several salient features of ALLMAPS.
- Clear, computable objective which is to maximize the colinearity to multiple input maps - reproducible
- Allow variable weights in input maps, leading to better control in conflict resolution - flexible
- Can incorporate many mapping evidence, requiring minimal transformation -powerful
- Streamline the genome build (with FASTA and AGP outputs) and genome update (with CHAIN output) - easy-to-use
- Demo data download: https://dl.dropboxusercontent.com/u/15937715/Data/ALLMAPS/ALLMAPS-testdata.zip
- Resources: https://github.com/tanghaibao/jcvi/wiki/ALLMAPS
- Command line:
- Step 1: python -m jcvi.assembly.allmaps merge JMMale.csv JMFemale.csv -o JM-2.bed -w weights.txt
- Step 2: python -m jcvi.assembly.allmaps path JM-2.bed scaffolds.fasta -w weights.txt
- One or more map files (can be genetic maps, genomic maps, etc):
- Example data are JMMale.csv JMFemale.csv
- Format for map data: https://github.com/tanghaibao/jcvi/wiki/ALLMAPS#step-1-prepare-input-data
Parameters Used in App
No Parameters are used in this workflow.
Can be viewed in the example data directory: /iplant/home/shared/iplantcollaborative/example_data/ALLMAPS-testdata/Example_output_PATH