Medaka<\/a> is an open-source application from Oxford Nanopore<\/a>. It is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. <\/p>\n Versions 0.9.2 for CPU and GPU are installed on the CSF.<\/p>\n There are no restrictions on accessing the software on the CSF. It is released under the Mozilla Public License 2.0<\/a> and all usage must adhere to that license.<\/p>\n We now recommend loading modulefiles within your jobscript so that you have a full record of how the job was run. See the example jobscript below for how to do this. Alternatively, you may load modulefiles on the login node and let the job inherit these settings<\/abbr>.<\/p>\n Load one of the following modulefiles:<\/p>\n The above modulefiles will load any required dependency modulefiles. This includes the modulefiles for tabix, samtools and minimap2 (in the CPU version. The GPU version has been compiled from source and so these tools are compiled as part of the Please do not run Medaka on the login node. Jobs should be submitted to the compute nodes via batch.<\/p>\n Create a batch submission script (which will load the modulefile in the jobscript), for example:<\/p>\n Submit the jobscript using: <\/p>\n where scriptname<\/em> is the name of your jobscript.<\/p>\n Create a batch submission script (which will load the modulefile in the jobscript), for example:<\/p>\n Submit the jobscript using: <\/p>\n where scriptname<\/em> is the name of your jobscript.<\/p>\n Create a batch submission script (which will load the modulefile in the jobscript), for example:<\/p>\n The Medaka<\/a> website advises the following setting may<\/em> be required when running on the GPU:<\/p>\n If your GPU jobs fail with GPU-memory errors, try adding the above line to your jobscript.<\/p>\n None.<\/p>\n","protected":false},"excerpt":{"rendered":" Overview Medaka is an open-source application from Oxford Nanopore. It is a tool to create a consensus sequence from nanopore sequencing data. This task is performed using neural networks applied from a pileup of individual sequencing reads against a draft assembly. Versions 0.9.2 for CPU and GPU are installed on the CSF. Restrictions on use There are no restrictions on accessing the software on the CSF. It is released under the Mozilla Public License 2.0.. Read more »<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":86,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-4076","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/pages\/4076","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/comments?post=4076"}],"version-history":[{"count":3,"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/pages\/4076\/revisions"}],"predecessor-version":[{"id":4080,"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/pages\/4076\/revisions\/4080"}],"up":[{"embeddable":true,"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/pages\/86"}],"wp:attachment":[{"href":"https:\/\/ri.itservices.manchester.ac.uk\/csf3\/wp-json\/wp\/v2\/media?parent=4076"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Restrictions on use<\/h2>\n
Set up procedure<\/h2>\n
\r\n# Load one of the following modulefiles\r\nmodule load apps\/python\/medaka\/0.9.2 # CPU only\r\nmodule load apps\/python\/medaka\/0.9.2-gpu # GPU (uses GPU-enable tensorflow)\r\n<\/pre>\n
\ninstallation process.) The CUDA modulefiles will be loaded for the GPU version.<\/p>\nRunning the application<\/h2>\n
Serial batch job submission<\/h3>\n
\r\n#!\/bin\/bash --login\r\n#$ -cwd # Job will run from the current directory\r\n # NO -V line - we load modulefiles in the jobscript\r\n\r\n# Load the required CPU version\r\nmodule load apps\/python\/medaka\/0.9.2\r\n\r\n# Note that $NSLOTS is set to the number of cores: 1 for a serial\r\nmedaka_consensus -i basecalls.fa<\/em> -d assm_final.fa<\/em> -o resultsdir<\/em> -t $NSLOTS -m r941_min_high<\/em>\r\n<\/pre>\n
qsub scriptname<\/em><\/pre>\n
Parallel batch job submission<\/h3>\n
\r\n#!\/bin\/bash --login\r\n#$ -cwd # Job will run from the current directory\r\n#$ -pe smp.pe 8 # Number of cores\r\n\r\n# Load the required CPU version\r\nmodule load apps\/python\/medaka\/0.9.2\r\n\r\n# Note that $NSLOTS is set to the number of cores requested above\r\nmedaka_consensus -i basecalls.fa<\/em> -d assm_final.fa<\/em> -o resultsdir<\/em> -t $NSLOTS -m r941_min_high<\/em>\r\n<\/pre>\n
qsub scriptname<\/em><\/pre>\n
GPU batch job submission<\/h3>\n
\r\n#!\/bin\/bash --login\r\n#$ -cwd # Job will run from the current directory\r\n#$ -v100 # Run on a single Nvidia v100 GPU\r\n#$ -pe smp.pe 8 # Number of cores (can use up to 8 cores per GPU)\r\n\r\n# Load the required GPU<\/strong> version\r\nmodule load apps\/python\/medaka\/0.9.2-gpu\r\n\r\n# Note that $NSLOTS is set to the number of cores requested above\r\nmedaka_consensus -i basecalls.fa<\/em> -d assm_final.fa<\/em> -o resultsdir<\/em> -t $NSLOTS -m r941_min_high<\/em>\r\n<\/pre>\n
\r\nexport TF_FORCE_GPU_ALLOW_GROWTH=true\r\n<\/pre>\n
Further info<\/h2>\n
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Updates<\/h2>\n