wget https://swift.rc.nectar.org.au/v1/AUTH_810/CVL-Singularity-External-Files/virtualgl_2.6.2_amd64.deb\n\
\ndpkg -i virtualgl_2.6.2_amd64.deb\n![]()
\n"
- stargazers_count: 11
+ stargazers_count: 13
subscribers_count: 2
topics:
- collision-avoidance
- gazebo-simulator
- robot-navigation
- updated_at: 1701582273.0
+ updated_at: 1702557024.0
TheJacksonLaboratory/acm-bcb-singularity2021:
data_format: 2
description: Singularity Course Repo for 2021 ACM-BCB Conference
@@ -57730,15 +57730,15 @@ TheNoyesLab/WGS_SNP_pipelines:
data_format: 2
description: Collection of SNP pipelines to analyze assembled genomes
filenames:
- - containers/Singularity
- - containers/Singularity.etoki
- - containers/Singularity.atlas
- - containers/Singularity.lyveset1
- containers/Singularity.cfsansnp
- containers/Singularity.tree_analysis
+ - containers/Singularity.peppa
- containers/Singularity.lyveset
- containers/Singularity.kSNP3_cfsansnp
- - containers/Singularity.peppa
+ - containers/Singularity
+ - containers/Singularity.lyveset1
+ - containers/Singularity.etoki
+ - containers/Singularity.atlas
full_name: TheNoyesLab/WGS_SNP_pipelines
latest_release: null
readme: '![]()
'
- stargazers_count: 8334
+ stargazers_count: 8341
subscribers_count: 349
topics:
- c-plus-plus
@@ -62171,7 +62171,7 @@ VowpalWabbit/vowpal_wabbit:
- active-learning
- learning-to-search
- cpp
- updated_at: 1702263131.0
+ updated_at: 1702776911.0
WIPACrepo/monitoring-scripts:
data_format: 2
description: Tools for monitoring HTCondor and other things
@@ -64092,8 +64092,8 @@ aardk/jupyter-casa:
data_format: 2
description: A Jupyter kernel for CASA
filenames:
- - singularity/Singularity
- singularity/Singularity.docker
+ - singularity/Singularity
- singularity/Singularity.centos7
full_name: aardk/jupyter-casa
latest_release: null
@@ -64962,8 +64962,8 @@ accre/singularity:
description: Examples that use singularity containers to run arbitrary operating
systems on the ACCRE cluster.
filenames:
- - accre-internal/Singularity.accre.internal.base
- accre-internal/Singularity.accre.internal.rstudio.3.6.0
+ - accre-internal/Singularity.accre.internal.base
- accre-internal/Singularity.accre.internal.rstudio.4.0.5
- accre-internal/Singularity.accre.internal.rstudio.gpu.3.6.0
- accre-internal/Singularity.accre.internal.rstudio.3.4.3
@@ -69757,29 +69757,29 @@ apptainer/singularity:
project to the Linux Foundation. This repo has been persisted as a snapshot right
before the changes.
filenames:
+ - e2e/testdata/Singularity
- examples/instances/Singularity
- - examples/opensuse/Singularity
- - examples/apps/Singularity
- - examples/apps/Singularity.cowsay
- examples/debian/Singularity
- - examples/arch/Singularity
- - examples/scientific/Singularity
- examples/shub/Singularity
- - examples/multistage/Singularity
- - examples/asciinema/Singularity
+ - examples/docker/Singularity
+ - examples/ubuntu/Singularity
+ - examples/raspbian/Singularity
+ - examples/apps/Singularity.cowsay
+ - examples/apps/Singularity
+ - examples/opensuse/Singularity
+ - examples/busybox/Singularity
+ - examples/sle/Singularity
+ - examples/arch/Singularity
- examples/centos/Singularity
- examples/self/Singularity
+ - examples/multistage/Singularity
+ - examples/scratch/Singularity.alpine
+ - examples/scratch/Singularity.busybox
- examples/centos-arm64/Singularity
- - examples/sle/Singularity
- - examples/raspbian/Singularity
- examples/library/Singularity
- examples/opensuse-arm64/Singularity
- - examples/scratch/Singularity.busybox
- - examples/scratch/Singularity.alpine
- - examples/docker/Singularity
- - examples/busybox/Singularity
- - examples/ubuntu/Singularity
- - e2e/testdata/Singularity
+ - examples/scientific/Singularity
+ - examples/asciinema/Singularity
full_name: apptainer/singularity
latest_release: v3.8.7
readme: '.
'
- stargazers_count: 2474
+ stargazers_count: 2476
subscribers_count: 91
topics:
- containers
@@ -70002,7 +70002,7 @@ apptainer/singularity:
- portability
- rootless-containers
- cloud-native
- updated_at: 1701850261.0
+ updated_at: 1702753123.0
ar90n/lab:
data_format: 2
description: my laboratory
@@ -71574,21 +71574,21 @@ automl/HPOBench:
description: Collection of hyperparameter optimization benchmark problems
filenames:
- hpobench/container/recipes/Singularity.template
- - hpobench/container/recipes/ml/Singularity.XGBoostBenchmark
+ - hpobench/container/recipes/od/Singularity.ODBenchmarks
+ - hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation
+ - hpobench/container/recipes/ml/Singularity.ml_mmfb
- hpobench/container/recipes/ml/Singularity.SupportVectorMachine
+ - hpobench/container/recipes/ml/Singularity.XGBoostBenchmark
- hpobench/container/recipes/ml/Singularity.PyBNN
- hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark
- - hpobench/container/recipes/ml/Singularity.ml_mmfb
- - hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation
- - hpobench/container/recipes/od/Singularity.ODBenchmarks
- - hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine
- - hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark
- - hpobench/container/recipes/rl/Singularity.Cartpole
- - hpobench/container/recipes/rl/Singularity.learnaBenchmark
- hpobench/container/recipes/nas/Singularity.nasbench_1shot1
- - hpobench/container/recipes/nas/Singularity.TabularBenchmarks
- - hpobench/container/recipes/nas/Singularity.nasbench_201
- hpobench/container/recipes/nas/Singularity.nasbench_101
+ - hpobench/container/recipes/nas/Singularity.nasbench_201
+ - hpobench/container/recipes/nas/Singularity.TabularBenchmarks
+ - hpobench/container/recipes/rl/Singularity.Cartpole
+ - hpobench/container/recipes/rl/Singularity.learnaBenchmark
+ - hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine
+ - hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark
full_name: automl/HPOBench
latest_release: 0.0.10
readme: "Description\n\
-
This BIDS App enables generation and subsequent group analysis of structural\
- \ connectomes\ngenerated from diffusion MRI data. The analysis pipeline relies\
- \ primarily on the MRtrix3\nsoftware package, and includes a number of\
- \ state-of-the-art methods for image processing,\ntractography reconstruction,\
- \ connectome generation and inter-subject connection density\nnormalisation.
\n\
- NOTE: App is still under development; script is not guaranteed\
- \ to be operational\nfor all use cases.
\nRequirements
\nDue to use of the Anatomically-Constrained\
- \ Tractography (ACT) framework, correction of\nEPI susceptibility distortions\
- \ is a prerequisite for this pipeline. Currently, this is\nonly possible within\
- \ this pipeline through use of the FSL tool topup, which relies\n\
- on the presence of spin-echo EPI images with differences in phase encoding to\
- \ estimate\nthe causative inhomogeneity field. In the absence of such data, this\
- \ pipeline is not\ncurrently applicable; though recommendations for alternative\
- \ mechanisms for such\ncorrection in the Issues page are welcome, and development\
- \ of novel techniques for\nperforming this correction are additionally underway.
\n\
- While many common DICOM conversion software are capable of providing data characterising\n\
- the phase and slice encoding performed in the acquisition protocol, which are\
- \ subsequently\nused by this pipeline to automate DWI data pre-processing, for\
- \ some softwares and/or\nsome data (particularly those not acquired on a Siemens\
- \ platform), such data may not be\npresent in the sidecar JSON files for files\
- \ in the BIDS dwi/ and fmap/ directories.\nIn this circumstance,\
- \ it will be necessary for users to manually enter the relevant\ninformation into\
- \ these files in order for this script to be capable of processing the\ndata.\
- \ Every JSON file in these two directories should contain the BIDS fields\nPhaseEncodingDirection\
+ readme: "
Description
\nThis BIDS App enables generation\
+ \ and subsequent group analysis of structural connectomes\ngenerated from diffusion\
+ \ MRI data. The analysis pipeline relies primarily on the MRtrix3\nsoftware\
+ \ package, and includes a number of state-of-the-art methods for image processing,\n\
+ tractography reconstruction, connectome generation and inter-subject connection\
+ \ density\nnormalisation.
\nNOTE: App is still under development;\
+ \ script is not guaranteed to be operational\nfor all use cases.
\nRequirements
\nDue to use of the Anatomically-Constrained Tractography\
+ \ (ACT) framework, correction of\nEPI susceptibility distortions is a prerequisite\
+ \ for this pipeline. Currently, this is\nonly possible within this pipeline through\
+ \ use of the FSL tool topup, which relies\non the presence of spin-echo\
+ \ EPI images with differences in phase encoding to estimate\nthe causative inhomogeneity\
+ \ field. In the absence of such data, this pipeline is not\ncurrently applicable;\
+ \ though recommendations for alternative mechanisms for such\ncorrection in the\
+ \ Issues page are welcome, and development of novel techniques for\nperforming\
+ \ this correction are additionally underway.
\nWhile many common DICOM conversion\
+ \ software are capable of providing data characterising\nthe phase and slice encoding\
+ \ performed in the acquisition protocol, which are subsequently\nused by this\
+ \ pipeline to automate DWI data pre-processing, for some softwares and/or\nsome\
+ \ data (particularly those not acquired on a Siemens platform), such data may\
+ \ not be\npresent in the sidecar JSON files for files in the BIDS dwi/\
+ \ and fmap/ directories.\nIn this circumstance, it will be necessary\
+ \ for users to manually enter the relevant\ninformation into these files in order\
+ \ for this script to be capable of processing the\ndata. Every JSON file in these\
+ \ two directories should contain the BIDS fields\nPhaseEncodingDirection\
\ and TotalReadoutTime. For DWI data, it is also preferable to\n\
provide the SliceEncodingDirection and SliceTiming fields.\
\ More information on these\ndata can be found in the BIDS documentation.
\nInstructions
\nMulti-stage\
- \ analysis pipeline
\n\
+ \ rel=\"nofollow\">BIDS documentation.\nInstructions
\n\
+ Multi-stage analysis pipeline
\n\
The tool consists of three explicit analysis levels:
\n\n- \n
\"\
preproc\": This performs basic DWI (and if necessary T1-weighted\
\ image) pre-processing\nbased on the raw BIDS data input, writing the resulting\
@@ -76166,11 +76168,11 @@ bids-apps/MRtrix3_connectome:
\ performing\nappropriate inter-subject connection density normalisation as described\
\ in\nthis manuscript.\
\ Results are written to sub-directory\n\"MRtrix3_connectome-group\"\
- \ within the specified output directory.
\n \n
\nExecution platforms
\nThis\
- \ script can be utilised in one of three ways:
\n\n- \n
As a stand-alone\
- \ MRtrix3 script
\nThe script mrtrix3_connectome.py\
+ \ within the specified output directory.
\n \n
\nExecution\
+ \ platforms
\nThis script can be utilised in one of three ways:
\n\n\
+ - \n
As a stand-alone MRtrix3 script
\nThe script mrtrix3_connectome.py\
\ can additionally be used outside of this Docker\ncontainer, as a stand-alone\
\ Python script build against the MRtrix3 Python libraries.\nUsing the\
\ script in this way requires setting the PYTHONPATH environment\
@@ -76203,50 +76205,52 @@ bids-apps/MRtrix3_connectome:
\n
The resulting container file \"MRtrix3_connectome.sif\"\
\ can be run as a stand-alone\nexecutable, as long as the system on which the\
\ file is executed has a version of\nSingularity installed that is compatible\
- \ with that of the system used to build the\ncontainer.
\n \n
\nDocumentation\n\
- The help page of the tool itself can be generated by executing the script without\n\
- providing any command-line options. The help page is additionally presented at\
- \ the\nbottom of this README page for reference. Documentation regarding the underlying\n\
- MRtrix3 tools can be found in the official\nMRtrix3 documentation. Additional information\n\
- may be found in the online\
- \ MRtrix3 community forum.
\nError Reporting
\nExperiencing problems?\
- \ You can either post a private message to me on the\nMRtrix3 community forum, or you can report it\n\
- directly to the \n\n\n
Documentation\n
The help page of the tool itself can be generated\
+ \ by executing the script without\nproviding any command-line options. The help\
+ \ page is additionally presented at the\nbottom of this README page for reference.\
+ \ Documentation regarding the underlying\nMRtrix3 tools can be found\
+ \ in the official\nMRtrix3 documentation. Additional information\nmay be found in the\
+ \ online MRtrix3\
+ \ community forum.
\n
\n\
+ Experiencing problems? You can either post a private message to me on the\n\
+ MRtrix3\
+ \ community forum, or you can report it\ndirectly to the GitHub issues list.\nIn both cases, please include as much information as\
\ possible; this may include re-running\nthe script using the --debug\
\ option, which will provide additional information at the\nterminal, and preserve\
\ temporary files generated by the script within your target output\ndirectory,\
- \ which can be forwarded to the developer.
\nAcknowledgements
\nDevelopment of this\
- \ tool was made possible through funding from the National Health\nand Medical\
- \ Research Council (NHMRC) of Australia.
\nThe developer acknowledges the\
- \ facilities and scientific and technical assistance of\nthe National Imaging\
- \ Facility, a National Collaborative Research Infrastructure Strategy\n(NCRIS)\
- \ capability, at the Florey Institute of Neuroscience and Mental Health.
\n\
- The Florey Institute of Neuroscience and Mental Health acknowledges support\
- \ from the\nVictorian Government and in particular the funding from the Operational\
- \ Infrastructure\nSupport Grant.
\nRobert Smith is supported by fellowship\
- \ funding from the National Imaging Facility (NIF),\nan Australian Government\
- \ National Collaborative Research Infrastructure Strategy (NCRIS)\ncapability.
\n\
- Citation
\n\
- When using this pipeline, please use the following snippet to acknowledge the\
- \ relevant\nwork (amend as appropriate depending on options used):
\nStructural\
- \ connectomes were generated using the MRtrix3_connectome BIDS App (Smith\n\
- et al., 2019), which operates principally using tools provided in the MRtrix3\n\
- software package (Tournier et al., 2019; http://mrtrix.org). This included: DWI\ndenoising (Veraart et al.,\
- \ 2016), Gibbs ringing removal (Kellner et al., 2016),\npre-processing (Andersson\
- \ et al., 2003; Andersson and Sotiropoulos, 2016; Andersson\net al., 2016; (IF\
- \ USING EDDY_CUDA: Andersson et al., 2017)); and bias field correction\n(Tustison\
- \ et al., 2010 OR Zhang et al., 2001); inter-modal registration (Bhushan et al.,\n\
+ \ which can be forwarded to the developer.
\nAcknowledgements
\n\
+ Development of this tool was made possible through funding from the National\
+ \ Health\nand Medical Research Council (NHMRC) of Australia.
\nThe developer\
+ \ acknowledges the facilities and scientific and technical assistance of\nthe\
+ \ National Imaging Facility, a National Collaborative Research Infrastructure\
+ \ Strategy\n(NCRIS) capability, at the Florey Institute of Neuroscience and Mental\
+ \ Health.
\nThe Florey Institute of Neuroscience and Mental Health acknowledges\
+ \ support from the\nVictorian Government and in particular the funding from the\
+ \ Operational Infrastructure\nSupport Grant.
\nRobert Smith is supported\
+ \ by fellowship funding from the National Imaging Facility (NIF),\nan Australian\
+ \ Government National Collaborative Research Infrastructure Strategy (NCRIS)\n\
+ capability.
\nCitation
\nWhen using this pipeline,\
+ \ please use the following snippet to acknowledge the relevant\nwork (amend as\
+ \ appropriate depending on options used):
\nStructural connectomes were\
+ \ generated using the MRtrix3_connectome BIDS App (Smith\net al., 2019),\
+ \ which operates principally using tools provided in the MRtrix3\nsoftware\
+ \ package (Tournier et al., 2019; http://mrtrix.org). This included: DWI\ndenoising (Veraart et al., 2016),\
+ \ Gibbs ringing removal (Kellner et al., 2016),\npre-processing (Andersson et\
+ \ al., 2003; Andersson and Sotiropoulos, 2016; Andersson\net al., 2016; (IF USING\
+ \ EDDY_CUDA: Andersson et al., 2017)); and bias field correction\n(Tustison et\
+ \ al., 2010 OR Zhang et al., 2001); inter-modal registration (Bhushan et al.,\n\
2015); brain extraction (Smith, 2002 OR Iglesias et al., 2011), T1 tissue segmentation\n\
(Zhang et al., 2001; Smith, 2002; Patenaude et al., 2011; Smith et al., 2012);\
\ spherical\ndeconvolution (Tournier et al., 2004; Jeurissen et al., 2014); probabilistic\
@@ -76356,73 +76360,75 @@ bids-apps/MRtrix3_connectome:
\ 2010, 50, 970-983\nZhang, Y.; Brady, M. & Smith, S. Segmentation of brain\
\ MR images through a hidden Markov random field model and the expectation-maximization\
\ algorithm. IEEE Transactions on Medical Imaging, 2001, 20, 45-57\n\n\
-
Help page
\n\
- The following help page can equivalently be generated by executing the tool\
- \ without\nproviding any command-line arguments (within a container environment;\
- \ note interface\nis slightly different if run natively).
\n
\nSynopsis
\nGenerate\
- \ structural connectomes based on diffusion-weighted and T1-weighted image data\
- \ using state-of-the-art reconstruction tools, particularly those provided in\
- \ MRtrix3
\nUsage
\nmrtrix3_connectome.py bids_dir output_dir analysis_level\
+ Help page
\nThe following help page can equivalently be generated\
+ \ by executing the tool without\nproviding any command-line arguments (within\
+ \ a container environment; note interface\nis slightly different if run natively).
\n\
+
\nSynopsis
\nGenerate structural connectomes\
+ \ based on diffusion-weighted and T1-weighted image data using state-of-the-art\
+ \ reconstruction tools, particularly those provided in MRtrix3
\n\
+ Usage
\nmrtrix3_connectome.py bids_dir output_dir analysis_level\
\ [ options ]\n
\n\n- \n
bids_dir: The directory\
\ with the input dataset formatted according to the BIDS standard.
\n \n\
- \n
output_dir: The directory where the output files should be stored.
\n\
\n- \n
analysis_level: Level of analysis that will be performed;\
- \ options are: preproc, participant, group.
\n \n
\nDescription
\nWhile preproc-level\
- \ analysis only requires data within the BIDS directory, participant-level analysis\
- \ requires that the output directory be pre-populated with the results from preproc-level\
- \ processing; similarly, group-level analysis requires that the output directory\
- \ be pre-populated with the results from participant-level analysis.
\nThe\
- \ operations performed by each of the three levels of analysis are as follows:
\n\
- \"preproc\": DWI: Denoising; Gibbs ringing removal; motion, eddy current and\
- \ EPI distortion correction and outlier detection & replacement; brain masking,\
- \ bias field correction and intensity normalisation; rigid-body registration &\
- \ transformation to T1-weighted image. T1-weighted image: bias field correction;\
- \ brain masking.
\n\"participant\": DWI: Response function estimation; FOD\
- \ estimation. T1-weighted image (if -parcellation is not none): Tissue segmentation;\
- \ grey matter parcellation. Combined (if -parcellation is not none, or -streamlines\
- \ is provided): Whole-brain streamlines tractography; SIFT2; connectome construction.
\n\
- \"group\": Generation of FA-based population template; warping of template-based\
- \ white matter mask to subject spaces; calculation of group mean white matter\
- \ response function; scaling of connectomes based on white matter b=0 intensity,\
- \ response function used during participant-level analysis, and SIFT model proportioinality\
- \ coefficient; generation of group mean connectome.
\nThe label(s) provided\
- \ to the -participant_label and -session_label options correspond(s) to sub-<participant_label>\
- \ and ses-<session_label> from the BIDS spec (so they do not include\
- \ \"sub-\" or \"ses-\"). Multiple participants / sessions can be specified with\
- \ a space-separated list.
\nFor both preproc-level and participant-level\
- \ analyses, if no specific participants or sessions are nominated by the user\
- \ (or the user explicitly specifies multiple participants / sessions), the script\
- \ will process each of these in series. It is additionally possible for the user\
- \ to invoke multiple instances of this script in order to process multiple subjects\
- \ at once in parallel, ensuring that no single participant / session is being\
- \ processed in parallel, and that preproc-level output data are written fully\
- \ before commencing participant-level analysis.
\nThe -output_verbosity\
- \ option principally affects the participant-level analysis, modulating how many\
- \ derivative files are written to the output directory. Permitted values are from\
- \ 1 to 4: 1 writes only those files requisite for group-level analysis; 2 additionally\
- \ writes files typically useful for post-hoc analysis (the default); 3 additionally\
- \ generates files for enhanced connectome visualisation and copies the entire\
- \ whole-brain tractogram; 4 additionally generates a full copy of the script scratch\
- \ directory (with all intermediate files retained) to the output directory (and\
- \ this applies to all analysis levels)
\nIf running participant-level analysis\
- \ using the script as a standalone tool rather than inside the provided container,\
- \ data pertaining to atlas parcellations can no longer be guaranteed to be stored\
- \ at a specific location on the filesystem. In this case, the user will most likely\
- \ need to manually specify the location where the corresponding parcellation is\
- \ stored using the -atlas_path option.
\nOptions
\n\n- \n--output_verbosity
The\
- \ verbosity of script output (number from 1 to 4). \n
\nOptions that are relevant to participant-level analysis
\n\n- \n
--parcellation
The\
+ \ options are: preproc, participant, group.
\n \n
\nDescription
\n\
+
'
- stargazers_count: 8334
+ stargazers_count: 8341
subscribers_count: 349
topics:
- c-plus-plus
@@ -62171,7 +62171,7 @@ VowpalWabbit/vowpal_wabbit:
- active-learning
- learning-to-search
- cpp
- updated_at: 1702263131.0
+ updated_at: 1702776911.0
WIPACrepo/monitoring-scripts:
data_format: 2
description: Tools for monitoring HTCondor and other things
@@ -64092,8 +64092,8 @@ aardk/jupyter-casa:
data_format: 2
description: A Jupyter kernel for CASA
filenames:
- - singularity/Singularity
- singularity/Singularity.docker
+ - singularity/Singularity
- singularity/Singularity.centos7
full_name: aardk/jupyter-casa
latest_release: null
@@ -64962,8 +64962,8 @@ accre/singularity:
description: Examples that use singularity containers to run arbitrary operating
systems on the ACCRE cluster.
filenames:
- - accre-internal/Singularity.accre.internal.base
- accre-internal/Singularity.accre.internal.rstudio.3.6.0
+ - accre-internal/Singularity.accre.internal.base
- accre-internal/Singularity.accre.internal.rstudio.4.0.5
- accre-internal/Singularity.accre.internal.rstudio.gpu.3.6.0
- accre-internal/Singularity.accre.internal.rstudio.3.4.3
@@ -69757,29 +69757,29 @@ apptainer/singularity:
project to the Linux Foundation. This repo has been persisted as a snapshot right
before the changes.
filenames:
+ - e2e/testdata/Singularity
- examples/instances/Singularity
- - examples/opensuse/Singularity
- - examples/apps/Singularity
- - examples/apps/Singularity.cowsay
- examples/debian/Singularity
- - examples/arch/Singularity
- - examples/scientific/Singularity
- examples/shub/Singularity
- - examples/multistage/Singularity
- - examples/asciinema/Singularity
+ - examples/docker/Singularity
+ - examples/ubuntu/Singularity
+ - examples/raspbian/Singularity
+ - examples/apps/Singularity.cowsay
+ - examples/apps/Singularity
+ - examples/opensuse/Singularity
+ - examples/busybox/Singularity
+ - examples/sle/Singularity
+ - examples/arch/Singularity
- examples/centos/Singularity
- examples/self/Singularity
+ - examples/multistage/Singularity
+ - examples/scratch/Singularity.alpine
+ - examples/scratch/Singularity.busybox
- examples/centos-arm64/Singularity
- - examples/sle/Singularity
- - examples/raspbian/Singularity
- examples/library/Singularity
- examples/opensuse-arm64/Singularity
- - examples/scratch/Singularity.busybox
- - examples/scratch/Singularity.alpine
- - examples/docker/Singularity
- - examples/busybox/Singularity
- - examples/ubuntu/Singularity
- - e2e/testdata/Singularity
+ - examples/scientific/Singularity
+ - examples/asciinema/Singularity
full_name: apptainer/singularity
latest_release: v3.8.7
readme: '.
'
- stargazers_count: 2474
+ stargazers_count: 2476
subscribers_count: 91
topics:
- containers
@@ -70002,7 +70002,7 @@ apptainer/singularity:
- portability
- rootless-containers
- cloud-native
- updated_at: 1701850261.0
+ updated_at: 1702753123.0
ar90n/lab:
data_format: 2
description: my laboratory
@@ -71574,21 +71574,21 @@ automl/HPOBench:
description: Collection of hyperparameter optimization benchmark problems
filenames:
- hpobench/container/recipes/Singularity.template
- - hpobench/container/recipes/ml/Singularity.XGBoostBenchmark
+ - hpobench/container/recipes/od/Singularity.ODBenchmarks
+ - hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation
+ - hpobench/container/recipes/ml/Singularity.ml_mmfb
- hpobench/container/recipes/ml/Singularity.SupportVectorMachine
+ - hpobench/container/recipes/ml/Singularity.XGBoostBenchmark
- hpobench/container/recipes/ml/Singularity.PyBNN
- hpobench/container/recipes/ml/Singularity.ml_tabular_benchmark
- - hpobench/container/recipes/ml/Singularity.ml_mmfb
- - hpobench/container/recipes/od/Singularity.ODKernelDensityEstimation
- - hpobench/container/recipes/od/Singularity.ODBenchmarks
- - hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine
- - hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark
- - hpobench/container/recipes/rl/Singularity.Cartpole
- - hpobench/container/recipes/rl/Singularity.learnaBenchmark
- hpobench/container/recipes/nas/Singularity.nasbench_1shot1
- - hpobench/container/recipes/nas/Singularity.TabularBenchmarks
- - hpobench/container/recipes/nas/Singularity.nasbench_201
- hpobench/container/recipes/nas/Singularity.nasbench_101
+ - hpobench/container/recipes/nas/Singularity.nasbench_201
+ - hpobench/container/recipes/nas/Singularity.TabularBenchmarks
+ - hpobench/container/recipes/rl/Singularity.Cartpole
+ - hpobench/container/recipes/rl/Singularity.learnaBenchmark
+ - hpobench/container/recipes/surrogates/Singularity.SupportVectorMachine
+ - hpobench/container/recipes/surrogates/Singularity.ParamnetBenchmark
full_name: automl/HPOBench
latest_release: 0.0.10
readme: "Description\n\
-
This BIDS App enables generation and subsequent group analysis of structural\ - \ connectomes\ngenerated from diffusion MRI data. The analysis pipeline relies\ - \ primarily on the MRtrix3\nsoftware package, and includes a number of\ - \ state-of-the-art methods for image processing,\ntractography reconstruction,\ - \ connectome generation and inter-subject connection density\nnormalisation.
\n\ -NOTE: App is still under development; script is not guaranteed\ - \ to be operational\nfor all use cases.
\nRequirements
\nDue to use of the Anatomically-Constrained\
- \ Tractography (ACT) framework, correction of\nEPI susceptibility distortions\
- \ is a prerequisite for this pipeline. Currently, this is\nonly possible within\
- \ this pipeline through use of the FSL tool topup, which relies\n\
- on the presence of spin-echo EPI images with differences in phase encoding to\
- \ estimate\nthe causative inhomogeneity field. In the absence of such data, this\
- \ pipeline is not\ncurrently applicable; though recommendations for alternative\
- \ mechanisms for such\ncorrection in the Issues page are welcome, and development\
- \ of novel techniques for\nperforming this correction are additionally underway.
While many common DICOM conversion software are capable of providing data characterising\n\
- the phase and slice encoding performed in the acquisition protocol, which are\
- \ subsequently\nused by this pipeline to automate DWI data pre-processing, for\
- \ some softwares and/or\nsome data (particularly those not acquired on a Siemens\
- \ platform), such data may not be\npresent in the sidecar JSON files for files\
- \ in the BIDS dwi/ and fmap/ directories.\nIn this circumstance,\
- \ it will be necessary for users to manually enter the relevant\ninformation into\
- \ these files in order for this script to be capable of processing the\ndata.\
- \ Every JSON file in these two directories should contain the BIDS fields\nPhaseEncodingDirection\
+ readme: "
Description
\nThis BIDS App enables generation\ + \ and subsequent group analysis of structural connectomes\ngenerated from diffusion\ + \ MRI data. The analysis pipeline relies primarily on the MRtrix3\nsoftware\ + \ package, and includes a number of state-of-the-art methods for image processing,\n\ + tractography reconstruction, connectome generation and inter-subject connection\ + \ density\nnormalisation.
\nNOTE: App is still under development;\ + \ script is not guaranteed to be operational\nfor all use cases.
\nRequirements
\nDue to use of the Anatomically-Constrained Tractography\
+ \ (ACT) framework, correction of\nEPI susceptibility distortions is a prerequisite\
+ \ for this pipeline. Currently, this is\nonly possible within this pipeline through\
+ \ use of the FSL tool topup, which relies\non the presence of spin-echo\
+ \ EPI images with differences in phase encoding to estimate\nthe causative inhomogeneity\
+ \ field. In the absence of such data, this pipeline is not\ncurrently applicable;\
+ \ though recommendations for alternative mechanisms for such\ncorrection in the\
+ \ Issues page are welcome, and development of novel techniques for\nperforming\
+ \ this correction are additionally underway.
While many common DICOM conversion\
+ \ software are capable of providing data characterising\nthe phase and slice encoding\
+ \ performed in the acquisition protocol, which are subsequently\nused by this\
+ \ pipeline to automate DWI data pre-processing, for some softwares and/or\nsome\
+ \ data (particularly those not acquired on a Siemens platform), such data may\
+ \ not be\npresent in the sidecar JSON files for files in the BIDS dwi/\
+ \ and fmap/ directories.\nIn this circumstance, it will be necessary\
+ \ for users to manually enter the relevant\ninformation into these files in order\
+ \ for this script to be capable of processing the\ndata. Every JSON file in these\
+ \ two directories should contain the BIDS fields\nPhaseEncodingDirection\
\ and TotalReadoutTime. For DWI data, it is also preferable to\n\
provide the SliceEncodingDirection and SliceTiming fields.\
\ More information on these\ndata can be found in the BIDS documentation.
Instructions
\nMulti-stage\ - \ analysis pipeline
\n\ + \ rel=\"nofollow\">BIDS documentation.\nInstructions
\n\ +Multi-stage analysis pipeline
\n\\"\
preproc\": This performs basic DWI (and if necessary T1-weighted\
\ image) pre-processing\nbased on the raw BIDS data input, writing the resulting\
@@ -76166,11 +76168,11 @@ bids-apps/MRtrix3_connectome:
\ performing\nappropriate inter-subject connection density normalisation as described\
\ in\nthis manuscript.\
\ Results are written to sub-directory\n\"MRtrix3_connectome-group\"\
- \ within the specified output directory.
As a stand-alone\ - \ MRtrix3 script
\nThe script mrtrix3_connectome.py\
+ \ within the specified output directory.
As a stand-alone MRtrix3 script
\nThe script mrtrix3_connectome.py\
\ can additionally be used outside of this Docker\ncontainer, as a stand-alone\
\ Python script build against the MRtrix3 Python libraries.\nUsing the\
\ script in this way requires setting the PYTHONPATH environment\
@@ -76203,50 +76205,52 @@ bids-apps/MRtrix3_connectome:
\n
The resulting container file \"MRtrix3_connectome.sif\"\
\ can be run as a stand-alone\nexecutable, as long as the system on which the\
\ file is executed has a version of\nSingularity installed that is compatible\
- \ with that of the system used to build the\ncontainer.
The help page of the tool itself can be generated by executing the script without\n\ - providing any command-line options. The help page is additionally presented at\ - \ the\nbottom of this README page for reference. Documentation regarding the underlying\n\ - MRtrix3 tools can be found in the official\nMRtrix3 documentation. Additional information\n\ - may be found in the online\ - \ MRtrix3 community forum.
\nError Reporting
\nExperiencing problems?\
- \ You can either post a private message to me on the\nMRtrix3 community forum, or you can report it\n\
- directly to the \n\n\n
Documentation\n
The help page of the tool itself can be generated\ + \ by executing the script without\nproviding any command-line options. The help\ + \ page is additionally presented at the\nbottom of this README page for reference.\ + \ Documentation regarding the underlying\nMRtrix3 tools can be found\ + \ in the official\nMRtrix3 documentation. Additional information\nmay be found in the\ + \ online MRtrix3\ + \ community forum.
\n
\n\
+ Experiencing problems? You can either post a private message to me on the\n\
+ MRtrix3\
+ \ community forum, or you can report it\ndirectly to the GitHub issues list.\nIn both cases, please include as much information as\
\ possible; this may include re-running\nthe script using the --debug\
\ option, which will provide additional information at the\nterminal, and preserve\
\ temporary files generated by the script within your target output\ndirectory,\
- \ which can be forwarded to the developer.
Acknowledgements
\nDevelopment of this\ - \ tool was made possible through funding from the National Health\nand Medical\ - \ Research Council (NHMRC) of Australia.
\nThe developer acknowledges the\ - \ facilities and scientific and technical assistance of\nthe National Imaging\ - \ Facility, a National Collaborative Research Infrastructure Strategy\n(NCRIS)\ - \ capability, at the Florey Institute of Neuroscience and Mental Health.
\n\ -The Florey Institute of Neuroscience and Mental Health acknowledges support\ - \ from the\nVictorian Government and in particular the funding from the Operational\ - \ Infrastructure\nSupport Grant.
\nRobert Smith is supported by fellowship\ - \ funding from the National Imaging Facility (NIF),\nan Australian Government\ - \ National Collaborative Research Infrastructure Strategy (NCRIS)\ncapability.
\n\ -Citation
\n\ -When using this pipeline, please use the following snippet to acknowledge the\ - \ relevant\nwork (amend as appropriate depending on options used):
\nStructural\ - \ connectomes were generated using the MRtrix3_connectome BIDS App (Smith\n\ - et al., 2019), which operates principally using tools provided in the MRtrix3\n\ - software package (Tournier et al., 2019; http://mrtrix.org). This included: DWI\ndenoising (Veraart et al.,\ - \ 2016), Gibbs ringing removal (Kellner et al., 2016),\npre-processing (Andersson\ - \ et al., 2003; Andersson and Sotiropoulos, 2016; Andersson\net al., 2016; (IF\ - \ USING EDDY_CUDA: Andersson et al., 2017)); and bias field correction\n(Tustison\ - \ et al., 2010 OR Zhang et al., 2001); inter-modal registration (Bhushan et al.,\n\ + \ which can be forwarded to the developer.
\nAcknowledgements
\n\ +Development of this tool was made possible through funding from the National\ + \ Health\nand Medical Research Council (NHMRC) of Australia.
\nThe developer\ + \ acknowledges the facilities and scientific and technical assistance of\nthe\ + \ National Imaging Facility, a National Collaborative Research Infrastructure\ + \ Strategy\n(NCRIS) capability, at the Florey Institute of Neuroscience and Mental\ + \ Health.
\nThe Florey Institute of Neuroscience and Mental Health acknowledges\ + \ support from the\nVictorian Government and in particular the funding from the\ + \ Operational Infrastructure\nSupport Grant.
\nRobert Smith is supported\ + \ by fellowship funding from the National Imaging Facility (NIF),\nan Australian\ + \ Government National Collaborative Research Infrastructure Strategy (NCRIS)\n\ + capability.
\nCitation
\nWhen using this pipeline,\ + \ please use the following snippet to acknowledge the relevant\nwork (amend as\ + \ appropriate depending on options used):
\nStructural connectomes were\ + \ generated using the MRtrix3_connectome BIDS App (Smith\net al., 2019),\ + \ which operates principally using tools provided in the MRtrix3\nsoftware\ + \ package (Tournier et al., 2019; http://mrtrix.org). This included: DWI\ndenoising (Veraart et al., 2016),\ + \ Gibbs ringing removal (Kellner et al., 2016),\npre-processing (Andersson et\ + \ al., 2003; Andersson and Sotiropoulos, 2016; Andersson\net al., 2016; (IF USING\ + \ EDDY_CUDA: Andersson et al., 2017)); and bias field correction\n(Tustison et\ + \ al., 2010 OR Zhang et al., 2001); inter-modal registration (Bhushan et al.,\n\ 2015); brain extraction (Smith, 2002 OR Iglesias et al., 2011), T1 tissue segmentation\n\ (Zhang et al., 2001; Smith, 2002; Patenaude et al., 2011; Smith et al., 2012);\ \ spherical\ndeconvolution (Tournier et al., 2004; Jeurissen et al., 2014); probabilistic\ @@ -76356,73 +76360,75 @@ bids-apps/MRtrix3_connectome: \ 2010, 50, 970-983\nZhang, Y.; Brady, M. & Smith, S. Segmentation of brain\ \ MR images through a hidden Markov random field model and the expectation-maximization\ \ algorithm. IEEE Transactions on Medical Imaging, 2001, 20, 45-57\n\n\ -
Help page
\n\ -The following help page can equivalently be generated by executing the tool\ - \ without\nproviding any command-line arguments (within a container environment;\ - \ note interface\nis slightly different if run natively).
\n\n
Synopsis
\nGenerate\ - \ structural connectomes based on diffusion-weighted and T1-weighted image data\ - \ using state-of-the-art reconstruction tools, particularly those provided in\ - \ MRtrix3
\nUsage
\nmrtrix3_connectome.py bids_dir output_dir analysis_level\
+ Help page
\nThe following help page can equivalently be generated\
+ \ by executing the tool without\nproviding any command-line arguments (within\
+ \ a container environment; note interface\nis slightly different if run natively).
\n\
+
\nSynopsis
\nGenerate structural connectomes\
+ \ based on diffusion-weighted and T1-weighted image data using state-of-the-art\
+ \ reconstruction tools, particularly those provided in MRtrix3
\n\
+ Usage
\nmrtrix3_connectome.py bids_dir output_dir analysis_level\
\ [ options ]\n
\n\n- \n
bids_dir: The directory\
\ with the input dataset formatted according to the BIDS standard.
\n \n\
- \n
output_dir: The directory where the output files should be stored.
\n\
\n- \n
analysis_level: Level of analysis that will be performed;\
- \ options are: preproc, participant, group.
\n \n
\nDescription
\nWhile preproc-level\
- \ analysis only requires data within the BIDS directory, participant-level analysis\
- \ requires that the output directory be pre-populated with the results from preproc-level\
- \ processing; similarly, group-level analysis requires that the output directory\
- \ be pre-populated with the results from participant-level analysis.
\nThe\
- \ operations performed by each of the three levels of analysis are as follows:
\n\
- \"preproc\": DWI: Denoising; Gibbs ringing removal; motion, eddy current and\
- \ EPI distortion correction and outlier detection & replacement; brain masking,\
- \ bias field correction and intensity normalisation; rigid-body registration &\
- \ transformation to T1-weighted image. T1-weighted image: bias field correction;\
- \ brain masking.
\n\"participant\": DWI: Response function estimation; FOD\
- \ estimation. T1-weighted image (if -parcellation is not none): Tissue segmentation;\
- \ grey matter parcellation. Combined (if -parcellation is not none, or -streamlines\
- \ is provided): Whole-brain streamlines tractography; SIFT2; connectome construction.
\n\
- \"group\": Generation of FA-based population template; warping of template-based\
- \ white matter mask to subject spaces; calculation of group mean white matter\
- \ response function; scaling of connectomes based on white matter b=0 intensity,\
- \ response function used during participant-level analysis, and SIFT model proportioinality\
- \ coefficient; generation of group mean connectome.
\nThe label(s) provided\
- \ to the -participant_label and -session_label options correspond(s) to sub-<participant_label>\
- \ and ses-<session_label> from the BIDS spec (so they do not include\
- \ \"sub-\" or \"ses-\"). Multiple participants / sessions can be specified with\
- \ a space-separated list.
\nFor both preproc-level and participant-level\
- \ analyses, if no specific participants or sessions are nominated by the user\
- \ (or the user explicitly specifies multiple participants / sessions), the script\
- \ will process each of these in series. It is additionally possible for the user\
- \ to invoke multiple instances of this script in order to process multiple subjects\
- \ at once in parallel, ensuring that no single participant / session is being\
- \ processed in parallel, and that preproc-level output data are written fully\
- \ before commencing participant-level analysis.
\nThe -output_verbosity\
- \ option principally affects the participant-level analysis, modulating how many\
- \ derivative files are written to the output directory. Permitted values are from\
- \ 1 to 4: 1 writes only those files requisite for group-level analysis; 2 additionally\
- \ writes files typically useful for post-hoc analysis (the default); 3 additionally\
- \ generates files for enhanced connectome visualisation and copies the entire\
- \ whole-brain tractogram; 4 additionally generates a full copy of the script scratch\
- \ directory (with all intermediate files retained) to the output directory (and\
- \ this applies to all analysis levels)
\nIf running participant-level analysis\
- \ using the script as a standalone tool rather than inside the provided container,\
- \ data pertaining to atlas parcellations can no longer be guaranteed to be stored\
- \ at a specific location on the filesystem. In this case, the user will most likely\
- \ need to manually specify the location where the corresponding parcellation is\
- \ stored using the -atlas_path option.
\nOptions
The\ - \ verbosity of script output (number from 1 to 4).
--parcellation
The\
+ \ options are: preproc, participant, group.
While preproc-level analysis only requires data within the BIDS directory,\ + \ participant-level analysis requires that the output directory be pre-populated\ + \ with the results from preproc-level processing; similarly, group-level analysis\ + \ requires that the output directory be pre-populated with the results from participant-level\ + \ analysis.
\nThe operations performed by each of the three levels of analysis\ + \ are as follows:
\n\"preproc\": DWI: Denoising; Gibbs ringing removal;\ + \ motion, eddy current and EPI distortion correction and outlier detection &\ + \ replacement; brain masking, bias field correction and intensity normalisation;\ + \ rigid-body registration & transformation to T1-weighted image. T1-weighted\ + \ image: bias field correction; brain masking.
\n\"participant\": DWI: Response\ + \ function estimation; FOD estimation. T1-weighted image (if -parcellation is\ + \ not none): Tissue segmentation; grey matter parcellation. Combined (if -parcellation\ + \ is not none, or -streamlines is provided): Whole-brain streamlines tractography;\ + \ SIFT2; connectome construction.
\n\"group\": Generation of FA-based population\ + \ template; warping of template-based white matter mask to subject spaces; calculation\ + \ of group mean white matter response function; scaling of connectomes based on\ + \ white matter b=0 intensity, response function used during participant-level\ + \ analysis, and SIFT model proportioinality coefficient; generation of group mean\ + \ connectome.
\nThe label(s) provided to the -participant_label and -session_label\ + \ options correspond(s) to sub-<participant_label> and ses-<session_label>\ + \ from the BIDS spec (so they do not include \"sub-\" or \"ses-\"). Multiple\ + \ participants / sessions can be specified with a space-separated list.
\n\ +For both preproc-level and participant-level analyses, if no specific participants\ + \ or sessions are nominated by the user (or the user explicitly specifies multiple\ + \ participants / sessions), the script will process each of these in series. It\ + \ is additionally possible for the user to invoke multiple instances of this script\ + \ in order to process multiple subjects at once in parallel, ensuring that no\ + \ single participant / session is being processed in parallel, and that preproc-level\ + \ output data are written fully before commencing participant-level analysis.
\n\ +The -output_verbosity option principally affects the participant-level analysis,\ + \ modulating how many derivative files are written to the output directory. Permitted\ + \ values are from 1 to 4: 1 writes only those files requisite for group-level\ + \ analysis; 2 additionally writes files typically useful for post-hoc analysis\ + \ (the default); 3 additionally generates files for enhanced connectome visualisation\ + \ and copies the entire whole-brain tractogram; 4 additionally generates a full\ + \ copy of the script scratch directory (with all intermediate files retained)\ + \ to the output directory (and this applies to all analysis levels)
\nIf\ + \ running participant-level analysis using the script as a standalone tool rather\ + \ than inside the provided container, data pertaining to atlas parcellations can\ + \ no longer be guaranteed to be stored at a specific location on the filesystem.\ + \ In this case, the user will most likely need to manually specify the location\ + \ where the corresponding parcellation is stored using the -atlas_path option.
\n\ +Options
\n- \n
- \n--output_verbosity
The\ + \ verbosity of script output (number from 1 to 4). \n
Options\ + \ that are relevant to participant-level analysis
\n- \n
- \n
--parcellation
\n\ @@ -76430,24 +76436,24 @@ bids-apps/MRtrix3_connectome: \ generate for each subject (will be determined heuristically if not explicitly\ \ set).\n
The\ \ choice of connectome parcellation scheme (compulsory for participant-level analysis);\ \ options are: aal, aal2, brainnetome246fs, brainnetome246mni, craddock200, craddock400,\ \ desikan, destrieux, hcpmmp1, none, perry512, yeo7fs, yeo7mni, yeo17fs, yeo17mni. \n - \n
--template_reg software
\n
The choice\ \ of registration software for mapping subject to template space; options are:\ - \ ants, fsl. \n
Options that are relevant to both preproc-level and participant-level analyses
\n- \n\
-
- \n--t1w_preproc path
Provide a path by which pre-processed\ + \ ants, fsl.\n \n
Options\ + \ that are relevant to both preproc-level and participant-level analyses
\n\ +- \n
- \n--t1w_preproc path
Provide a path by which pre-processed\ \ T1-weighted image data may be found for the processed participant(s) / session(s) \n\
-
Options specific to the batch processing of participant data
\n- \n
- \n
--participant_label
The\ +
Options\ + \ specific to the batch processing of participant data
\n- \n
- \n
--participant_label
\n
The\ \ label(s) of the participant(s) that should be analyzed. \n - \n
--session_label
\n
The\ \ session(s) within each participant that should be analyzed. \n
Standard options
\n\ -- \n
- \n
-d/--debug
\n\ -
display debugging messages. \n - \n
-h/--help
\n
display this information page and\ - \ exit. \n - \n
-n/--n_cpus number
\n\ +
use this number\ - \ of threads in multi-threaded applications (set to 0 to disable multi-threading).Standard options
\n- \n
- \n
-d/--debug
\n
display\ + \ debugging messages. \n - \n
-h/--help
\n
display\ + \ this information page and exit. \n - \n
-n/--n_cpus number
\n\
use\ + \ this number of threads in multi-threaded applications (set to 0 to disable multi-threading). \n - \n
--scratch /path/to/scratch/
\n
manually specify\ \ the path in which to generate the scratch directory. \n - \n
--skip-bids-validator
\n
Skip\ \ BIDS validation \n - \n
-v/--version
display\ @@ -76470,7 +76476,7 @@ bids-apps/MRtrix3_connectome: - bidsapp - diffusion-mri - mri - updated_at: 1695969616.0 + updated_at: 1700828672.0 bids-apps/freesurfer: data_format: 2 description: BIDS app wrapping recon-all from FreeSurfer @@ -84853,40 +84859,40 @@ connor-lab/singularity-recipes: data_format: 2 description: A collection of Singularity recipes useful for our nextflow pipelines filenames: - - Singularity.sierrapy - - Singularity.variant_calling - - Singularity.kraken2 - - Singularity.quast - - Singularity.centrifuge - - Singularity.shovill - - Singularity.mash - - Singularity.abricate + - Singularity.prokka - Singularity.bbtools - - Singularity.seqtk - Singularity.taxonkit - - Singularity.cd-hit - - Singularity.wtdbg2 - - Singularity.deeptools - - Singularity.rtg - - Singularity.minimap2 - Singularity.iqtree + - Singularity.mlst + - Singularity.iva + - Singularity.minimap2 + - Singularity.shiver + - Singularity.vsearch - Singularity.trim_galore + - Singularity.kraken2 + - Singularity.shovill + - Singularity.interop + - Singularity.centrifuge + - Singularity.variant_calling + - Singularity.multiqc - Singularity.snapperdb_v3 + - Singularity.ariba + - Singularity.canu + - Singularity.abricate + - Singularity.kronatools + - Singularity.cd-hit + - Singularity.sierrapy + - Singularity.seqtk + - Singularity.rtg - Singularity.snapperdb - - Singularity.mykrobe-atlas - - Singularity.mlst + - Singularity.deeptools + - Singularity.wtdbg2 - Singularity.shiver_init - - Singularity.prokka - - Singularity.canu - - Singularity.multiqc - - Singularity.pengu-ddt - - Singularity.interop + - Singularity.mash + - Singularity.quast - Singularity.assembly_improvement - - Singularity.kronatools - - Singularity.iva - - Singularity.ariba - - Singularity.vsearch - - Singularity.shiver + - Singularity.mykrobe-atlas + - Singularity.pengu-ddt full_name: connor-lab/singularity-recipes latest_release: null readme: 'Description:\n
For a given CCS dataset generated from short-insert\ \ library, 6mASCOPE will examine if there are contaminating species and calculate\ \ the proportion of reads mapped to the reference and top 50 contaminated species\ - \ from reads that do not map to the eukaryotic species of interest.
\nInputs:
\n- \n\
-
- CCS reads file capturing all the genetic material in a gDNA sample (.fasta,\ - \ pre-computed in the following example) \n
- Eukaryotic reference of genome\ - \ of interest (.fasta) \n
Outputs:
\nFor a given CCS dataset generated from short-insert\ - \ library,
\n\ -6mASCOPEwill examine if there are contaminating species\ - \ and calculate the proportion of reads mapped to the reference and top 50 contaminated\ - \ species from reads that do not map to the eukaryotic species of interest.Example of the Output:
\n
\nRemove 8491 possible inter-species\ - \ chimeric reads for further analysis\n#total_CCS\tmapped_to_goi\tcontaminants\n\ - 666159\t640345 (96.1249%)\t25814 (3.87505%)\n\nTop 50 mapped species outside goi\ - \ reference\n#Count\tSpecies\n 10836 Saccharomyces cerevisiae\n 2413 Acetobacter\ - \ tropicalis\n 1524 Acetobacter pasteurianus\n 1479 Lactobacillus plantarum\n\ - \ 882 Acetobacter sp.\n ...\n(Full species list can be viewed\ - \ in
\ntest.contam.estimate.txt)Example commands:
\n
\n\ + \ from reads that do not map to the eukaryotic species of interest.\n6mASCOPE contam\ - \ -c test.ccs.fasta -r test.ref.fasta -o test.contam.estimate.txt\n
- \n
- \n
- CCS reads file capturing all the genetic material\ + \ in a gDNA sample (.fasta, pre-computed in the following example) \n
- Eukaryotic\ + \ reference of genome of interest (.fasta) \n
![\"The](\"/docs/figures/test.6mASCOPE.ReadsProportion.png\"\)
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