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| align="center" | [https://arrow.apache.org arrow] | | align="center" | [https://arrow.apache.org arrow] | ||
| align="center" | tools | | align="center" | tools | ||
| align="center" | 0.11.1, 0.15.1, 0.16.0, 0.17.1 | | align="center" | 0.11.1, 0.15.1, 0.16.0, 0.17.1, 2.0.0, 5.0.0, 7.0.0, 8.0.0, 9.0.0, 10.0.1, 11.0.0, 12.0.1, 13.0.0, 14.0.0, 14.0.1, 15.0.1, 16.1.0 | ||
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Apache Arrow is a cross-language development platform for in-memory data. Homepage: https://arrow.apache.org URL: https://arrow.apache.org Compatible modules: python/3.11, python/3.10 Keyword:tools<br /><br /><br /></div> | | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Apache Arrow is a cross-language development platform for in-memory data. Homepage: https://arrow.apache.org URL: https://arrow.apache.org Compatible modules: python/3.11, python/3.10 Keyword:tools<br /><br /><br /></div> | ||
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| align="center" | - | | align="center" | - | ||
| align="center" | 1.03 | | align="center" | 1.03 | ||
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: bgc implements Bayesian estimation of genomic clines to quantify introgression at many loci. Models are available for linked loci, genotype uncertainty and sequence errors. Models for genotype uncertainty and sequence error can be used to estimate genomic cline parameters from next-generation sequence data. Homepage | | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: bgc implements Bayesian estimation of genomic clines to quantify introgression at many loci. Models are available for linked loci, genotype uncertainty and sequence errors. Models for genotype uncertainty and sequence error can be used to estimate genomic cline parameters from next-generation sequence data. Homepage: https://sites.google.com/site/bgcsoftware/<br /><br /><br /></div> | ||
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| align="center" | [https://bitbucket.org/gavinband/bgen/src/default/ bgen] | | align="center" | [https://bitbucket.org/gavinband/bgen/src/default/ bgen] | ||
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| align="center" | [https://github.com/nanoporetech/dorado dorado] | | align="center" | [https://github.com/nanoporetech/dorado dorado] | ||
| align="center" | - | | align="center" | - | ||
| align="center" | 0.2.1, 0.2.2, 0.3.0, 0.4.1, 0.4.3, 0.5.3, 0.6.1 | | align="center" | 0.2.1, 0.2.2, 0.3.0, 0.4.1, 0.4.3, 0.5.3, 0.6.1, 0.7.0 | ||
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Dorado is a high-performance, easy-to-use, open source basecaller for Oxford Nanopore reads. Homepage: https://github.com/nanoporetech/dorado URL: https://github.com/nanoporetech/dorado<br /><br /><br /></div> | | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Dorado is a high-performance, easy-to-use, open source basecaller for Oxford Nanopore reads. Homepage: https://github.com/nanoporetech/dorado URL: https://github.com/nanoporetech/dorado<br /><br /><br /></div> | ||
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| align="center" | bio | | align="center" | bio | ||
| align="center" | 1.30, 1.31 | | align="center" | 1.30, 1.31 | ||
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: FragGeneScan is an application for finding (fragmented) genes in short reads. Homepage: http://omics.informatics.indiana.edu/FragGeneScan/ Keyword:bio<br /><br /><br /></div> | | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: FragGeneScan is an application for finding (fragmented) genes in short reads. Homepage: http://omics.informatics.indiana.edu/FragGeneScan/ URL: http://omics.informatics.indiana.edu/FragGeneScan/ Keyword:bio<br /><br /><br /></div> | ||
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| align="center" | [http://grigoriefflab.janelia.org/frealign frealign] | | align="center" | [http://grigoriefflab.janelia.org/frealign frealign] | ||
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| align="center" | [https://github.com/NVIDIA/gdrcopy gdrcopy] | | align="center" | [https://github.com/NVIDIA/gdrcopy gdrcopy] | ||
| align="center" | - | | align="center" | - | ||
| align="center" | 2.1 | | align="center" | 2.1, 2.3.1 | ||
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: A low-latency GPU memory copy library based on NVIDIA GPUDirect RDMA technology. Homepage: https://github.com/NVIDIA/gdrcopy URL: https://github.com/NVIDIA/gdrcopy<br /><br /><br /></div> | | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: A low-latency GPU memory copy library based on NVIDIA GPUDirect RDMA technology. Homepage: https://github.com/NVIDIA/gdrcopy URL: https://github.com/NVIDIA/gdrcopy<br /><br /><br /></div> | ||
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| align="center" | - | | align="center" | - | ||
| align="center" | 3.3.2, 3.3.8 | | align="center" | 3.3.2, 3.3.8 | ||
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: GLFW is an Open Source, multi-platform library for OpenGL, OpenGL ES and Vulkan development on the desktop Homepage: https://www.glfw.org URL: https://www.glfw.org | | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: GLFW is an Open Source, multi-platform library for OpenGL, OpenGL ES and Vulkan development on the desktop Homepage: https://www.glfw.org URL: https://www.glfw.org<br /><br /><br /></div> | ||
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| align="center" | [https://ccb.jhu.edu/software/glimmerhmm glimmerhmm] | | align="center" | [https://ccb.jhu.edu/software/glimmerhmm glimmerhmm] | ||
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| align="center" | chem | | align="center" | chem | ||
| align="center" | 2021.7-0.5.1 | | align="center" | 2021.7-0.5.1 | ||
| Documentation: [[GROMACS]]<div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Modified GROMACS for small-angle scattering calculations (SAXS/WAXS/SANS). This is a | | Documentation: [[GROMACS]]<div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: Modified GROMACS for small-angle scattering calculations (SAXS/WAXS/SANS). This is a CPU only build, containing both MPI and threadMPI builds. - CC-Wiki: GROMACS Homepage: https://cbjh.gitlab.io/gromacs-swaxs-docs/ URL: https://cbjh.gitlab.io/gromacs-swaxs-docs/ Keyword:chem<br /><br /><br /></div> | ||
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| align="center" | [https://www.gnu.org/software/gsl/ gsl] | | align="center" | [https://www.gnu.org/software/gsl/ gsl] | ||
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| align="center" | math | | align="center" | math | ||
| align="center" | 4.0.3, 5.1.0 | | align="center" | 4.0.3, 5.1.0 | ||
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: METIS is a set of serial programs for partitioning graphs, partitioning finite element meshes, and producing fill reducing orderings for sparse matrices. The algorithms implemented in METIS are based on the multilevel recursive-bisection, multilevel k-way, and multi-constraint partitioning schemes. Homepage: http://glaros.dtc.umn.edu/gkhome/metis/metis/overview Keyword:math<br /><br /><br /></div> | | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: METIS is a set of serial programs for partitioning graphs, partitioning finite element meshes, and producing fill reducing orderings for sparse matrices. The algorithms implemented in METIS are based on the multilevel recursive-bisection, multilevel k-way, and multi-constraint partitioning schemes. Homepage: http://glaros.dtc.umn.edu/gkhome/metis/metis/overview URL: http://glaros.dtc.umn.edu/gkhome/metis/metis/overview Keyword:math<br /><br /><br /></div> | ||
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| align="center" | [http://glaros.dtc.umn.edu/gkhome/metis/metis/overview metis-64idx] | | align="center" | [http://glaros.dtc.umn.edu/gkhome/metis/metis/overview metis-64idx] | ||
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| align="center" | - | | align="center" | - | ||
| align="center" | 6.0.1.5, 8.6.1.6 | | align="center" | 6.0.1.5, 8.6.1.6 | ||
| <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: NVIDIA TensorRT is a platform for high-performance deep learning inference Homepage: https://developer.nvidia.com/tensorrt URL: https://developer.nvidia.com/tensorrt Compatible modules: | | <div class="mw-collapsible mw-collapsed" style="white-space: pre-line;"><br />Description: NVIDIA TensorRT is a platform for high-performance deep learning inference Homepage: https://developer.nvidia.com/tensorrt URL: https://developer.nvidia.com/tensorrt Compatible modules: python/3.10, python/3.11<br /><br /><br /></div> | ||
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| align="center" | [https://github.com/tesseract-ocr/tesseract tesseract] | | align="center" | [https://github.com/tesseract-ocr/tesseract tesseract] |