How to Install and Uninstall swarm Package on Kali Linux
Last updated: January 11,2025
1. Install "swarm" package
This is a short guide on how to install swarm on Kali Linux
$
sudo apt update
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$
sudo apt install
swarm
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2. Uninstall "swarm" package
Please follow the steps below to uninstall swarm on Kali Linux:
$
sudo apt remove
swarm
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the swarm package on Kali Linux
Package: swarm
Source: swarm-cluster
Version: 3.1.4+dfsg-1
Installed-Size: 755
Maintainer: Debian Med Packaging Team
Architecture: amd64
Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 13.1)
Recommends: python3
Size: 634392
SHA256: 63462152b32ab656485b16ff4fa8b9f8a49f9bd03bb8984349f9ef55c02a5f40
SHA1: 65cb2bd5b5350618bb72d50b3115d93f762550fd
MD5sum: c2658f7ccfcab0f25732228d35750dd3
Description: robust and fast clustering method for amplicon-based studies
The purpose of swarm is to provide a novel clustering algorithm to handle large
sets of amplicons. Traditional clustering algorithms results are strongly
input-order dependent, and rely on an arbitrary global clustering threshold.
swarm results are resilient to input-order changes and rely on a small local
linking threshold d, the maximum number of differences between two amplicons.
swarm forms stable high-resolution clusters, with a high yield of biological
information.
Description-md5:
Homepage: https://github.com/torognes/swarm
Section: science
Priority: optional
Filename: pool/main/s/swarm-cluster/swarm_3.1.4+dfsg-1_amd64.deb
Source: swarm-cluster
Version: 3.1.4+dfsg-1
Installed-Size: 755
Maintainer: Debian Med Packaging Team
Architecture: amd64
Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.3.1), libstdc++6 (>= 13.1)
Recommends: python3
Size: 634392
SHA256: 63462152b32ab656485b16ff4fa8b9f8a49f9bd03bb8984349f9ef55c02a5f40
SHA1: 65cb2bd5b5350618bb72d50b3115d93f762550fd
MD5sum: c2658f7ccfcab0f25732228d35750dd3
Description: robust and fast clustering method for amplicon-based studies
The purpose of swarm is to provide a novel clustering algorithm to handle large
sets of amplicons. Traditional clustering algorithms results are strongly
input-order dependent, and rely on an arbitrary global clustering threshold.
swarm results are resilient to input-order changes and rely on a small local
linking threshold d, the maximum number of differences between two amplicons.
swarm forms stable high-resolution clusters, with a high yield of biological
information.
Description-md5:
Homepage: https://github.com/torognes/swarm
Section: science
Priority: optional
Filename: pool/main/s/swarm-cluster/swarm_3.1.4+dfsg-1_amd64.deb