How to Install and Uninstall norsnet Package on Ubuntu 21.10 (Impish Indri)

Last updated: November 26,2024

1. Install "norsnet" package

This guide covers the steps necessary to install norsnet on Ubuntu 21.10 (Impish Indri)

$ sudo apt update $ sudo apt install norsnet

2. Uninstall "norsnet" package

Learn how to uninstall norsnet on Ubuntu 21.10 (Impish Indri):

$ sudo apt remove norsnet $ sudo apt autoclean && sudo apt autoremove

3. Information about the norsnet package on Ubuntu 21.10 (Impish Indri)

Package: norsnet
Architecture: all
Version: 1.0.17-6
Priority: extra
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 213
Depends: profnet-norsnet
Recommends: blast2, librg-utils-perl, profphd, profbval
Suggests: pp-popularity-contest
Filename: pool/universe/n/norsnet/norsnet_1.0.17-6_all.deb
Size: 43132
MD5sum: 27798d8e0d755cac1a629e515a6a0af2
SHA1: a77474fa258c03712c27c04bbea88591c7b684f0
SHA256: 53ecd9f0f2a6e4329ca93154a4dcb30b4565ca5b6617bca60f8f302e53ad6738
SHA512: b035ec39e22892f9c0683c37324c884030d12ddd3f2784406ce0027a5f8f4efec602f5f7adc8ea5181bcd3c7cbcc94b46b88de130b9c951ab953ec02f774659e
Homepage: https://www.rostlab.org/owiki/index.php/Norsnet
Description-en: tool to identify unstructured loops in proteins
NORSnet can distinguish between very long contiguous segments with
non-regular secondary structure (NORS regions) and well-folded proteins.
.
NORSnet was trained on predicted information rather than on experimental data.
This allows NORSnet to reach into regions in sequence space that are not
covered by specialized disorder predictors. One disadvantage of this approach
is that it is not optimal for the identification of the "average" disordered
region.
.
NORSnet takes the following input, further described on norsnet(1):
* a protein sequence in a FASTA file
* secondary structure and solvent accessibility prediction by prof(1)
* an HSSP file
* flexible/rigid residues prediction by profbval(1)
Description-md5: dd08110009953d3d011d1374634e144b