Ubuntu
mafft package

mafft 7.505-1 source package in Ubuntu

Changelog

mafft (7.505-1) unstable; urgency=medium

  * Team upload.
  * New upstream version 7.505
  * Removed myself from uploaders

 -- Nilesh Patra <email address hidden>  Sun, 26 Jun 2022 00:45:31 +0530

Upload details

Uploaded by:
Debian Med
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
any
Section:
science
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Oracular release universe science
Noble release universe science
Mantic release universe science
Lunar release universe science

Downloads

File Size SHA-256 Checksum
mafft_7.505-1.dsc 1.9 KiB bc3eafbf0dc94f25205906b5874e22181afa1ed14c7f515070b7de9ade592782
mafft_7.505.orig.tar.gz 606.9 KiB 9b9ee0dea1d493f0fad6f9c227bcc291ddedd045b5558b7fea30dc5932db33b8
mafft_7.505-1.debian.tar.xz 7.0 KiB d0317b4d2b4cd3afd5a27657bec696ea2f7e0f1101c858b8c0006c3a1b7a8d54

Available diffs

No changes file available.

Binary packages built by this source

mafft: Multiple alignment program for amino acid or nucleotide sequences

 MAFFT is a multiple sequence alignment program which offers three
 accuracy-oriented methods:
  * L-INS-i (probably most accurate; recommended for <200 sequences;
    iterative refinement method incorporating local pairwise alignment
    information),
  * G-INS-i (suitable for sequences of similar lengths; recommended for
    <200 sequences; iterative refinement method incorporating global
    pairwise alignment information),
  * E-INS-i (suitable for sequences containing large unalignable regions;
    recommended for <200 sequences),
 and five speed-oriented methods:
  * FFT-NS-i (iterative refinement method; two cycles only),
  * FFT-NS-i (iterative refinement method; max. 1000 iterations),
  * FFT-NS-2 (fast; progressive method),
  * FFT-NS-1 (very fast; recommended for >2000 sequences; progressive
    method with a rough guide tree),
  * NW-NS-PartTree-1 (recommended for ∼50,000 sequences; progressive
    method with the PartTree algorithm).