fsa 1.15.9+dfsg-4build1 source package in Ubuntu

Changelog

fsa (1.15.9+dfsg-4build1) focal; urgency=medium

  * No-change rebuild for libgcc-s1 package name change.

 -- Matthias Klose <email address hidden>  Mon, 23 Mar 2020 07:14:31 +0100

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Uploaded by:
Matthias Klose
Uploaded to:
Focal
Original maintainer:
Debian Med
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

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Series Pocket Published Component Section
Focal release universe misc

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File Size SHA-256 Checksum
fsa_1.15.9+dfsg.orig.tar.xz 545.6 KiB c3a845122e08f5e3c0fb96ef5133270a386c4ac6198856fc56aa2a796c0a6377
fsa_1.15.9+dfsg-4build1.debian.tar.xz 12.8 KiB 0dfbf8b6375898bb76ec9833321eba178b4f9e28b009639cf69cbe4f9ad85130
fsa_1.15.9+dfsg-4build1.dsc 1.9 KiB e2f649dcd70e0bb9e3b5dd501c7731ac5193151667391f5cff0c41633b231c4d

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Binary packages built by this source

fsa: Fast Statistical Alignment of protein, RNA or DNA sequences

 FSA is a probabilistic multiple sequence alignment algorithm which uses
 a "distance-based" approach to aligning homologous protein, RNA or DNA
 sequences. Much as distance-based phylogenetic reconstruction methods
 like Neighbor-Joining build a phylogeny using only pairwise divergence
 estimates, FSA builds a multiple alignment using only pairwise
 estimations of homology. This is made possible by the sequence annealing
 technique for constructing a multiple alignment from pairwise
 comparisons, developed by Ariel Schwartz.
 .
 FSA brings the high accuracies previously available only for
 small-scale analyses of proteins or RNAs to large-scale problems such as
 aligning thousands of sequences or megabase-long sequences. FSA
 introduces several novel methods for constructing better alignments:
  * FSA uses machine-learning techniques to estimate gap and
    substitution parameters on the fly for each set of input sequences.
    This "query-specific learning" alignment method makes FSA very robust:
    it can produce superior alignments of sets of homologous sequences
    which are subject to very different evolutionary constraints.
  * FSA is capable of aligning hundreds or even thousands of sequences
    using a randomized inference algorithm to reduce the computational
    cost of multiple alignment. This randomized inference can be over ten
    times faster than a direct approach with little loss of accuracy.
  * FSA can quickly align very long sequences using the "anchor
    annealing" technique for resolving anchors and projecting them with
    transitive anchoring. It then stitches together the alignment between
    the anchors using the methods described above.
  * The included GUI, MAD (Multiple Alignment Display), can display the
    intermediate alignments produced by FSA, where each character is
    colored according to the probability that it is correctly aligned

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