nixpkgs/pkgs/applications/science/biology/hmmer/default.nix
Profpatsch 4a7f99d55d treewide: with stdenv.lib; in meta -> with lib;
Part of: https://github.com/NixOS/nixpkgs/issues/108938

meta = with stdenv.lib;

is a widely used pattern. We want to slowly remove
the `stdenv.lib` indirection and encourage people
to use `lib` directly. Thus let’s start with the meta
field.

This used a rewriting script to mostly automatically
replace all occurances of this pattern, and add the
`lib` argument to the package header if it doesn’t
exist yet.

The script in its current form is available at
https://cs.tvl.fyi/depot@2f807d7f141068d2d60676a89213eaa5353ca6e0/-/blob/users/Profpatsch/nixpkgs-rewriter/default.nix
2021-01-11 10:38:22 +01:00

26 lines
1.6 KiB
Nix

{ lib, stdenv, fetchurl }:
stdenv.mkDerivation rec {
version = "3.3.2";
pname = "hmmer";
src = fetchurl {
url = "http://eddylab.org/software/hmmer/${pname}-${version}.tar.gz";
sha256 = "0s9wf6n0qanbx8qs6igfl3vyjikwbrvh4d9d6mv54yp3xysykzlj";
};
meta = with lib; {
description = "Biosequence analysis using profile hidden Markov models";
longDescription = ''
HMMER is used for searching sequence databases for sequence homologs, and for making sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs).
HMMER is often used together with a profile database, such as Pfam or many of the databases that participate in Interpro. But HMMER can also work with query sequences, not just profiles, just like BLAST. For example, you can search a protein query sequence against a database with phmmer, or do an iterative search with jackhmmer.
HMMER is designed to detect remote homologs as sensitively as possible, relying on the strength of its underlying probability models. In the past, this strength came at significant computational expense, but as of the new HMMER3 project, HMMER is now essentially as fast as BLAST.
HMMER can be downloaded and installed as a command line tool on your own hardware, and now it is also more widely accessible to the scientific community via new search servers at the European Bioinformatics Institute.
'';
homepage = "http://hmmer.org/";
license = licenses.gpl3;
maintainers = [ maintainers.iimog ];
platforms = platforms.unix;
};
}