Free four levels of protein structure software for linux

XML::Structured is a simple conversion API from XML to perl structures and back.

SYNOPSIS

use XML::Structured;

$dtd = [
element =>
attribute1,
attribute2,
[],
element1,
[ element2 ],
[ element3 =>
...
],
[[ element4 =>
...
]],
];

$hashref = XMLin($dtd, $xmlstring);
$hashref = XMLinfile($dtd, $filename_or_glob);
$xmlstring = XMLout($dtd, $hashref);

The XML::Structured module provides a way to convert xml data into a predefined perl data structure and back to xml. Unlike with modules like XML::Simple it is an error if the xml data does not match the provided skeleton (the "dtd"). Another advantage is that the order of the attributes and elements is taken from the dtd when converting back to xml.

MKDoc::Text::Structured::Inline is a Perl module to convert text to HTML without handling block-level tags.

SYNOPSIS

my $text = some_structured_text();
my $this = MKDoc::Text::Structured::Inline::process ($text);
my $that = MKDoc::Text::Structured::Inline::process_entities_only ($text);

Project Foundry provides a integrated project management tool.
Project Foundry is a tool to create and maintain information used during the development of software projects.
It is oriented towards the high level management of the projects, and aims to better integrate the usual tasks of management.
In particular, one can keep track of:
- Meetings minutes
- Stakeholders
- Requirements
- Models of GUI
- Work breakdown structure (Gantt chart scheduler)
- A glossary so that important words are defined and used correctly all around the project

KnowledgeRoot is a knowledge base system that lets you structure your content in a tree.
KnowledgeRoot project integrates FCKeditor to make adding content easy. Knowledgeroot use PHP and MySQL or PostgreSQL.
Enhancements:
- This release fixes insecure upload of fckeditor.

Image::MetaData::JPEG::Structures is a Perl module that describes the structure of a JPEG file; it is an appendix to the main manual page of the Image::MetaData::JPEG module, which the reader should refer to for further details and the general scope.

THE STRUCTURE OF JPEG PICTURES

The JPEG (Joint Photographic Experts Group) ISO standard is a lossy image compression mechanism developed before 1990, designed for compressing colour or gray-scale photo-realistic images (it performs poorly on line-art drawings). The JPEG mechanism is designed to exploit known limitations of the human eye, in particular the fact that colour changes are perceived less accurately than changes in brightness.

Note that although the "baseline" JPEG format is patent-free, there are patents associated with some optional features, namely arithmetic coding and hierarchical storage. For this reason, these optional features are never used on the Web (good job, patent fans ...). JPEG refers only to a class of compression algorithms, not to a specific file format. In order to produce files with embedded JPEG streams, a number of file format (semi-)standards have been adapted or devised, some of which are listed in the format section. The structure of a well formed JPEG file can be described by the following pseudo production rules (for sake of simplicity, some additional constraints between tables and SOF segments are neglected).

JPEG --> (SOI)(misc)*(image)?(EOI)
(image) --> (hierarch.)|(non-hier.)
(hierarch.) --> (DHP)(frame)+
(frame) --> (misc)*(EXP)?(non-hier.)
(non-hier.) --> (SOF)(scan)+
(scan) --> (misc)*(SOS)(data)*(ECS)(DNL)?
(data) --> (ECS)(RST)
(misc) --> (DQT)|(DHT)|(DAC)|(DRI)|(COM)|(APP)

(SOI) = Start Of Image
(EOI) = End Of Image
(SOF) = Start Of Frame header (10 types)
(SOS) = Start Of Scan header
(ECS) = Entropy Coded Segment (row data, not a real segment)
(DNL) = Define Number of Lines segment
(DHP) = Define Hierarchical P??? segment
(EXP) = EXPansion segment
(RST) = ReSTart segment (8 types)
(DQT) = Define Quantisation Table
(DHT) = Define Huffman coding Table
(DAC) = Define Arithmetic coding Table
(DRI) = Define Restart Interval
(COM) = COMment segment
(APP) = APPlication segment

This package does not check that a JPEG file is really correct; it accepts a looser syntax, were segments and ECS blocks are just contiguous (basically, because it does not need to display the image!). All meta-data information is concentrated in the (COM*) and (APP) Segments, exception made for some records in the (SOF*) segment (e.g. image dimensions).

Reference: B< "Digital compression and coding of continuous-tone still
images: requirements and guidelines", CCITT recommend. T.81, 1992,
The International Telegraph and Telephone Consultative Committee,
standard ISO/IEC IS 10918-1 or ITU-T Recommendation T.81 >.
Also: B< "The JPEG still picture compression standard", G.K.Wallace,
1991, IEEE Transactions on Consumer Electronics >.

Folding@Gnome project is an applet is a GUI that keeps track of the background protein folding progress done by Folding@Home

The Folding@Gnome applet is merely a GUI in which you can adjust your Folding@Home settings and view the folding progress. Folding@Home is a background process that uses spare CPU cycles to calculate the folding of proteins. For more info, please visit Stanfords Folding@Home homepage.

Bio::Tools::Run::TribeMCL is a method for clustering proteins into related groups, which are termed protein families.
SYNOPSIS
use Bio::Tools::Run::TribeMCL;
use Bio::SearchIO;
# 3 methods to input the blast results
# straight forward raw blast output (NCBI or WU-BLAST)
my @params = (inputtype=>blastfile);
# OR
# markov program format
# protein_id1 protein_id2 evalue_magnitude evalue_factor
# for example:
# proteins ENSP00000257547 and ENSP00000261659
# with a blast score evalue of 1e-50
# and proteins O42187 and ENSP00000257547
# with a blast score evalue of 1e-119
# entry would be
my @array = [[qw(ENSP00000257547 ENSP00000261659 1 50)],
[qw(O42187 ENSP00000257547 1 119)]];
my @params = (pairs=>@array,I=>2.0);
# OR
# pass in a searchio object
# slowest of the 3 methods as it does more rigourous parsing
# than required for us here
my $sio = Bio::SearchIO->new(-format=>blast,
-file=>blast.out);
my @params=(inputtype=>searchio,I=>2.0);
# you can specify the path to the executable manually in the following way
my @params=(inputtype=>blastfile,I=>2.0,
mcl=>/home/shawn/software/mcl-02-150/src/shmcl/mcl,
matrix=>/home/shawn/software/mcl-02-150/src/contrib/tribe/tribe-matrix);
my $fact = Bio::Tools::Run::TribeMCL->new(@params);
# OR
$fact->matrix_executable(/home/shawn/software/mcl-02-150/src/contrib/tribe/tribe-matrix);
$fact->mcl_executable(/home/shawn/software/mcl-02-150/src/shmcl/mcl);
# to run
my $fact = Bio::Tools::Run::TribeMCL->new(@params);
# Run the program
# returns an array reference to clusters where members are the ids
# for example :2 clusters with 3 members per cluster:
# $fam = [ [mem1 mem2 mem3],[mem1 mem2 mem3]]
# pass in either the blastfile path/searchio obj/the array ref to scores
my $fam = $fact->run($sio);
# print out your clusters
for (my $i = 0; $i
print "Cluster $i t ".scalar(@{$fam->[$i]})." membersn";
foreach my $member (@{$fam->[$i]}){
print "t$membern";
}
}
This clustering is achieved by analysing similarity patterns between proteins in a given dataset, and using these patterns to assign proteins into related groups. In many cases, proteins in the same protein family will have similar functional properties.
Enhancements:
- Perl