GBrowse Tutorial

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{{#icon: 2010SummerSchoolAmericas300.png|2010 GMOD Summer School - Americas 2010 GMOD Summer School - Americas}} GBrowse Session

2010 GMOD Summer School - Americas
May 6-9, 2010
Scott Cain

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There are several GBrowse tutorials:
GBrowse User Tutorial at OpenHelix
Demonstrates the GBrowse user interface.
GBrowse2 Admin Tutorial
Step by step guide on how to configure and load data into GBrowse. Administration tutorials are available for both the GBrowse2 Admin Tutorial, and the earlier 1.x versions.
GBrowse NGS Tutorial
Instructions on how to visualize next generation sequencing data in GBrowse using SAMtools. The tutorial includes a starting VMware image, and uses the example data that comes with SAMtools.
This tutorial
This tutorial was originally taught by Scott Cain at the 2010 GMOD Summer School - Americas. It walks you through setting up and running GBrowse with some sample data. It provides a VMware image to work on, and relies heavily on the GBrowse2 Admin Tutorial.



VMware

This tutorial was taught using a VMware system image as a starting point. If you want to start with that same system, download and install the Starting image.

See VMware for what software you need to use a VMware system image, and for directions on how to get the image setup and running on your machine.

Download
Starting Image

Ending Image

Logins
Purpose Username Password
Shell gmod gmodamericas2010
MySQL root gmodamericas2010


Caveats

Important Note

This tutorial describes the world as it existed on the day the tutorial was given. Please be aware that things like CPAN modules, Java libraries, and Linux packages change over time, and that the instructions in the tutorial will slowly drift over time. Newer versions of tutorials will be posted as they become available.

Prerequisites

Installed before using apt or cpan.

Install GBrowse

Easily installed via the cpan shell:

 sudo cpan
 cpan> install Bio::Graphics::Browser2

Which gets all of the prereqs that aren't installed on the machine.

Tutorial

Go to http://localhost/gbrowse



Basic Chado Configuration (if we have time)

Bio::DB::Das::Chado was installed when we created the image, but I've since released a new version, so we can install the new version with the cpan shell:

 sudo cpan
 cpan> install Bio::DB::Das::Chado</enter>

Simple config file in /etc/gbrowse2/pythium.conf

Some simple tweaks and additions:

  • fix the dbi string
  • add nucleotide matches
  • strip out stuff that is in /etc/gbrowse2/GBrowse.conf

Materialized views for searching

Chado comes with a tool to materialize views written by developers at the SOL Genomics Network (SGN). A materialized view is faster (at the expense of more disk space) to search than a regular view (which is really a query over potentially several tables). To create a materialized view that makes searching a GBrowse Chado instance a faster, we can do this:

 gmod_materialized_view_tool.pl -c

which will ask us several obscure questions for which we need to provide obscure answers:

 Give your materialized view a name (word characters only):
 all_feature_names
 Where will this MV be located? (schemaname.tablename):
 public.all_feature_names
 A view with this name already exists; do you want to replace it
 with a materialized view? [y|n]
 y
 How often, in seconds, should the MV be refreshed?
 You can also type 'daily', 'weekly', 'monthly' (30 days), or 'yearly' (365 days):
 weekly
 Enter specifications for the materialized view, OR provide a file in which
 the specs are written ('? for help):
 feature_id integer,name varchar(255)
 Enter the SQL query for the materialized view,
 or a file containing only the query:
 SELECT feature_id,CAST(substring(uniquename from 0 for 255) as varchar(255)) as name FROM feature UNION SELECT feature_id, name FROM feature where name is not null UNION SELECT fs.feature_id,s.name FROM feature_synonym fs, synonym s WHERE fs.synonym_id = s.synonym_id
 Enter a comma separated list of fields to index (or return for none):
 feature_id,name
 Enter the SQL queries for special indexes,
 or a file containing only the query (or return for none):
 create index all_feature_names_lower_name on all_feature_names (lower(name))
 Enter 'y' to confirm, 'n' to re-enter data:
 y