Difference between revisions of "GTRD"

From BioUML platform
Jump to: navigation, search
(→‎Start page)
 
(5 intermediate revisions by 3 users not shown)
Line 1: Line 1:
 
[[Category:Databases]]
 
[[Category:Databases]]
−
'''Gene Transcription Regulation Database''' ([http://gtrd.biouml.org GTRD]) is a database of transcription factor binding sites identified from ChIP-seq experiments that were systematically collected and uniformly processed using special workflow (pipeline) for BioUML platform. Raw ChIP-seq data and experiment information were collected from:
+
'''Gene Transcription Regulation Database''' ([http://gtrd.biouml.org GTRD]) is a database of transcription factor (TF) binding sites identified from ChIP-seq experiments that were systematically collected and uniformly processed using a special workflow (pipeline) for a BioUML platform (http://www.biouml.org). Raw ChIP-seq data and experiment information were collected from:
 
* literature
 
* literature
 
* [http://www.ncbi.nlm.nih.gov/geo/ GEO]
 
* [http://www.ncbi.nlm.nih.gov/geo/ GEO]
Line 6: Line 6:
 
* [http://genome.ucsc.edu/ENCODE/ ENCODE].
 
* [http://genome.ucsc.edu/ENCODE/ ENCODE].
  
−
Initial ChIP-seq and DNase-seq raw data were uniformly processed using specially developed workflow (pipeline) for BioUML platform.
+
Initial ChIP-seq and DNase-seq raw data were uniformly processed using specially developed workflow (pipeline) for the BioUML platform.
−
ChIP-seq processing pipeline:
+
ChIP-seq processing pipeline included the following steps:
−
* sequenced reads were aligned to reference genome using Bowtie2;
+
* sequenced reads were aligned to the corresponding reference genome using Bowtie2;
−
* peaks were identified using MACS, SISSR, GEM and PICS peak callers;
+
* peaks were identified using MACS2, SISSR, GEM and PICS peak callers;
 
* peaks computed for the same TF and peak calling method, but different experiment conditions (e.g., cell line, treatment, etc.) were joined into clusters;
 
* peaks computed for the same TF and peak calling method, but different experiment conditions (e.g., cell line, treatment, etc.) were joined into clusters;
 
* clusters for the same TF revealed by different peak calling methods were joined into metaclusters.
 
* clusters for the same TF revealed by different peak calling methods were joined into metaclusters.
−
DNase-seq processing pipeline:
+
DNase-seq processing pipeline included the following steps:
−
* sequenced reads were aligned to reference genome using Bowtie2;
+
* sequenced reads were aligned to the corresponding reference genome using Bowtie2;
 
* regions of open chromatin were identified using MACS2 and Hotspot2;
 
* regions of open chromatin were identified using MACS2 and Hotspot2;
−
* to reveal de novo putative protein-DNA interactions digital genomic footprinting tool Wellington was used.  
+
* de novo putative protein-DNA interactions were revealed using a digital genomic footprinting tool Wellington.  
  
 
[[GTRD_Workflow|Learn more about GTRD build process]]
 
[[GTRD_Workflow|Learn more about GTRD build process]]
Line 22: Line 22:
  
 
==Database statistics (18.06 version)==
 
==Database statistics (18.06 version)==
−
GTRD uses 17485 ChIP-seq experiments correspond to 2399 uniprot IDs. [[File:GTRD statistics-by-species3.png|thumb|ChIP-seq experiments by species]]
+
GTRD uses 17485 ChIP-seq experiments corresponding to 2399 UniProt IDs. [[File:GTRD statistics-by-species3.png|thumb|ChIP-seq experiments by species]]
  
−
Most of ChIP-seq experiments (79.2%) have corresponding control experiment. [[File:GTRD statistics-control3.png|thumb|Control experiments]]
+
Most of ChIP-seq experiments (79.2%) have corresponding control experiments. [[File:GTRD statistics-control3.png|thumb|Control experiments]]
  
 
General statistics:
 
General statistics:
Line 38: Line 38:
 
|}
 
|}
  
−
In average each TF has been measured in 7.28 ChIP-seq experiments, 52% of TFs have been measured in more than one experiment. [[File:GTRD tf-statistics-by-species3.png|thumb|Transription factors by species]]
+
In average, each TF has been measured in 7.28 ChIP-seq experiments, 52% of TFs have been measured in more than one experiment. [[File:GTRD tf-statistics-by-species3.png|thumb|Transription factors by species]]
  
−
The ten most studied transcription factors are listed bellow:
+
Ten most studied TFs are listed bellow:
  
 
{| class="wikitable"
 
{| class="wikitable"
Line 73: Line 73:
  
 
Each ChIP-seq and DNase-seq experiment has a row in 'chip_experiments' or 'dnase-experiments' table, respectively, which assigns id and stores basic information about experiment.
 
Each ChIP-seq and DNase-seq experiment has a row in 'chip_experiments' or 'dnase-experiments' table, respectively, which assigns id and stores basic information about experiment.
−
'chip_experiments' table has following structure:
+
'chip_experiments' table has the following structure:
  
 
{| class="wikitable"
 
{| class="wikitable"
Line 96: Line 96:
 
|}
 
|}
  
−
'dnase_experiments' table has following structure:
+
'dnase_experiments' table has the following structure:
  
 
{| class="wikitable"
 
{| class="wikitable"
Line 124: Line 124:
 
|}
 
|}
  
−
GTRD uses following object identifiers:
+
GTRD uses the following object identifiers:
 
{| class="wikitable"
 
{| class="wikitable"
 
|-
 
|-
Line 146: Line 146:
 
|}
 
|}
  
−
The relationship between these objects is provided by 'hub' table:
+
The relationship between these objects is provided by a 'hub' table:
 
{| class="wikitable"
 
{| class="wikitable"
 
|-
 
|-
Line 194: Line 194:
  
 
==Web interface==
 
==Web interface==
−
{{stub|screenshots}}
 
  
 
Web interface to GTRD is available [http://gtrd.biouml.org/bioumlweb/gtrd.html here].
 
Web interface to GTRD is available [http://gtrd.biouml.org/bioumlweb/gtrd.html here].
Line 210: Line 209:
 
The ChIP-seq experiments contained in GTRD can be queried from the search box on the Start page.
 
The ChIP-seq experiments contained in GTRD can be queried from the search box on the Start page.
 
GTRD uses [http://en.wikipedia.org/wiki/Lucene Lucene] engine for indexing and quering ChIP-seq experiments that provides rich [http://lucene.apache.org/core/2_9_4/queryparsersyntax.html syntax] for searching.
 
GTRD uses [http://en.wikipedia.org/wiki/Lucene Lucene] engine for indexing and quering ChIP-seq experiments that provides rich [http://lucene.apache.org/core/2_9_4/queryparsersyntax.html syntax] for searching.
−
The search can be performed by transcription factor(name or uniprot ID), cell line, antibody or treatment/conditions.
+
The search can be performed by transcription factor(name or UniProt ID), cell line, antibody or treatment/conditions.
  
−
For example, to search for STAT transcription factors enter stat* in the search field and press enter.
+
For example, to search for STAT transcription factors you should input 'stat*' in the search field and press 'Enter'.
−
You can restrict query to HeLa cells treated with interferon with following query:
+
You also can restrict the query to a certain cell line and treatment, e.g. to HeLa cells treated with interferon, using the following query:
 
   tfTitle:stat* AND cell:hela AND treatment:IFN
 
   tfTitle:stat* AND cell:hela AND treatment:IFN
 +
Similarly, it is possible to search by publication author, year or title. To search by full article title, quote it:
 +
  "Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing."
 +
To search by author or publication date, use "articles:" prefix. For example to search for experiments authored by Snyder in 2007:
 +
  articles:Snyder AND  articles:2007
  
 
The list of matching ChIP-seq experiments will appear in the 'Search result' tab.
 
The list of matching ChIP-seq experiments will appear in the 'Search result' tab.

Latest revision as of 14:13, 22 October 2019

Gene Transcription Regulation Database (GTRD) is a database of transcription factor (TF) binding sites identified from ChIP-seq experiments that were systematically collected and uniformly processed using a special workflow (pipeline) for a BioUML platform (http://www.biouml.org). Raw ChIP-seq data and experiment information were collected from:

Initial ChIP-seq and DNase-seq raw data were uniformly processed using specially developed workflow (pipeline) for the BioUML platform. ChIP-seq processing pipeline included the following steps:

  • sequenced reads were aligned to the corresponding reference genome using Bowtie2;
  • peaks were identified using MACS2, SISSR, GEM and PICS peak callers;
  • peaks computed for the same TF and peak calling method, but different experiment conditions (e.g., cell line, treatment, etc.) were joined into clusters;
  • clusters for the same TF revealed by different peak calling methods were joined into metaclusters.

DNase-seq processing pipeline included the following steps:

  • sequenced reads were aligned to the corresponding reference genome using Bowtie2;
  • regions of open chromatin were identified using MACS2 and Hotspot2;
  • de novo putative protein-DNA interactions were revealed using a digital genomic footprinting tool Wellington.

Learn more about GTRD build process

GTRD database is freely available for non-commercial organizations.

Contents

[edit] Database statistics (18.06 version)

GTRD uses 17485 ChIP-seq experiments corresponding to 2399 UniProt IDs.
ChIP-seq experiments by species
Most of ChIP-seq experiments (79.2%) have corresponding control experiments.
Control experiments

General statistics:

Object type Total count Per ChIP-seq experiment
ChIP-seq reads 719.3 × 109 41.1 × 106
Reads aligned 539.8 × 109 30.9 × 106
ChIP-seq peaks >1.1 × 109 depends on peak caller
In average, each TF has been measured in 7.28 ChIP-seq experiments, 52% of TFs have been measured in more than one experiment.
Transription factors by species

Ten most studied TFs are listed bellow:

uniprot ID Transcription Factor Species Number of ChIP-seq experiments
P49711 CTCF Homo sapiens 432
P03372 SR1 Homo sapiens 280
P10275 ANDR Homo sapiens 259
Q61164 CTCF Mus musculus 255
P17433 SPI1 Mus musculus 156
P55317 FOXA1 Homo sapiens 125
P01106 MYC Homo sapiens 122
P28033 CEBPB Mus musculus 100
Q04206 TF65 Homo sapiens 93
P04150 GCR Homo sapiens 90
GTRD contains 843 DNase-seq experiments.
DNase-seq experiments by species

[edit] Database structure

The metadata concerning GTRD is stored in MySQL tables.

Each ChIP-seq and DNase-seq experiment has a row in 'chip_experiments' or 'dnase-experiments' table, respectively, which assigns id and stores basic information about experiment. 'chip_experiments' table has the following structure:

Column Description Example value
id Unique experiment identifier EXP000489
antibody Antibody used in chromatin immunoprecipitation sc-345
specie Species latin name Homo sapiens
treatment Cell treatment or conditions IFN gamma
control_id Id of control experiment, NULL for control experiments or experiments without control EXP000490
cell_id Studied cell line ID 423
experiment_type unspecified
tf_uniprot_id Protein id in Uniprot database P42224

'dnase_experiments' table has the following structure:

Column Description Example value
id Unique experiment identifier DEXP000044
organism Species latin name Homo sapiens
treatment Cell treatment or conditions 100nM dexamethasone (CHEBI:41879) for 6 hour
cell_id Studied cell line ID 38

The links to external databases stored in 'external_refs' table:

Column Description Example values
id Experiment identifier EXP000489
external_db External database name GEO or PUBMED or ENCODE or SRA
external_db_id Identifier in external database GSM320736

GTRD uses the following object identifiers:

Template Object type Example
EXPXXXXXX ChIP-seq experiment EXP000489
READSXXXXXX Collection of ChIP-seq reads READS000770
ALIGNSXXXXXX Collection of ChIP-seq reads alignments ALIGNS010001
PEAKSXXXXXX Collection of DNase-seq peaks PEAKS010000
DEXPXXXXXX DNase-seq experiment DEXP000044
DREADSXXXXXX Collection of DNase-seq reads DREADS000232
DALIGNSXXXXXX Collection of DNase-seq reads alignments DALIGNS000044
DPEAKSXXXXXX Collection of DNase-seq peaks DPEAKS000044

The relationship between these objects is provided by a 'hub' table:

Column Description Example values
input Input object identifier READS000770
input_type Type of input object ReadsGTRDType
output Output object identifier EXP000489
output_type Type of output object ExperimentGTRDType

ChIP-seq and DNase-seq reads, alignments and peaks links to experiments with hub table in the following way:

input input_type output output_type specie
READS000770 ReadsGTRDType EXP000489 ExperimentGTRDType Homo sapiens
READS000771 ReadsGTRDType EXP000489 ExperimentGTRDType Homo sapiens
READS000772 ReadsGTRDType EXP000489 ExperimentGTRDType Homo sapiens
READS000773 ReadsGTRDType EXP000489 ExperimentGTRDType Homo sapiens
READS000774 ReadsGTRDType EXP000489 ExperimentGTRDType Homo sapiens
READS000775 ReadsGTRDType EXP000489 ExperimentGTRDType Homo sapiens
ALIGNS033052 AlignmentsGTRDType EXP000489 ExperimentGTRDType Homo sapiens
PEAKS033050 PeaksGTRDType EXP000489 ExperimentGTRDType Homo sapiens
DALIGNS000044 DNaseAlignments DEXP000044 DNaseExperiment Homo sapiens
DPEAKS000044 DNasePeaks DEXP000044 DNaseExperiment Homo sapiens
DREADS000232 DNaseReads DEXP000044 DNaseExperiment Homo sapiens
DREADS000233 DNaseReads DEXP000044 DNaseExperiment Homo sapiens
DREADS000234 DNaseReads DEXP000044 DNaseExperiment Homo sapiens
DREADS000235 DNaseReads DEXP000044 DNaseExperiment Homo sapiens

[edit] Web interface

Web interface to GTRD is available here. It provides capabilities for searching and browsing GTRD.

[edit] Start page

Start page contains search boxes and the links to browse all experiments in the repository tree, documentation, help (wiki pages) and transcription factor classification tree.

GTRD start page

[edit] Search capabilities

Search GTRD
Open ChIP-seq peaks

The ChIP-seq experiments contained in GTRD can be queried from the search box on the Start page. GTRD uses Lucene engine for indexing and quering ChIP-seq experiments that provides rich syntax for searching. The search can be performed by transcription factor(name or UniProt ID), cell line, antibody or treatment/conditions.

For example, to search for STAT transcription factors you should input 'stat*' in the search field and press 'Enter'. You also can restrict the query to a certain cell line and treatment, e.g. to HeLa cells treated with interferon, using the following query: 

 tfTitle:stat* AND cell:hela AND treatment:IFN

Similarly, it is possible to search by publication author, year or title. To search by full article title, quote it: 

 "Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing."

To search by author or publication date, use "articles:" prefix. For example to search for experiments authored by Snyder in 2007: 

 articles:Snyder AND  articles:2007

The list of matching ChIP-seq experiments will appear in the 'Search result' tab. Select one of them to view detailed ChIP-seq experiment information in the information box. The information box also provides links to the experiment data: reads, alignments and peaks.

To view peaks or alignments click the link in the ChIP-seq experiment information box, the Type-track-icon.png track will be opened as table. The Type-track-icon.png track can be exported by pressing 'Export' button or opened in the genome browser by pressing 'Open as track' button in the general control panel.

[edit] Repository structure

GTRD is organized in hierarchical Repository.

GTRD repository

The GTRD/Data folder contains following items:

  • DNase experiments - DNase-seq experiments metainformation
  • alignments - ChIP-seq read alignments
  • clusters - ChIP-seq-derived meta-clusters
  • experiments - ChIP-seq experiments metainformation
  • peaks - ChIP-seq and DNase-seq peaks identified by peak callers

GTRD/Dictionaries/classification is TFClass classification tree used by GTRD to reference transcription factors. GTRD/Dictionaries/cells is a collection of cells and tissues used by GTRD

Retrieved from "https://wiki.biouml.org/index.php?title=GTRD&oldid=8423"
Personal tools
Namespaces

Variants
Actions
BioUML platform
Community
Modelling
Analysis & Workflows
Collaborative research
Development
Virtual biology
Wiki
Toolbox