基因功能註解工具 - PowerPoint Presentation

Slide1

基因功能註解工具

:DAVIDSlide2
Slide3

D

atabase for

A

nnotation,

V

isualization and

I

ntegrated Discovery (DAVID )

Functional Annotation Tool

Gene Ontology

Protein interaction

Protein domain

Pathway

Disease

Gene ID

Conversion

Gene Functional ClassificationSlide4

上傳基因列表到網站

DAVID

操作流程

Gene Name Batch Viewer

Gene Functional Classification

Functional Annotation Tool

選定

類別以進行分析

取得結果Slide5

上傳基因列表

AFFYMETRIX_3PRIME_IVT_ID

AFFYMETRIX_EXON_GENE_ID

AFFYMETRIX_SNP_ID

AGILENT_CHIP_ID

AGILENT_ID

AGILENT_OLIGO_ID

ENSEMBL_GENE_IDENSEMBL_TRANSCRIPT_IDENTREZ_GENE_IDFLYBASE_GENE_IDFLYBASE_TRANSCRIPT_ID

GENBANK_ACCESSION

GENOMIC_GI_ACCESSION

GENPEPT_ACCESSION

ILLUMINA_ID

IPI_ID

MGI_ID

OFFICIAL_GENE_SYMBOL

PFAM_ID

PIR_ID

PROTEIN_GI_ACCESSION

REFSEQ_GENOMIC

REFSEQ_MRNA

REFSEQ_PROTEIN

REFSEQ_RNA

RGD_ID

SGD_ID

TAIR_ID

UCSC_GENE_ID

UNIGENE

UNIPROT_ACCESSIONUNIPROT_IDUNIREF100_IDWORMBASE_GENE_IDWORMPEP_IDZFIN_IDNot SureSlide6

1.

確定物種

2.

選定後使用

3.Slide7

Functional Annotation Tool

DAVID Gene ID:

It is an internal ID generated on "DAVID Gene Concept"  in DAVID system. One DAVID gene ID represents one unique gene cluster belonging to one single gene entry

.

Input Gene list : 817

Map to David Database : 754

David IDs : 734

1.

Genes from your list involved in this annotation categories

2

.

4

.

Single chart report only for this annotation categories.

3.

99 / 734Slide8

Functional Annotation

Chart

Chart Report is an annotation-term-focused view which lists annotation terms and their associated genes under study. To avoid over counting duplicated genes, the Fisher Exact statistics is calculated based on corresponding DAVID gene IDs by which all redundancies in original IDs are removed. All result of Chart Report has to pass the thresholds (by default,

Max.Prob

.<=0.1 and

Min.Count

>=2)  in Chart Option section to ensure only statistically significant ones displayed.

Functional Annotation Charta modified Fisher Exact P-Value (

EASE Score

)

每頁可顯示多少結果

List

Total(LT)

- number of genes in the gene list mapping to the

category

of which the term is a

member

Population Hits(PH)

- number of genes in the background gene list mapping to a specific

term

Population Total(PT)

- number of genes in the background gene list mapping to the

category

RT (Related

Term)

Related Term Search can identify other similar termsSlide9

RT (Related Term)

Any given gene is associating with a set of annotation terms. If genes share similar set of those terms, they are most likely involved in similar biological mechanisms. The algorithm adopts

kappa statistics to quantitatively measure the degree of the agreement how genes share the similar annotation terms

. Kappa result ranges from 0 to 1. The higher the value of Kappa, the stronger the agreement.

Any a biological process/term coming from all functional categories listed in

DAVID.Slide10

COG_ONTOLOGY

refers to an ontology from NCBI's COG

database

The

database of Clusters of Orthologous Groups of proteins (COGs): a tool for

genome-scale

analysis of protein functions and

evolutionSP_PIR_KEYWORDS are keywords defined by the SwissProt/Uniprot and PIR (Protein Information Resource) UP_SEQ_FEATURE refers to the annotation category, Uniprot Sequence Feature, found at the Uniprot site, within their report.

Annotation Category - Functional CategoriesSlide11

Annotation Category – Protein domain & Protein Interaction

Protein structureSlide12

GOTerms

are categorized into 3 groups:

BP

- Biological Process

MF

- Molecular Function

CC

- Cellular ComponentGOTERM_BP_1 -> GO term under Biological Process (BP) in the Level 1. GOTERM_BP_ALL -> GO term under Biological Process (BP) in the ALL possible Levels.GOTERM_BP_FAT - Basically this test exams the significance of enriched annotation(GO FAT) filters out very broad GO terms based on a measured specificity of each term (not level-specificity)

Annotation Category - Gene OntologySlide13

Annotation Category-Pathways

Biocarta

KEGGSlide14

挑選

11

項

C

ategories

總共

11

項

C

ategories

Combined View AnnotationSlide15

Functional

Annotation Cluster

Functional Annotation

Clustering

Due to the redundant nature of annotations, Functional Annotation Chart presents similar/relevant annotations repeatedly. It dilutes the focus of the biology in the report.  To reduce the redundancy, the newly developed

Functional Annotation Clustering report groups/displays similar annotations together which makes the biology clearer and more focused to be read vs. traditional chart report

. The Functional Annotation Clustering integrates the same techniques of  Kappa statistics to measure the degree of the common genes between two annotations, and  fuzzy heuristic clustering to classify the groups of similar annotations according kappa values. All gene involved in this annotation cluster

Ease score (modified fisher exact test)

Heat map

調整

Kappa

statistics

的參數

調整

fuzzy heuristic clustering

的參數

P_valueSlide16

Enrichment Score = [ -log(

P_value

1) + -log(

P_value

2) + -log(

P_value

N) ] / n

Initial Group Members (any value >=2; default = 4): the minimum gene number in a seeding group, which affects the minimum size of each functional group in the final. In general, the lower value attempts to include more genes in functional groups, particularly generates a lot small size groups. Final Group Members (any value >=2; default = 4): the minimum gene number in one final group after “cleanup” procedure. In general, the lower value attempts to include more genes in functional groups, particularly generates a lot small size groups. It co-functions with previous parameters to control the minimum size of functional groups. In the final cluster, the number of terms that a cluster must have to be presented in the output.Multi-linkage Threshold (any value between 0% to 100%; default = 50%): It controls how seeding groups merge each other, i.e. two groups sharing the same gene members over the percentage will become one group. The higher percentage, in general, gives sharper separation

i.e. it generates more final functional groups with more tightly associated genes in each group. In addition, changing the parameter does not contribute extra genes into

unclustered

group. Slide17

If

you run both functions with

defualt

setting, they will not be totally overlapped. In general, clustering result may contain more result than chart. In clustering, some 'non-significant' terms could be included due to the link of their 'significant' neigthbors

(co-members in on cluster

).

If

you want to completely cross link the two reports, you should run chart report by setting p-value cutoff to "1" (ground level). Thus, you will have all possible terms with significant or insignificant p-values.Chart vs ClusterSlide18

上傳基因列表到網站

Gene Name Batch Viewer

Gene Functional Classification

Functional Annotation Tool

選定

類別以進行分析

取得結果Slide19

Another Tools in DAVIDSlide20

Gene Name Batch ViewerSlide21

Gene Functional Classification Tool

Term reportSlide22

Gene Functional Classification Tool - Create

sublistSlide23

Gene ID Conversion ToolSlide24

Thank you for your attention