New resources for functional analysis of omics data for the genus Aspergillus [original]

Benjamin M. Nitsche, Jonathan Crabtree, Gustavo C. Cerqueira, Vera Meyer,

Arthur F.J. Ram, Jennifer R. Wortman

New resources for functional analysis of

omics data for the genus Aspergillus

Article, Published version

This version is available at http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-70178.

Suggested Citation

Nitsche, Benjamin M. ; Crabtree, Jonathan ; Cerqueira, Gustavo C. ; Meyer, Vera ; Ram, Arthur F.J. ;

Wortman, Jennifer R. : New resources for functional analysis of omics data for the genus Aspergillus. -

In: BMC Genomics. - ISSN 1471-2164 (online). - 12 (2011), art. 486. - doi:10.1186/1471-2164-12-486.

This work is licensed under a CC BY 2.0 License (Creative

Commons Attribution 2.0 Generic). For more information see

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RESEARCH ARTICLE Open Access

New resources for functional analysis of omics

data for the genus Aspergillus

Benjamin M Nitsche

, Jonathan Crabtree

, Gustavo C Cerqueira

, Vera Meyer

1,4,5

, Arthur FJ Ram

1,4

and

Jennifer R Wortman

Abstract

Background: Detailed and comprehensive genome annotation can be considered a prerequisite for effective

analysis and interpretation of omics data. As such, Gene Ontology (GO) annotation has become a well accepted

framework for functional annotation. The genus Aspergillus comprises fungal species that are important model

organisms, plant and human pathogens as well as industrial workhorses. However, GO annotation based on both

computational predictions and extended manual curation has so far only been available for one of its species,

namely A. nidulans.

Results: Based on protein homology, we mapped 97% of the 3,498 GO annotated A. nidulans genes to at least

one of seven other Aspergillus species: A. niger,A. fumigatus,A. flavus,A. clavatus,A. terreus,A. oryzae and

Neosartorya fischeri. GO annotation files compatible with diverse publicly available tools have been generated and

deposited online. To further improve their accessibility, we developed a web application for GO enrichment

analysis named FetGOat and integrated GO annotations for all Aspergillus species with public genome sequences.

Both the annotation files and the web application FetGOat are accessible via the Broad Institute’s website (http://

www.broadinstitute.org/fetgoat/index.html). To demonstrate the value of those new resources for functional

analysis of omics data for the genus Aspergillus, we performed two case studies analyzing microarray data recently

published for A. nidulans,A. niger and A. oryzae.

Conclusions: We mapped A. nidulans GO annotation to seven other Aspergilli. By depositing the newly mapped

GO annotation online as well as integrating it into the web tool FetGOat, we provide new, valuable and easily

accessible resources for omics data analysis and interpretation for the genus Aspergillus. Furthermore, we have

given a general example of how a well annotated genome can help improving GO annotation of related species

to subsequently facilitate the interpretation of omics data.

Background

Gene Ontology (GO) is a framework for functional

annotation of gene products aiming to provide a unique

vocabulary for living systems [1]. It comprises Biological

Process (BP), Molecular Function (MF) and Cellular

Component (CC) ontologies. GO terms are organized as

directed acyclic graphs (DAG) meaning that GO terms

are connected as nodes by directed edges defining hier-

archical parent-child relationships. As a consequence,

the specificity of GO terms increases with increasing

distance from their root node. Enrichment analysis of

GO terms is a well accepted approach to dissecting

omics data in a non-biased manner. It has been used in

many studies to highlight major trends in genomic, tran-

scriptomic or proteomic datasets and describe them

with a controlled vocabulary [2-5]. If the frequency of

specific GO terms in a list of genes or proteins is higher

than expected by chance, it is likely that these enriched

GO terms are related to the biological processes under

investigation.

The genus Aspergillus covers a group of filamentous

fungi that includes saprophytes, human and plant patho-

gens as well as species being exploited in biotechnology.

Whereas A. nidulans has been comprehensively studied

andusedasmodelorganism,A. niger,A. oryzae and A.

terreus are important industrial workhorses for the

* Correspondence: bmnitsche@gmail.com

Institute of Biology Leiden, Leiden University, Sylviusweg 72, 2333 BE

Leiden, The Netherlands

Full list of author information is available at the end of the article

Nitsche et al.BMC Genomics 2011, 12:486

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Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in

any medium, provided the original work is properly cited.

production of various enzymes and organic acids. In

medical research, A. fumigatus and Neosartorya fischeri

are intensively studied because of their importance as

allergens and pathogens of immunocompromised

patients. The aflatoxin producing fungus A. flavus is

well known to cause spoilage of a great variety of agri-

cultural goods. With genome sequences publicly avail-

able for eight of its species, the genus Aspergillus

provides an important group of related fungal species

for comparative genomics [6]. The exceptional role of

this genus in the genomics of filamentous fungi is

further emphasized by a community sequencing project

(CSP#350), which has recently been initiated by the

DOE Joint Genome Institute (JGI), aiming to sequence

nine additional Aspergillus species. However, despite the

importance of the genus Aspergillus, A. nidulans has so

far been the only species with a genome-scale GO anno-

tation inferred from both orthology mapping and

intense manual curation [7-9], thus providing a valuable

resource for the analysis of omics data.

In this work, we have generated a new central reposi-

tory for functional analysis of omics data for the genus

Aspergillus using GO annotation. Firstly, we extended

the GO annotation of A. nidulans to all Aspergillus spe-

cies with publicly available genome sequences and gen-

erated annotation files compatible with diverse publicly

available tools for GO enrichment analysis. Secondly, we

further improved the accessibility of the GO annotation

for the genus Aspergillus by integrating it into a web

tool for GO enrichment analysis and graph visualization

named Fisher’s exact test Gene Ontology annotation

tool (FetGOat). Finally, we performed two case studies

to demonstrate the value and flexibility of the newly

generated resources for functional analysis of omics data

for the genus Aspergillus.

Results

Mapping of GO annotation

A. nidulans is the only Aspergillus species for which

comprehensive GO annotation based on both computa-

tional prediction and extended manual curation of gene-

specific literature is available [9]. It constitutes a valu-

able resource for GO enrichment analysis, which has

proven to be a powerful tool for dissecting omics data,

for example sets of differentially expressed genes. The

GO annotation of A. nidulans available at the Aspergil-

lus Genome Database (AspGD) [9] covers 33% (3,498)

of its predicted transcripts and associates them with

3,340 GO terms. Including all parental nodes, the list of

GO terms extends to 5,508 comprising 3,061 (55%) BP,

1,753 MF (32%) and 694 (13%) CC terms.

To extend this valuable resource to other species of its

genus, we mapped the A. nidulans GO annotation to all

Aspergillus strains with published genome sequences

(see Table 1). Groups of orthologous and close paralo-

gous proteins were compiled with the Sybil comparative

analysis package [10], which applies a modified recipro-

cal best-hit approach comprising two clustering cycles.

Roughly 89% (99,679) of all predicted proteins from the

ten analyzed Aspergillus strains constituted 13,179 Jac-

card orthologous clusters. For A. nidulans, 9,250 of its

predicted proteins were organized in Jaccard ortholo-

gous clusters, meaning that roughly 80% of all A. nidu-

lans proteins were linked to at least one ortholog of

another Aspergillus species. Of the 3,498 GO annotated

A. nidulans genes, 97% were contained in Jaccard ortho-

logous clusters, meaning that their associated annota-

tions could be mapped to at least one other Aspergillus

species (see Figure 1). Overall, mapping resulted in an

average of 3,484 GO annotated transcripts per genome

ranging from 3,403 (A. clavatus) to 3,574 (A. flavus). On

average, their GO annotations comprise 5,436 terms,

(see Table 1). These numbers correspond well to the

GO annotation of A. nidulans and indicate that the

majority (97%) of the A. nidulans GO annotated genes

could be efficiently mapped to the other Aspergilli.

Availability of GO resources for the genus Aspergillus

The newly mapped GO annotations were deposited at

the Broad Institute’s website (http://www.broadinstitute.

org/fetgoat/index.html). Different annotation file formats

were generated that can be used with diverse public

tools for GO enrichment analysis, such as: the Gene Set

Enrichment Analysis tool (GSEA) [11], the functional

annotation suite Blast2GO [12], the Cytoscape plug-in

BiNGO [13] and the Bioconductor package TopGO

[14]. To further improve its accessibility, we have imple-

mented Fisher’s exact test [15], a well-accepted

approach for GO enrichment analysis, in the web appli-

cation FetGOat and integrated the newly mapped GO

annotations. FetGOat can be accessed via a web

Table 1 Mapping of A. nidulans GO annotation

Transcripts

Species Strain Predicted GO annotated (%) GO terms

A. nidulans FGSC A4 10546 3498 (33) 5508

A. fumigatus AF2937 9846 3443 (35) 5445

A. fumigatus A1163 10109 3450 (34) 5446

A. flavus NRRL 3357 13487 3574 (26) 5463

A. niger CBS 513.88 14366 3540 (25) 5430

A. niger ATCC 1015 11200 3487 (31) 5412

A. oryzae RIB40 12319 3502 (28) 5434

A. terreus NIH 2624 10402 3414 (33) 5406

A. clavatus NRRL 1 9379 3403 (36) 5449

N. fischeri NRRL 181 10728 3543 (33) 5445

Summary of mapping A. nidulans GO annotation to seven other Aspergilli. The

number of predicted transcripts were obtained from the Central Aspergillus

Data REpository (CADRE) [40].

Nitsche et al.BMC Genomics 2011, 12:486

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interface at the Broad Institute’s website (http://www.

broadinstitute.org/fetgoat/index.html). It combines GO

annotations for all Aspergillus species with public gen-

ome sequences and a widely used statistical methodol-

ogy to identify overrepresented GO terms. Via the web

interface, a list of gene identifiers can be uploaded to

the server and statistical parameters can easily be

adjusted with end-user computational skills. After com-

pletion of the analysis on the server-side, the enrich-

ment results are sent by Email. The results consist of

plain text and spreadsheet files as well as scalable vector

graphics representing graphs of enriched GO terms.

Case studies

To demonstrate the flexibility and value of the newly

generated resources for omics data analysis, we per-

formed two case studies analyzing transcriptomic data-

sets recently published for the genus Aspergillus.Inthe

first case study, we demonstrate that the generated

resources can be used with various methods for enrich-

ment analysis. We analyze a set of maltose-induced

genes from A. niger using FetGOat and two alternative

tools for enrichment analysis to subsequently compare

their results. In the second case study, we highlight the

advantage of having GO annotations that are as

comprehensive as possible available for different species.

We use FetGOat to analyze sets of glycerol-induced

genes derived from a three-species microarray study to

highlight major differences in the transcriptional

responses for A. nidulans,A. niger and A. oryzae.

Maltose-induced genes

The first dataset reflects the transcriptomic responses of

A. niger to growth in maltose and xylose-limited chemo-

stat cultures at identical growth rates. From manual

analysis of roughly 700 upregulated genes, Jørgensen et

al. [16] concluded a concerted induction of secretory

pathway genes in maltose compared to xylose-limited

cultures.

Using three alternative approaches, we repeated the

analysis of the maltose induced genes in an automated

and un-biased manner to subsequently compare their

enrichment results. First, we performed the analysis

using the web application FetGOat. We identified 73

enriched GO terms, which were reduced to 19 most-

specific GO terms by removing redundant higher hierar-

chy terms with less detailed annotations. In correspon-

dence to the findings by Jørgensen et al., the enriched

GO terms are related to important steps involved in

protein secretion: Translocation to the endoplasmic reti-

culum, glycosylation and transport between the endo-

plasmic reticulum and the Golgi apparatus (see Table 2).

For comparison of FetGOat with alternative programs,

we used the generated annotation files and repeated the

enrichment analysis with two publicly available tools,

Blast2GO [12] and GSEA [11]. The numbers of enriched

GO terms found with Blast2GO and GSEA are in the

same range compared to the results from FetGOat, they

identified 76 and 47 enriched GO terms, respectively. To

compare the enrichment results from the three tools, we

computed semantic similarity scores with the G-SESAME

tool [17]. For both FetGOat and Blast2GO, the enrich-

ment statistic is based on Fisher’s exact test and thus

their results are theoretically expected to be identical

resulting in a semantic similarity score of 1. A similarity

score of 0.983 confirms that their results are virtually

identical, with minor differences that are likely due to dif-

ferences in their implementations. In contrast to FetGOat

(and Blast2GO), the GSEA results are based on running-

sum statistics computed from the complete expression

data set. Therefore, the similarity between their results

can be expected to be less. Accordingly, G-SESAME

determined a smaller semantic similarity score of 0.863

for the results obtained with FetGOat and the GSEA tool.

In addition to the GO terms identified by both Fish-

er’s exact test based tools, GSEA computed an enrich-

ment of GO terms related to oxidative phosphorylation

(GO:0006119), carbohydrate transport (GO:0008643)

and glucosidase activity (GO:0015926). Comparing mal-

tose to xylose limitation, an enrichment of those GO

ll transcripts

(10546)

In Jaccard cluster

(9250)

GO annotated

(3498)

5845

3405

1203

Figure 1 Mapping A. nidulans GO annotation to Jaccard

orthologous clusters. Area-proportional Venn diagram [39]

showing fractions of all A. nidulans transcripts (red) annotated by

GO (green) and/or associated with Jaccard orthologous protein

clusters (blue). The intersection of all circles (gray), comprising 3405

transcripts, was used to map A. nidulans GO annotation to seven

other Aspergillus species.

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terms fits our expectations. Under maltose-limitation, A.

niger breaks down the disaccharide into its monomer

glucosebyenzymeshavingglucosidase activity. Subse-

quently, glucose is taken up by carbohydrate transpor-

ters, which can be expected to be different from those

required for the uptake of xylose. Finally, 1 mole of glu-

cose yields more ATP than 1 mole of xylose, thereby

explaining an induction of oxidative phosphorylation.

These differences in the enrichment results are poten-

tially inherited by the statistics applied by Jørgensen et

al. to define the set of maltose-induced genes. In con-

trast to the GSEA tool, which analyzes the complete

expression data, FetGOat and Blast2GO are depending

on a-priori performed statistics that were applied to

generate subsets of genes or proteins of interest.

Jørgensen et al. used the Affymetrix MAS 5.0 algorithm

for data pre-processing in combination with the stu-

dent’s t-test to define their set of maltose induced

genes. In current literature, this approach is critically

discussed [18,19]. To assess the effect of those a-priori

applied statistics on the differences between the results

from FetGOat and the GSEA tool, we generated an

alternative set of maltose-induced genes. We computed

RMA expression data [18] from the raw data (CEL files)

and subsequently applied a moderated t-statistic [20] to

identify upregulated genes (data not shown). Interest-

ingly, FetGOat also identified enriched GO terms related

to glucosidase activity and carbohydrate transport for

this alternative set of maltose-induced genes. However,

no enrichment of genes related to oxidative phosphory-

lation was found. Genes annotated with the GO term

oxidative phosphorylation were only marginally induced

and their FDR values were rather high (data not shown).

Interestingly, similar differences between Fisher’sexact

test based methods and the GSEA tool were reported in

another study. In muscle tissue from diabetics, the

GSEA tool identified a joint downregulation of genes

related to oxidative phosphorylation compared to

healthy controls, while no enrichment was found in the

set of downregulated genes [21]. For tightly regulated

essential cellular processes that show only minor fold

changes, the GSEA tool seems to be superior to gene-

by-gene differential expression studies.

Glycerol-induced genes

In the second case study, we used FetGOat to analyze

transcriptomic data generated by Salazar et al. [22].

Table 2 FetGOat enrichment analysis of maltose-induced genes

Transcripts

GO term Description FDR Ontology Induced Predicted

Translocation to ER

GO:0031204 posttranslational protein targeting 2.7E-04 BP 6 6

GO:0031207 Sec62/Sec63 complex 6.1E-03 CC 3 3

GO:0005787 signal peptidase complex 6.1E-03 CC 3 3

GO:0006616 SRP-dependent cotranslational protein targeting 1.7E-02 BP 4 5

GO:0051605 protein maturation by peptide bond cleavage 1.6E-02 BP 5 8

Glycosylation in ER

GO:0005788 endoplasmic reticulum lumen 3.3E-03 CC 4 5

GO:0008250 oligosaccharyltransferase complex 6.9E-03 CC 4 6

GO:0006487 protein amino acid N-linked glycosylation 4.2E-02 BP 8 24

GO:0016758 transferase activity, transferring hexosyl groups 3.4E-02 MF 14 54

Transport between ER and golgi

GO:0030126 COPI vesicle coat 1.3E-02 CC 4 7

GO:0030127 COPII vesicle coat 6.9E-03 CC 4 6

GO:0006888 ER to Golgi vesicle-mediated transport 4.8E-03 BP 22 92

GO:0030173 integral to Golgi membrane 12.0E-02 CC 5 12

Starch metabolism

GO:0005982 starch metabolic process 4.2E-02 BP 5 10

Miscellaneous

GO:0006066 alcohol metabolic process 4.2E-02 BP 33 199

GO:0003756 protein disulfide isomerase activity 8.2E-03 MF 4 4

GO:0006083 acetate metabolic process 4.2E-02 BP 7 19

GO:0015812 gamma-aminobutyric acid transport 4.2E-02 BP 6 14

GO:0015935 small ribosomal subunit 5.6E-03 CC 12 44

Most specific GO terms identified by FetGOat as being enriched in the maltose-induced gene set. GO terms were grouped in five arbitrary categories:

Translocation into endoplasmic reticulum (ER), glycosylation in ER, transport between ER and golgi, starch metabolism and miscellaneous.

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