Positional clustering of differentially expressed genes on human chromosomes 20, 21 and 22

gb-2003-4-2-p1 GBJ Deposited research article Positional clustering of differentially expressed genes on human chromosomes 20, 21 and 22 Mégy Karine Audic Stéphane Claverie Jean-Michel Jean-Michel.Claverie@igs.cnrs-mrs.fr

Genomic and Structural Information, UMR 1998 CNRS / Aventis, 31, chemin Joseph Aiguier, 13402 Marseille Cedex 20, France

Genome Biology 1465-6906 2003 4 2 P1 http://genomebiology.com/2003/4/2/P1 This was the first version of this article to be made available publicly. This article was submitted to Genome Biology for peer reiew. 10.1186/gb-2003-4-2-p1 12620117 6 1 2003 10 1 2003 2003 BioMed Central Ltd

The relationship between gene co-expression and chromosomal co-localization in the human genome is expolored by analyzing the expression status of the genes along the best-annotated chromosomes 20, 21 and 22.

Abstract

Background

Clusters of genes co-expressed are known in prokaryotes (operons) and were recently described in several eukaryote organisms, including Human. According to some studies, these clusters consist of housekeeping genes, whereas other studies suggest that these clustered genes exhibit similar tissue specificity. Here we further explore the relationship between co-expression and chromosomal co-localization in the human genome by analyzing the expression status of the genes along the best-annotated chromosomes 20, 21 and 22.

Methods

Gene expression levels were estimated according to their publicly available ESTs and gene differential expressions were assessed using a previously described and validated statistical test. Gene sequences for chromosomes 20, 21 and 22 were taken from the Ensembl annotation.

Results

We identified clusters of genes specifically expressed in similar tissues along chromosomes 20, 21 and 22. These co-expression clusters occurred more frequently than expected by chance and may thus be biologically significant.

Conclusion

The co-expression of co-localized genes might be due to higher chromatin structures influencing the gene availability for transcription in a given tissue or cell type.

Genome studies Bioinformatics Molecular biology

Background

Since the publication of two "complete" first drafts of the human genome [1, 2], a huge continuing effort is being made to annotate the human genome. Whereas some regions remain poorly annotated, the exact positions of most - protein coding - genes are now defined. This allows the systematic analysis of the influence of the position of genes on various of their properties, such as their expression level and tissue distribution. The positional clustering of co-expressed genes is common in prokaryotes (operons) and was recently described in Saccharomyces cerevisiae [3], in Caenorabditis elegans [4, 5] and in Drosophila melanogaster [6, 7]. Throughout the human genome, it is often supposed that genes are randomly distributed, except for tandem duplicates. However, clusters of highly expressed genes were recently revealed in the Human genome [8, 9]. To date, no clear functional relationships between genes in these clusters have been identified and their biological meaning, if any, is yet to be determined.

Two studies were carried out on the expression level of sets of co-localized human genes. Caron et al. [8] analyzed the gene expression profiles for any chromosomal regions in various tissue types (Human Transcriptome Map). The genes studied corresponded to about 24,000 UniGene clusters and expression levels were estimated from 12 SAGE libraries made in different conditions. This study revealed about 50 large regions, called RIDGEs (Region of IncreaseD Gene Expression), showing a clustering of highly expressed genes. A similar study by Lercher et al. [9] (based on 11,000 UniGene clusters and 14 SAGE libraries) suggested that such RIDGEs might mostly consist of housekeeping genes and no clusters of genes with similar tissue expression profiles were identified.

In order to specifically analyze tissue specific expression, other studies were based on sets of genes expressed in a given tissue. Gabrielsson et al. [10] performed a micro-array analysis of genes expressed in the adipose tissue. Mapping these genes back on the human genome, revealed clusters of adipose tissue specific genes on chromosomes 11, 19 and 22. Using ESTs, Dempsey et al. [11] focused on genes from chromosomes 21 and 22 expressed in the cardio-vascular system (CVS). They showed some chromosomal clustering of these genes. Bortoluzi et al. [12] performed a similar study on genes expressed in the skeletal muscle. They identified positional clusters of skeletal muscle genes on chromosomes 17, 19 and X. Finally, an EST analysis of the murine placenta by Ko et al. [13] identified clusters of placenta specific genes on chromosomes 2, 7, 9 and 17. Overall, these studies suggest that clusters of tissue specific genes do exist, and might be more frequent than initially thought.

Previous studies were based on the whole set of genes expressed in a particular tissue, irrespective of the behavior of these genes in other tissues. In order to evaluate the clustering of genes specifically expressed in any tissue - not specified in advance -, we performed a comprehensive analyzed of the expression profiles of all genes identified along human chromosomes 20, 21 and 22. These chromosomes were chosen as the most complete and best annotated available human chromosomes. For each gene, we first estimated the expression level in various tissues from the public EST database and then computed the probability of differential expression in each tissue. We then compared these probabilities with those calculated for the neighboring genes and looked for a succession of genes specifically over-expressed (SOGs) in a given tissue. This procedure revealed more of such clusters than expected at random.

Results

Relationship between Gene Expression and Tissue type

The following analyses were based on the number of specifically expressed genes (SEGs) in each tissue category and on chromosomes 20, 21 and 22 (using a p-value > 0.90). Tissue categories were pooled in three groups according to their origin: a diseased group (DIZ), a healthy and infant group (INF), and a healthy and adult group (ADLT).

Chromosome analysis. On each chromosome, 80% of the genes were found differentially expressed in at least one tissue category. The same proportion was found by Su et al. [14] in an analysis of the human transcriptome map. The remaining 20% represents genes ubiquitously expressed (i.e. housekeeping genes), or weakly expressed genes. The expression level of such genes - represented by low EST numbers - cannot be reliably estimated nor their differential expression status.

Genes with erratic expression levels. The number of tissue types associated with significant differential expression (p > 0.90) was estimated for each gene. We noticed that some genes were statistically identified as "differentially expressed" in more than 50% of the tissue types (Table 2). Our statistical test is performed by comparing the number of cognate ESTs found for each library type to the number found for all other library types aggregated as one virtual "average" tissue. With this procedure, genes exhibiting expression levels fluctuating highly above or below the average (over all the other tissues), may appear significantly differentially over- or under- expressed in numerous libraries. The genes we found exhibiting this erractic behavior were all highly expressed, corresponding to a large number of ESTs such as ribosomal proteins, known to be found in all tissues. This strongly suggests that the erratic EST counts (from almost none, to much higher than average) has an artifactual origin, e.g. an untold "normalization" procedure. Indeed, it is (and was) customary for a number of EST sequencing laboratories not to record (or even not to pick the clones corresponding to) the many instances of the most abundant transcripts (such as ribosomal proteins, elongation factor EF-Tu, and the like). This ad hoc -but not consistent- subtraction of the most abundant ESTs (even though the libraries are not normalized) is the most probable cause for the corresponding gene to appear either over- or under-expressed in many tissues. We thus removed them from our subsequent analyses.

Table 1

Number of ESTs and keywords characterizing the tissue types

Tissues

Keywords

INF

DIZ

ADLT

Stem_cell

stem hematopoietic

1,592

Embryo

whole body trophoblast

10,346

Placenta

placent

110,502

43,395

Bone

bone osteo ewings

16,551

7,407

Cartilage

cartilage

5,017

5,224

Ear

cochlea ear

14,966

Eye

eye retina retino cornea ocular

6,370

28,235

30,036

Skin

skin keratinocyt melanoma melanocytic psoriasis derm

93,511

10,457

Cardio-vascular

Heart

myocardium valve heart cardiac

20,979

8,263

Vascular

artery vein aorta endotheli blood vascul hemopoietic venous venae huvec platelet

39,737

Head_neck

mouth tongue tonsil head oral cavity gingiva

5,817

2,628

5,787

Muscle_skeletal

skeletal rhabdomyosarcom

5,442

40,819

Soft_tissue

adipose peritoneum omentum synovium fibroblast connective synovial epithelioid fibrosarcoma liposarcoma

3,721

4,182

5,692

Endocrine

adrenal

16,620

10,483

thyroid

thyroid parathyroid

4,294

5,125

pineal

pineal pituitary

12,155

17,458

Exocrines_breast

breast mammary nipple areola

31,204

22,379

Respiratory

lung pleura trachea bronchi larynx pharynx nasal nasopharyn laryngal bronchi laryngeal olfact

4,406

82,164

38,108

Digestive

liver

liver hepato bile gallbladder

90,480

38,103

43,846

saliv

salivary parotid paratid

11,193

pancreas

pancrea langerhans

61,178

17,042

stomach

stomach gastric gastro

23,580

esophagus

esophag buccal

3,807

bowel

bowel intestine appendix cecum colon duodenu ileum jejunum colit

70,282

9,933

Genitourinary

femal_ovary

ovary oviduct ovarian

65,196

6,106

femal_uterus

uterus endometrium pregnant exocervical

10,5749

985

femal_others

cervix fallopian

29,380

testis

testis epididym seminal vesicle

25,484

6,034

prostate

55,609

22,785

Urinaire kidney

kidney wilms

1,427

57,891

13,776

Urinaire others

bladder ureter urethra

20,736

others

gonad genitourina wolffian germ germinal mesonephros Mullerian paramesonephros urogenital

11,930

4,341

Lymphoreticular

immuno

leukocyte monocyt macrophag t-cell lymph b-cell bone marrow leukemi leukaemia mononuclear myelo lymphoblastoid T-lymphoc B-lymphoc

75,877

72,618

spleen

93,645

4,460

thymus

4,680

Central Nervous System

brain

brain amygdala medulla oblongata cerebrum cortex frontal occipital hippocampus cerebellum corpus callosum basal ganglia striatum globus pallidus putamen caudate substantia nigra subthalamic tectum prosencephalon diencephalon thalamus corpora mesencephalon mesencephali quadrigemina glioblastoma astrocyt neuron cranial dura mater

108,992

99,576

61,843

dorsal root ganglion

dorsal root

1,520

Peripheric Nervous System

sympathetic nerve nervous spinal neuronal dendrit

18,360

22,456

Neural system

neuro

26,463

NO TISSU_Sp

662

88,540

'DIZ' is for disease tissues, 'INF' for infant and foetal healthy tissues and 'ADLT' for adult healthy tissues. '-' represents categories with less than 1,000 ESTs

Table 2

Genes from chromosomes 20, 21 and 22 expressed in most of the tissues

Chromosomes

Ensembl gene identifiant

Function of the gene

chromosome 20

ENSG00000132668

ribosomal protein

chromosome 21

ENSG00000128093

cyclophilin A

chromosome 22

ENSG00000128327

ribosomal protein

ENSG00000128360

ribosomal protein

ENSG00000100316

ribosomal protein

Correlation Islands

We searched for correlation islands, defined as clusters of at least three successive SOGs in a common tissue (see Material and methods). Nine, 5 and 17 clusters of SOGs were found for chromosomes 20, 21 and 22, respectively. To assess the statistical significance of these results, we computed the probability of finding such a number of clusters under a random permutation of the gene order along the chromosomes. This probability was found to be very low (Table 3). We can thus confidently conclude that there are more clusters that expected by chance, and further explore their potential biological meaning.

Table 3

Number of clusters for chromosomes 20, 21 and 22

Real Nc

Random Nc

Probability

chr.20

5.2

3.8 × 10^-2

chr.21

1.7

2.8 × 10^-2

chr.22

6.8

8 × 10^-4

Number of clusters at real position (Real Nc), mean number of clusters at random position (Random Nc) and probability to find the actual number of clusters by chance

The functional annotation of these gene clusters is shown in Tables 4a, 4b and 4c. No functional correlation was identified within the clusters, but such a correlation would be hard to establish given the lack of a defined function for many of the genes.

Table 4a

Clusters of successive genes from chromosomes 20

Chr.20

Tissue Category

Ensembl gene identifiant (chromosomic order)

Gene function

NCBI gene identifiant

PNS_ADLT

ENSG00000132646

proliferating cell nuclear Ag (PCNA) - cyclin

NM_002592

ENSG00000101290

CDP-diacylglycerol synthase 2 (CDS2)

Y16521

ENSG00000149345

ubiquitin-conjugating enzyme E2D 3

NM_003340

II.

Breast_DIZ

ENSG00000101339

N-acetyltransferase 5 (ARD1 homolog, yeast)

NM_016100

ENSG00000101343

Cm, crooked neck-like 1 (CGI-201)

NM_016652

ENSG00000089101

no defined function

HSJ1178H5

III.

Eye_ADLT

ENSG00000125966

matrix metalloproteinase 24 (MMP24)

XM_047216

ENSG00000126005

integrin beta 4 binding protein

BC019305

ENSG00000125965

groADLTh differentiation factor 5 (GDF5)

NM_000557

ENSG00000126001

centrosomal protein 2 (CEP2 = C-Nap1)

NM_007186

IV.

Genito_urinair_other _DIZ

ENSG00000125995

no defined function

AK000548

ENSG00000131051

splicing factor

NM_004902

ENSG00000126002

no defined function

XM_087888

Testis_ADLT

ENSG00000124177

KIAA protein

XM_029763

ENSG00000149593

no defined function

NM_032221

ENSG00000149598

no defined function

AY034072

VI.

Pineal _DIZ

ENSG00000100982

no defined function

XM_053387

ENSG00000124137

hypothetical C2H2 zinc finger protein

NM_022095

ENSG00000100985

matrix metalloproteinase 9 (gelatinas collagenas)

NM_004994

VII.

Respiratory INF

ENSG00000130706

cell membrane glycoprotein (surface antigen)

BC003059

ENSG00000130702

laminin alpha5 chain precursor (forte expr. lung)

NM_005560

ENSG00000130705

ribosomal protein

NM_001024

VIII.

Eye_DIZ

ENSG00000088876

No defined function

HSJ734P14

ENSG00000101361

Nucleolar protein

XM_044915

ENSG00000101365

isocitrate dehydrogenase 3 (NAD+) beta

NM_006899

IX.

Immuno ADLT

ENSG00000101146

RAE1 - mRNA export protein

NM_003610

ENSG00000132819

Seb4D(CLL-associated antigen KW-5)

AF432218

ENSG00000124097

chromosomal protein

AF076674

The function and the Ensembl and NCBI identification numbers are given for each gene.

Table 4b

Clusters of successive genes from chromosome 21

Chr.21

Tissue Category

Ensembl gene identifiant (chromosomic order)

Gene function

NCBI gene identifiant

Pancreas ADLT

ENSG00000099582

adenovirus receptor

NM_001338

ENSG00000099583

BTG family - mb 3 (antiproliferative protein)

NM_000606

ENSG00000099585

no defined function

NM_017447

II.

Genital femal others DIZ

ENSG00000099580

no defined function (similar to a rat kinase)

NM_017447

ENSG00000023067

heat shock transcription factor 2 binding ptn

NM_007031

ENSG00000128150

H2B histone family

NM_080593

ENSG00000099597

no defined function

XM_035973

III.

NS DIZ

ENSG00000099522

no defined function

NM_032261

ENSG00000099524

lanosterol synthase

NM_002340

ENSG00000074707

germinal associated nuclear protein

AJ01009

IV.

Respiratory ADLT

ENSG00000023120

Phosphofructokinase, liver (PFKL)

XM_036042

ENSG00000099439

candidate gene for APECED

HSY11392

ENSG00000099440

transient recept potential cation channel TRPM2

XM_009803

Pineal_DIZ

ENSG00000099500

collagen, type VI, alpha 1 (COL6A1)

NM_001848

ENSG00000099505

collagen, type VI, alpha 2 (COL6A2)

NM_001849

ENSG00000139071

collagen, type VI, alpha 2 (COL6A2)

XM_086775

The function and the Ensembl and NCBI identification numbers are given for each gene.

Table 4c

Clusters of successive genes from chromosome 22

Chr.22

Tissue Category

Ensembl gene identifiant (chromosomic order)

Gene function

NCBI gene identifiant

Lympho-reticular ADLT

ENSG00000128256

immunoglobulin lambda gene

HSIGLV

ENSG00000128275

immunoglobulin lambda gene

D87016

ENSG00000128280

immunoglobulin light chain V11

HSU03902

ENSG00000100089

immunoglobulin lambda light chain

HSZ85009

ENSG00000128273

immunoglobulin lambda light chain

HUMIGLVF

II.

Lympho-reticular ADLT

ENSG00000128299

immunoglobulin light chain

HUMIGLZI

ENSG00000128291

immunoglobulin lambda gene

D86994

ENSG00000128265

immunoglobulin lambda light chain

HSZ85032

III.

Kidney_DIZ

ENSG0000099964

macrophage migration inhibitory factor

HSMMIHFA

Pineal_ADLT

ENSG0000099974

D-dopachrome tautomerase

HSU84143

ENSG0000099977

D-dopachrome tautomerase

HSU49785

IV.

Skin_DIZ

ENSG00000100099

no defined function

HS1048E94

ENSG00000100104

similar to mouse tuftelin-interacting protein 10

HS1048E9A

ENSG00000100109

similar to mouse tuftelin-interacting protein 10

HS1048E9A

ENSG00000100118

high-mobility group (nonhistone K.al) ptn 1

NM_002128

Eye_ADLT

ENSG00000100284

target of myb1 (chicken) (TOM1)

NM_005488

ENSG00000100292

heme oxygenase (decycling) 1 (HMOX1)

NM_002133

ENSG00000100297

P1-Cdc46

HSP1CDC46

VI.

Spleen_ADLT

ENSG00000128340

no defined function

HS151B14

ENSG00000100051

Rac3 (small G protein)

AF008591

ENSG00000100055

cytohesin-4 (CYT4)

AF075458

VII.

Exocrine_breast_DIZ

ENSG00000100097

galectin

HSLEC14K

ENSG00000100101

no defined function

HS37E167

ENSG00000100106

no defined function

HS37E16

VIII.

CNS_brain_DIZ

ENSG00000100106

no defined function

HS37E16

ENSG00000138967

H1 histone family, member 0 (H1F0)

NM_005318

ENSG00000100116

glycine C-acetyltransferase (GCAT)

XM_009974

IX.

CNS_brain ADLT

ENSG00000100311

platelet-derived groADLTh factor beta plptd

XM_009997

ENSG00000100316

ribosomal protein

NM_000967

ENSG00000100321

Synaptogyrin 1 (SYNGR1)

XM_009999

Immuno ADLT

ENSG00000100389

ribosomal protein

XM_050589

ENSG00000100393

E1A binding protein p300 (EP300)

XM_010013

ENSG00000100395

H-1(3)mbt-like protein

HSA305227

XI.

Bone_DIZ

ENSG00000100387

Ring-box 1

BC017370

ENSG00000100389

ribosomal protein

XM_050589

ENSG00000100393

E1A binding protein p300 (EP300)

XM_010013

XII.

CNS_brain DIZ

ENSG00000100399

no defined function

HS756G23

ENSG00000100401

RanGTPase activating ptn

HsRanGAP1

ENSG00000100403

ubiquitous tetratricopptd containg ptn RoXaN

AF188530

XIII.

Skin_DIZ

ENSG00000100401

RanGTPase activating ptn

HsRanGAP1

ENSG00000100403

ubiquit. tetratricopptd containg ptn RoXaN

AF188530

ENSG00000100410

no defined function

HS223H9

ENSG00000100412

nuclear aconitase

HSU80040

ENSG00000100413

no defined function

XM_039448

ENSG00000100417

phosphomannomutase

HSU86070

ENSG00000100419

Thyroid autoantigen 70kD (Ku antigen)

BC018259

ENSG00000100138

RNA binding protein (OTK27)

AF155235

XIV.

Breast_DIZ

ENSG00000100294

similar to fatty acid synthase

XM_040148

Bowel_ADLT

ENSG00000100300

peripheral benzodiazepine receptor related

HumBenza

ENSG00000100304

no defined function

NM_015140

XV.

Skin_DIZ

ENSG00000100344

no defined function

HS796I171

ENSG00000100347

no defined function

XM_043614

ENSG00000022364

beta parvin (PARVB = CLINT = affixin)

NM_013327

XVI.

Skin_ADLT

ENSG00000100201

DEA-box protein p72

HSU59321

ENSG00000100211

no defined function

HS508I15

ENSG00000100216

Tom22 - mitochondrial import receptor

AB041906

XVII.

Brain INF

ENSG00000100423

no defined function

AF131851

ENSG00000100425

bromodomain containing protein 1

NM_014577

ENSG00000100426

no defined function

XM_010055

The function and the Ensembl and NCBI identification numbers are given for each gene.

Two clusters (III and IV on chromosome 22) were each composed of three genes, two of them being annotated as having exactly the same function. These genes are from computer prediction and may correspond to a single gene erroneously interpreted as two different genes. As this particularity concern only two clusters, we did not consider them further.

Discussion

The analysis of all genes along chromosomes 20, 21 and 22, identified clusters of co-expressed genes (e.g. the known immunoglobulin cluster), and genes expressed in every tissue (e.g. some ribosomal proteins). The visualization of SEGs in various conditions allowed expression variations to be detected in diseased vs. healthy or infant vs. adult tissues. For instance, we noticed that a small cluster of immunoglobulin apparently specific of ovary diseased tissues. These immunoglobulins may be involved in an immune response specific to this pathology.

ESTs were grouped according to the tissue type: organ, developmental and pathological states. While comparing the gene expression across adult healthy tissues is biologically meaningful, comparing gene expression across pathological states (DIZ group) is more problematic as it involves treating different pathological conditions as one. For instance, different cancer types - each with its specific expression patterns- may arise in the same organ [15]. In principle, only diseased tissues corresponding exactly to the same disorder should be pooled. When dealing with diseased tissues, our protocol was thus expected to provide a distorted view of their gene expression patterns.

As in all statistical studies, sample size is important. As less fetal/infant libraries were available, less fetal or infant tissue specific gene clusters were detected.

In a study of Drosophila gene clusters, Spellman et al. [6] found that no functional relationship could be detected between the genes within a cluster. Our study again failed to reveal any relationships between the gene forming co-expressed/co-localized clusters. However, the large proportion of genes with no defined function is not allowing any final conclusion to be drawn.

Chromatin is usually described as been divided into "open" domains, where genes have the potential to be expressed, and domains of "closed" regions, where gene expression is shut down. The existence of co-expressed/co-localized gene clusters is consistent with a model where large chromatin regions would change their activity (openness) status in a tissue specific manner, allowing neighboring genes to be transcribed or shut down in a coordinated way. Such a model, confirmed by our study, has been around for quite sometimes, although experimental evidence have been obtained for only a few tissues and cell types [16, 17].

Materials and methods

EST and Libraries

Human ESTs were obtained from dbEST (release Oct.2001) [18]. Pooled, subtracted or normalized libraries were removed from the study. The remaining 1270 libraries were classified in three groups: 489 libraries from diseased tissues, whatever their developmental stage (DIZ), 194 libraries from healthy fetal or infant tissues (INF) and 587 libraries from healthy adult tissues (ADLT). The classification was made with the data extracted from the 'keywords' and 'developmental stage' fields of the library description. A similar analysis was performed on the three groups. ESTs were then masked for vector, common repeats and low complexity sequences using RepeatMasker (URL: http://repeatmasker.genome.washington.edu/.) and Repbase [19]. After these steps, 2,251,840 ESTs remained: 1,147,369 in the diseased libraries group, 478,320 in the infant libraries group and 626,151 in the adult libraries group. In each group, libraries were categorized into 40 organs as described in Table 1. Each library was classified in a tissue category if at least one of its keywords characterized the tissue category. Libraries were individually characterized by keywords extracted from the library description, in the 'lib', 'keywords', 'tissue description', 'tissue type', and 'cell type' and 'organ' fields. Tissue categories were individually characterized by representative keywords, such as the name of the category or its synonyms. A library could only belong to a single category. Finally, the classification was visually verified. Categories with less than 1,000 ESTs were removed. The numbers of EST for every tissue category in the groups DIZ, INF and ADLT are shown in Table 1. The list of the libraries composing each tissue category is given as supplementary data.

Genes on Chromosomes 20, 21 and 22

Gene sequences were downloaded from Ensembl (release Nov.2001). As we analyzed the gene expression along the chromosomes, the various transcripts of a single gene were not considered. We used both known and novel genes predicted by Ensembl. Respectively, 694, 243 and 595 gene sequences were found for chromosomes 20, 21 and 22. As the Ensembl sequence identifier might change from one release to the next, a correspondence between the Ensembl sequences and the NCBI sequences is given in Tables 4a, 4b and 4c.

Gene Expression Profiles

Every gene was compared to the total EST set of the corresponding group at high stringency (%identity > 95 and match length > 66% of the query sequence) with BLAST 2.2.1. The expression profile was derived from the cognate ESTs in each tissue category relative to the total number of ESTs in the tissue category of this group. All expression profiles were stored in a matrix with rows corresponding to genes and columns corresponding to tissue categories. The Mij element thus correspond to the relative frequency of cognate ESTs for gene i in tissue category j.

Differential Gene Expression

To assess its differential expression in a tissue category and for a given group (DIZ, INF or ADLT), every contig was compared to the total EST set of this group at high stringency (previously described matrix). The hit list of cognate matches was then separated in two groups: ESTs from the corresponding tissue category vs. any other tissue categories. The statistical significance of the difference in frequencies between these two groups was computed according to a previously published formula [20]. The groups of diseased, infant or adult tissues were treated independently.

Correlation Island

Correlation islands were considered as clusters of at least three successive SOGs (p-value > 0.90) in the same tissue category. To assess the biological meaning of these clusters, we estimated the probability of finding such a number of clusters under a randomization of the gene position along the chromosomes (5,000 randomizations). The probabilities are presented in Table 3.

Additional data files

The list of the libraries composing each tissue category is given as supplementary data.

Additional data file 1

A list of the libraries composing each tissue category

Click here for additional data file

Acknowledgements

KM was supported by a grant from the Region Provence-Alpes Cote d'Azur and AVENTIS Pharma. We thank Deborah Byrne for reading the manuscript.

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