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APPENDIX 1

A new algorithm for identification of Stress responsive

TranscrIption Factor binding sites (STIF) and a database of

abiotic stress responsive transcription factors in Arabidopsis

thaliana (STIFDB)

Publications from this chapter:

• K. Shameer, S. Ambika, S. M. Varghese, N. Karaba, M. Udayakumar and R. Sowdhamini: STIFDB – Arabidopsis Stress responsive TranscrIption Factor DataBase, (2009) et.al; Int. Journal of Plant Genomics: 583429

• Sundar AS, Varghese SM, K. Shameer, Karaba N, Udayakumar M, R. Sowdhamini: STIF: Identification of stress-upregulated transcription factor binding sites in Arabidopsis thaliana. (2008) Bioinformation. 30; 2(10).

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A: 1.1 Introduction

Transcription factors play a pivotal role in the cell by regulating differential expression of

genes required for a particular molecular function of biological process in the complex

cellular environment. The expressions of proteins in the cell are carefully regulated by

transcription factors that interact with their downstream targets in specific signal transduction

cascades. Our understanding of the regulation of functional genes responsive to a plant

abiotic stress signals is still nascent [638]. Understanding the molecular mechanisms that

underlie stress tolerance would be the first step in the generation of abiotic stress tolerant

crops. To understand plant abiotic stress responses, unraveling the mechanisms of regulation

of abiotic stress responsive genes assumes paramount importance. Gene regulation by

Transcription Factors (TFs) is an important facet of stress responsive signal transduction

cascades. Transcription factors are regulatory proteins that implement their functions by

binding directly to the promoters of target genes in a sequence-specific manner to either

activate or repress the transcription of downstream target genes. Arabidopsis thaliana is a

convenient plant model system to study fundamental questions related to regulation of the

stress transcriptome. Microarray experiments of the A. thaliana transcriptome indicate that

several genes could be up regulated during multiple stresses, such as cold, salinity, drought

etc. Experimental biochemical validations have proved the involvement of several

transcription factors could be involved in the up regulation of these stress responsive genes

[3, 639]. Bioinformatics approaches are widely employed in multiple domains of plant

biology to understand various aspects in the context of fundamental, cellular or biochemical

level [640]. In order to follow the intricate and complicated networks of transcription factors

and genes that respond to plant abiotic stress situations in plants, a new algorithm for the

identification of key transcription factor binding sites that are present in the upstream of

genes of interest was developed. Hidden Markov models of the transcription factor binding

sites enable the identification of predicted sites upstream of abiotic stress genes in A.

thaliana. The search algorithm named as ‘STIF’ was assessed for its performance using a set

of genes reported to be up regulated during abiotic stress response in A. thaliana [636]. The

algorithm performed well, with more than 90% sensitivity, when tested on experimentally

validated positions of transcription factor binding sites on a dataset of 29 plant abiotic stress

up regulated genes. Further, the algorithm is applied on a larger dataset of 2, 629 genes from

A. thaliana genome. The genes are extracted from various public microarray datasets related

to abiotic stress response experiments in A. thaliana. 2, 629 genes are scanned using the

algorithm for potential abiotic stress responsive transcription factor binding sites. A new

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database called “STIFDB [Stress responsive TranscrIption Factor DataBase]” is compiled,

developed and provided in the public domain [637, 641]. STIFDB is developed a database of

plant abiotic stress responsive genes and their predicted abiotic transcription factor binding

sites in A. thaliana. STIFDB will be a useful resource for researchers to understand the

abiotic stress regulome and transcriptome of this important model plant system. This Chapter

details various aspects of the new HMM based algorithm for the identification of plant abiotic

stress responsive transcription factor binding sites, database and also discusses about the

generic trends of the genes and transcription factors available in STIFDB.

A: 1.2 STIF Algorithm

The interactions between regulatory proteins and DNA control many important processes and

responses to abiotic stresses, and defects in these interactions can contribute to inefficient

stress responses. Numerous studies have shown that transcription factors are important in

regulating plant responses to stress. One important step in the control of stress responses is

the transcriptional activation or repression (regulation) of genes. Databases, such as

ATHAMAP, offer information about the chromosomal positions of genes of interest and

possible location of their transcription factors and binding sites [642]. Multiple signaling

pathways regulate the stress responses of plants and there is significant overlap between the

patterns of gene expression that are induced in plants in response to different stresses [643].

Many genes induced by stress challenges, including those encoding transcription factors,

have been identified and some of them have been shown to be essential for stress tolerance

[644]. Many studies have also revealed some of the complexity and overlap in the responses

to different stresses, and are likely to lead to new ways to enhance crop tolerance to disease

and environmental stress. The binding specificities of only a small number of transcription

factors (TFs) are well characterized. Transcription-factor binding sites (TFBSs) are usually

short in length (around 5-15 base-pairs (bp)) and they frequently contain degenerate sequence

motifs. The sequence degeneracy of TFBSs has been selected through evolution and is

beneficial, because it confers different levels of activity upon different promoters. Much of

the information on TF binding specificity has been determined using traditional

methodologies, such as foot-printing methods, (that identify the region of DNA protected by

a bound protein), nitrocellulose binding assays, South-western blotting (of both DNA and

protein) or reporter constructs. These methods are generally quite time-consuming and are

not readily scalable to a whole genome [645]. One of the promising approaches is to identify

the transcription factors by computational techniques at a whole genome level so as to choose

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promising targets for detailed experimental investigation. Well-known eukaryotic

transcription factors and their binding sites are recorded in TRANSFAC database [646].

Computational tools are available to facilitate the retrieval of transcription factor binding site

information from TRANSFAC database, but for the human genome [647]. Several existing

algorithms use position-specific profiles [648, 649] based scoring schemes or probabilistic

models to recognize putative binding sites. Even though various bioinformatics tools are

available in the public domain for transcription factor binding site prediction, most of the

servers and algorithms are largely for eukaryotic general-purpose transcription factors and

not specific for plant genomes or plant abiotic stress responsive genes. There are other

computational algorithms to search for possible genes that are downstream of classical

transcription factor binding sites, where the binding site data are encoded as HMMs and

searched all around the genome of interest. These methods are called as ‘targeted gene

finding’ since they begin from known transcription factor binding sites [648]. However, this

approach is complicated for plant stress genes since stress TF-binding site signatures could

potentially be upstream of constitutive genes as well and there could also be overlap in

various transcription factor binding sites. Data of a set of 10 well-known plant abiotic stress

specific transcription factors were curated from literature and generated Hidden Markov

Models (HMM) of known transcription factor binding sites. This knowledge-based approach,

by building HMM models through well-known abiotic stress cis-elements, has been tested

extensively to standardize thresholds for scores. ‘STIF’ is basically an HMM based algorithm

developed to predict transcription factor binding sites in the upstream and 5'UTR regions of

genes extracted from TAIR. Program based on STIF algorithm accepts a DNA sequence

(Upstream region + 5'UTR) in FASTA format as the input. Extensive experimental results

show that abiotic stress responsive transcription factors fall into ten transcription factor

families [650, 651]. These are ABI3/VP1, AP2/EREBP, ARF, bHLH, bZIP, HB, HSF, Myb,

NAC and WRKY families, which have a total of 22 subfamilies. Abiotic stress responsive

transcription factors largely belong to one of these 22 TF subfamilies (Table A3). Input

sequence is scanned using library of these 22 pre-constructed stress responsive transcription

factor HMMs obtained from literature. Input sequences are scanned for matches to the HMM

models. Subsequent to the HMM search, scores of all possible matches in forward and

reverse orientations in the upstream regions of stress genes are calculated along with standard

deviation and average. Based on STIF search results, hits are scored using significant scoring

method. In the final step Standard deviation, average and significant score base on hits are

used to calculate the Z-score and normalization. Hidden Markov Model (HMM) is used for

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transcription factor binding site detection in STIF algorithm. The consensus (S) of length (L)

was taken from the literature and the probabilistic score (P(S)) and log-odd score were

calculated.

P(S) = F * T

Where P(S) – Probability of consensus

F – Frequency (i.e. No:. of particular nucleotide/ Total no in column)

T – Transition probability

The log odd-score for consensus

(S) = log P(S) – L (AT) log 0.375 + L(GC) log 0.125

As plant sequences are rich in GC content, higher weight is assigned to AT than GC in log-

odd score. Schematic representation of the STIF algorithm is provided in Figure A1.

A: 1.3 Implementation of STIF Algorithm

STIF algorithm and associated scripts for HMM related computation, searching, calculation

of statistics and input - output parsing and other calculations like Z-Score and normalization

were coded in Perl. Flowchart of the algorithm is provided in Figure A2.

A:1.4 Statistical Validation of STIF Algorithm

A new transcription factor binding site prediction algorithm ‘STIF’ was been developed to

identify potential TFBS of stress-specific transcription factors, using the Hidden Markov

Models. The HMM models of cis-elements, based on abiotic stress transcription factor

families, were validated using Jackknifing method. HMM-based search algorithm STIF is

used to search 100 base pairs upstream of the gene with its 5’UTR. A set of 29 abiotic stress

genes from public microarray databases based on the high stress-induced expression profiles

were selected for the candidate genes for validation [652] . To evaluate the method further,

sequence searches are performed against 1000 base pairs with its 5’UTR. In the validation

data set, at a Z-score of 2.0 when searched 100 base pairs with 5’UTR, the sensitivity of the

method is found to be very high and the method identified 18 out of 20 hits (95% coverage)

with only two false negatives. Based on the statistical observations, a Z-score of 2.0 or more

could be defined as effective to search and predict transcription factor binding sites 100 base

pairs with 5’UTR. In several instances, more than one transcription factor has been recorded

for a stress gene of interest (for instance, COR15a has both DREB_AP2_EREBP and

G_ABRE_bZIP (Figure A3, Table A1). The 29 stress genes considered for validation is

known to be upregulated during different types of stress – such as cold, dehydration, salinity

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etc. It is possible that, during a particular type of abiotic stress, any one of these transcription

factors would selectively respond by binding upstream of the gene of interest. Due to few

‘validated’ transcription factor binding sites mapped in the 100 base pairs upstream of stress

genes, validation searches where extended to 1000 base pairs upstream of the gene and

likewise a Z-score threshold of 1.5 is appropriate for 1000 base pairs with 5’UTR (Figure A4,

Table A2). 90% sensitivity is achieved in STIF, where 71 out of 78 hits could be correctly

identified with Z-scores above the threshold. As with most other algorithms, method is not

highly specific and can generate false positives. The specificities for searches in the

validation set, by searching 100 base pairs and 1000 base pairs, is 57 and 18.6 (for Z-score

threshold of 1.5) and 54 and 20.4 (for Z-score threshold of 2.0), respectively. The difficulty in

obtaining high specificities has been due to simple and short nucleotide patterns that describe

some of the transcription factors like bHLH. Such TFs would respond frequently and that too

with very good match with HMM and are reflected as high scores. An alternate normalized

score were proposed for these frequently responding TFs in Arabidopsis genome. STIF

employs Hidden Markov Models of binding site information of well-known plant

transcription factors in abiotic stress. Microarray results of key stress up regulated genes in

plants have shown that a large number of these genes are up regulated in response to a variety

of genes generating redundancy in the dataset of stress up regulated plant genes. Further, the

experimentally ‘validated’ results also indicate that more than one transcription factor can

induce the expression of the stress genes in archived in STIFDB. The scoring schemes and

thresholds established should be useful for dealing with redundancy and occurrence of

multiple true positives.

A: 1.5 STIFDB [Arabidopsis Stress responsive Transcription Factor Database]

The list of 2,629 genes in STIFDB was compiled from various abiotic stress-related

microarray experiments. Genes were obtained from gene expression databases like the

Nottingham Arabidopsis Stock Centre (NASC) [653], Database Resource for Analysis of

Signal Transduction in Cells (DRASTIC) [654], Microarray Expression Data Search of the

Riken Arabidopsis Genome Encyclopedia (RARGE-MAEDA) [655], and the StressLink

Database [656]. Genes that are consistently upregulated in at least 3 replicates) of microarray

experiments in response to various stress treatments like dehydration, drought, osmotic stress,

salinity stress, ABA, cold, high light, and oxidative stress across various microarray

experiments have been considered as stress responsive and included in the database. In cases

where fold increases in expression levels were available, genes with a 4-fold expression

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change was used to consider the gene as a probable candidate for STIFDB. Sequence

segments (1000 bp, 100 bp, and 5*UTR) of genes were obtained from TAIR. The collected

sequences were scanned further to identify potential abiotic stress responsive TFBS using the

STIF algorithm [636]. 22 HMM-based models of the 10 specific families including

subfamilies are used in the STIF algorithm to scan for binding sites using STIF algorithm

(see Table A3 and Table A4). Literature is consulted to validate the transcription factor

binding sites predicted by the STIF algorithm for 29 genes. STIFDB provides the 1000+bp

promoter regions, along with their 5*UTR sequences, extracted from TAIR, and identifies

known transcription factor binding sites/cis-elements bound by abiotic stress responsive

transcription factors. Flow chart of the data integration steps involved in the development of

STIFDB is provided in Figure A5.

A: 1.5 Features of STIFDB

STIFDB offers several unique features as well as integrated data from public resources that

will be useful for the better understanding of the TFBS and function of the downstream

genes.

A: 1.5.1 TFmap

TFmap [657] is a graphical representation of the upstream regions of the stress genes in

Arabidopsis thaliana with the predicted and the validated transcription factor binding sites

marked along with their Z-Scores. TFmaps are generated using Bio::Graphics module from

Bioperl [267]. TFmap with the validated and predicted transcription factors marked on the

1000bp upstream of the gene AT1G02920 is given in Figure A6.

A: 1.5.2 TAIR ID

The Arabidopsis Information Resource (TAIR) [658, 659] maintains a database of genetic

and molecular biology data for the plant model system A. thaliana. TAIR ID is used in

STIFDB to access the gene-based contents. Users can query the database using TAIR ID.

A: 1.5.3 Gene Ontology

GO annotations [6, 660] of the genes in STIFDB are obtained from TAIR. GO annotations

will help the users to understand the known functional associations of genes in STIFDB.

A: 1.5.4 Gene Description

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Gene description provides a short description of genes along with predicted domain

associations from InterPro database [164, 661]. Gene descriptions for genes reported in

STIFDB are obtained from TAIR [658, 659].

A: 1.5.5 Gene Names [Including Aliases]

Users can access STIFDB using standard gene names or its aliases reported in TAIR

database. For Example: TAIR ID - AT4G23600 refers to the single entry in the database with

different aliases CORI3, CORONATINE INDUCED 1, JASMONIC ACID RESPONSIVE 2

and JR2.

A: 1.5.6 Chromosome Position

Chromosome Position refers to the exact location of the given stress gene among the 5 A.

thaliana chromosomes.

A: 1.5.7 References to Publication and Related Resources

References to publications and related resources are provided along with individual gene

related information.

A: 1.5.8 Transcription Factor Family Name

This refers to the Transcription Factor Family whose binding site sequence has been

located/predicted on a given promoter sequence. This database identifies binding sites of the

ten stress responsive transcription factor families and their subfamilies.

A: 1.5.9 Binding Site Information

Binding site refers to the core binding sequence to which a transcription factor binds. The

binding site sequences have been characterized in literature reports and the accompanying

references are provided.

A: 1.5.10 Orientation of Binding Sites

Orientation of Binding Sites refers to the DNA strand on which the transcription factor-

binding site has been located. It can be either on the forward strand or on the reverse DNA

strand.

A: 1.5.11 Stress Signals

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Stress Signal refers to the type of stress, which according to literature reports, regulates the

transcription factor. Most of the transcription factors dealt with here are regulated by various

abiotic stress signals like drought, cold, heat, light etc. A dedicated URL is provided to access

the individual genes affected by different stress signals [662]. An example stress profile of

abiotic stress signal category 'COLD-DROUGHT-SALT' is provided in Figure A7.

A: 1.5.12 Z-Score

Z =Score ! Mean

SD!(A: 1.1)

Where Z = Z-score

Score = HMM score of the hit

Mean = Mean of scores of all window slides of query sequence ad the window size depends

on the transcription factor binding sites

SD = Standard Deviation of mean of all window slides of query sequence.

This algorithm is validated with an experimental data set of 27 stress genes from Arabidopsis

thaliana. During the validation of STIF algorithm, Z-score for 100bp and its 5’UTR regions

can be seen above 2.0 and for 1000bp and its 5’UTR regions can be seen above 1.5.

A: 1.5.13 Normalization Score

Normalization =!"

!(A: 1.2)

Where ! is a factor that denotes Top 1st rank of z-score of binding site for given TFBS and

stress gene/Total number of binding sites for given TFBS and ! is a factor that denotes Total

number of binding sites for all TFBS library and stress gene/Total number of binding sites for

all TFBS library and all stress genes. The normalization score explains the distribution of

particular TFBS (Transcription Factor Binding Site) in the whole data set of the stress genes.

If the normalization numbers are low, then it means that it is well distributed among the data

set.

A: 1.5.14 Utilities in STIFDB

STIFDB is organized such that the users can browse using four criteria like chromosome

number, transcription factors, stress signal profiles and sorted list of TAIR locus IDs. Users

can search the STIFDB using TAIR locus IDs, Gene alias names and stress signals. A

BLAST (blastn, nucleotide version) [139] based search tool is also implemented to search the

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database of 1000bp promoter sequences of 2629 genes in STIFDB. A detailed screenshot of

STIFDB with various features are provided in Figure A8.

A: 1.5.15 Technical Details

STIFDB is developed on a MySQL backend [326]. Web interface of STIFDB is developed

using HTML, and JavaScript. Perl-CGI programs are used for the development of search,

query and retrieval system.

A: 1.6 Discussion

Chromosome wise distribution of genes compiled in STIFDB indicates that abiotic stress

responsive genes seem to be roughly the same numbers on all chromosomes. Chromosome

wise distribution of abiotic stress responsive genes in STIFDB is provided in Figure A9.

Distribution of genes responsive to specific abiotic stress signals indicates that numerous

genes are regulated in response to cold, drought, salinity, light and external ABA, and a lesser

subset of genes that respond to oxidative stress and rehydration. Distribution of stress signals

that affect various genes in STIFDB is provided in Figure A10. There are also 41 genes that

are expressed in response to multiple abiotic signals, cold, drought and salinity. Analyzing

these genes as subsets or individually, would offer clues to understanding the individual

stress transciptomes better, and analyzing the promoters of these genes could provide insights

into the regulation of these genes in response to their specific stress signal. Analysis of the

frequency of transcription factor binding sites on the promoters of the abiotic stress

responsive genes provides varying numbers of stress specific transcription factor binding

sites. There seem to be greater numbers of certain transcription factor binding sites than

others. This could partly be due to the differences in the length of these cis elements.

Frequency of individual transcription factor binding sites on 2,629 genes in STIFDB is

provided in Figure A11. STIFDB would be a very useful tool to understand abiotic

transcriptome and the regulatory events of abiotic stress genes in A.thaliana genome.

Experimental validation and evidence about how many of these transcription factor binding

sites actually bind a TF to bring about regulation of their downstream gene in vivo, is still

lacking suggesting that further analysis using annotations may required to the seeming false

positives. It also needs to be determined if a greater number of stress specific transcription

factor binding sites on the promoter a particular gene, means a greater role of that particular

TF in its regulation. It is also worthwhile to analyze the promoters of subsets of genes that are

regulated by specific stresses, to identify patterns of transcription factor binding sites, which

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would have potential roles in the regulation of downstream genes responsive to a particular

stress. Therefore, STIFDB provides a platform to understand the stress-regulome of abiotic

stress responsive genes in plants. STIFDB will be a highly useful resource for a researcher

working on abiotic stress responses in plants.

A: 1.7 Conclusion

The challenge of maintaining a balance between a swelling population and the capacity to

produce food is increasing day by day. Consequently, food security has become a burning

issue for agricultural scientists and economists alike. Increasing crop productivity in view of

the escalating population and diminishing cultivable land and natural resources has become

vital. However, environmental stresses like drought, salinity, high and low temperatures, high

light, etc., along with biotic agents like pests and diseases, reduce agricultural yields

significantly, and consequently affect food security [638]. Developing crops that tolerate

environmental stresses, while maintaining productivity, will become a critical requirement for

enhancing agriculture in the twenty first century. Understanding the molecular mechanisms

that underlie the stress regulome would be the first step in the generation of abiotic stress

tolerant crops. To understand plant stress responses, unraveling the mechanisms of regulation

of stress responsive genes assumes paramount importance. Gene regulation by Transcription

Factors (TFs) is an important facet of stress responsive signal transduction cascades.

Computational transcription factor binding site prediction is a mature domain in the field of

Bioinformatics. Various algorithms, stand-alone software and web servers are available for

the effective prediction of transcription start from sequence information using knowledge

based and motif based methods [645, 663]. A wide array of TFBS prediction programs are

available based on different biological contexts. For example a novel method for prokaryotic

promoter prediction based on DNA stability that utilizes structural properties of DNA is

developed and analyzed across different prokaryotic organisms [664], time-delay neural

network based method (NNPP), is available specifically for the analysis of Drosophila

melanogaster promoter regions [665]. STIF algorithm explained in this Chapter is developed

using HMM models of known Abiotic stress factors will be useful for further analysis and

understanding of stress gene regulation in the plant model system Arabidopsis thaliana. Since

no bioinformatics tool provides a complete solution for the transcription factor identification

problem, it is always better to analyze the promoter regions with more than one algorithm or

program that based on the biological context. The new algorithm STIF [636] developed for

the identification of stress responsive transcription factor binding sites and the associated

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database STIFDB [637] with information about 2, 629 genes from A. thaliana genome will be

an valuable resources for the better understanding of the abiotic stress regulome in plants.

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Table and Figures of Appendix 1:

TAIR ID Number of hits* using different thresholds 1.0 1.5 2.0 2.5 3.0

AT1G02930 2,0 2,0

2,1

1,1

1,1

AT1G20450 3,0 3,0 3,0 0,0 0,3

AT2G15970 6,0 3,0 3,0 2,0 2,0

AT2G33380 1,0 1,0 1,0 1,0 1,0

AT2G40880 2,0 1,0 1,0 1,2 1,2

AT2G42540 7,0 4,0 3,0 2,0 2,0

AT2G46270 11,0 10,0 10,0 8,1 1,1

AT4G01120 5,0 5,0 5,0 4,0 1,1 AT4G37070 3,0 3,0 0,0 0,0 0,0 AT5G15970 1,0 1,0 1,0 1,0 1,0 AT5G51070 3,0 3,0 2,1 0,1 0,1 Total 44,0 36,0 31,2 20,5 10,9

Table A1: Statistical validation of search using STIF algorithm for Transcription Factor

Binding Sites (TFBS) 100 bp upstream of 11 stress genes. (* Total number of false positives

(x) and total number of false negatives (y) for a set threshold of Z-score. The numbers are

expressed as x,y for different thresholds imposed.)

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TAIR ID Number of hits* using different thresholds

1.0 1.5 2.0 2.5 3.0

AT1G02920 8,0 8,0 5,0 1,1 1,1

AT1G02930 9,0 9,0 7,0 6,1 3,1

AT1G05680 7,0 7,0 7,0 2,0 1,0

AT1G07890 17,1 14,1 8,1 8,1 0,1

AT1G20440 4,0 4,0 4,0 2,0 1,0

AT1G20450 6,0 6,0 5,0 4,0 2,0

AT1G52400 6,0 6,0 3,0 3,0 0,0

AT1G67090 12,0 12,0 12,0 12,0 3,0

AT1G77120 9,0 9,0 9,0 3,0 2,0

AT2G14610 4,0 4,0 4,3 4,3 4,3

AT2G14960 8,0 6,0 6,0 1,0 0,2

AT2G15970 13,0 13,0 12,0 4,0 3,0

AT2G21330 4,0 4,0 4,0 4,0 4,0

AT2G33380 11,0 11,0 9,0 7,0 2,0

AT2G40880 9,0 8,0 7,0 6,0 1,3

AT2G42540 16,0 6,0 5,0 4,0 4,0

AT2G46270 17,0 16,0 16,0 14,2 4,2

AT3G02480 7,0 5,0 4,0 3,0 3,1

AT3G04720 11,0 11,0 11,0 5,1 1,1

AT3G15500 10,0 10,0 10,0 4,0 3,1

AT4G00340 5,0 5,1 5,1 5,1 3,1

AT4G01120 12,0 12,0 12,0 10,1 2,1

AT4G02380 7,0 7,0 7,1 5,1 5,1

AT4G23130 9,0 9,0 9,2 2,2 0,2

AT4G37070 7,0 7,0 7,0 4,0 3,1

AT5G15970 11,0 11,0 5,0 3,0 1,2

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AT5G44420 9,0 9,0 9,0 8,0 2,0

AT5G51070 7,0 7,0 7,0 6,0 4,0

AT5G52310 6,0 6,0 4,0 3,0 3,0

Total 261,1 242,2 213,8 143,14 65,24 Table A2: Statistical validation of search using STIF algorithm for Transcription Factor Binding Sites (TFBS) 1000 bp upstream of 29 stress genes. (Total number of false positives (x) and total number of false negatives (y) for a set threshold of Z-score. The numbers are expressed as x,y for different thresholds imposed)

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Transcription factor Family name

Stress signal

Name of the Cis-element

Cis-element Reference (Stress signal / Cis-element)

ABI3/ VP1 ABA distB ABRE

GCCACTTGTC

[666]

AP2/ EREBP (EREBP-ERF sub-family)

Cold, Drought

GCC-box GCCGCC [667]

DREB sub-family

Cold, Drought

CRT/DRE (A/G)CCGAC

[668]

ARF Auxin AuxREs TGTCTC [669] BHLH/ myc NACL,

ABA, Drought

N box CACG(G/A)C

[670]

G box CACGTG [671] bZIP ABA,

Drought G box1

CCACGTGG

[672] [673]

G box2 TGACG(T/C)

[673]

G/ABRE (C/T)ACGTGGC

[674]

C/ABRE CGCGTG [674] HB ABA,

Drought CAATNAT

TG [675], [676]

HSF Drought, Cold, Heavy-metal stress and oxidative stress

HSE TTCNNGAA GAANNTTC

[677], [676]

MYB Dehydration, Wounding

(T/C)AAC(G/T) G

[678], [679]

CC(T/A)ACC

[680]

TAACTG [681] CC(TA)AA

CC [680]

(C/T)AACN(A/G)

[682]

NAC Drought, high salinity and ABA

CATGTG [683], [684]

WRKY Biotic W box (T)TGAC( [685], [686]

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stress (pathogen attack) Abiotic Stress (wind, rain, hail)

C/T)

Table A3: Transcription factors used in the study

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No. Transcription Factor Family

Subfamily Members

1 MYB Myb_box1, Myb_box2, Myb_box3, Myb_box4, Myb_box5

2 bZIP C_ABRE, G_ABRE, G_box1, G_box2 3 EREBP DREB_AP2, GCC_box_AP2 4 bHLH G_box, N_box 5 VP1 ABRE_ABI3 6 ARF AuxRE 7 WRKY W_box 8 NAC Nac_box 9 HB HBE 10 HSF HSE1 Table A4: Details of transcription factors and subfamily members available in STIFDB

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Figure A1: Flow chart diagram of STIF search Algorithm (Reproduced with permissions

from Bioinformation. 2008; 2(10): 431–437.)

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Figure A2: Schematic representation of STIF approach for construction of a Hidden Markov

Model of transcription factor binding sites given the experimentally observed nucleotide

patterns (Reproduced with permissions from Bioinformation. 2008; 2(10): 431–437.)

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Figure A3: The validation set of 11 stress responsive genes when searched for 100 base pairs

with its 5’UTR with 11 stress responsive genes. The total number of false positives obtained

during the search was compared against the total number of false negatives for various Z-

score thresholds applied for the statistical tests. (Reproduced with permissions from

Bioinformation. 2008; 2(10): 431–437.)

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Figure A4: Same as Figure A3 but for a validation set of 29 stress genes where search for

TFBS was performed 1000 base pairs with its 5’UTR. (Reproduced with permissions from

Bioinformation. 2008; 2(10): 431–437.)

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Figure A5: Flow chart of steps involved in the development of STIFDB (Reproduced with

permissions from International Journal of Plant Genomics, vol. 2009, Article ID 583429, 8

pages, 2009. doi:10.1155/2009/583429)

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Figure A6: TFmap with the validated and predicted transcription factors marked on the

1000bp upstream of the gene AT1G02920 (Reproduced with permissions from International

Journal of Plant Genomics, vol. 2009, Article ID 583429, 8 pages, 2009.

doi:10.1155/2009/583429)

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Figure A7: Stress signal profile from STIFDB for stress category ‘COLD-DROUGHT-

SALT’

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Figure A8: Screenshot of STIFDB (Reproduced with permissions from International Journal

of Plant Genomics, vol. 2009, Article ID 583429, 8 pages, 2009. doi:10.1155/2009/583429)

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Figure A9: Chromosome wise distribution of abiotic stress responsive genes in STIFDB

(Reproduced with permissions from International Journal of Plant Genomics, vol. 2009,

Article ID 583429, 8 pages, 2009. doi:10.1155/2009/583429)

!316

Figure A10: Distribution of individual stress signal that affect genes in STIFDB (Reproduced

with permissions from International Journal of Plant Genomics, vol. 2009, Article ID 583429,

8 pages, 2009. doi:10.1155/2009/583429)

!317

Figure A11: Frequency of transcription factor binding sites in STIFDB (Reproduced with

permissions from International Journal of Plant Genomics, vol. 2009, Article ID 583429, 8

pages, 2009. doi:10.1155/2009/583429)