our aim was to create a tool that would allow the construction of gene lists with homology to any specified protein domain , coupled with the capability to analyze the expression levels of these genes as provided by gene microarray experiments .
our motivation to develop such a resource originated from the general lack of such interactivity between existing highly used online resources .
we specifically intended to develop a resource that could rapidly provide exhaustive gene - type classification information , concomitantly with expression information of the entire set of members within a specific species type . such a working profile suits numerous areas of investigation , as it is often the case that a priori knowledge of gene family involvement in a given biological process is available , but the individual gene member is unknown .
however , if exhaustive gene family lists could be generated with ease , the coupling of such lists to an extensive gene expression base , should help in narrowing down family members playing tissue - specific roles .
if such a tool was available , we reasoned it would benefit investigators in a diverse range of disciplines .
the core knowledgebase of genespeed is the comprehensive homology categorization of all pfam ( 1 ) protein domain homology within the unigene transcriptomes ( 2 ) of human , mouse , worm and fly , without which its development would have been impossible .
this dataset facilitates the generation of gene lists with homology to any specified protein domain by first allowing the user to identify a single protein domain that may characterize a certain protein family of interest and then searching the transcriptome of one of these species for all translations containing homology to this domain .
the current version of genespeed only allows queries of single domains ; however , we plan to implement the ability for multiple domain searches in a future version .
the resultant list will contain all genes in the transcriptome with homology to the protein domain of interest ( displayed with decreasing similarity ) . in order to assist the user in identifying an initial protein domain of interest
the first takes advantage of a custom / manually curated transcription factor characterization of all domains within pfam ( currently 7973 domains ) so that an investigator may browse by transcription factor type .
second , we incorporated the gene ontology ( go ) characterization of domains ( 3 ) , which allows browsing by the go organizational tree and the ability to generate a gene list from any specified go node .
the third technique allows a more traditional query by gene / domain name , symbol or accession identifier . finally , the capability to search the genespeed database by fold expression level or by tissue specificity .
this is made possible through the incorporation of the novartis symatlas2 dataset ( 4 ) , containing expression data from 280 microarrays of 61 mouse and 79 human tissues .
these integrated expression data also serve an exploratory role , in that the genes within gene lists generated by the other query techniques mentioned above may be analyzed for fold - change from median , tissue specificity as well as absolute expression level .
all resulting datasets and tools were organized into a lamp ( linux / apache / mysql / php ) based dynamic website named genespeed and is available at .
the process for populating the genespeed database is illustrated in figure 1 , and is nearly fully automated .
( i ) the first step involves obtaining the full set of pfam domains from the pfam database ( currently representing 7973 domain families ) .
( ii ) a single domain family is selected out of the total of 7973 ( in this example the c2h2-zinc finger domain will be used ) .
full pfam alignment file for the c2h2-zinc finger domain ( containing 32  874 distinct c2h2-zinc finger protein sequences from a multitude of different species ) is generated .
this process involves using the tblastn algorithm ( 5 ) to search for homology for each of the 38  874 c2h2-zinc finger domains within the unigene est databases for mus musculus , homo sapiens , caenorhabditis elegans and drosophila melanogaster . in order to preserve protein id / aa - aa / e - score value
custom perl and python scripts were utilized to perform all tblastn homology searches using the blast pbh cluster maintained at massively parallel technologies inc .
( v ) the tblastn output for this pfam domain is subsequently parsed for expectancy score ( e - score ) , unigene i d , genbank i d , pfam i d , domain range and domain size . these results are sorted first by e - score and then by unigene i d and subsequently all redundancy was eliminated by retaining only the most significant e - score containing record for each unique homologous unigene hit .
( vii ) this process is then repeated for each of the 7973 pfam domains .
( viii ) in addition to the results of the tblastn population process , several other sources of data were incorporated into the genespeed database .
information from external databases including unigene ( 2 ) , interpro ( 3 ) , go ( 6 ) and the novartis symatlas2 ( 4 ) were included . from the most recent build of the unigene database we incorporated the unigene i d , gene symbol , gene name , entrez gene , chromosome and cytoband location .
in addition , through retired unigene information tables , we implemented a tool that automatically checks for unigene ids that have been retired from the database .
when uploading a custom gene list into genespeed , this script scans the list for all retired or changed unigene identifiers .
once a retired unigene i d is discovered , the most recent identifier(s ) is displayed for the user . from interpro , we obtained all interpro ids and gene ontology ( go ) information relating domain families to go categories .
this information allowed the development of a go browsing tool to search for genes containing domains that have an associated go classification ( 3,6 ) .
as a result , these incorporated go classifications may allow for gene - to - gene inference based on an original go - member domain .
however , care should be exercised , as protein homology does not always equate functional similarity , especially at the lower go - node levels .
as given in the figure : ( 1 ) all domain information and alignments in the pfam database are downloaded from pfam ; ( 2 ) a single domain is selected ( c2h2-zinc finger in this example ) ; ( 3 ) the  full alignment ( in this case the c2h2-zinc finger domain 
full alignment contains 32  874 distinct protein sequences ) is used in a ( 4 ) batch ncbi - based tblastn using the massively parallel pbh blast server ; ( 5 ) tblastn output is parsed and redundancy eliminated ; ( 6 ) non - redundant data are banked into a custom mysql database ; ( 7 ) the process is repeated for all domain families in pfam ( currently 7973 ) ; ( 8) other datasets are integrated with the database . as mentioned above ,
array data for the 79 human and 61 mouse tissues ( in duplicate ) of the novartis symatlas2 dataset ( 4 ) were included into the genespeed database .
these array data were downloaded from novartis ( ) in its mas5 ( affymetrix ) normalized form .
in addition to incorporating the absolute expression normalized values for each probeset into the database , we also pre - computed the fold - change differential from median as well as performing an anova statistical test to determine a p - value for tissue specificity . this anova computation compared the mean expression value of a gene for one tissue against the mean expression value of the same gene in all other tissues .
such a calculation was repeated for all genes within each tissue type for both human and mouse tissues .
all computations were performed within the r statistical environment ( ) running on a linux mandrake 10.2 operating system ( ) .
given personal interests in the regulatory dna - binding factors , we also incorporated a custom transcription factor classification of all 7973 pfam protein domains .
modeling the outline of a previous classification scheme ( 7 ) , domains within the pfam database were manually evaluated for their 
relatedness with transcription factors and classified into three hierarchies of superclass , class and subclass .
 relatedness in this case refers to any domains characteristic of or associated with known transcription factors ( i.e. associated with text queries of 
 relatedness , however , does not imply an absolute transcription factor classification for any particular domain . to clarify this point with an example , the user may browse the genespeed m.musculus division of the database for the transcription factor superclass of  helix turn helix ( hth ) and class of  fork head / winged helix. this yields a list of domains including the fork head domain as well as 13 other domains and a total of 480 genes .
the fork head domain is the signature domain that definitively characterizes the hth and fork head / winged helix transcription factor ; however , 13 other domains have also been found to be associated with these transcription factor proteins .
thus , if an interpro - based sub - search of this list is performed for all genes in genespeed that only contain the fork head domain ( pf00250 , ipr001766 ) , the gene list shrinks from 480 to 38 . in translation , 480 genes were identified that contain domains ( albeit some with very low similarity ) that have been associated with fork head / winged helix transcription factors and 38 genes contain a definitive fork head domain .
an update consists of running the complete database generation process from the most recent versions of all information / database sources .
a general navigation overview for the genespeed database is illustrated in figure 2 . in order to permit users to save custom gene lists while searching the genespeed database , all users of the database
, they may browse genespeed , perform queries , create custom gene lists and save these lists into their account .
guest users have full access to all database content ; however , to save gene lists , a free account must be established .
there is also an upload tool implemented allowing users to upload their own gene lists into the database .
this tool receives ( as upload from user ) a unigene gene list , scans the list for all retired or changed unigene identifiers and updates all retired unigene identifiers to the most recent build of the unigene database .
any discrepancies , such as retired or divided clusters , will be displayed to the user .
once the user 's external list has been uploaded into the database , all the tools of genespeed may be used on the new uploaded list . the primary rationale for including the import tool is to provide rapid domain assignment and other annotation within the genespeed environment for gene lists obtained from external experiments .
users may log back into the database at any time to access their previously saved custom gene lists .
each user 's gene list information is protected by login name and password and may not be observed by any other user .
if using genespeed as a guest user , only a single gene list may be created and manipulated at a given time .
( a. search ) a search is started by first selecting the organism and then the search type .
( b. results ) the results page will display the number of unigene clusters ( genes ) found and the user then has the option to select additional specific information they would like displayed concerning these genes .
( c. sub - searches / tool ) other sub - searches / tools are available to refine the original search including domain sub - search ( finds all domains in a specific gene ) , interpro sub - search ( finds all genes with a specific domain ) , external links ( www links to outside databases ) and novartis symatlas2 ( investigate the expression level of any number of genes with the data contained within the norvartis symatlas2 dataset including 79 human and 61 mouse tissues ) .
( d. user account ) each user is provided with a private account , in which they may store any number of user - specified gene lists to keep for analysis at any later time point .
upload tools are also provided , allowing users to analyze any gene list not generated within the genespeed database .
a new search ( figure 2 , a. search ) starts with selecting the organism and then the type of search , which includes ( i ) keyword , search the  gene name and 
( ii ) i d or accession , search by unigene , entrez gene , chromosome or interpro .
( iv ) transcription factor classification , to browse genespeed 's custom transcription factor classification hierarchy .
( v ) microarray expression data , allowing the user to query the database by fold - change from median as well as tissue specificity ( anova p - value ) .
once a search has produced a new list ( or a previously saved list is selected from the account page ) the user is prompted to select what information to display for the genes in the resulting list ( figure 2 , b. results ) .
output choices include various identifiers ( unigene , pfam , interpro , ensembl , entrez , go ) , gene descriptors ( gene symbol , gene name ) , domain descriptors ( name , size , number ) , mapping information ( chromosome , cytoband ) , transcription factor classification ( superclass , class , subclass ) and blast homology information ( e - score , genbank i d and amino acid range of the original blast query sequence ) .
the domain descriptors refer to the name and size of each domain as well as the number of different domains containing homology in each gene sequence .
the genbank i d and amino acid range of all original blast queries are also provided so that homology with any domain may be traced back to its original source .
the e - score field provides a drop down menu of different e - score values allowing the user to set the e - score cutoff for the resultant output . elaborating on the e - score further , when designing the genespeed database we sought to allow a flexible and open environment allowing the user to have precise control over various selection criteria . because genespeed is a similarity - driven database , the most important criterion for the evaluation of domain sequences in the transcriptome is the expectation - score ( e - score ) .
the e - score describes the number of hits that one can expect to encounter by chance when searching by homology in a database .
consequently , the score is influenced by the size of the database , and by the size of the query sequence .
for any particular hit , as the e - score approaches 0 ( becomes smaller ) , the hit exhibits a more significant degree of similarity with the query .
the purpose of an e - score cutoff is to exclude hits generated from the similarity search that do not have sufficient resemblance to the original domain of interest and thus do not represent a homologous domain .
unfortunately , due to the dramatic size differences between domain families , and the variations in conserved residues even between similarly sized domains , there is not a clearcut e - score cutoff that may be used for all domains and their corresponding hits in the database .
rather , we leave such critical decisions up to the user because both size and content characteristics of each domain must be taken into consideration when evaluating an appropriate e - score cutoff . despite these complications ,
in most cases , an inverse correlation exists between the domain size and e - score ; therefore , a suitable cutoff may be selected using these criteria . in other less common situations ,
domain size may not be a suitable tool for selecting the e - score cutoff value .
explanations and recommendations for choosing an appropriate e - score cutoff may be found on the genespeed website in the  background section .
after the results have been displayed , the user has a choice of using other links and tools to further investigate the genes in their list ( figure 2 , c. sub - searches / tools ) : ( i ) domain sub - search identifies all the domains with homology in a specified gene .
( ii ) interpro sub - search identifies all other genes in the genespeed database within the currently selected species displaying homology to the specified domain .
( iv ) novartis symatlas2 , when working in the human or mouse section of the database , the average absolute expression , fold change above median and anova tissue specificity statistical information pre - computed from the symatlas2 dataset may be accessed and visualized for genes contained within a gene list .
we have provided several additional resources on the genespeed website to help users navigate and use the database with success .
these include a section of  real biological scenarios , where we present possible scientific questions and full descriptions on how genespeed may answer these questions .
similarly , we have included an faq page as well as a glossary of helpful definitions and descriptions .
in addition , we have also included a tutorial section that provides several short tutorials on how to effectively use the tools within the genespeed database .
we here describe the construction and use of genespeed , a database providing an extensive homology compilation of all protein domains within the transcriptomes of human , mouse , worm and fly . at present , we have restricted this domain compilation to these four organisms .
although unigene databases exist for many species ( at present 76 ) , the transcriptome sequencing effort for most is not approaching the saturated level of the four chosen organisms , and incorporating these additional species , although not limited in a practical sense , was deemed premature .
these can later be added , as the sequencing effort and their respective genomics platforms become expanded and further utilized .
the homology assemblage employed in the creation of genespeed is based on a computationally demanding batch tblastn population process , which to our knowledge has not been used previously for this purpose . when developing this population process it was necessary to choose an efficient homology detection algorithm .
others have shown that a hidden markov model ( hmm ) based approach achieves better performance than gapped blast techniques for homology identification ( 8,9 ) .
we , on the other hand , have observed that by using a very large number of domains ( each used as an individual query ) derived from all types of species in the similarity search , a greater number of true hits is possible with the blast algorithm than hmm - based techniques ( results to be published elsewhere ) . by using a large batch of query sequences from a diversity of organisms all representative of a particular protein domain family , it may be possible to more accurately reflect the natural diversity and evolution of amino acid combinations within that family ( details of these experiments and their results are available on the genespeed website ) .
included with the domain analysis of these transcriptomes , genespeed provides a toolset for the exploration of these domains , their protein families and the genes in which they reside .
relatedness with known transcription factors , go classification , full name , average size , blast domain data and various protein domain identifiers ( pfam , interpro and genbank ) .
moreover , with the incorporated blast e - score cutoff tool , the user has control governing the amount of homology displayed when analyzing query results in order to accommodate any domain type or the specific biological question at hand .
this is both a unique and indispensable feature of the database , as an appropriate cutoff may significantly fluctuate depending on the size of a domain , type of domain or even the taxonomic limitation / category of a particular protein domain .
we have found it highly useful to be able to follow low levels of homology as this often carries certain functional relevance , which may link very distantly related members and often allows bridging between protein superfamilies .
such information is normally not available at other domain - based sites as only near - perfect matches are curated as true members ( additional discussion on this topic is available at the genespeed website ) .
in essence , genespeed provides users with a very  domain - centric online suite for analyzing the transcriptome .
it is this domain focus that affords the genespeed database its many unique benefits , such as providing the user the ability to infer protein type classification of a gene based on the composition and homology of its protein domain content .
this is particularly noticeable in almost any genespeed query result , where many of the resultant hits are genes that have not been classified in any of the common databases ( genbank , entrez gene , unigene , etc . ) .
as an example , a genespeed query for the homeobox domain in human yields 217 genes with significant e - scores . of these ,
24 have not been characterized as homeoboxes and 16 have no previous classification associated with them . in such instances
the domain content of these genes may be quickly and easily analyzed in the genespeed environment , thereby revealing distinct clues about the possible classification of these unknown genes . to our knowledge
, few online biological knowledgebases are dedicated to the task of helping investigators define all genes in a particular proteomic category , which is both species specific and non - redundant , as well as linking this information to searchable gene expression information at this level of detail .
only recently have large databases such as ncbi and embl started to interlink genetic , functional and expression datasets .
even so , there are still no tools allowing the analysis of large user defined gene lists or the ability to save this information in a private account within the database of interest .
furthermore , there is no current resource where an investigator may study protein domain families in a non - redundant manner and within a select organism of choice .
we have developed genespeed in an attempt to fulfill these needs within a single database and provide access from a globally accessible website .
we have used the genespeed database with significant success in our own research not only to identify particular protein families based on domain content , but also to analyze these gene lists for expression level in our tissue of interest in order to define candidates for follow up in our laboratory .
the success by which genespeed has facilitated this process for our research should exemplify its potential value for other investigators .
 the genespeed database ( ) is an online database and resource tool facilitating the detailed study of protein domain homology in the transcriptomes of homo sapiens , mus musculus , drosophila melanogaster and caenorhabditis elegans . 
 the population schema for the genespeed database takes advantage of howard parallel cluster technology ( ) and performs exhaustive tblastn searches covering all pre - assigned pfam domain classes in all species ( currently 7973 domain families ) against the respective unigene est databases of the selected four transcriptomes . 
 the resulting database provides a complete annotation of presumed protein domain presence for each unigene cluster . to complement this domain annotation 
 we have also performed a custom transcription factor - family curation of all pfam domains , incorporated the gene ontology classifications for these domains as well as integrated the novartis symatlas2 dataset for both human and mouse which provides rapid and easy access to tissue - based expression analysis . 
 consequently , the genespeed database provides the user with the capability to browse or search the database by any of these specialized criteria as well as more traditional means ( gene identifier , gene symbol , etc . ) , thereby enabling a supervised analysis of gene families through a top - down hierarchical basis defined by domain content , all directly linked to an optimized gene expression dataset . 
INTRODUCTION
DATABASE CONSTRUCTION
NAVIGATION AND USE
DISCUSSION