in the last 30 years , massive resources have been deployed to understand the molecular components and processes of human cells , both for clinical and fundamental research applications . while this effort has been first targeted toward the sequencing of the genome and the mapping of its transcriptome , it has now shifted toward the studies of the major actors of life , proteins . the molecular and functional complexity of human proteins is challenging and requires bioinformatics resources specifically aimed at capturing , integrating and maintaining up - to - date the available knowledge about them . in a step toward this end , the uniprot / swiss - prot group has completed the manual annotation of the full set of human proteins , derived from about 20  000 genes , in september 2008 ( 1 ) . the proteomic space generated from these gene products is enormous , up to an estimated 1 million different protein species derived from dna recombination , alternative mrna splicing and the wealth of protein post - translational modifications ( ptms ) . however , as estimated from the uniprotkb / swiss - prot knowledgebase content , 25% of those proteins ( i.e. around 5000 ) have not yet been studied experimentally . for the remainder , many proteins have not been completely analyzed with respect to their abundance , distribution , subcellular localization and interactions with other biomolecules , post - translational modifications or  even more critical  function . the more complete our understanding of human proteins is the better equipped we will be to understand the functioning of the human body at molecular level . much undiscovered knowledge is hidden in large sets of heterogeneous and noisy data distributed across a multitude of resources and web sites . the problem is intensified by the fact that databases regularly become obsolete after a few years due to lack of financial support . this trend is especially true for research on human biology , owing to the sheer quantity of resources at the disposal of researchers . to address these issues , we have created nextprot ( http://www.nextprot.org/ ) , a web - based protein knowledge platform on human proteins ( see screenshot of the home page in figure 1 ) . the ultimate goal for nextprot is to serve for research on human the same role that model organism databases ( mods ) serve for model species . nextprot is developed within the swiss institute of bioinformatics ( sib ) ( www.isb-sib.ch ) , which has extensive expertise in building high - quality protein - centric resources such as uniprotkb / swiss - prot ( 2 ) , prosite ( 3 ) , enzyme ( 4 ) , string ( 5 ) and the swiss - model repository ( 6 ) . users can sign - in ( top right ) to create a personal account that allows them to personalize their usage of the platform by keeping a history of their queries and favoring or tagging search results . the home page also links to pages with more information about the current content of the platform in term of integrated resources (  release details link at the bottom right ) . users can sign - in ( top right ) to create a personal account that allows them to personalize their usage of the platform by keeping a history of their queries and favoring or tagging search results . the home page also links to pages with more information about the current content of the platform in term of integrated resources (  release details link at the bottom right ) . nextprot is being developed as a service for the community , and is using the knowledge and the expertise of the community to populate it with very high quality data and tools . for each data type we need to incorporate , we identify groups that have expertise in that area and collaborate with them to integrate data . in addition to making nextprot and its users benefit from expert data in all areas , this philosophy helps us ensure that our data are up to date and helps advertise both nextprot and our collaborators ' resources to our respective user communities via reciprocal cross - links . the primary data set in nextprot comes from the high - quality solid work that has been the hallmark of uniprotkb / swiss - prot since its inception in 1986 : we integrate all the information from the swiss - prot human entries . the information captured by swiss - prot , however , is only a small fraction of what is available . the fact that nextprot is centered on a single species , human , makes it possible to widen not only the quantity but also the range of data being captured . while we are still early in the nextprot development path , we have already integrated a significant amount of additional information relevant to human proteins , notably : 
 extensive protein expression information obtained by immunohistochemistry on healthy tissues from the human protein atlas ( hpa ) ( 7).micro - array and cdna expression information in healthy tissues originating from arrayexpress ( 8) and unigene ( 9,10 ) . this rna - based expression data have been meta - analyzed by the sib evolutionary bioinformatics group and is available in the bgee resource ( 11).subcellular localization results from two different high - throughput projects : dkfz gfp - cdna localization ( 12,13 ) ; and weizmann institute of science 's kahn dynamic proteomics database ( 14).we have started to integrate high - quality mass spectrometry - derived proteomics information and , in particular , a number of published sets of n - glycosylation and phosphorylation sites . we also store peptide / protein identification results from experiments carried out in the context of the hupo plasma ( 15 ) and brain ( unpublished ) initiatives obtained from peptideatlas ( 16 ) , as well as some sets directly submitted to us by a network of collaborators.the gene ontology ( go ) ( 17,18 ) annotations of all human proteins as captured by goa ( 19).the mapping of proteins to their genomic transcripts on the human genome using ensembl ( 20).additional single - amino acid polymorphism ( saps ) variants obtained from dbsnp ( 9 ) and ensembl.additional identifiers , including cdna clone names encoding for the proteins , affymetrix and illumina dna probesets ; cross - references to ccds ( 21 ) and hprd ( 22).abstracts of all articles from pubmed that are cited in human swiss - prot entries as well as some cited by other resources such as entrez gene ( generifs ) ( 9 ) , mint ( 23 ) and pdb ( 24 ) and which have been computationally mapped to the relevant protein entry by the uniprot consortium . extensive protein expression information obtained by immunohistochemistry on healthy tissues from the human protein atlas ( hpa ) ( 7 ) . micro - array and cdna expression information in healthy tissues originating from arrayexpress ( 8) and unigene ( 9,10 ) . this rna - based expression data have been meta - analyzed by the sib evolutionary bioinformatics group and is available in the bgee resource ( 11 ) . subcellular localization results from two different high - throughput projects : dkfz gfp - cdna localization ( 12,13 ) ; and weizmann institute of science 's kahn dynamic proteomics database ( 14 ) . we have started to integrate high - quality mass spectrometry - derived proteomics information and , in particular , a number of published sets of n - glycosylation and phosphorylation sites . we also store peptide / protein identification results from experiments carried out in the context of the hupo plasma ( 15 ) and brain ( unpublished ) initiatives obtained from peptideatlas ( 16 ) , as well as some sets directly submitted to us by a network of collaborators . the gene ontology ( go ) ( 17,18 ) annotations of all human proteins as captured by goa ( 19 ) . the mapping of proteins to their genomic transcripts on the human genome using ensembl ( 20 ) . additional single - amino acid polymorphism ( saps ) variants obtained from dbsnp ( 9 ) and ensembl . additional identifiers , including cdna clone names encoding for the proteins , affymetrix and illumina dna probesets ; cross - references to ccds ( 21 ) and hprd ( 22 ) . abstracts of all articles from pubmed that are cited in human swiss - prot entries as well as some cited by other resources such as entrez gene ( generifs ) ( 9 ) , mint ( 23 ) and pdb ( 24 ) and which have been computationally mapped to the relevant protein entry by the uniprot consortium . ontologies and controlled vocabularies ( cvs ) are essential for consistent annotation and powerful data retrieval . it is a challenge to choose the most appropriate vocabularies with respect to completeness , how well it represents the data we are capturing and how much interoperability it provides with other resources . we have imported into nextprot the gene ontology ( go ) , uniprot disease , keyword , post - translational modification and subcellular location ontologies , unipathway ( 25 ) , enzyme classification ( enzyme ) and part of the medical subject headings ( mesh ) ( 26 ) . we also created mini - cvs based on uniprotkb annotations to cater for domains , protein families , protein - bound metal ligands and topology . available ontologies and controlled vocabularies , including mesh , evoc ( 27 ) , brenda tissue ontology ( 28 ) and fma ( 29 ) , describe human anatomy with different scopes , coverage and precision levels . since none of them allowed us to integrate and compare data from different resources ( e.g. microarrays / ests from bgee and immunohistochemistry from hpa ) keeping the original granularity , we developed our own tissue and cell - type ontology . users access the platform through an intuitive , simple interface centered on a google - like search functionality that enables both simple ( free text ) and relatively complex queries ( through the use of search topics ) ( figure 2 ) . users can choose to search in nextprot for protein entries , publications or terminologies ( ontologies and controlled vocabularies ) . once a search has been made , it is possible to filter the results according to a number of criteria . the search results are displayed either as simple lists or as mini - summaries . nextprot is indexed across several biological areas , corresponding to the different views found on each entry ( some data are present in multiple indexes ) . this allows users to make complex searches , for example finding all proteins localized to the lysosomes and expressed in the brain . from the search results page , it is possible to do various exports : obtain the list of proteins as an excel file ; the protein sequences as fasta or peff , or the entire entry in xml format . when a user is logged in , checkboxes appear next to each entry , so that s / he is able to select specific entries for which the data are exported . nextprot is indexed across several biological areas , corresponding to the different views found on each entry ( some data are present in multiple indexes ) . this allows users to make complex searches , for example finding all proteins localized to the lysosomes and expressed in the brain . from the search results page , it is possible to do various exports : obtain the list of proteins as an excel file ; the protein sequences as fasta or peff , or the entire entry in xml format . when a user is logged in , checkboxes appear next to each entry , so that s / he is able to select specific entries for which the data are exported . users of nextprot can sign - in to create a personal account that allows them to personalize their usage of the platform by keeping a history of their queries and favoring or tagging the search results . nextprot provides an original way of visualizing proteins entries : they can be seen from three different perspectives : the the protein and gene perspectives are further subdivided in views that put the available information in context : function , medical , expression ( figure 3 ) , interactions , localization , sequence ( figure 4 ) , proteomics , structures , exons ( figure 5 ) and protein and gene identifiers . sequence view , the different splice isoforms can be graphically compared , highlighting the shared and specific sequence features ( domains , sites , etc . ) of each form . figure 3.the expression data view displays data via a browser of the nextprot human anatomy ontology . currently , the data presented come from two different sources : bgee and hpa ( see text ) . data are captured and displayed at the original granularity level ( loupe symbols ) , and is propagated to higher levels using the ontology to be comparable across the data sets . the tool provides a menu to toggle between showing only gold data and showing both gold and silver data . figure 4.the sequence viewer , accessible from the sequence view , displays - in addition to the sequence itself , the different features of the sequence ( processing , regions , modified residues , topological information , variants , sequence conflicts , etc . ) as a graphical overview and a table view . when a feature is selected , either from the graphical viewer or from the feature table , the corresponding sequence is immediately highlighted on the right . the sequence viewer also provides direct access to the blast tool , which has the option of using the full sequence , a selection corresponding to a sequence feature or any other sub - sequence selected by the user . figure 5.the exon view , available from the gene perspective , gives the precise coordinates of all protein isoforms that can be mapped on ensembl transcripts , based on exons . for each exon , its position on the gene is shown , as well as the length if the exon in nucleotides . the coding regions are represented by a small glyph , in which coding fragments are shown with a large green line , and non - coding sequences by a thin gray line . the first and the last amino acid of each exon are shown , and the reading frame of each exon is indicated by red labeling of the amino acids . for example , val 96ile 110 means that the first amino acid of that exon ( val ) is completely encoded within that exon , while only the first nucleotide of the last one ( ile ) is encoded in that exon . the expression data view displays data via a browser of the nextprot human anatomy ontology . currently , the data presented come from two different sources : bgee and hpa ( see text ) . data are captured and displayed at the original granularity level ( loupe symbols ) , and is propagated to higher levels using the ontology to be comparable across the data sets . the tool provides a menu to toggle between showing only gold data and showing both gold and silver data . the sequence viewer , accessible from the sequence view , displays - in addition to the sequence itself , the different features of the sequence ( processing , regions , modified residues , topological information , variants , sequence conflicts , etc . ) as a graphical overview and a table view . when a feature is selected , either from the graphical viewer or from the feature table , the corresponding sequence is immediately highlighted on the right . the sequence viewer also provides direct access to the blast tool , which has the option of using the full sequence , a selection corresponding to a sequence feature or any other sub - sequence selected by the user . the exon view , available from the gene perspective , gives the precise coordinates of all protein isoforms that can be mapped on ensembl transcripts , based on exons . for each exon , its position on the gene is shown , as well as the length if the exon in nucleotides . the coding regions are represented by a small glyph , in which coding fragments are shown with a large green line , and non - coding sequences by a thin gray line . the first and the last amino acid of each exon are shown , and the reading frame of each exon is indicated by red labeling of the amino acids . for example , val 96ile 110 means that the first amino acid of that exon ( val ) is completely encoded within that exon , while only the first nucleotide of the last one ( ile ) is encoded in that exon . nextprot also provides a dedicated page for each term from our controlled vocabularies and ontologies . these pages display graphical and tree representations of the ontologies , as well as links to proteins annotated with these terms or their children ( figure 6 ) . similarly , there are pages for publications : these pages display the full publication record , including the abstract as well as the list of proteins that were annotated with that publication . figure 6.for each term within the ontologies and hierarchical controlled vocabularies , a dedicated page shows its definition , synonyms and cross - references to other ontologies . in addition , the graphical ontology viewer shows the relationship of that term to all its parents . for each term within the ontologies and hierarchical controlled vocabularies , a dedicated page shows its definition , synonyms and cross - references to other ontologies . in addition , the graphical ontology viewer shows the relationship of that term to all its parents . in term of tools , nextprot provides access to a simple blast ( 30 ) implementation and we are currently beta - testing a tool to analyze enrichment of lists of proteins in term of various categories of annotations such as go terms , domains , subcellular locations , etc . nextprot provides export functionality , namely , the download of lists of protein entries as text or excel files , the corresponding sequences in fasta format and the complete set of annotations in xml . to cater the needs of the proteomics community , we are the first resource to have implemented export of sequences and annotations of ptms and variants in the peff format ( 31 ) which has been developed in the context of the hupo proteomics standards initiative . bulk download of the full complement of sequence and annotations is also available through our anonymous ftp site ( ftp.nextprot.org ) . through the ftp site not all data published or available in public repositories are of the same quality . however , this fact has rarely been captured in databases , whose attitude is often that the user should be able to view all data to make a judgment on the reliability of the information s / he is presented with . this attitude tends to overwhelm the user with too much information , often making it simply impracticable to evaluate it ; and requires that all users have expertise in all fields . in an attempt to overcome this problem , we are providing nextprot users with a data integration philosophy based on a three - tier quality system : 
 gold : highest quality data , corresponding to error rates of < 1%.silver : good quality data , corresponding to error rates of < 5% . silver data are marked as such in the annotations.bronze : data deemed of a lower quality that we do not integrate in nextprot . bronze : data deemed of a lower quality that we do not integrate in nextprot . within nextprot , users can choose to view and search only gold data ( the default option ) , or view both  gold and  silver. the grading of experimental data is not a trivial process and there is no simple rule that can be applied across the large landscape of high - throughput technologies that produce the data that need to be integrated into nextprot . to make our quality - grading criteria transparent to users , we are documenting these criteria in a metadata information record linked to the relevant experiments . whenever possible , we establish the quality thresholds  bronze , silver and gold  with the group who has produced the data . we expect that quality grading will be a dynamic process where users feedback will play an important role . much undiscovered knowledge is hidden in large sets of heterogeneous and noisy data distributed across a multitude of resources and web sites . the problem is intensified by the fact that databases regularly become obsolete after a few years due to lack of financial support . this trend is especially true for research on human biology , owing to the sheer quantity of resources at the disposal of researchers . to address these issues , we have created nextprot ( http://www.nextprot.org/ ) , a web - based protein knowledge platform on human proteins ( see screenshot of the home page in figure 1 ) . the ultimate goal for nextprot is to serve for research on human the same role that model organism databases ( mods ) serve for model species . nextprot is developed within the swiss institute of bioinformatics ( sib ) ( www.isb-sib.ch ) , which has extensive expertise in building high - quality protein - centric resources such as uniprotkb / swiss - prot ( 2 ) , prosite ( 3 ) , enzyme ( 4 ) , string ( 5 ) and the swiss - model repository ( 6 ) . users can sign - in ( top right ) to create a personal account that allows them to personalize their usage of the platform by keeping a history of their queries and favoring or tagging search results . the home page also links to pages with more information about the current content of the platform in term of integrated resources (  release details link at the bottom right ) . users can sign - in ( top right ) to create a personal account that allows them to personalize their usage of the platform by keeping a history of their queries and favoring or tagging search results . the home page also links to pages with more information about the current content of the platform in term of integrated resources (  release details link at the bottom right ) . nextprot is being developed as a service for the community , and is using the knowledge and the expertise of the community to populate it with very high quality data and tools . for each data type we need to incorporate , we identify groups that have expertise in that area and collaborate with them to integrate data . in addition to making nextprot and its users benefit from expert data in all areas , this philosophy helps us ensure that our data are up to date and helps advertise both nextprot and our collaborators ' resources to our respective user communities via reciprocal cross - links . the primary data set in nextprot comes from the high - quality solid work that has been the hallmark of uniprotkb / swiss - prot since its inception in 1986 : we integrate all the information from the swiss - prot human entries . the information captured by swiss - prot , however , is only a small fraction of what is available . the fact that nextprot is centered on a single species , human , makes it possible to widen not only the quantity but also the range of data being captured . while we are still early in the nextprot development path , we have already integrated a significant amount of additional information relevant to human proteins , notably : 
 extensive protein expression information obtained by immunohistochemistry on healthy tissues from the human protein atlas ( hpa ) ( 7).micro - array and cdna expression information in healthy tissues originating from arrayexpress ( 8) and unigene ( 9,10 ) . this rna - based expression data have been meta - analyzed by the sib evolutionary bioinformatics group and is available in the bgee resource ( 11).subcellular localization results from two different high - throughput projects : dkfz gfp - cdna localization ( 12,13 ) ; and weizmann institute of science 's kahn dynamic proteomics database ( 14).we have started to integrate high - quality mass spectrometry - derived proteomics information and , in particular , a number of published sets of n - glycosylation and phosphorylation sites . we also store peptide / protein identification results from experiments carried out in the context of the hupo plasma ( 15 ) and brain ( unpublished ) initiatives obtained from peptideatlas ( 16 ) , as well as some sets directly submitted to us by a network of collaborators.the gene ontology ( go ) ( 17,18 ) annotations of all human proteins as captured by goa ( 19).the mapping of proteins to their genomic transcripts on the human genome using ensembl ( 20).additional single - amino acid polymorphism ( saps ) variants obtained from dbsnp ( 9 ) and ensembl.additional identifiers , including cdna clone names encoding for the proteins , affymetrix and illumina dna probesets ; cross - references to ccds ( 21 ) and hprd ( 22).abstracts of all articles from pubmed that are cited in human swiss - prot entries as well as some cited by other resources such as entrez gene ( generifs ) ( 9 ) , mint ( 23 ) and pdb ( 24 ) and which have been computationally mapped to the relevant protein entry by the uniprot consortium . extensive protein expression information obtained by immunohistochemistry on healthy tissues from the human protein atlas ( hpa ) ( 7 ) . micro - array and cdna expression information in healthy tissues originating from arrayexpress ( 8) and unigene ( 9,10 ) . this rna - based expression data have been meta - analyzed by the sib evolutionary bioinformatics group and is available in the bgee resource ( 11 ) . subcellular localization results from two different high - throughput projects : dkfz gfp - cdna localization ( 12,13 ) ; and weizmann institute of science 's kahn dynamic proteomics database ( 14 ) . we have started to integrate high - quality mass spectrometry - derived proteomics information and , in particular , a number of published sets of n - glycosylation and phosphorylation sites . we also store peptide / protein identification results from experiments carried out in the context of the hupo plasma ( 15 ) and brain ( unpublished ) initiatives obtained from peptideatlas ( 16 ) , as well as some sets directly submitted to us by a network of collaborators . the gene ontology ( go ) ( 17,18 ) annotations of all human proteins as captured by goa ( 19 ) . the mapping of proteins to their genomic transcripts on the human genome using ensembl ( 20 ) . additional single - amino acid polymorphism ( saps ) variants obtained from dbsnp ( 9 ) and ensembl . additional identifiers , including cdna clone names encoding for the proteins , affymetrix and illumina dna probesets ; cross - references to ccds ( 21 ) and hprd ( 22 ) . abstracts of all articles from pubmed that are cited in human swiss - prot entries as well as some cited by other resources such as entrez gene ( generifs ) ( 9 ) , mint ( 23 ) and pdb ( 24 ) and which have been computationally mapped to the relevant protein entry by the uniprot consortium . ontologies and controlled vocabularies ( cvs ) are essential for consistent annotation and powerful data retrieval . it is a challenge to choose the most appropriate vocabularies with respect to completeness , how well it represents the data we are capturing and how much interoperability it provides with other resources . we have imported into nextprot the gene ontology ( go ) , uniprot disease , keyword , post - translational modification and subcellular location ontologies , unipathway ( 25 ) , enzyme classification ( enzyme ) and part of the medical subject headings ( mesh ) ( 26 ) . we also created mini - cvs based on uniprotkb annotations to cater for domains , protein families , protein - bound metal ligands and topology . available ontologies and controlled vocabularies , including mesh , evoc ( 27 ) , brenda tissue ontology ( 28 ) and fma ( 29 ) , describe human anatomy with different scopes , coverage and precision levels . since none of them allowed us to integrate and compare data from different resources ( e.g. microarrays / ests from bgee and immunohistochemistry from hpa ) keeping the original granularity , we developed our own tissue and cell - type ontology . users access the platform through an intuitive , simple interface centered on a google - like search functionality that enables both simple ( free text ) and relatively complex queries ( through the use of search topics ) ( figure 2 ) . users can choose to search in nextprot for protein entries , publications or terminologies ( ontologies and controlled vocabularies ) . once a search has been made , it is possible to filter the results according to a number of criteria . the search results are displayed either as simple lists or as mini - summaries . nextprot is indexed across several biological areas , corresponding to the different views found on each entry ( some data are present in multiple indexes ) . this allows users to make complex searches , for example finding all proteins localized to the lysosomes and expressed in the brain . from the search results page , it is possible to do various exports : obtain the list of proteins as an excel file ; the protein sequences as fasta or peff , or the entire entry in xml format . when a user is logged in , checkboxes appear next to each entry , so that s / he is able to select specific entries for which the data are exported . nextprot is indexed across several biological areas , corresponding to the different views found on each entry ( some data are present in multiple indexes ) . this allows users to make complex searches , for example finding all proteins localized to the lysosomes and expressed in the brain . from the search results page , it is possible to do various exports : obtain the list of proteins as an excel file ; the protein sequences as fasta or peff , or the entire entry in xml format . when a user is logged in , checkboxes appear next to each entry , so that s / he is able to select specific entries for which the data are exported . users of nextprot can sign - in to create a personal account that allows them to personalize their usage of the platform by keeping a history of their queries and favoring or tagging the search results . nextprot provides an original way of visualizing proteins entries : they can be seen from three different perspectives : the the protein and gene perspectives are further subdivided in views that put the available information in context : function , medical , expression ( figure 3 ) , interactions , localization , sequence ( figure 4 ) , proteomics , structures , exons ( figure 5 ) and protein and gene identifiers . for example , in the  sequence view , the different splice isoforms can be graphically compared , highlighting the shared and specific sequence features ( domains , sites , etc . ) of each form . figure 3.the expression data view displays data via a browser of the nextprot human anatomy ontology . currently , the data presented come from two different sources : bgee and hpa ( see text ) . data are captured and displayed at the original granularity level ( loupe symbols ) , and is propagated to higher levels using the ontology to be comparable across the data sets . the tool provides a menu to toggle between showing only gold data and showing both gold and silver data . figure 4.the sequence viewer , accessible from the sequence view , displays - in addition to the sequence itself , the different features of the sequence ( processing , regions , modified residues , topological information , variants , sequence conflicts , etc . ) as a graphical overview and a table view . when a feature is selected , either from the graphical viewer or from the feature table , the corresponding sequence is immediately highlighted on the right . the sequence viewer also provides direct access to the blast tool , which has the option of using the full sequence , a selection corresponding to a sequence feature or any other sub - sequence selected by the user . 
 figure 5.the exon view , available from the gene perspective , gives the precise coordinates of all protein isoforms that can be mapped on ensembl transcripts , based on exons . for each exon , its position on the gene is shown , as well as the length if the exon in nucleotides . the coding regions are represented by a small glyph , in which coding fragments are shown with a large green line , and non - coding sequences by a thin gray line . the first and the last amino acid of each exon are shown , and the reading frame of each exon is indicated by red labeling of the amino acids . for example , val 96ile 110 means that the first amino acid of that exon ( val ) is completely encoded within that exon , while only the first nucleotide of the last one ( ile ) is encoded in that exon . the expression data view displays data via a browser of the nextprot human anatomy ontology . currently , the data presented come from two different sources : bgee and hpa ( see text ) . data are captured and displayed at the original granularity level ( loupe symbols ) , and is propagated to higher levels using the ontology to be comparable across the data sets . the tool provides a menu to toggle between showing only gold data and showing both gold and silver data . the sequence viewer , accessible from the sequence view , displays - in addition to the sequence itself , the different features of the sequence ( processing , regions , modified residues , topological information , variants , sequence conflicts , etc . ) as a graphical overview and a table view . when a feature is selected , either from the graphical viewer or from the feature table , the corresponding sequence is immediately highlighted on the right . the sequence viewer also provides direct access to the blast tool , which has the option of using the full sequence , a selection corresponding to a sequence feature or any other sub - sequence selected by the user . the exon view , available from the gene perspective , gives the precise coordinates of all protein isoforms that can be mapped on ensembl transcripts , based on exons . for each exon , its position on the gene is shown , as well as the length if the exon in nucleotides . the coding regions are represented by a small glyph , in which coding fragments are shown with a large green line , and non - coding sequences by a thin gray line . the first and the last amino acid of each exon are shown , and the reading frame of each exon is indicated by red labeling of the amino acids . for example , val 96ile 110 means that the first amino acid of that exon ( val ) is completely encoded within that exon , while only the first nucleotide of the last one ( ile ) is encoded in that exon . nextprot also provides a dedicated page for each term from our controlled vocabularies and ontologies . these pages display graphical and tree representations of the ontologies , as well as links to proteins annotated with these terms or their children ( figure 6 ) . similarly , there are pages for publications : these pages display the full publication record , including the abstract as well as the list of proteins that were annotated with that publication . figure 6.for each term within the ontologies and hierarchical controlled vocabularies , a dedicated page shows its definition , synonyms and cross - references to other ontologies . in addition , the graphical ontology viewer shows the relationship of that term to all its parents . for each term within the ontologies and hierarchical controlled vocabularies , a dedicated page shows its definition , synonyms and cross - references to other ontologies . in addition , the graphical ontology viewer shows the relationship of that term to all its parents . in term of tools , nextprot provides access to a simple blast ( 30 ) implementation and we are currently beta - testing a tool to analyze enrichment of lists of proteins in term of various categories of annotations such as go terms , domains , subcellular locations , etc . nextprot provides export functionality , namely , the download of lists of protein entries as text or excel files , the corresponding sequences in fasta format and the complete set of annotations in xml . to cater the needs of the proteomics community , we are the first resource to have implemented export of sequences and annotations of ptms and variants in the peff format ( 31 ) which has been developed in the context of the hupo proteomics standards initiative . bulk download of the full complement of sequence and annotations is also available through our anonymous ftp site ( ftp.nextprot.org ) . through the ftp site however , this fact has rarely been captured in databases , whose attitude is often that the user should be able to view all data to make a judgment on the reliability of the information s / he is presented with . this attitude tends to overwhelm the user with too much information , often making it simply impracticable to evaluate it ; and requires that all users have expertise in all fields . in an attempt to overcome this problem , we are providing nextprot users with a data integration philosophy based on a three - tier quality system : 
 gold : highest quality data , corresponding to error rates of < 1%.silver : good quality data , corresponding to error rates of < 5% . silver data are marked as such in the annotations.bronze : data deemed of a lower quality that we do not integrate in nextprot . gold : highest quality data , corresponding to error rates of < 1% . silver : good quality data , corresponding to error rates of < 5% . bronze : data deemed of a lower quality that we do not integrate in nextprot . within nextprot , users can choose to view and search only gold data ( the default option ) , or view both  gold and  silver. the grading of experimental data is not a trivial process and there is no simple rule that can be applied across the large landscape of high - throughput technologies that produce the data that need to be integrated into nextprot . to make our quality - grading criteria transparent to users , we are documenting these criteria in a metadata information record linked to the relevant experiments . whenever possible , we establish the quality thresholds  bronze , silver and gold  with the group who has produced the data . we expect that quality grading will be a dynamic process where users feedback will play an important role . nextprot aims to act as a central hub for all knowledge on human proteins . to achieve this nextprot has been selected to be the knowledge platform for the newly launched hupo human proteome project ( hpp ) ( 32 ) . to this end , nextprot will need to integrate data and tools aimed to support the hpp . among other developments , this means increasing the amount of proteomics data ( post - translational modifications and peptide identification ) and extending its scope toward quantification results obtained from selected reaction monitoring ( srm ) experiments . we are collaborating with the string group ( http://string-db.org/ ) to integrate human protein network information ( 5 ) . protein interaction data provided by intact ( 33 ) and other members of the imex consortium of interaction databases ( 34 ) , will allow nextprot users to explore graphically the functional protein complexes and their dynamic and spatial regulation through a cytoscape plugin ( 35 ) . information on protein networks will be complemented by data on interactions between proteins and small molecules ( such as drugs ) and between proteins and nucleic acids . while nextprot only caters for human proteins , we want to provide the phylogenetic range of species in which a given human protein exists . we will also extract from swiss - prot experimental information carried out in organisms other than human but providing information directly relevant to the cognate human protein(s ) . for example , selected phenotypes from knock out or knock down experiments in mouse or zebrafish or enzyme characterization of bovine or pig counterparts . in terms of tools and interface , we want to build an intuitive and powerful system , having capacities that are not yet available in other life sciences platforms . this is why we want to add a number of tools to nextprot . among them , we are planning to provide an advanced search option that will allow to specifically retrieve any stored data item and to carry out complex ( including boolean and analytical ) queries ; a multiple sequence aligner with a user - friendly interface and a 3d structure viewer that enables protein sequence annotations ( ptms , domains , variants , etc . ) to be displayed overlaid on the structural view . we are also exploring how we can allow users who have created personal accounts to customize our platform and to allow them to participate in group discussions and data sharing activities . currently , urls for searches and displayed pages are rest - compatible but this is not sufficient to allow third party developers to make full use of our platform and of the data available in nextprot . this is why we are currently developing an application programming interface ( api ) for nextprot . this api will be used to integrate the future 3d structure viewer developed by bionext ( http://www.bio-next.com ) in the context of a collaborative research project . it extends the high - quality uniprotkb / swiss - prot annotations for human proteins to include several new data types . the development of nextprot is just beginning and will continue to expand with respect to the quantity and scope of data presented . we are convinced that the comprehensive biocuration of human proteins is a community endeavor . with this in mind , nextprot is being built as a participative platform and we look forward to receiving users ' input for its future development . the sib ; genebio sa ; the swiss confederation 's commission for technology and innovation ( cti , grant 10214.1 pfls - ls ) ; the nextprot server is hosted by vitalit ; the bioinformatics competence center that supports and collaborates with life scientists in switzerland . funding for open access charge :