Large-scale genomic studies based on transcriptome technologies provide clusters of genes that need to be functionally annotated. The Gene Ontology (GO) implements a controlled vocabulary organised into three hierarchies: cellular components, molecular functions and biological processes. This terminology allows a coherent and consistent description of the knowledge about gene functions. The GO terms related to genes come primarily from semi-automatic annotations made by trained biologists (annotation based on evidence) or text-mining of the published scientific literature (literature profiling).in vitro enterocyte differentiation model (CaCo-2 cells).We report an original functional annotation method based on a combination of evidence and literature that overcomes the weaknesses and the limitations of each approach. It relies on the Gene Ontology Annotation database (GOA Human) and the PubGene biomedical literature index. We support these annotations with statistically associated GO terms and retrieve associative relations across the three GO hierarchies to emphasise the major pathways involved by a gene cluster. Both annotation methods and associative relations were quantitatively evaluated with a reference set of 7397 genes and a multi-cluster study of 14 clusters. We also validated the biological appropriateness of our hybrid method with the annotation of a single gene (cdc2) and that of a down-regulated cluster of 37 genes identified by a transcriptome study of an The combination of both approaches is more informative than either separate approach: literature mining can enrich an annotation based only on evidence. Text-mining of the literature can also find valuable associated MEDLINE references that confirm the relevance of the annotation. Eventually, GO terms networks can be built with associative relations in order to highlight cooperative and competitive pathways and their connected molecular functions. The numerous gene clusters identified thus far in molecular biology by high throughput analyses such as transcriptomic or proteomic technologies need to be understood according to the biological conditions under study. However, often only highly specialized individual biologists have an in-depth knowledge about a gene or gene product and therefore this knowledge is limited to relatively narrow research fields. The functional annotation of groups of gene products identified by genomic studies is a large challenge and new tools are needed to help in this task.de facto standard for formalising our knowledge about biological processes, molecular functions and cellular components, in three independent hierarchies [is_a or part_of relationships. As the defined terms can have more than one parent, the structure of this ontology is called a Directed Acyclic Graph (DAG). Furthermore, each GO term is associated with a unique identifier (GO ID) in order to allow a biological database to link to the GO and to ensure interoperability between different biological databases. These GO IDs are used in several biological databases – from almost 20 experimental organisms such as animals, plants, fungi, bacteria and viruses – to tag gene products and assign functions, biological roles and sub-cellular locations to them. Therefore, a user can identify the gene products associated with a specific GO term as well as all of the GO terms associated with this gene product by using an appropriate browser, such as AmiGo [GenNav [FatiGO [GoMiner [MAPPFinder [GOTree Machine [Onto-Tools [GOToolBox [Ontologies are widely used in informatics and are now becoming important in bioinformatics. They can make the large amounts of biological knowledge found in textbooks and research papers generally accessible in a structured way . Althougrarchies . It contas AmiGo  or GenNa [GenNav . The inv [FatiGO , GoMiner[GoMiner , MAPPFinPPFinder , GOTree  Machine , Onto-Toto-Tools  or GOTooOToolBox , that ofOToolBox ,13.Nevertheless, annotating genes with a controlled vocabulary is laborious and needs an expert to inspect carefully the literature associated with each gene to determine the appropriate terms. As our knowledge of biology increases, becomes more refined, and expands into new areas such a process will no longer be sufficient . It is tPubGene index [PubGene method uses a probabilistic score to reflect the gene-term association strength. This score takes into account the frequencies of both gene and term in the 14 million article records of the database. We discard weak associations  to improve the precision of the PubGene method. The two sets of GO terms are then merged and GO terms having statistically enriched gene numbers are identified to aid the biological interpretation of the cluster.We report here an original method for the functional annotation of gene clusters based on both evidence and literature profiles that aims to overcome the weaknesses and the limitations of each approach (annotation based on evidence and literature mining). We can functionally annotate a gene cluster by retrieving associated GO terms from two different sources of information. The first is an annotation database built on evidence: the Gene Ontology Annotation (GOA) database , and thene index . The PubWe evaluated the precision of each method and the overlap between them. We also evaluated the relevance of the bibliographic references associated with the gene cluster by the literature mining method. As we were seeking the biological meaning of a gene set, we focused on identifying metabolic pathways. This therefore limited the annotations to the Process hierarchy of GO.is_a relationship) and meronymy (part_of relationship) are the backbone of GO and make it a proper ontology in the computational sense, it however lacks associative relations within and especiallly across its three hierarchies. These relations would be very helpful and informative. For example, they could show that a certain molecular function is involved in a certain biological process and that a certain cellular component is the location of a certain biological process. We previously investigated three non-lexical approaches for identifying associative relations between GO terms [Although subsumption (GO terms . We haveResults section describes how we compared evidence (GOA) and literature (PUB) using an exhaustive reference set of 7397 genes annotated by both methods. We then explain how we evaluated the contribution of statistical dependences (DEP) on the same reference set. The methodology was quantitatively evaluated in a multi-cluster analysis concerning 14 clusters chosen from 7 independent studies (Table in vitro model of enterocyte differentiation (Up and Down clusters). A qualitative evaluation was also performed for two oncogenomic studies: a glioblastoma cluster (glioGBM) and a leukemia cluster (bcr-abl). The Discussion section describes the benefits and limitations of the evidence and literature annotations. We then comment on the major contributions of the bibliographic aspects of our method and that of the statistical dependence between GO terms. The Methods section details the technical and statistical aspects of our methodology.This paper is organised as follows. The The evidence annotation of the reference set provided 1625 Process terms whereas the literature annotation provided 3226. The two methods shared 1079 terms (24.9%). Although the reference set represented only 49.6% of the overall Process hierarchy of GO (8730 terms), we checked its relevance by evaluating its representativeness compared to all the GO Process terms available in the GOA Human database [In the evidence annotation, many gene-term associations were based on electronic inference: 38.5% of the terms retrieved in GOA were associated with the "Inferred from Electronic Annotation" evidence code (IEA) . The remPubGene retrieved 269172 gene-term associations of which 38703 (14.4%) had a scored below 0.01. Among the 3236 Process terms associated with the set, 10 (0.3%) were obsolete and 3075 (95.3%) had a score below 0.01.The generalised estimating equations (gee) showed that the annotation method did not affect the number of genes associated with a given term . Terms with significantly enriched gene numbers could then be compared between methods.2 = 1886.863, df = 1, p-value < 2.2e-16).The literature annotation provided more than twice as many terms for a given gene than the evidence annotation .There were many more references associated with one gene in the literature annotation than in the evidence annotation . The median depth was seven and was consistent with the overall granularity of the GO Process hierarchy.We found no significant difference in the granularities of the two annotations  or literature alone  for the dependent set versus the random set  is a measure of its relative number of annotated parents and children terms. PQIs for the combination of both evidence and literature were significantly different from evidence alone , all were found by literature profiling and/or associative relations. The literature annotation retrieved 154 Process terms, 23 of which had scores below 0.01. These significant terms were associated with 266 MEDLINE references. A systematic reading of the title and abstract of these references showed that these were relevant for the associations brought out and related all the important steps of the cdc2 characterisation  in several species, descriptions of the various substrates and inhibitors of cdc2, etc.). Furthermore, half the references provided by the literature annotation were less than 5 years old.Terms from the evidence and literature annotations were also associated with 153 terms in the associative relation database. Only 38 of these had a non-zero PQI. A selective part of the network of terms associated by dependence is presented in Figure A Venn diagram for the 37 down-regulated genes during CaCo-2 cells differentiation is shown in Figure As for the reference set, genes from the Down cluster were primarily annotated with three evidence codes: IEA (47.1%), TAS (33.3%) and NAS (12.6%). TAS, NAS and IDA evidence codes were associated with 28 MEDLINE references. Manual inspection of the 87 gene-term associations confirmed the accuracy of the evidence annotation and the robustness of the inference methods used in building annotation databases. Less than 2% of the terms were unexploitable. These were either misassociated, for example "perception of sound" (GO:0007605) with ITM2B, or not very biologically informative, for example "biological_process unknown" (GO:0000004) for TRIP6.e.g., "copper ion transport" (GO:0006825) with ATP7B, "DNA replication initiation" (GO:0006270) with MCM3, "chromatin silencing" (GO:0006342) and "DNA packaging" (GO:0006323) with CBX1, or "ornithine catabolism" (GO:0006593) and "putrescine catabolism" (GO:0009447) with ODC1. There were very few false positives associations (1.2%). The remaining 17.3% of the associations were correct but imprecise. The gene symbol and the term were both found effectively in the title/abstract but there was either: (i) no biological relationship between them, for example, ATP7B associated with "mRNA metabolism" (GO:0016071) in a study of mRNA expression levels (and thus transcription) of ATP7B itself [The Down cluster was annotated with 259 significant GO terms associated with 3377 MEDLINE references. Manual inspection of all the 626 significant gene-term associations retrieved by PubGene showed that 81.5% had a direct link between the gene and the term, " GO:00060 with MCB itself  on sum1+B itself  in a stu2 = 48.9203, df = 1, p-value = 2.666e-12) or literature alone   Figure .Enriched GO terms for the Down cluster are shown in Figure 2 = 255.7346, df = 1, p-value < 2.2e-16) for the dependent set versus the random set . Bad associations are mostly indirect rather than entirely false. For example, the "perception of sound" (GO:0007605) associated with ITM2B comes from a spkw2go mapping  as this cdc2 was identified ten years ago, its action on CDKN1A and TOP2A was only recently characterised [Text mining of biomedical literature combined with probabilistic scoring of the gene-term associations is also a powerful annotation technique. For a given gene set, it retrieved more terms per gene than evidence annotation and with a similar precision. Although common terms highlighted the major pathways, supplementary terms were a valuable source of information for reinforcing those pathways, by adding parent, sibling and child nodes. For example, in the annotation of cdc2 Figure , the litcterised .in vitro kinase assay. Moreover, the annotators linked this article to the Cellular Component "nucleus" (GO:0005634) whereas it would be better associated to "negative regulation of cell cycle" (GO:0045786) and "negative regulation of cyclin-dependent protein kinase activity" (GO:0045736). Likewise, HMGA2 was TAS-associated with "development" (GO:0007275) [e,g., ODC1, CBX1, LAMB1).Scientific literature is the optimal resource for validating a functional annotation. However, GOA provides few MEDLINE references to support its annotation. Despite there being abundant literature on cdc2 only one article was retrieved: a general study by Laronga et al.  on cycli GO:00457. Likewis0007275) . Most ofThe significant increase in the associated MEDLINE references in the literature annotation corroborates the enrichment of the GO terms. The considerably higher number of associated references and their accuracy (up to 90% precision) make PubGene an excellent bibliographic tool for validating the biological interpretation of a cluster. In the Down cluster, PubGene was able to retrieve very informative references highlighting the main biological implications of one gene. For example, the study by Chen et al.  was assoAs expected, the primary source of errors found in the literature approach was linked to the ambiguity of the gene symbols: CBX1 was associated with "secretion" (GO:0046903) whereas the abstract of Dodic et al.  referredOur data strongly suggest that networks of statistically inter-dependent GO terms highlight the leading features of a gene or gene cluster: a synthetic and simplified interpretation of its annotation. Most of the main processes identified in the functional annotation of the Down cluster  between GO terms across hierarchies, such as the "signal transduction" (GO:0007165) process with the "receptor binding" (GO:0005102) function in the Down cluster annotation, or the "mitosis" (GO:0007067) process and the "cyclin-dependent protein kinase activity" (GO:0004693) function in the cdc2 annotation; and (ii) between GO terms belonging to different sub-DAGs of the same hierarchy, such as the "regulation of cell cycle" (GO:0000074) and "apoptosis" (GO:0006915) processes in both the Down cluster and cdc2 annotations.We have presented here an application of the associative relations to the functional interpretation of experimental results. We deliberately restricted their contribution to reinforce the evidence or literature annotated pathways and to identify between annotated terms the relationships across hierarchies. This improvement needs to be evaluated in terms of the precision and specificity of each non-lexical approach and the term-term associations could also be filtered with respect to their similarity coefficient.The biological interpretation of a gene cluster will surely be facilitated by the identification of the GO sub-DAGs having a high number of annotated nodes. Each term in the gene cluster annotation has a PQI that measures its annotation degree: its relative number of co-annotated kinship terms. Using the distribution of the PQI within the DAG, it is therefore possible to identify statistically over-annotated sub-DAGs – possibly biological pathways – linked to a specific biological condition. Nevertheless, this measure needs to be normalised in order to be independent from the size of the gene cluster and, consequently, from the number of GO terms in the annotation.We used the associative relations to identify possible interactions between processes and functions but this method is general to GO and not specific to a gene cluster. At least two other approaches could be explored at the cluster level. The first and most obvious one is to link terms that share one or more genes (co-annotated genes). These terms are likely to be the enriched terms from the annotation. Such approach can yet be elusive as most of the terms are only annotated with one gene. A second approach is to link sub-DAGs with high PQI terms (co-annotated terms) if we consider the PQI as a quantification of a sub-DAG (pathway) relevance for the gene cluster .Despite their obvious differences, semi-automatic annotation based on evidence and literature mining combined with statistical scoring of the gene-term associations are both efficient methods for associating relevant GO Process terms to a gene cluster. The significantly higher PQIs obtained using a combination of both methods is an indication of their synergy: they do not contain the same information. We achieved a more robust and complete annotation by combining the coherence of GOA with PubGene's exploratory and bibliographic qualities. Eventually, GO terms networks can be built with associative relations in order to highlight cooperative and competitive pathways and their connected molecular functions. Our methodology is an effort to improve the actual situation which is clearly suboptimal. It is, however, not demonstrated to what precise degree this improvement goes. This remains to be determined but is outside the scope of the present paper.The GOA database aims to provide high-quality supplementary GO annotation to proteins in the UniProt (SWISS-PROT/TrEMBL) databases. Most of the GOA content comes from the manual curation of scientific literature, with semi-automatic and electronic techniques being used to support the annotation process. Therefore, an evidence code assesses the reliability of the gene-term association. These codes are established by the GO Consortium  and rangi.e., specified in the term_comment attribute) and obsolete terms with no updated term were discarded from the literature annotation. Terms being poorly associated with the gene cluster  were also discarded.PubGene is a web-based database of gene-gene and gene-term associations based on co-occurrences in biomedical literature. It provides a full-scale literature network for 25,000 human genes extracted from the titles and abstracts of over 14 million article records from the MEDLINE citation database of the National Library of Medecine (NLM). The method assumes that if two genes are mentioned in the same MEDLINE record there should be an underlying biological relationship. Genes are linked to terms from the Gene Ontology and a probabilistic score is computed that reflects the gene-term association strength which can be used to assess the relevance of each individual term. The computation of this probabilistic score assumes that occurrences of the gene and the term are independent. Therefore, a binomial formula can be used to estimate the probability of finding the gene and the term together in an article based on their respective frequencies in the whole database. Assuming a normal distribution, the expected number of articles mentioning the gene and the term is then compared to the number of times they actually occur together  product, involved in the G2/M transition of the cell cycle [We wanted to determine what the method was able to retrieve for a minimal cluster, that is, for a single gene. We chose the cell division cycle 2  and 37 down-regulated genes (Down cluster). Evaluations for both Up and Down clusters were quantitatively and qualitatively similar. We will therefore only detail here the results obtained for the down-regulated cluster (Down cluster). See The methodology was quantitatively evaluated in a multi-cluster analysis concerning 14 clusters chosen from 7 independent studies. Detailed informations on these clusters can be found in Table GO terms that best characterise this set. These terms will be among those relevant to a high number of genes. We used the hypergeometric distribution to identify statistically significant enrichments . We carried out analyses with a generalized estimating equation (gee) model to estimate parameters for correlated data, assuming a Poisson error and a log-link function. We used the R package 'geepack' [We measured, for each GO term, and for each annotation method, the number of occurrences and the number of associated genes. We carried out statistical analyses only on the number of genes per term because these two variables were strongly correlated . For the terms only found by both methods, we tested the number of genes per term against the annotation method /N, where N is the total number of parents and children nodes in the sub-DAG, NPa is the number of annotated parent nodes and NCa is the number of annotated child nodes. We used the PQI to compare the evidence and literature annotations to a combination of both annotations. We also used it to evaluate the global relevance of the GO terms found only by dependence. In this case, we calculated the PQIs for the combination of the evidence and literature and compared it to the PQIs obtained after the addition of: (i) the terms found only by dependence, and (ii) a random term set of equal size. We compared PQIs using the Kruskal-Wallis Rank Sum Test with a Bonferroni correction for multiple comparisons. We eventually used the PQI to filter out the associative relations and to limit them to reinforcing the annotation: dependent terms with a zero PQI  were discarded.A Directed Acyclic Graph (DAG) is a hierarchy in which a node can have multiple parents and children. The highest node, the one having no parents is called the root node and the deepest nodes, those with no children, are the leaves. Thus, a node can be characterised by its position within the DAG. The depth or granularity of the node is its minimum distance from the root node . We compe.g., a molecular mechanism implied by or associated with the gene activity but not the gene activity itself) or, inferred by a sequence similarity, etc.We carried out a systematic inspection of each gene-term association retrieved from the GOA Human database. Associations were sorted into three categories depending on their relevance: good associations in which the GO term was directly linked to the gene product, bad associations in which the GO term was misassociated with the gene product or non-informative, and doubtful associations in which the GO term could be indirectly linked to the gene product  relations and Across Hierarchies (AH) relations.The version of GO used throughout this study is the February 2005 monthly release, available from the GO website. DAG graphical representations were achieved using dot v1.10 and Graphviz 1.13(v16). All other graphics and statistical analyses were done using the R language version 2.1.0.All computational tasks and statistical analyses were carried out by MA. The biological relevance of the method was primarily evaluated by AM and assisted by CC, JM and MA. Expertises on glioblastomas and leukemias were respectively supplied by MdT and MG. AB and JM supervised this study and contributed to continuous discussions about its shortcomings. All authors have read and approved the final manuscript.GO terms associated with the Down cluster: Venn category, GO ID, Name, GOA Human annotation with evidence codes and genes, PubGene annotation with significant scores and genes.Click here for fileUp cluster. (A) Boxplots of the PQIs for the Evidence (GOA), Literature (PUB) and combination of both (GOAPUB). (B) Boxplots of the PQIs for the evidence and literature terms (GOAPUB), for the same set enriched with the associative relations (+Dep.) or a random term set of the same size (+Random).Click here for fileGlioblastomas clusters. (A) Boxplots of the PQIs for the Evidence (GOA), Literature (PUB) and combination of both (GOAPUB). (B) Boxplots of the PQIs for the evidence and literature terms (GOAPUB), for the same set enriched with the associative relations (+Dep.) or a random term set of the same size (+Random).Click here for fileAcute Lymphocyte Leukemias (ALL) clusters. (A) Boxplots of the PQIs for the Evidence (GOA), Literature (PUB) and combination of both (GOA-PUB). (B) Boxplots of the PQIs for the evidence and literature terms (GOAPUB), for the same set enriched with the associative relations (+Dep.) or a random term set of the same size (+Random).Click here for fileCircadian cluster. (A) Boxplots of the PQIs for the Evidence (GOA), Literature (PUB) and combination of both (GOAPUB). (B) Boxplots of the PQIs for the evidence and literature terms (GOAPUB), for the same set enriched with the associative relations (+Dep.) or a random term set of the same size (+Random).Click here for fileLung cluster. (A) Boxplots of the PQIs for the Evidence (GOA), Literature (PUB) and combination of both (GOAPUB). (B) Boxplots of the PQIs for the evidence and literature terms (GOAPUB), for the same set enriched with the associative relations (+Dep.) or a random term set of the same size (+Random).Click here for fileRetina clusters. (A) Boxplots of the PQIs for the Evidence (GOA), Literature (PUB) and combination of both (GOAPUB). (B) Boxplots of the PQIs for the evidence and literature terms (GOAPUB), for the same set enriched with the associative relations (+Dep.) or a random term set of the same size (+Random).Click here for fileAlzheimer's disease cluster. (A) Boxplots of the PQIs for the Evidence (GOA), Literature (PUB) and combination of both (GOAPUB). (B) Boxplots of the PQIs for the evidence and literature terms (GOAPUB), for the same set enriched with the associative relations (+Dep.) or a random term set of the same size (+Random).Click here for fileEnriched GO Process terms (p < = 0.05) associated with at least 4 genes in the glioGBM cluster. Color: red = terms found in GOA and PubGene, green = terms only found in GOA, blue = terms only found in PubGene. Shape: rectangle = significantly enriched annotated terms (p < = 0.05) ; ellipse = non-significantly enri-ched annotated terms (p > 0.05).Click here for fileEnriched GO Process terms associated with at least 4 genes in the bcr-abl cluster. Color: red = terms found in GOA and PubGene, green = terms only found in GOA, blue = terms only found in PubGene. Shape: rectangle = significantly enriched annotated terms (p < = 0.05) ; ellipse = non-significantly enriched annotated terms (p > 0.05).Click here for fileLocusLink IDs, symbols, aliases and names of the down regulated genes.Click here for file


Genomic scale projects have compounded the need for rapid and reliable functional annotation methods. Traditional experimental approaches have become outpaced resulting in an ever-increasing proportion of missing annotations. Computational approaches, including those based on sequence, expression, interaction and tertiary structure, have the potential to impact the growing annotation deficit.Despite a recent increase in the number and variety of prediction methods, the computational annotation of protein function remains difficult. This stems from a combination of issues such as the inherent limitations of current tools and databases, the difficulty of assessing the predictive power of different methods and more fundamental problems related to the ambiguity of the definition of function itself.a-là CASP  keep revisiting the problems [it presents]". Function prediction is indeed a challenging endeavor that is further hampered by the lack of a standard assessment framework ,33. It wFor further information and updates on AFP meetings see:


Campylobacter jejuni is the leading bacterial cause of human gastroenteritis in the developed world. To improve our understanding of this important human pathogen, the C. jejuni NCTC11168 genome was sequenced and published in 2000. The original annotation was a milestone in Campylobacter research, but is outdated. We now describe the complete re-annotation and re-analysis of the C. jejuni NCTC11168 genome using current database information, novel tools and annotation techniques not used during the original annotation.Campylobacter strains and species not available during the original annotation. Re-annotation was accompanied by a full literature search that was incorporated into the updated EMBL file [EMBL: AL111168]. The C. jejuni NCTC11168 re-annotation reduced the total number of coding sequences from 1654 to 1643, of which 90.0% have additional information regarding the identification of new motifs and/or relevant literature. Re-annotation has led to 18.2% of coding sequence product functions being revised.Re-annotation was carried out using sequence database searches such as FASTA, along with programs such as TMHMM for additional support. The re-annotation also utilises sequence data from additional O- and N-linked glycosylation. This re-annotation will be a key resource for Campylobacter research and will also provide a prototype for the re-annotation and re-interpretation of other bacterial genomes.Major updates were made to genes involved in the biosynthesis of important surface structures such as lipooligosaccharide, capsule and both  Campylobacter jejuni is the leading bacterial cause of human gastroenteritis in the developed world [C. jejuni infection has also been associated with post-infection sequelae including septicaemia and neuropathies such as Guillain-Barré Syndrome (GBS) [C. jejuni NCTC11168 genome project published in 2000 [ed world . C. jejume (GBS) . Infectime (GBS) . The lac in 2000 , and equC. jejuni NCTC11168 genome sequence in 2000, there has been a spectacular increase in research on this important human pathogen. One result of this has been significant revisions of the genetic loci that code for important surface structures on C. jejuni strains. The surface polysaccharide region has since been identified as a capsule locus (Cj1413c – Cj1448c) [O-linked glycosylation pathway (Cj1293 – Cj1342) [N-linked glycosylation pathway has been identified in C. jejuni (Cj1119 – Cj1130) [N-linked general glycosylation system was initially thought to only be present in eukaryotes. To date, up to 30 proteins modified with the same heptasaccharide glycan structure have been identified. Research over the last 7 years on C. jejuni, coupled with the publication of a further 2 C. jejuni genome sequences [Campylobacter species [Since the publication of the Cj1448c) -7. The fequences ,16 and a species , has heiRe-annotation is defined as the process of annotating a previously annotated genome . ExampleC. jejuni NCTC11168 genome. Manual re-annotation of all coding sequences (CDSs) was carried out using current annotation techniques. Literature searches, updates to genome structure and additional unique genome searches were carried out to produce the most comprehensive annotation of any Campylobacter genome to date. The re-annotation of the C. jejuni NCTC11168 genome also represents a useful model for the re-evaluation of other bacterial genomes.In this study, we describe the re-annotation and re-analysis of the C. jejuni NCTC11168 genome was performed resulting in the reduction of the total number of CDSs from 1654 to 1643. This reduction was due to the merging of adjacent CDSs or the removal of CDSs. Three CDSs originally designated as pseudogenes were removed as a result of merging with adjacent pseudogenes. CDSs designated as pseudogenes were also updated to reflect the complete amino acid sequence for the encoded protein regardless of expression. Phase-variable CDSs that contained an intersecting homopolymeric region between adjacent CDSs on separate frames were merged. This allowed the complete amino acid sequence for appropriate genes to be obtained regardless of phase. Re-interpretation of phase-variable CDSs resulted in removal of seven CDSs. CDS (Cj1520) was removed because of the recently discovered CRISPR structural moieties [A complete re-annotation of the  CDS Cj15 was remoCampylobacter species/strains or any orthologs in similar microorganisms. Additionally, the 'updated' note qualifier also contains reasoning for including 'putative' or not within the product function. Putative designations infer an accepted product function without definitive evidence. For each CDS, a full literature search was performed. In total, 64.5% of CDSs have had one or more literature qualifier added. Interestingly, from all the literature added (2092), 50.5% have been published after the year 2000. Considering there was no literature qualifier in the original annotation, this data illustrates the depth of research that has been carried out since 2000 and further supports the need to make use of this information in a re-annotation. Detailed statistics on genome modifications are given in Table A systematic re-annotation of all CDSs was performed. For the purpose of this re-annotation, all CDSs with additional information have had an 'updated' note qualifier attached. This qualifier contains consistent free-hand descriptions on recently identified motifs, relevant similarity search results and any characterisation work carried out within re added 092, 50.5C. jejuni N-linked glycosylation pathway , has been fully characterised [pglA-K (protein glycosylation) genes and has updated all product functions for genes Cj1119c – Cj1130c. The LOS locus (Cj1131c – Cj1152c) described in the original annotation was updated to include recent product functions and gene names including neuA1, B1, C1 and hldDE [O-linked glycosylation loci (Cj1293 – Cj1342) involved in flagellar glycosylation, has been updated to include neu, pse and maf genes [Cj1413c – Cj1448c), has now been updated to include kps and hdd genes [Since the original annotation, significant new information has been derived on the genetic loci encoding the four main carbohydrate surface structures. The cterised ,12-14. Tnd hldDE -24. The af genes -11. Finadd genes .C. jejuni NCTC11168 genome is provided in Additional File Additional genome-wide updates were also carried out, of which a large proportion entailed adding specificity to existing product function. For example, the identification of a new PFAM or PROSITE motif has allowed the product function to become further specified e.g. putative transport protein modified to putative MFS (Major Facilitator System) transport protein. A complete list of changes throughout the C. jejuni NCTC11168 genome was 20. We carried out a re-analysis on all pseudogenes in the NCTC11168 genome. The majority of revisions we carried out incorporated multiple features created from different coordinates on more than one frame. This process is often complicated with support needed from FASTA and TBLASTX search results. Completion of this re-analysis resulted in modification of 19 out of 20 pseudogenes (Table Cj0968/Cj0969).Pseudogene identification is a challenging process where discrepancies exist between pseudogene assignment techniques . Identifes Table . The finCj0522, Cj0523 and Cj0524 within C. jejuni NCTC11168. These three CDSs are represented as one whole CDS on a single frame within C. jejuni RM1221 (Cje0628). The three CDSs are large enough to be represented as individual CDSs and in C. jejuni NCTC11168 have been represented on more than one frame. The question can be asked as to whether these CDSs (which are intact in C. jejuni RM1221), represent a pseudogene in C. jejuni NCTC11168. Given the fact that in C. jejuni RM1221 these three CDSs do actually code for a product , it is more likely that they represent a pseudogene in C. jejuni NCTC11168. In this re-annotation, our intention was to carry out a full mark up of existing pseudogenes, however, the potential for a pseudogene has been noted.An example of the difficulty and complexity associated with pseudogene designation is observed when viewing the CDSs C. jejuni NCTC11168 and C. jejuni RM1221 are compared in Additional File C. jejuni 81–176 genome has not been fully annotated so could not be used in this comparison. This is also the case for C. coli RM2228, C. lari RM2100 and C. upsaliensis 3195 which only have an estimation of pseudogene numbers based on a subset of genes [C. jejuni NCTC11168, 63% (12/19) of the pseudogenes are shared with C. jejuni RM1221. In contrast to 19 pseudogenes in C. jejuni NCTC11168, C. jejuni RM1221 contains 47 pseudogenes. Assuming these are genuine pseudogenes this would imply C. jejuni NCTC11168  and C. jejuni RM1221 , share a core set of ancestral pseudogenes. Even with the variation of isolation dates, source and geographical location, there is substantial conservation of pseudogene type. It is speculative to suggest when and how the additional pseudogenes in C. jejuni RM1221 arose, or when and how the C. jejuni NCTC11168 genome lost CDSs as pseudogenes since divergence occurred.The frequency and importance of pseudogene formation in microorganisms has attained added significance in recent years with the emergence of genome reduction theories and enhanced virulence through pathoadaptive mutations ,28. Receof genes . In C. jEscherichia coli K-12, which has predicted an additional 161 from the original single pseudogene identified [Campylobacter strains and species along with additional epsilon proteobacteria species will aid our understanding on this emerging area of interest. Also, greater understanding of pseudogene dynamics and in particular innovative pseudogene identification techniques will yield more information about the actual number and purpose of these entities within microorganisms.The significance of pseudogenes in early genome annotations were frequently ignored, as these were considered as sequencing artefacts. However, given the recent realisation of the importance of pseudogenes in pathoadaptive mutations, a greater significance is placed on their identification ,29. An eentified . The samentified . Pseudogentified . AnalysiC. jejuni genomic shotgun sequence [O-linked glycosylation and capsule loci. Further research on these loci have illustrated the impact of phase-variation on microorganism pathogenicity [Phase-variable CDSs containing hypervariable regions were also analysed. The initial annotation gave a number of hypervariable sequences found within the sequence . These hgenicity ,31,32. TCampylobacter [C. jejuni and this has now been incorporated within the genome. As a result, one CDS (Cj1520) has been removed. This CDS was previously annotated as having five repeat regions. Thus, the genome now contains a CRISPR repeat region in place of the removed CDS.As well as CDS updates, novel features were also added to the re-annotation. For example, the incorporation of the recently identified Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) regions within lobacter ,33,34. CCj0046. The SRP is a universally conserved ribonucleoprotein involved in the co-translational targeting of proteins to membranes [Cj0453 (thiamin biosynthesis protein ThiC). The RFAM motif is a conserved structure (THI element), involved in thiamin-regulation [Additional genome searches included RFAM database search to discover any non-coding RNAs. This search identified two new non-coding RNA structures. RFAM RF00169, a bacterial signal recognition particle (SRP) RNA, was identified upstream of embranes ,36. Alsogulation .The final step of the re-annotation process was the incorporation of Gene Ontology (GO) annotation. GO annotation attempts to link three structured, controlled vocabularies (ontologies) that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species-independent manner . This reC. jejuni NCTC11168 genome sequence has led to substantial updates across the entire genome, incorporating a vast amount of research information performed since the original annotation in 2000 and also integrating data from additional Campylobacter species and strains. Major updates include noteworthy modifications to the 4 main surface structure loci in the genome, 18.2% of genome product functions being updated and 90.0% of all CDSs now having additional information. The inclusion of literature searches and a GO annotation alongside genome wide structural modifications has resulted in C. jejuni NCTC11168 being the most comprehensively annotated Campylobacter genome to date.In summary, the re-annotation and re-analysis of the C. jejuni NCTC11168 CDSs [Manual re-annotation of all previously annotated 168 CDSs  was carr168 CDSs  and FAST168 CDSs  sequence168 CDSs . Additio168 CDSs  and PROS168 CDSs  motif da168 CDSs  database168 CDSs  and SIGN168 CDSs .C. jejuni RM1221 and adopting the GO annotation of orthologous CDSs.This re-annotation included a complete literature search of all CDS numbers and gene names using PubMed , HighWirAdvances in genome annotation techniques that were unavailable during the original annotation have led to updated interpretation of pseudogenes and phase-variable CDSs. Using guidance from TBLASTX search results, we carried out a full re-analysis of all pseudogenes. CDSs designated as pseudogenes have been updated to reflect the complete amino acid sequence for the encoded protein regardless of expression. This has caused differences from the amino acid sequence of the previous annotation. Some pseudogene modifications entailed merging two or more adjacent, in frame CDSs (previously annotated as separate pseudogene CDSs), to create a single pseudogene containing internal stop codons. In other cases, pseudogene features were created with multiple coordinates representing one or more frameshift in the CDS – these had previously only detailed the start and stop coordinates so did not reflect the true position of the non-mutated CDS. In both cases the assignment of coordinates was based on matches to homologues determined through FASTA searches.CDSs containing an intersecting homopolymeric tract were merged to reflect the complete amino acid sequence for appropriate genes regardless of phase. This is analogous to the scenario described above for frameshifted pseudogenes. This modification was carried out for two CDSs with an intersecting homopolymeric tract. The joining of such CDSs was not undertaken in the original annotation.OG carried out the re-annotation process and drafted the manuscript. SDB assisted with the re-annotation process. MTH assisted with running additional programs used in the re-annotation. JP, ND and BWW participated in the conception and supervised the design of the study. All authors submitted comments on drafts and read and approved the final manuscript.C. jejuni functional classification (created at Sanger Institute).Click here for fileDistribution of functional classification before/after re-annotation.Click here for fileChanges to functional classification categories before and after the re-annotation.Click here for fileChanges to CDS functions and functional classifications.Click here for fileC. jejuni NCTC11168 re-annotation.CDSs modified in Click here for fileC. jejuni NCTC11168 and C. jejuni RM1221.Pseudogene comparison between Click here for file


Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity.The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions.The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart. The number of sequences generated by many genome projects is soaring exponentially but most of them have not been characterized experimentally. Manual annotation methods have been proposed by experts and are popular for use at the genome centers, but their annotation capacities are exceeded by the fast growing genome data. An automatic annotation scheme is in urgent need to speed up reliable functional annotation on new sequences produced. Automatic annotation provides an efficient procedure for analyzing the gene sequences. Most automatic solutions used to characterize the gene sequences are based on a high-level sequence similarity search against some known protein databases such as using the BLAST or FASTA program. The correlation between sequence composition and functional characterization provides the foundation for transferring functional knowledge from a biochemically characterized protein to a homologous but uncharacterized one. However, sequence composition bias and database updating commonly influence the results of similarity searches, and they do not yield the exact share between biological function and domain composition based on the similarity threshold used . Many anIn the post-genomic era, the functional annotations are of great importance in understanding the real cellular processes. A variety of enzymes and pathway databases including Ecocyc, Enzyme, and KEGG, have been built to facilitate the prediction of the metabolic pathway. Such databases are supplied as reference databases in virtual construction of the metabolic networks of other organisms. On the pathway map, enzymes are the main components used for linking the metabolic networks. The fundamental units of enzyme structure governing folding and function are domains of protein . A domaiet al. [et al. [This work proposes a machine learning method for identifying enzyme classes according to the rules that are related to the protein domain composition. Using rules generated by machine learning algorithms, Kretschmann et al.  and Bazz [et al.  have suc [et al. ,8. In th [et al.  and medi [et al. . This inThis work seeks to annotate unknown genes and establishes virtual metabolic pathways using the bioinformatics approach based on progress made in the Monascus genome project at the authors' institute. Only few Monascus genes have been biochemically characterized so far. Numerous well-characterized proteins have been stored in a public database so that it is feasible to mine the classified rules from a protein knowledgebase. The BLAST is a fast but insufficient method for annotating unknown genes because it does not provide information on the functional domain. Analyzing the constituent domains of a gene enables the determination of possible functions of the gene. However, making a decision regarding the annotation of a multi-domain protein is difficult. In this study, an annotation model was established by applying rules derived from the domain compositions in some well-characterized proteins. The concept of annotation using the domain composition was further investigated. Five datasets Table  were useMany data mining methods have been applied in the biological researches. For example, a decision tree has been used in keyword annotation in the Swiss-Prot  and PIGSAs presented in Table Table Furthermore, the accuracy of the presented method was compared with the rules obtained from the InterPro database. These rules were parsed where the IPR Acc's were cross-referenced to ENZYME in the entry_xref table of the InterPro database. The rules such as {IPR001711, EC 3.1.4.11} were retained for providing the enzyme identification. There were five testing datasets used to evaluate the parsed ones. As shown in Table The precision and confidence of each EC class was also evaluated in the fungus dataset. Both quantities were varied among all the EC classes tested (data not shown here). However, a precision of greater than 75% was obtained for 60% of the EC classes tested (data not shown.). In this study, the Swiss-Prot entries were chosen as the training while the TrEMBL entries as the test set. We aimed to find the EC classifying rules that are hidden in the protein knowledgebase and to estimate the accuracy of the classifying method. The rules mined and presented here can be used by an annotator to perform manual annotation. They can be also implemented in an automatic annotation flowchart. They are also feasible to be used in identifying enzyme classes based on their IPR signature.Apriori one.This report proposed an alternative approach on employing the association algorithm. The association algorithm is commonly used to identify large and frequent item sets and mine hidden relationships among items. The concept can be applied in many fields other than market basket analysis. The method is extended here to mine the association rules which are then applied to identify enzyme classes. The current prediction scheme emphasizes on identifying enzymes of taxonomically closed datasets. Rule sets generated from the eukaryote training datasets can be used to assign the EC classes accurately to poorly annotated entries whose real enzyme function remain unknown. Extending the method to predict other types of data, including the transcription factors and structure proteins, is also worthwhile. However, the low coverage is a shortcoming of the presented scheme. The matching coverage depends on the quality of the training dataset which may be extended as a combination of various datasets with each being closed in taxonomic relationship. Moreover, more rules may be generated using other association algorithms except the There were five distinctly taxonomic datasets referring to the NEWT  UniProt. The entThe WEKA machine learning package  which isApriori [The Apriori ,15 modulP(A+B)     (1)Support (AB) = P(B|A)     (2)Confidence (AB) = P(B|A) was the conditional probability of B given A, and P(A) or P(B) was the probability of A or B over all instances. The probability was defined as the observed frequency in the data set. The support of the rule was the relative frequency of transactions containing both A and B. The lift was the related measure of strength of the association. Positive correlation was indicated by lift > 1 while negative correlation was indicated by lift < 1. A large frequent itemsets were subdivided into smaller ones in numerous ways to generate the candidate association rules. The candidate association rules were redundant and many of them were subsets of larger frequent itemsets. However, the rules mined herein were of the form {A,B,C}⇒{D} but not {A,B}⇒{C,D} or {A}⇒{B,C,D}. For example, {IPR000873, IPR006163, IPR010080} ⇒ {1.2.1.31}. Because most of the support values of items were between 1 and 50, a minimum support value of 0.09% was set herein to indicate that the attribute must appear 2.7 times in 3000 instances. The threshold of confidence was 0.6 and the corresponding lift value was between 10 and 30.where et al. [The criterion satisfied rules were stored in the MySQL database for further evaluation. The testing dataset was used to evaluate the candidate rules governing the enzyme domain composition. Each test datum (separated by commas) was treated as a single string and matched with the set of rules  to find the corresponding EC class. The precision of EC class matching (testing dataset to rules set) and the confidence were evaluated using the following equations as given by Kretschmann et al. .n = TP + FP     (6)TP represents the "True Positives" and FP represents the "False Positives" and z is a constant, 1.96 (for 95% confidence).where SHC implemented the computational approach, performed the analysis and drafted the manuscript. CCC and GYF participated in the design of this study. THL participated in the design of this study, interpreted the results, and wrote the manuscript. All authors read and approved the final manuscript.All rules generated from the fungus training dataset.Click here for file


Plasmodium falciparum, confirming SIFTER's prediction. The results illustrate the predictive power of exploiting a statistical model of function evolution in phylogenomic problems. A software implementation of SIFTER is available from the authors.We present a statistical graphical model to infer specific molecular function for unannotated protein sequences using homology. Based on phylogenomic principles, SIFTER  accurately predicts molecular function for members of a protein family given a reconciled phylogeny and available function annotations, even when the data are sparse or noisy. Our method produced specific and consistent molecular function predictions across 100 Pfam families in comparison to the Gene Ontology annotation database, BLAST, GOtcha, and Orthostrapper. We performed a more detailed exploration of functional predictions on the adenosine-5′-monophosphate/adenosine deaminase family and the lactate/malate dehydrogenase family, in the former case comparing the predictions against a gold standard set of published functional characterizations. Given function annotations for 3% of the proteins in the deaminase family, SIFTER achieves 96% accuracy in predicting molecular function for experimentally characterized proteins as reported in the literature. The accuracy of SIFTER on this dataset is a significant improvement over other currently available methods such as BLAST (75%), GeneQuiz (64%), GOtcha (89%), and Orthostrapper (11%). We also experimentally characterized the adenosine deaminase from  New genome sequences continue to be published at a prodigious rate. However, unannotated sequences are of limited use to biologists. To computationally annotate a hypothetical protein for molecular function, researchers generally attempt to carry out some form of information transfer from evolutionarily related proteins. Such transfer is most successfully achieved within the context of phylogenetic relationships, exploiting the comprehensive knowledge that is available regarding molecular evolution within a given protein family. This general approach to molecular function annotation is known as phylogenomics, and it is the best method currently available for providing high-quality annotations. A drawback of phylogenomics, however, is that it is a time-consuming manual process requiring expert knowledge. In the current paper, the authors have developed a statistical approach—referred to as SIFTER —that allows phylogenomic analyses to be carried out automatically.The authors present the results of running SIFTER on a collection of 100 protein families. They also validate their method on a specific family for which a gold standard set of experimental annotations is available. They show that SIFTER annotates 96% of the gold standard proteins correctly, outperforming popular annotation methods including BLAST-based annotation (75%), GOtcha (89%), GeneQuiz (64%), and Orthostrapper (11%). The results support the feasibility of carrying out high-quality phylogenomic analyses of entire genomes. The post-genomic era has revealed the nucleic and amino acid sequences for large numbers of genes and proteins, but the rate of sequence acquisition far surpasses the rate of accurate protein function determination. Sequences that lack molecular function annotation are of limited use to researchers, so automated methods for molecular function annotation attempt to make up for this deficiency. But the large number of errors in protein function annotation propagated by automated methods reduces their reliability and utility –3.E-value, as an indicator of homology. A functional annotation is heuristically transferred to the query sequence based on reported functions of similar sequences.Most of the well-known methods or resources for molecular function annotation, such as BLAST , GOFigurSIFTER  takes a different approach to function annotation. Phylogenetic information, if leveraged correctly, addresses many of the weaknesses of sequence-similarity-based annotation transfer , such asOther approaches, referred to as context methods, predict protein function using evolutionary information and protein expression and interaction data –26. ThesPhylogenomics is a methodology for annotating the specific molecular function of a protein using the evolutionary history of that protein as captured by a phylogenetic tree . PhylogePhylogenomics applies knowledge about how molecular function evolves to improve function prediction. Specifically, phylogenomics is based on the assertion that protein function evolves in parallel with sequence , implyinIt is broadly recognized that this method produces high-quality results for annotating proteins with specific molecular functions . Three pBayesian methodologies have influenced computational biology for many years . BayesiaThree properties of the Bayesian approach make it uniquely suited to molecular function prediction. First, Bayesian inference exploits all of the available observations, a feature that proves to be essential in this inherently observation-sparse problem. Second, the constraints of phylogenomics—that function mutation tends to occur after a duplication event or that function evolution proceeds parsimoniously—are imposed as prior biases, not as hard constraints. This provides a degree of robustness to assumptions that is important in a biological context. Third, Bayesian methods also tend to be robust to errors in the data. This is critical in our setting, not only because of existing errors in functional annotations, but also because phylogeny reconstruction and reconciliation often imperfectly reflect evolutionary history.The current instantiation of SIFTER uses Bayesian inference to combine all molecular function evidence within a single phylogenetic tree, using an evolutionary model of molecular function. A fully Bayesian approach to phylogenomics would integrate over all sources of uncertainty in the function annotation problem, including uncertainty in the phylogeny and its reconciliation, and uncertainty in the evolutionary model for molecular function. It is important to be clear at the outset that the current instantiation of SIFTER stops well short of full Bayesian integration. Rather, we have focused on a key inferential problem that is readily treated with Bayesian methods and is not accommodated by current tools in the literature—that of combining all of the evidence within a single inferred tree using probabilistic methods. Technically, this limited use of the Bayesian formalism is referred to as “empirical Bayes” .Extensions to a more fully Bayesian methodology are readily contemplated; for example, we could use techniques such as those used by MrBayes  to integAll automated function annotation methods require a vocabulary of molecular function names, whether the names are from the set of Enzyme Commission (EC) numbers, Gene Ontology (GO) molecular function names , or wordSIFTER builds upon phylogenomics by employing statistical inference algorithms to propagate available function annotations within a phylogeny, instead of relying on manual inference, as fully described in A “duplication event” captures a single instance of a gene duplicating into divergent copies of that gene within a single genome; a “speciation event” captures a single instance of a gene in an ancestral species evolving into divergent copies of a gene in distinct genomes of different species. Each of the internal nodes of a phylogeny represents one of these two events, although a standard phylogeny does not distinguish between the two. The reconciled phylogeny for a protein family, which discriminates duplication events and speciation events ,46, specThe available, or observed, function annotations, associated with individual proteins at the leaves of the phylogeny, are propagated towards the root of the phylogeny and then propagated back out to the leaves of the phylogeny, based on a set of update equations defined by the model of function evolution. The result of the inference procedure is a posterior probability of each molecular function for every node in the tree (including the leaves), conditioned on the set of observed functions. The posterior probabilities at each node do not actually select a unique functional annotation for that node, so functional predictions may be selected using a decision rule based on the posterior probabilities of all of the molecular functions. This procedure gives statistical meaning to the phylogenomic notion of propagating functional annotations throughout each clade descendant from a molecular function mutation event. We do not require that the mutation event coincide with a duplication event.The inference algorithm used in SIFTER has linear complexity in the size of the tree and thus is viable for large families. The complexity of SIFTER is exponential in the number of possible molecular functions in a family, owing to the fact that we compute posterior probabilities for all possible subsets of functions. In the families that we studied, the number of functions was small and this computation was not rate-limiting; in general, however, it may be necessary to restrict the computation to smaller collections of subsets. The rate-limiting step of applying SIFTER is phylogeny reconstruction; a full-genome analysis, given limited computational resources, might use lower-quality or precomputed phylogenies along with bootstrapping, or a subset of closely related species for the larger protein families. We found that lower-quality trees do not significantly diminish the quality of the results (results will be detailed elsewhere).In this report, we use only GO IDA- and IMP-derived annotations as observations for SIFTER, because of the high error rate and contradictions in the non-experimental annotations . However, SIFTER can also incorporate other types of annotations, weighted according to their reliability.We first present results for SIFTER's performance on a large set of proteins to show general trends in prediction and to evaluate the scalability of SIFTER. We then present results for a single protein family with a gold standard set of function characterizations to evaluate prediction quality in detail. We also describe results for the lactate/malate dehydrogenase family, although it does not have a gold standard dataset. The decisive benefit of a statistical approach to phylogenomics is evidenced on each of these different datasets.To evaluate the scalability, applicability, and relative performance of SIFTER, we predicted molecular function for proteins from 100 protein families available in Pfam , using eFor each family in our 100-family dataset, we ran SIFTER on the associated reconciled tree with the experimental annotations (IDA and IMP) from the GOA database. SIFTER produced a total of 23,514 function predictions; we selected the subset of 18,736 that had non-experimental annotations from the GOA database and applied BLASTC, GOtcha, and Orthostrapper to this set . We did We chose these 100 families to meet one of the following two criteria: (1) greater than 10% proteins with experimental annotations (and more than 25 proteins), or (2) more than nine experimental annotations. Families with fewer than two incompatible experimental GO functions were excluded. The families had an average of 235 proteins, ranging from 25 to 1,116 proteins. On average, 3.3% of the proteins in a family had IDA annotations, and 0.4% had IMP annotations. Both SIFTER and Orthostrapper relied on this particularly sparse dataset for inference; evaluative techniques involving the removal of any of these annotations from inference tended to trivialize the results . Selecting well-annotated families via these criteria assists SIFTER, but it should also enhance the performance of all of the function transfer methods evaluated here. Note also that SIFTER does not require this level of annotation accuracy to be effective, as discussed below. Finally, it is important to note that many of the IEA annotations from the GOA database may come from one of the assessed methods, so we can expect consistency to be quite high.Of the 8,501 SIFTER predictions that were either identical or incompatible to the GO non-experimental annotations, 83.1% were identical. The average percentage of identical function predictions by family was 82.9%, signifying that the size of the family does not appear to impact this percentage. The median identity by family was 90.7%, and the mode was 100% (representing 25 families). The minimum identity was 14.4% (Pfam family PF00536). We estimate that 38 of the families contained non-enzyme proteins, and we found no difference in the identity percentage of SIFTER on enzyme families versus non-enzyme families. Similarly, the total number of functional annotations used as observations in SIFTER does not appear to impact the identity percentage . These data suggest that a large percentage of incompatibility is concentrated within a few families. It is not entirely clear what property of those families contributes to the greater incompatibility; it may reflect how well studied the families are relative to the number of proteins in the family.Not all of the annotation methods predict functions for 100% of the proteins. Indeed, as shown in The percentage of Orthostrapper predictions that were identical or compatible with the non-experimental GOA database function annotations in the 100-family dataset was 88%, but only 7% of proteins received Orthostrapper predictions . When fuThe difficulties encountered by Orthostrapper arise from the small number of proteins that are placed in statistically supported clusters, and the lack of annotations in these clusters. The latter limits the usefulness of the method to protein families with a high percentage of known protein functions, or to observed annotations with a low error rate. These results highlight the impact of the modeling choices in SIFTER and Orthostrapper. SIFTER uses Bayesian inference in a single phylogeny, addressing uncertainty in the ancestral variables in the phylogeny but presently not addressing uncertainty in the phylogeny itself. In contrast, Orthostrapper's approach of bootstrapped orthology addresses uncertainty in the phylogeny, but neglects uncertainty in the ancestral variables. Our results indicate that the gains to be realized by treating uncertainty within a tree may outweigh those to be realized by incorporating uncertainty among trees, but it would certainly be of interest to implement a more fully Bayesian version of SIFTER that accounts for both sources of uncertainty.We compared SIFTER's prediction (the function with the single highest posterior probability) to the top-ranked prediction from BLAST-based methods, the top-ranked prediction from GOtcha, the unranked set of non-experimental terms from the GOA database, and unranked Orthostrapper predictions. On this broad set of proteins, SIFTER's predictions were compatible or identical with the non-experimental annotations from the GOA database for 80% of the predictions, while 67% of BLAST-based predictions, 80% of GOtcha predictions, and 78% of GOtcha-ni predictions were compatible or identical to the non-experimental GOA database annotations. It is not entirely clear what these numbers represent, in particular because some unknowable fraction of the IEA annotations in the GOA database were derived using these or related methods. Orthostrapper predictions achieved 88% (Ortho) and 92% (Ortho-ns) compatibility or identity with the GOA database, but because of the small percentage of proteins receiving predictions using Orthostrapper, the absolute number of compatible or identical annotations is much lower. All pairwise comparison data are in The number of incompatible annotations is noteworthy: exact term agreement ranges from 16% to 91%, and the percentage of compatible or identical terms ranges from 45% to 95%. Collectively the methods must be producing a large number of incorrect annotations as evidenced by the high percentage of disagreement in predictions. It appears that there is no gold standard for comparison in the case of electronic annotation methods other than experimental characterization.We selected a well-characterized protein family, the adenosine-5′-monophosphate (AMP)/adenosine deaminase family, for evaluation of SIFTER's predictions against a gold standard set of function annotations. We assessed these using experimental annotations that we manually identified in the literature, accepting only first-hand experimental results that were successful in unambiguously characterizing the specific chemical reaction in question. References are provided in The AMP/adenosine deaminase Pfam family contains 128 proteins. Based on five proteins with experimental annotations from the GOA database, we ran SIFTER to make predictions for the remaining 123 proteins. Of these remaining proteins, 28 had experimental characterizations found by the manual literature search. SIFTER achieved 96% accuracy (27 of 28) for predicting a correct function against this gold standard dataset. SIFTER performed better than BLAST, GeneQuiz, GOtcha, GOtcha-exp , and Orthostrapper . The comparative results are summarized in Homo sapiens results in one form of severe combined immune deficiency syndrome )SIFTER's primary role may be to reliably predict protein function for many of the Pfam families or more generic sets of homologous proteins. The argument can be made that no automated function annotation method should be used in some of these cases because the data within a family are too sparse to support annotation transfer. Thus, a second role for SIFTER may be to quantify the reliability of function transfer in under-annotated sets of homologous proteins, by using the posterior probabilities as a measure of confidence in annotation transfer. A third role may be to select targets for functional assays so as to provide maximum coverage based on function transfer for automated annotation techniques. Because of its Bayesian foundations, SIFTER is uniquely qualified to address these alternate questions in a quantifiable and robust way.Molecular function predictions cannot replace direct experimental evidence for producing flawless function annotations . HoweverIn this section we first present the modeling, algorithmic, and implementational choices that were made in SIFTER. We then turn to a discussion of the methods that we chose for empirical comparisons. Finally, we present the protocol followed for the deaminase activity assays.In classical phylogenetic analysis, probabilistic methods are used to model the evolution of characters  along the branches of a phylogenetic tree  and to mSIFTER borrows much of the probabilistic machinery of phylogenetic analysis in the service of an inference procedure for molecular function evolution. The major new issues include the following: (1) given our choice of GO as a source of functional labels, functions are not a simple list of mutually exclusive characters, but are vertices in a DAG; (2) we require a model akin to Jukes–Cantor but appropriate for molecular function; (3) generally only a small subset of the proteins in a family are annotated, and the annotations have different degrees of reliability. We describe our approach to these issues below.The first step of SIFTER is conventional sequence-based phylogenetic reconstruction and reconciliation.Phylogenetic reconstruction is the computational bottleneck in the application of SIFTER. Thus, in the current implementation of SIFTER we have made use of parsimony methods instead of more computationally intense likelihood-based or Bayesian methods in phylogenetic reconstruction. This “empirical Bayes” simplification makes it possible to apply SIFTER to genome-scale problems.b10 [In detail, the steps of phylogenetic reconstruction implemented in SIFTER are as follows. Given a query protein, we (1) find a Pfam family of a homologous domain , and extb10 , using pb10  to estimThe result of this procedure is a “reconciled phylogeny,” a rooted phylogenetic tree with branch lengths and duplication events annotated at the internal nodes ,46.Subsequent stages of SIFTER retain these structural elements of the phylogeny, but replace the amino acid characters with vectors of molecular function annotations and place a model of molecular function evolution on the branches of the phylogeny.We use the following process to define a vector of candidate molecular function annotations for a given query protein and for the other proteins in the phylogeny.Given a Pfam family of a homologous domain for a query protein, we index into the GOA database   associated with some of the proteins in the family. To accommodate the fact that IDA annotations may be more reliable than IMP annotations according to the experiments by which they are generated, and to allow users to make use of other, possibly less reliable, annotations, SIFTER distinguishes between a notion of “true function” and “annotated function,” and defines a likelihood function linking these variables. In particular, the current implementation of SIFTER defines expert-elicited probabilities that an experimentally derived annotation is correct given the method of annotation: IDA annotations are treated as having a likelihood of 0.9 of being correct, and IMP as having a likelihood of 0.8.Q(S) = 1/ηS||, where S is an arbitrary subset of the nad nodes, |S| is the cardinality of S, and the value of η is fixed by the requirement that GOA database annotations are not restricted to the leaves of the ontology but can be found throughout the DAG. To incorporate all such annotations in SIFTER, we need to propagate annotations to the nad subset. In particular, annotations at nodes that are ancestors to nad nodes need to be propagated downward to the nad nodes.  We do this by treating evidence at an ancestor node as evidence for all possible combinations of its descendants, according to the distribution We turn to a description of the model of molecular function evolution that SIFTER associates with the branches of the phylogeny. For each node in the phylogeny, corresponding to a single protein, this model defines the conditional probability for the vector of function annotations at the node, conditioning on the value of the vector of function annotations at the ancestor of the node. We chose a statistical model known as a loglinear model for the model of function evolution. We make no claims for any theoretical justification of this model. It is simply a phenomenological model that captures in broad outlines some of the desiderata of an evolutionary model for function and has worked well in practice in our phylogenomic setting.iX denote the Boolean vector of candidate molecular function annotations for node i and let mth component of this vector. Let M denote the number of components of this vector. Let πi denote the immediate ancestor of node i in the phylogeny, so that i to i as follows:Let id and m,nq are parametric functions of branch lengths in the phylogeny and path lengths in GO, respectively. This functional form is known as a “noisy-OR” function [m and is equal to zero for all other values of m.  Suppose that id is equal to one. Then the probability that node i has the nth function .where function , and it m,nq to be a decreasing function of the path length m,nl in GO. Specifically, we let , where s is a free parameter. This parameter is taken to be different for speciation and duplication events; in particular, it is larger in the latter case, corresponding to the phylogenomic assumption that evolutionary transitions are more rapid following a duplication event. To set the parameters sspeciation and sduplication, we can in principle make use of resampling methods such as cross-validation or the bootstrap. In the case of the deaminase family, however, the number of observed data points (five) is too small for these methods to yield reasonable results, and in our analyses of this family we simply fixed the parameters to the values sspeciation = 3 and sduplication = 4 and did not consider other values. For the 100-family dataset, we ran each family with a few different parameter settings, because the number of annotations available for the families was in general prohibitively small, and fixed them at the set of values that produced predictions most closely aligned with the non-experimental annotations from the GOA database. We define m,mq = (1/r)s/2 for self-transitions; this normalizes the self-transition probability with respect to the number of components of the annotation vector.To capture the notion that a transition should be less probable the less “similar” two functions are, we defined id to be a decreasing nonlinear function of the branch length.  Specifically, we set ib is the most parsimonious number of amino acid mutations along the branch from πi to i.We also need to parameterize the transition rate as a function of the branch length in the phylogeny. This is achieved by defining Having defined a probabilistic transition model for the branches of the phylogeny, and having defined a mechanism whereby evidence is incorporated into the tree, it remains to solve the problem of computing the posterior probability of the unobserved functions in the tree conditional on the evidence.This problem is readily solved using standard probabilistic propagation algorithms. Specifically, all posterior probabilities can be obtained in linear time via the classical pruning algorithm , also knE-value cutoff of 0.01. We transferred annotation from the highest scoring non-identity protein (BLASTB), which was determined by checking the alignment for 100% identity and identical species name. We also transferred annotation from the highest scoring annotated non-identity protein (BLASTA), which was the highest scoring non-identity protein that had a functional description . Phrases modifying a functional annotation such as “putative” and “-related” were ignored. An annotation including an EC number was considered unambiguous.The BLAST version 2.2.4 [E-value) that had a function description from the appropriate set: for the deaminase family we searched for “adenosine deaminase,” “adenine deaminase,” “AMP deaminase,” and, for the results on multiple functions, “growth factor activity.” A reference could also be in the form of an EC number or unambiguous phrase . We plotted the false positives (one minus specificity) versus true positives (sensitivity) as the acceptance cutoff for E-values ranges from 0.01 to zero, where proteins were annotated with a function if the most significant E-value for a protein with that particular function was less than the acceptance cutoff.To build the ROC plots for the BLASTC comparison, for each protein in the selected families we searched the BLAST output for the highest scoring sequence  from August 22, 2004, to September 1, 2004 . The funFor GOtcha, we ran the first publicly available version of the GOtcha software , kindly For Orthostrapper , we ran In each cluster, we transferred all experimentally derived GO annotations from member proteins onto the remaining proteins without experimentally derived GO annotations. If a cluster did not contain a protein with an experimentally derived GO annotation, no functions were transferred; if a protein was present in multiple clusters, it would receive annotations transferred within each of those clusters. This method yields an unranked set of predictions for each protein.Purified Q8IJA9_PLAFA was the kind gift of Erica Boni, Chris Mehlin, and Wim Hol of the Stuctural Genomics of Pathogenic Protozoa project at the University of Washington. Adenosine and adenine were from Sigma-Aldrich , AMP was from Schwarz Laboratories , and monobasic and dibasic potassium phosphate were from EMD Chemicals .−1 cm−1 [The loss of absorbance at 265 nm was monitored with an Agilent Technologies  8453 spectrophotometer. The Δɛ between substrate adenosine and product inosine is 7,740 AU M−1 cm−1 .Dataset S1(7.5 MB TGZ)Click here for additional data file.http://www.ebi.ac.uk/swissprot/) accession number for H. sapiens adenosine deaminase is P00813 and for P. falciparum adenosine deaminase is Q8IJA9. The Pfam (http://www.sanger.ac.uk/Software/Pfam/) accession number for the AMP/adenosine deaminase family is PF00962.The Swiss-Prot (