PlasmoDraft: a database of Plasmodium falciparum gene function predictions based on postgenomic data
© Bréhélin et al; licensee BioMed Central Ltd. 2008
Received: 13 June 2008
Accepted: 16 October 2008
Published: 16 October 2008
Of the 5 484 predicted proteins of Plasmodium falciparum, the main causative agent of malaria, about 60% do not have sufficient sequence similarity with proteins in other organisms to warrant provision of functional assignments. Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes.
We present PlasmoDraft http://atgc.lirmm.fr/PlasmoDraft/, a database of Gene Ontology (GO) annotation predictions for P. falciparum genes based on postgenomic data. Predictions of PlasmoDraft are achieved with a Guilt By Association method named Gonna. This involves (1) a predictor that proposes GO annotations for a gene based on the similarity of its profile (measured with transcriptome, proteome or interactome data) with genes already annotated by GeneDB; (2) a procedure that estimates the confidence of the predictions achieved with each data source; (3) a procedure that combines all data sources to provide a global summary and confidence estimate of the predictions. Gonna has been applied to all P. falciparum genes using most publicly available transcriptome, proteome and interactome data sources. Gonna provides predictions for numerous genes without any annotations. For example, 2 434 genes without any annotations in the Biological Process ontology are associated with specific GO terms (e.g. Rosetting, Antigenic variation), and among these, 841 have confidence values above 50%. In the Cellular Component and Molecular Function ontologies, 1 905 and 1 540 uncharacterized genes are associated with specific GO terms, respectively (740 and 329 with confidence value above 50%).
All predictions along with their confidence values have been compiled in PlasmoDraft, which thus provides an extensive database of GO annotation predictions that can be achieved with these data sources. The database can be accessed in different ways. A global view allows for a quick inspection of the GO terms that are predicted with high confidence, depending on the various data sources. A gene view and a GO term view allow for the search of potential GO terms attached to a given gene, and genes that potentially belong to a given GO term.
Malaria is one of the most prevalent disease in the world, infecting 400 million people every year, and causing 2.7 million deaths, mainly children under 5 years . Plasmodium falciparum, the main causative agent of this parasitic disease, develops drug resistance and no effective vaccine is available. Of the 5 484 coding genes of P. falciparum (http://plasmodb.org version 5.4), about 60% do not have sufficient similarity to proteins in other organisms to warrant provision of functional assignments. Thus, almost two-thirds of the proteins appear to be specific to P. falciparum, a much higher proportion than observed in other enkaryotes . However, this is likely exacerbated by the high evolutionary distance between P. falciparum and other sequenced eukaryotes, so homology detection is a hard task. Because of the extreme AT bias (80%), the high amino acid bias (six amino acids account for more than 50% of the protein composition) and the presence of a large number of low complexity repeat regions that are believed to form non-globular segments , standard sequence comparison methods based on BLAST  or HMMER  could be ineffective . Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes .
Methods based on post-genomic data (mainly gene expression and protein interaction) have been proposed. These are commonly called Guilt by Association (GBA) methods. Contrary to sequence homology which involves inter-species annotation transfers, i.e. genes characterized in other species are used to annotate genes of the newly sequenced genome, GBA approaches involve intra-species annotation transfers: the genes already characterized in the genome, e.g. by wet experiments or using sequence homology, are used for the annotation of the other genes (guilt by association principle). Gene expression data are often used, since genes with similar transcriptomic profiles likely share common functional roles [8, 9]. In the same way, protein interaction data are also used since proteins that share common interactors likely share common functions [10–12]. These methods provide functional predictions for the uncharacterized genes, and new clues to be compared with the predictions achieved by homology.
Part of these new post-genomic methods work in a non-supervised way: first a gene clustering algorithm is run on the post-genomic data to cluster the genes into several groups. Then, in each cluster and for each potential function, a statistical test is applied to compare the proportion of genes annotated with this function in the cluster with that in the complete set of genes. Functions that appear to be over-represented in one cluster are used to annotate the uncharacterized genes that belong to this cluster. Several genome-scale studies have been conducted using this principle, e.g. [8, 13, 14].
Some other GBA methods work in a supervised way: first, based on the post-genomic data of already characterized genes, a supervised learning algorithm is run to learn a predictor, i.e. a function that takes post-genomic measurements of a given gene as input, and outputs one or several functional predictions for that gene. This predictor is then used to annotate the uncharacterized genes. Typical examples of this approach are, e.g. [11, 15, 16]. Zhou et al.  presented OPI, a supervised method that predicts Gene Ontology annotations using gene expression profiles and was applied on P. falciparum. Alternative methods work in a semi-supervised way ; these use gene clustering as in the non-supervised approach, but clustering is not fully unsupervised as the function of the already characterized genes is used to define the clusters.
In this paper, we present PlasmoDraft http://atgc.lirmm.fr/PlasmoDraft/, a database of Gene Ontology (GO) annotation predictions for P. falciparum achieved by applying a GBA predictor named Gonna (for Gene Ontology Nearest Neighbor Approach) on several transcriptome (microarray), proteome (mass-spectrometry) and protein-protein interaction data. The Gonna system involves: (1) a supervised k-nearest-neighbor predictor that proposes predictions on the basis of each data source; (2) a cross-validation procedure that estimates the confidence of the predictions achieved with each data source; (3) a procedure that combines the results achieved with the different data sources to estimate a global confidence value of each prediction for each gene. The PlasmoDraft database provides all of these predictions along with their confidence values in a friendly interface that allows easy browsing and querying.
Gonna proposes annotation predictions in the GO framework. The GO Consortium http://www.geneontology.org has developed a systematic and standardized nomenclature to annotate genes in terms of their associated biological processes (BP), cellular components (CC) and molecular functions (MF), in a species-independent manner. Each ontology describes generalization relationships between hundreds of terms. The most general term is at the top of the ontology, while the bottom terms are the most specific ones. A gene may be annotated with several GO terms of the same ontology. Moreover, due to the generalization relationship, when a gene is annotated with a term t, then it is also annotated with all upper terms that generalize t (a principle known as the "true path rule" in GO context). In PlasmoDraft, the specificity of a term is evaluated by its prior probability, i.e. the proportion of already characterized genes of P. falciparum that belong to this term. In this way, the leaves of the ontologies are the most specific terms with low prior probabilities, while the root of the ontology is the most common term with a prior probability of 1. Gonna uses the GO annotations available on PlasmoDB and provided by GeneDB as prior knowledge database to propose new annotations. The GO consortium distinguishes between curator-assigned annotations and automatically-assigned annotations. Curator-assigned annotations involve annotations that come from experimental data (GO evidence codes IDA, IPI, etc.), or that have been inferred by sequence similarity and curated by an expert (GO evidence code ISS). Automatically-assigned annotations involve all electronically inferred annotations (usually by sequence similarity) that have not been reviewed by an expert (GO evidence code IEA). Here, due to the scarcity of the curator-assigned annotations for P. falciparum (~60% annotations possess IEA evidence code only), all available GO annotations are considered, without regard to their evidence code (this choice is further-discussed below). Every gene with an annotation in the considered ontology (whatever its evidence code) is then referred as "characterized".
Gonna uses a k-nearest neighbor approach . It takes as input two positive integers K and K' ≤ K (e.g. K = 6 and K' = 4), one ontology (MF, BP, or CC), and one postgenomic data source D (e.g. the microarray data of ). With this data source, Gonna computes a function S D that measures the similarity S D (g, h) of every gene pair (g, h). For example, if D is a transcriptomic data set then S D measures the similarity of profiles using the Pearson correlation coefficient. When asked for the GO categories of a gene g, Gonna uses the S D function to search for the K genes already characterized in the selected ontology by GeneDB, which have the highest level of similarity with g. Then, for each GO term t of the ontology, Gonna looks at these K genes, and if at least K' are associated with t, then g is predicted to be also associated with t; otherwise g is not considered to be in t. Note that when looking at the terms associated with the neighbor genes, Gonna considers all the upper terms generalizing the direct annotations (i.e. all terms in the true path rule).
Some choices are critical to insure that Gonna provides relevant and accurate predictions. The first critical choice is related to the similarity measure, which has to capture the "signature" of the gene functions in the data set at hand. When two genes appear to be similar, this should imply that they share common functions. For transcriptomic (microarray) and proteomic (mass-spectrometry) data, we use the Pearson correlation coefficient that gives high similarity to genes with correlated transcriptomic/proteomic profiles. Other similarity measures, as the classical Euclidean metric, could be possible, but the Pearson correlation measure has been shown to perform well to detect functional links in several analyses . For the protein-protein interaction data, we use the Czekanovski-Dice metric , which gives high similarity to pairs of genes that share many interactors, and has been shown to perform well to predict biological functions .
Another critical choice is related to the K and K' values. K should be neither too large (else some neighbors will not be similar to the studied gene) nor too low (to avoid reduced, non-representative gene samples). With K' the problem is different. If K' is high (close to K), then the proportion of good predictions is likely to be high, but only a few predictions could be achieved on the most specific terms of the ontology, and most of the predictions would involve the most general (and hence less interesting) terms. Conversely, if K' is low, then the proportion of good predictions declines, but more predictions are made on the most specific terms. In PlasmoDraft, we use two pairs of parameters (K, K') for each postgenomic data source: one stringent pair (K = 6, K' = 4) is used to achieve, for each GO term, a first set of predictions that usually has a high proportion of good predictions (see next section for an estimate of this proportion). Next, a second, non-stringent pair (K = 6, K' = 2) is used to come up with, for each GO term, another set of predictions that cannot be achieved with the stringent setting, but which usually contains a lower proportion of good predictions.
Assessing the predictions
Cross-validation (CV) is a well known procedure to estimate the error rate of supervised classification methods . The leave-one-out version of CV, which we use here, involves: (1) running Gonna on each gene already characterized in GeneDB as if it were an uncharacterized gene, and (2) comparing the predictions to the true annotations. Since no functional information on this gene is supplied to Gonna for the predictions, this procedure provides an unbiased estimate of the method performance . For a given GO term t, the correct predictions in CV involve the genes predicted in t, which are already annotated by this term in GeneDB (or by a specialization of t, cf. the true path rule); the wrong predictions involve the genes predicted in t which are already annotated in the ontology under consideration (MF, BP, or CC) but not with t (or one of its specializations). Genes without any annotation in the selected ontology are not taken into account. It is convenient to present all of these quantities in tabular form (see Table 1). For example, p a denotes the number of genes predicted in the GO term t which are annotated with t in GeneDB, while n n denotes the number of genes not annotated with t which have not been predicted in t in CV. Then, the True Discovery Rate (TDR) associated with this GO term (and for a given data source) is estimated byTDR = p a /(p a + p n ).
Correct and wrong predictions associated with a given GO term t in the CV procedure
not predicted in
not annotated with
The advantage of estimating the TDR of each GO term rather than estimating a global performance on the whole ontology is that it allows to differentiate GO terms that appear well suited for applying a GBA approach with the considered data source. Indeed, all GO terms cannot be predicted with the same accuracy. First because some terms are more general than others (and thus are a priori more likely). But also because some functions (GO terms) have a more apparent signature than others in the considered data source. For example, while they have a similar prior probability (~10%), GO terms antigenic variation (GO:0020033) and post-translational protein modification (GO:0043687) get 90% and 15% TDR s with the microarray data of , respectively.
Combining the data sources
Terms P(d i |t), P(¬d i |t), P(d i |¬t), and P(¬d i |¬t) are estimated with the quantities computed in the CV and displayed in Table 1. For example, P(d i |t) is the probability that data source i supports t when the gene belongs to t; it is estimated by the ratio p a /(p a + n a ). P(¬d i |¬t) is the probability that data source i does not support t when the gene does not belongs to t; it is estimated with n n /(p n + n n ).
Thus, from the three above equations, the conditional probability of t can be roughly estimated and it constitutes our global confidence score. This score reflects the likelihood of the predictions: genes with high (near 1) confidence scores are more likely to be associated with t than genes with low (near 0) confidence scores. However, due to the independence assumption, this score cannot be interpreted as the probability of t. Hence, it is discretized in 4 score categories (very low [0.0, 0.25], low ]0.25, 0.5], high ]0.5, 0.75], and very high ]0.75,1.0]). The true discovery rate associated with each category is estimated by way of a last cross-validation procedure: this is done by computing the proportion of successes among already characterized genes that have been predicted in the considered GO term with a confidence score in this category. These cross-validated true discovery rates then represent our GDB. For example, a prediction associated with a GDB of 80% means that 80% of the predictions belonging to the same score category in this GO term are correct in the CV procedure. As for the TDR s, we also compute the p-value of the GDB s. If this p-value is higher than 5%, then the GDB is not considered to be significantly higher than the prior probability of the term, and it appears in gray in PlasmoDraft.
The discretization procedure we use, sometimes known as the equal interval width method, could be replaced by other methods, such as the equal frequency interval method or more sophisticated methods based on entropy minimization . However, it is a classical and simple method that has shown to give good performance on numerous data sets .
The independence assumption is often used in statistical machine learning, and forms the basis of the "naive Bayes" predictor, which was shown to be fairly accurate in a number of applications . One interesting feature of this predictor (and hence of the GDB) is that it is not much affected by irrelevant or poor quality data sources . Indeed, when a source i is not relevant for a specific GO term t, either because it has not been designed for screening this type of information or because of the poor quality of the data, terms P(d i |t) and P(d i |¬t) tend to be equal. Therefore, the numerical quantities related to this data source tend to cancel in the numerator and denominator pairs of the confidence score. This prevents the GDB from pollution by irrelevant or too noisy data sources.
To produce the PlasmoDraft database, Gonna has been applied to most publicly available postgenomic data sources we were aware. 9 transcriptomic (microarray), 1 proteomic (mass-spectrometry), and 1 protein-protein interaction data sets were used. Below is a short description of each data set, indexed by the name used in PlasmoDraft.
LE03: Le Roch et al. (2003) data set . A transcriptomic data set that covers 9 stages of the entire cycle of strain 3D7: 6 asexual intraerythrocytic stages, plus the merozoite, gametocyte, and salivary gland sporozoite stages. Measurements for ~5 100 genes.
YO3D7: Young et al. (2005) data set . A transcriptomic data set that covers the sexual developmental cycle (gametocytes) of strain 3D7. Measurements for ~5 100 genes.
YONF54: Young et al. (2005) data set . Same data set as Y03D7, for strain NF54.
LLDd2: Llinas et al. (2006) data set . Same data set as LLHB3, for strain Dd2.
LL3D7: Llinas et al. (2006) data set . Same data set as LLHB3, for strain 3D7.
DA06: Dahl et al. (2006) data set . A transcriptomic data set that covers two 48 h life cycles of doxycyclin treated parasites. Measurements for ~5 300 genes.
SH07: Shock et al. (2007) data set . A transcriptomic data set analysing mRNA decay during the intraerythrocytic developmental cycle. Measurements for ~5 300 genes.
LE07: A transcriptomic data set analysing the parasite response to choline analog T4 during the intraerythrocytic life cycle. See series GSE4582 in the NCBI Gene Expression Omnibus http://www.ncbi.nlm.nih.gov/geo/. Measurements for ~5 100 genes.
LE04: Le Roch et al. (2004) data set [31, 32]. A proteomic data set that covers 7 stages of the entire cycle of strain 3D7: the ring, trophozoite, schizont, merozoite, gametocyte, gamete, and salivary gland sporozoite stages. Measurements for ~2 900 genes.
LA05: LaCount et al. (2005) data set . A protein-protein interaction data set. Measurements for ~1 300 genes.
The Gene Ontology file (revision 5.754) and the gene annotations file (revision 1.54) were downloaded from the GO website.
Accessing the database
Users can access the predictions by browsing the database or querying for a specific gene, GO term, or keyword. Results are displayed using three types of views: a global view, a gene view, and a GO term view. In each view, TDR s and GDB s are represented with a color code that ranges from red (0%) to light green (100%) via yellow (50%); non-significant TDR s or GDB s (see Method) are in gray.
The global views
There is one global view for each gene ontology (Molecular Function, Biological Process, and Cellular Component). A global view (see Figure 2) shows all GO terms of the selected ontology where predictions are made. These are represented in a hierarchical way which respects the ontology structure. Each term is followed by its prior probability, the best GDB found for a gene predicted in this term, and the best TDR associated with each data source for this term.
The GO term view
The gene view
Quantity and quality of the available annotations used in the prior knowledge database to generate the predictions is a key point of any GBA approach. For P. falciparum, both quantity and quality are questionable. For example, in the BP ontology, of the 1799 genes (35%) possessing annotations, only 228 (13%) have annotations with experimental evidence; annotations of the 1571 remaining genes come from sequence similarity with proteins of other organisms (ISS and IEA evidence codes), and for 1067 genes (68%) these annotations have IEA code, indicating that they have not been reviewed by a curator. Moreover, of the 431 different BP GO terms associated with the P. falciparum genes when considering all annotations, 172 (40%) are associated with IEA annotations only. For example, all annotations involving BP GO terms ATP biosynthetic process (GO:0006754), immune response (GO:0006955) or methylation (GO:0032259), as well as their descendants terms, possess IEA code only. Hence, we decided to consider all available GO annotations when generating the PlasmoDraft database. Removing all non-curated annotations from the prior knowledge database would eliminate not only numerous characterized genes, but also numerous GO terms, which would render impossible any new prediction in these parts of the ontology.
Experiments on a well annotated organism
Contents of the database
By browsing the PlasmoDraft database, several predictions clearly involve rare GO terms (i.e. with low prior probability) with high TDR s or GDB s. For example, in the BP ontology, 16 uncharacterized genes are predicted in establishment of localization (GO:0051234) (prior probability 15%, GDB 78%), 25 uncharacterized genes are predicted in Rosetting (GO:0020013) (prior probability 2%, GDB 78%), and 50 uncharacterized genes are in Pathogenesis (GO:0009405) (prior probability 4%, GDB 75%). Similarly (but with lower GDB s), 13 uncharacterized genes are predicted in Ubiquitin-dependent protein catabolic process (GO:0006511) (prior probability 2%, GDB 50%), and 12 uncharacterized genes are in Biopolymer catabolic process (GO:0043285) (prior probability 3%, GDB 56%). Moreover the best TDR s are not always achieved with the same data source. For example, for the Antigenic variation (GO:0020033) term, the LE03 data  provides more accurate predictions than the LLHB3/LLDd2/L13D7 series [27, 28], may be because this function has a more apparent expression signature when considering the entire life cycle of the organism. For functions such as DNA packaging (GO:0006323) however, the highest TDR is achieved with the LLHB3 data set  because the function is better monitored at the cell cycle level.
Assessment of the GDB s
TDR s and GDB s are estimated by cross-validation by applying Gonna on the already characterized genes. This procedure produces unbiased estimates of the method accuracy, provided that the uncharacterized genes share approximately the same distribution as the characterized ones . However, since TDR s and GDB s are sometimes estimated on small numbers of predictions, users should be aware that for some specific GO terms, the accuracy on the uncharacterized genes may differ from the reported TDR s and GDB s. Nonetheless, these measures provide valuable indications on the potential functions of genes by pointing out the most likely GO terms. To assess this point, we compared the PlasmoDraft predictions proposed for the uncharacterized genes to the annotations of their homologous genes in yeast when these are known. We looked on the 986 genes without BP annotations that have been predicted with high GDB (above 50%) on specific BP terms (prior probability below 25%). As expected, few genes among these 986 can be associated with a characterized orthologous gene in S. cerevisiae. However, a reciprocal best hit procedure using BLASTP with an e-value cutoff of 10-5 allows to find S. cerevisiae orthologues for 141 genes. Among these 141 orthologous pairs, 63 (45%) have "concordant" annotations with the high GDB predictions. Here we consider that annotations are concordant if at least half of the terms with prior probability below 25% are shared by the S. cerevisiae orthologue. As expected, this proportion decreases when using PlasmoDraft predictions with lower GDB s. For example, 2 271 genes without BP annotations are predicted with a GDB between 25% and 50% on a GO term with prior probability below 25%. Among these, 245 can be associated with S. cerevisiae orthologues by reciprocal best hit, and 71 (29%) have concordant annotations.
Comparison with the predictions of Zhou et al. (2008) 
During the writing of this article, another database  of gene function predictions based on the OPI method described in reference  was published. Briefly, OPI is a supervised method that works as follows. For each GO term, OPI uses a set of "seed" genes already annotated with this term to construct an average expression profile. Next, all genes (annotated or not) are ranked according to their similarity to this average profile and a statistical test is used to identify the rank cutoff that includes the largest number of seed genes within the smallest cluster size. All genes before this cutoff are then considered as potentially related to the GO term under consideration. The database  exploits a single new transcriptomic data set covering all life cycle stages of the parasite and combining gene expressions from both P. yoelii and P. falciparum. As both the methods and data sources are different, this database and PlasmoDraft provide different and complementary information. OPI provides BP annotations for 1 902 different genes, among which 1 036 have no BP annotations in GeneDB. When looking at the PlasmoDraft predictions with GDB above 50% (which involves 1 111 uncharacterized genes in BP), only 230 also have BP predictions in OPI. However, when both methods propose BP predictions for a gene, the predictions are often similar. Indeed, of the 230 common genes, 94 have concordant predictions – i.e. at least half of the predictions of one of the methods involving terms with a prior probability below 25% are also predicted by the other method. Differences in specificity of OPI and PlasmoDraft can also be observed by comparing the GDB and FDR (false discovery rate) estimates associated with a given GO term by PlasmoDraft and OPI, respectively. Recall that the GDB is actually the TDR associated with the predictor that combines all data sources. Moreover, by definition, the FDR equals 1 minus the TDR on this term. While FDR s of OPI are not estimated by cross-validation, we can nevertheless get a rough idea of which method provides the best results for a given GO term. For example, OPI obtains the highest TDR s on terms like Entry into host (GO:0044409), or Mitochodrion organization and biogenesis (GO:0007005) (~90% and ~30% vs. 36% and ~5%), while for terms like Interaction between organisms (GO:0044419) or Rosetting (GO:0020013), PlasmoDraft obtains the best results (77% and 78% vs. 25% and no statistically significant TDR). On the whole, it thus appears that the two databases use quite different data sources and provide interesting information on different types of functions and different genes, so the community will likely benefit from both.
We presented PlasmoDraft, an extensive database of GO annotation predictions that are achieved by Guilt By Association using most postgenomic data available to date for P. falciparum. All predictions come with a confidence estimate computed by cross-validation. The database is presented in a friendly interface that allows easy browsing and querying, and proposes high confidence annotations for several hundreds of genes without any annotations, as well as additional annotations for many already characterized genes. One prospect is the integration of compendiums of gene expression data sets as new data sources in PlasmoDraft. These data, obtained by concatenation of several data sets of diversified biological conditions, have shown to often provide strong biological function signatures . However, predictions based on these data may be difficult to interpret for biologists, and their integration opens new issues in data selection and combination.
As mentioned in the Methods, one advantage of Gonna concerns its genericness that allows its use on any new data, as long as a relevant similarity measure can be computed; a set of scripts then enables regeneration of the database to integrate the new data set in a fully automated way. This also holds for the GO annotations used as prior knowledge, and the new annotations provided by the community in the future will be easily integrated. Most notably, we are aware that a collegiate effort for re-annotating P. falciparum proteins should provide new/curated functional annotations in the near future. This should improve both the quantity and the quality of the PlasmoDraft predictions. In the same way, while in the current version of PlasmoDraft all GO annotations are considered (i.e. including automatically-assigned annotations) due to the scarcity of curated annotations, it is possible that the re-annotation effort will enable the use of only curator-assigned annotations in the subsequent versions of PlasmoDraft. Thanks to these new advances, PlasmoDraft should become more and more accurate and useful to the community.
Availability and requirements
PlasmoDraft is freely available at http://atgc.lirmm.fr/PlasmoDraft/
This research was supported by the PlasmoExplore project of the French National Research Agency (ANR-Q6-CIS6-MDCA-14). We thank all members of this project for useful discussions.
- Sachs J, Malancy P: The economic and social burden of malaria. Nature 2002, 415(6872):680–685. 10.1038/415680aView ArticlePubMedGoogle Scholar
- Gardner M, Hall N, Fung E, White O, Berriman M, Hyman R, Carlton J, Pain A, Nelson K, Bowman S, Paulson I, James K, Eisen J, Rutherford K, Salzberg S, Craig A, Kyes S, Chan M, None V, Shallom S, Suh B, Peterson J, Angiuoli S, Pertea M, Allen J, Selengut J, Haft D, Mather M, Vaidya A, Martin D, Fairlamb A, Fraunholz M, Roos D, Ralph S, McFadden G, Cummings L, Subramanian G, Mungall C, Venter J, Carucci D, Hoffman S, Newbold C, Davis R, Fraser C, Barrell B: Genome sequence of the human malaria parasite Plasmodium falciparum. Nature 2002, 419(6906):498–511. 10.1038/nature01097View ArticlePubMedGoogle Scholar
- Pizzi E, Frontali C: Low-complexity regions in Plasmodium falciparum proteins. Genome Res 2001, 11(2):218–229. 10.1101/gr.GR-1522RPubMed CentralView ArticlePubMedGoogle Scholar
- Altschul S, Gish W, Miller W, Myers E, Lipman D: Basic local alignment search tool. J Mol Biol 1990, 215(3):403–410.View ArticlePubMedGoogle Scholar
- Sonnhammer E, Eddy S, Birney E, Bateman A, Durbin R: Pfam: multiple sequence alignments and HMM-profiles of protein domains. Nucleic Acids Res 1998, 26: 320–322. 10.1093/nar/26.1.320PubMed CentralView ArticlePubMedGoogle Scholar
- Bastion O, Roy S, Marechal E: Construction of non-symmetric substitution matrices derived from proteomes with biased amino acid distributions. C R Biol 2005, 328(5):445–453. 10.1016/j.crvi.2005.02.002View ArticleGoogle Scholar
- Marcotte E: Computational genetics: finding protein function by nonhomology methods. Curr Opin Struct Biol 2000, 10(3):359–365. 10.1016/S0959-440X(00)00097-XView ArticlePubMedGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998, 95(25):14863–14868. 10.1073/pnas.95.25.14863PubMed CentralView ArticlePubMedGoogle Scholar
- Lockhart D, Winzeler E: Genomics, gene expression and DNA arrays. Nature 2000, 405(6788):827–836. 10.1038/35015701View ArticlePubMedGoogle Scholar
- Brun C, Chevenet F, Martin D, Wojcik J, Guenoche A, Jacq B: Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biology 2003., 5:Google Scholar
- Vazquez A, Flammini A, Maritan A, Vespignani A: Global protein function prediction from protein-protein interaction networks. Nat Biotechnol 2003, 21(6):697–700. 10.1038/nbt825View ArticlePubMedGoogle Scholar
- Chen Y, Xu D: Global protein function annotation through mining genome-scale data in yeast Saccharomyces cerevisiae. Nucleic Acids Research 2004, 32(21):6414–6424. 10.1093/nar/gkh978PubMed CentralView ArticlePubMedGoogle Scholar
- Wu L, Hughes T, Davierwala A, Robinson M, Stoughton R, Altschuler S: Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nat Genet 2002, 31(3):255–265. 10.1038/ng906View ArticlePubMedGoogle Scholar
- Le Roch K, Zhou Y, Blair P, Grainger M, Moch J, Haynes J, Vega PDL, Holder A, Batalov S, Carucci D, Winzeler E: Discovery of gene function by expression profiling of the malaria parasite life cycle. Science 2003, 301(5639):1503–1508. 10.1126/science.1087025View ArticlePubMedGoogle Scholar
- Brown MPS, Grundy WN, Lin D, Cristianini N, Sugnet C, Furey TS, Manuel Ares J, Haussler D: Knowledge-based Analysis of Microarray Gene Expression Data Using Support Vector Machines. Proc Natl Acad Sci USA 2000, 97(1):262–267. 10.1073/pnas.97.1.262PubMed CentralView ArticlePubMedGoogle Scholar
- Mateos A, Dopazo J, Janson R, Tu Y, Gerstein M, Stolovitzky G: Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons. Genome Research 2002, 12(11):1703–1715. 10.1101/gr.192502PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou Y, Young J, Santrosyan A, Chen K, Yan S, Winzeler E, Young J, Fivelman Q, Blair P, do la Vega P, KG LR, Zhou Y, Carucci D, Baker D, Winzeler E: In silico gene function prediction using ontology-based pattern identification. Bioinformatics 2005, 21(7):1237–1245. 10.1093/bioinformatics/bti111View ArticlePubMedGoogle Scholar
- Toronen P: Selection of informative clusters from hierarchical cluster tree with gene classes. BMC Bioinformatics 2004., 5:Google Scholar
- Hastie T, Tibshirani R, Friedman J: The elements of statistical learning. Springer; 2001.View ArticleGoogle Scholar
- Yona G, Dirks W, Rahman S, Lin D: Effective similarity measures for expression profiles. Bioinformatics 2006, 22(13):1616–1622. 10.1093/bioinformatics/btl127View ArticlePubMedGoogle Scholar
- Dice L: Measure of the amount of ecologic association between species. Ecology 1945, 26: 297–302. 10.2307/1932409View ArticleGoogle Scholar
- Reimand J, Kull M, Peterson H, Hansen J, Vilo J: g:Profiler–a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res 2007, (35 Web Server):W193-W200. 10.1093/nar/gkm226Google Scholar
- Chmielewski MR, Grzynala-Busse JW: Global discretization of continuous attributes as preprocessing for machine learning. International Journal of Approximate Reasoning 1996, 15: 319–331. 10.1016/S0888-613X(96)00074-6View ArticleGoogle Scholar
- Dougherty J, Kohavi R, Sahami M: Supervised and Unsupervised Discretization of Continuous Features. International Conference on Machine Learning 1995, 194–202.Google Scholar
- Langley P, Sage S: Induction of selective Bayesian classifiers. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann; 1994:399–406.Google Scholar
- Young J, Fivelman Q, Blair P, do la Vega P, Le Roch K, Zhou Y, Carncci D, Baker D, Winzeler E: The Plasmodium falciparum sexual development transcriptome: a microarray analysis using ontology-based pattern identification. Mol Biochem Parasitol 2005, 143: 67–79. 10.1016/j.molbiopara.2005.05.007View ArticlePubMedGoogle Scholar
- Bozdech Z, Llinas M, Pulliam B, Wong E, Zhu J, DeRisi J: The transcriptome of the intraerythrocytic developmental cycle of Plasmodium falciparum. PLoS Biol 2003., 1:Google Scholar
- Llinas M, Bozdech Z, Wong E, Adai A, DeRisi J: Comparative whole genome transcriptome analysis of three Plasmodium falciparum strains. Nucleic Acids Res 2006, 34(4):1166–1173. 10.1093/nar/gkj517PubMed CentralView ArticlePubMedGoogle Scholar
- Dahl E, Shock J, Shenai B, Gut J, DeRisi J, Rosenthal P: Tetracyclines specifically target the apicoplast of the malaria parasite Plasmodium falciparum. Antimicrob Agents Chemother 2006, 50(9):3124–3131. 10.1128/AAC.00394-06PubMed CentralView ArticlePubMedGoogle Scholar
- Shock J, Fischer K, Derisi J: Whole-genome analysis of mRNA decay in Plasmodium falciparum reveals a global lengthening of mRNA half-life during the intra-erythrocytic development cycle. Genome Biol 2007., 8(7):Google Scholar
- Florens L, Washburn M, Raine J, Anthony R, Grainger M, Haynes J, Moch J, Muster N, Sacci J, Tabb D, Witney A, Wolters D, Wu Y, Gardner M, Holder A, Sinden R, Yates J, Carucci D: A proteomic view of the Plasmodium falciparum life cycle. Nature 2002, 419(6906):520–526. 10.1038/nature01107View ArticlePubMedGoogle Scholar
- Le Roch K, Johnson J, Florens L, Zhou Y, Santrosyan A, Grainger M, Yan S, Williamson K, Holder A, Carucci D 3rd, Yates J, Winzeler E: Global analysis of transcript and protein levels across the Plasmodium falciparum life cycle. Genome Res 2004, 14(11):2308–2318. 10.1101/gr.2523904PubMed CentralView ArticlePubMedGoogle Scholar
- LaCount D, Vignali M, Chettier R, Phansalkar A, Bell R, Hesselberth J, Schoenfeld L, Ota I, Sahasrabudhe S, Kurschner C, Fields S, Hughes R: A protein interaction network of the malaria parasite Plasmodium falciparum. Nature 2005, 438(7064):103–107. 10.1038/nature04104View ArticlePubMedGoogle Scholar
- Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PC), Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 1998, 9(12):3273–3297.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou Y, Ramachandran V, Kumar KA, Westenberger S, Refour P, Zhou B, Li F, Young JA, Chen K, Plouffe D, Henson K, Nussenzweig V, Carlton J, Vinetz JM, Duraisingh MT, Winzeler EA: Evidence-Based Annotation of the Malaria Parasite's Genome Using Comparative Expression Profiling. PLoS ONE 2008, 3(2):e1570. 10.1371/journal.pone.0001570PubMed CentralView ArticlePubMedGoogle Scholar
- Gasch A, Spellman P, Kao C, Carmel-Harel O, Eisen M, Storz G, Botstein D, Brown P: Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 2000, 11(12):4241–4257.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.