MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis
- Koki Tsuyuzaki1, 5Email author,
- Gota Morota2, 3,
- Manabu Ishii5,
- Takeru Nakazato4,
- Satoru Miyazaki1 and
- Itoshi Nikaido5Email author
https://doi.org/10.1186/s12859-015-0453-z
© Tsuyuzaki et al.; licensee BioMed Central. 2015
Received: 3 July 2014
Accepted: 8 January 2015
Published: 15 February 2015
Abstract
Background
In genome-wide studies, over-representation analysis (ORA) against a set of genes is an essential step for biological interpretation. Many gene annotation resources and software platforms for ORA have been proposed. Recently, Medical Subject Headings (MeSH) terms, which are annotations of PubMed documents, have been used for ORA. MeSH enables the extraction of broader meaning from the gene lists and is expected to become an exhaustive annotation resource for ORA. However, the existing MeSH ORA software platforms are still not sufficient for several reasons.
Results
In this work, we developed an original MeSH ORA framework composed of six types of R packages, including MeSH.db, MeSH.AOR.db, MeSH.PCR.db, the org.MeSH.XXX.db-type packages, MeSHDbi, and meshr.
Conclusions
Using our framework, users can easily conduct MeSH ORA. By utilizing the enriched MeSH terms, related PubMed documents can be retrieved and saved on local machines within this framework.
Keywords
Background
Due to the rapid development of “-omics” technology, such as DNA microarrays [1] and next-generation sequencing (NGS) [2,3], scientists are now able to quantify large numbers of transcripts from organisms simultaneously. The data output of such high-throughput experiments typically becomes overwhelmingly large, and some statistical analyses must be performed to focus on the genes that are related to the experiment being conducted. For example, p-values indicating significance [4-14], fold changes [15,16], ranks [17,18], factor loadings in principal component analysis [19] and other scores [20] are calculated and considered. Genes of interest can be selected based on these criteria and are listed such as differentially expressed genes (DEGs), single nucleotide polymorphism (SNPs), insertion/deletion (INDEL) mutations, or copy number variations (CNVs), and so on, depending on the experimental paradigm used.
To extract biological meaning from these lists, over-representation analysis (ORA, or enrichment analysis) is widely employed [21,22]. ORA determines which types of biological terms are significantly enriched among the genes on a given list. The degree of enrichment is calculated as a probability that indicates that particular terms are detected in the gene lists more often than expected by chance. The hypergeometric test (or Fisher’s exact test) is widely used to calculate such probabilities. Several thousand statistical tests are conducted against the terms assigned to the list, and only significant terms are extracted. Such terms will help us to gain insight into the biological mechanisms behind the phenomena being investigated. Many annotation resources for ORA have been used, such as Gene Ontology (GO) [23], KEGG [24], Reactome [25], BioCyc [26], BioCarta [27], Disease Ontology [28], and MSigDB [29]. In addition, a wide variety of ORA software has also been developed, including programs such as DAVID/EASEonline [30], FatiGO [31], GOstats [32], topGO, GenMAPP [33], GOMiner [34], GOSurfer [35], FIDEA [36], GOseq [37], EnrichNet [38], OntoTools [39], IPA http://www.ingenuity.com/products/ipa, and BiNGO [40] (comprehensive review of ORA analyses and resources are available in [41-45]).
Recently, Medical Subject Headings (MeSH) [46] terms have also been used for ORA. MeSH is the annotation used for PubMed documents and is manually curated by the U. S. National Library of Medicine (NLM). MeSH has 16 categories and the size of its vocabulary is approximately twice as large as that of GO [47]. MeSH includes biological categories such as “Phenomena and Processes”, “Chemicals and Drugs” and “Anatomy”, as well as the other annotation resources such as GO. MeSH also has a “Diseases” category, which contains many disease-related terms (e.g., “Hypertension” and “Neoplasms”) that facilitate a medical interpretation of data.
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With the exception of the application programmable interfaces (API) of gene2mesh and metab2mesh, the available character user interface (CUI) environments are still insufficient. Many bioinformaticians often combine multiple CUI tools and construct an original analytic pipeline on a local machine. Because R developers (notably Bioconductor) provide thousands of R packages to support many types of data analysis and because R is widely used by many “-omics” data analyses, Bioconductor packages are ideal for MeSH ORA. All Bioconductor packages are freely downloadable and accessible for any user. With Bioconductor packages, the output of upstream analyses can be seamlessly input into the function of downstream analysis.
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As described above, in ORA, several thousand statistical tests are conducted simultaneously. Such an approach is categorized as a multiple testing problem. Existing MeSH ORA software does not account for this multiplicity. Therefore, type 1 errors accumulate, and the significance level (p-value) may cause a high incidence false-positives. Many bioinformatics tools have employed the false discovery rate (FDR) statistic [51] instead of p-value, which is appropriate for MeSH ORA.
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Many -omics studies focus on the functions of individual genes. To do so, the researcher must retrieve many references from the literature about each gene and investigate the details. This step is very time-consuming, and there is no existing informatics tool to support this step. Because MeSH itself is an annotation of the information contained in PubMed documents, enriched pairs of MeSH terms and Gene IDs can be used to retrieve relevant literature from PubMed based on the co-occurrence of these pairs. No other annotation resouce (such as GO) can perform this step because such terms in other resources correspond only to Gene IDs and not to PubMed IDs. This is the biggest advantage when using MeSH ORA; MeSH terms help users to retrieve relevant literature without manual web browsing. However, the existing MeSH ORA software does not implement such a function.
For these reasons, we designed an original MeSH ORA framework to meet the requirements stated above.
Implementation
Framework and implementation
MeSH ORA framework and dependency of the packages. Our MeSH framework consists of six types of R packages: the MeSH.db, MeSH.AOR.db-type packages, MeSH.PCR.db, org.MeSH.XXX.db, MeSHDbi, and meshr. MeSHDbi defines the class used in the MeSH.db, MeSH.AOR.db, MeSH.PCR.db, and org.MeSH.XXX.db-type packages and then unifies the behavior of these packages. meshr imports the data from MeSH.db and org.MeSH.XXX.db and performs ORA.
MeSH.db
Categories of MeSH: MeSH.db provides 16 categories of MeSH terms in 2013
Abbreviations | Categories | No. of terms | Examples |
|---|---|---|---|
A | Anatomy | 2882 | Muscles, Skeleton |
B | Organisms | 5169 | Gram-negative Bacteria |
C | Diseases | 111257 | Leukemia, Burns |
D | Chemicals and Drugs | 20633 | Fatty Acids, Ligases |
E | Analytical Diagnostic and Therapeutic | 4720 | Dental Care |
Techniques and Equipment | |||
F | Psychiatry and Psychology | 1127 | Behavior, Motivation |
G | Phenomena and Processes | 3352 | Antibody Formation |
H | Disciplines and Occupations | 495 | Biology, Clinical Medicine |
I | Anthropology, Education, Sociology and | 622 | Economics, Culture |
Social Phenomena | |||
J | Technology and Food and Beverages | 597 | Bread, Coffee, Tea |
K | Humanities | 216 | Music, Religion |
L | Information Science | 505 | Communications Media |
M | Persons | 245 | Adult Children, Drug Users |
N | Health Care | 2297 | Oral Health, Women’s Health |
V | Publication Type | 180 | Book Illustrations, Letter |
Z | Geographical Locations | 546 | Japan, China, Taiwan |
MeSH.db also provides qualifier terms, which are broader categories (e.g., pathology, history, and genetics) and synonyms, which are more colloquial words with that have same meanings as the MeSH terms.
MeSH.AOR.db and MeSH.PCR.db
MeSH.AOR.db and MeSH.PCR.db provide information regarding to the MeSH database structure. The MeSH database has a tree structure similar to that of the GO database; i.e., its structure is hierarchical, and the higher-order terms (e.g., cancer) include the lower-order terms (e.g., breast cancer). MeSH.AOR.db and MeSH.PCR.db provide ancestor-offspring relationships (AOR) and parent-child relationships (PCR), respectively.
org.MeSH.XXX.db-type packages
org.MeSH.XXX.db-type packages provide the correspondence between NCBI Entrez Gene IDs and NLM MeSH IDs for each organism. Only the data that have sufficiently high correspondence between the Entrez Gene and MeSH IDs were selected.
Genome-wide tools. We focused on organisms that are used in genome-wide tools such as the UCSC Genome Browser, GeneChip, Gene Ontology, Gendoo, and Bioconductor.
120 orgnisms. To construct org.MeSH.XXX.db-type packages, we focused on organisms satisfying three requirements: 1) use in at least one of five genome-wide tools; 2) possession of an Entrez Gene ID, rather than an Ensembl Gene ID; and 3) published data abailable in at least 100 papers. Finally, 120 organisms were selected for the framework.
Next, we focused on the organisms that had at least 1000 of Entrez Gene IDs. Some organisms have only Ensembl Gene IDs but no Entrez Gene IDs, and because our framework uses only Entrez Gene IDs, such organisms were rejected. After this step, 115 organisms remained.
Finally, we classified the remaining organisms as major or minor. Here we defined major and minor organisms by whether the organisms have at least 100 PubMed IDs. We defined 15 well-annotated organisms as “major organisms”: Arabidopsis thaliana, Bacillus subtilis subsp. spizizenii str. 168, Bos taurus, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Escherichia coli str. K-12 substr. MG1655, Gallus gallus, Homo sapiens, Mus musculus, Rattus norvegicus, Saccharomyces cerevisiae S288c, Shizosaccharomyces pombe 972h-, Sus scrofa, and Xenopus laevis. In some cases, thousands of Entrez Gene IDs are assigned in a single publication. Most such papers describe the determination of genome sequences or genome projects/databases rather than specific gene functions. Therefore, we omitted those papers that were assigned more than 1000 Entrez Gene IDs.
Data Sources. The data source of the 120 organisms in our framework. Three data sources - RBBH, gene2pubmed, Gendoo - were choosen.
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gene2pubmed provides the correspondence between the Entrez Gene and PubMed IDs assigned by NCBI manual curation. We converted these PubMed IDs to their corresponding MeSH terms using data licenced by PubMed (http://www.nlm.nih.gov/databases/license/license.html), and then converted these MeSH terms to MeSH IDs using the data from the NLM MeSH (Figure 5).
Figure 5Data retrieval schema for the construction of MeSH.db and org.MeSH.XXX.db . MeSH.db uses the data for MeSH terms from NLM. org.MeSH.XXX.db uses the data from Gendoo, gene2pubmed and RBBH.
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Due to the lack of relevant literature, minor organisms were poorly annotated by 1) gene2pubmed approarch. Because many gene and protein annotations are based on sequence similarity against other organisms such as FANTOM project [54,55], GO [23], blast2go [56], InterProScan [57], and RAPSearch [58], we conducted a sequence homology search of these minor organisms against the major organisms and then applied the MeSH IDs of the major organisms to these minor organisms (Figure 6). We performed a reciprocal BLAST [59] best-hit search among all possible combinations of the 15 major organisms and 100 minor organisms (Figure 7). A total of 3000 (100 minor organisms × 15 major organisms × 2-direction) blastp programs were performed (E-values are 50).
Figure 6Three types of correspondence between Entrez Gene ID and MeSH ID. org.MeSH.XXX.db-type packages provide three types of correspondence between Entrez Gene ID and MeSH ID: 1) Gendoo data, in which the correspondence is assigned by a text-mining technique; 2) gene2pubmed data, in which the correspondence is assigned by manual curation of NCBI; and 3) RBBH data, in which the correspondence is assigned by using reciprocal BLASTP best hits among all possible combinations of minor organisms and major organisms.
Figure 7RBBH. A comparison of 100 minor organisms with 15 major organisms by RBBH was conducted.
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The Entrez GeneID - MeSH ID relationship in Gendoo was assigned using a text-mining technique. The degree of relevance between the Entrez Gene and MeSH IDs was assessed by the Kullback-Leibler divergence [60].
A summary of the 120 organisms in each MeSH category is given in Additional file 1.
MeSHDbi
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MeSH.db, MeSH.AOR.db, MeSH.PCR.db and the org.MeSH.XXX.db-type packages follow the classes and methods of S4, which is the R OOP (Object-Oriented Programming) system. The MeSHDbi package defines an S4 class named “MeSHDb” and then unifies the grammar of the functions of these packages. Thus, the behavior of these packages is unified by MeSHDbi such that the user can input the same commands for all packages. This implementation is not only for the users but also for the developers, because it reduces the amount of source code.
The “MeSHDb” class has a “SELECT” function, which is used for data retrieval (Figure 8). The user specifies three optional parameters in SELECT: keys, cols and keytype. This grammar is very similar to that of SQL languages, which have been used for the management of relational databases.
Figure 8SELECT function in R. AnnotationDbi package declares that data are designed to be retrieved by the SELECT function. Its grammar is similar to that of SQL’s SELECT method and very simple.
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Although we implemented 120 org.MeSH.XXX.db-type packages, more genomes are being sequenced thanks to the rapid spread of NGS technology. Additionally, some users may want to utilize the correspondence between Gene IDs and MeSH IDs designed in other databases, which we currently do not support [47,49,50]. Therefore, we implemented the makeGeneMeSHPackage function, which enables the users to construct an original org.MeSH.XXX.db-type package.
meshr
where N is the total number of all genes, k is number of interesting genes (e.g., DEGs), M is the number of genes assigned to a MeSH term and x is the number of interesting genes (e.g., DEGs) assigned to a MeSH term.
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The BH method [51] assumes a uniform distribution of p-values when all null-hypotheses (e.g., non-DEGs) are true. The BH method defines the threshold of the q-value instead of the p-value. The q-value is an expectation value of FDR. The procedures of the BH method is as follows.
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Set an α threshold, where 0<α<1.
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Sort the observed p-values in ascending order, such as p 1≤p 2≤..≤p m , where m is the number of hypothesis tests.
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For each ith p-value, calculate \(q_{i} = \frac {p_{i} \times N}{i}\), where N is the number of p-values.
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If the kth q-value is less than α, then reject the null-hypotheses corresponding to p 1≤p 2≤...≤p k . Otherwise, reject nothing.
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In contrast to the BH method, the Q-value [61,62] and the local FDR [63,64] methods hypothesize that p-values are from mixture distribution of null-hypotheses and alternative hypotheses (e.g., DEGs), where the mixture ratio is π 0 : 1 - π 0.
In Q-value, π 0 is estimated from a p-value histogram using a natural cubic spline curve [62]. The estimated π 0 is multiplied to q-value in BH method:$$ Q_{i} = \frac{\pi_{0} \times p_{i} \times N}{i}, $$(2)whereas, local FDR uses a Bayesian approach. π 0 is assumed to be a prior distribution in Bayes’ theorem, and FDR is estimated using the following equation:$$ LocalFDR = \frac{f_{0}(p) \times \pi_{0}}{f(p)}, $$(3)where, f 0(p) is the null-hypothesis density distribution function and f(p) is the density distribution function of the observed p-values.
These methods are expected to work appropriately even if the p-values are not uniformly distributed. Therefore, the users should first observe the distribution of p-values and then choose the appropriate method.
meshr also retrieves PubMed documents related to enriched MeSH terms, which saves the researcher some of the time that would have been spent searching the related literature. The documents can be saved in PDF or HTML format on a local machine, and a directory can be organized by Gene ID, MeSH ID or PubMed ID. Therefore, a researcher can directly call the publications in which a gene of interest was studied.
Results and discussion
Summary of MeSH assignment to Entrez gene ID
MeSH IDs and Gene IDs are linked from the data sources gendoo, gene2pubmed, and RBBH. Here, we demonstrate how many genes are assigned to MeSH, how much MeSH and existing annotation are duplicated and how many poorly annotated genes are newly annotated by MeSH, for each organism meeting our criteria.
Summary of MeSH Assignment to Entrez Gene ID. Detailed coverage of MeSH against all genes of 115 organisms without Synechocystis, Miyakogusa, Mesorhizobium, Anabaena, and Silkworm. We hypothesized that genes assigned to gene name is well-annotated genes and genes assigned to only locus tag is not-annotated genes. (A) The genes assigned to gene name (red), the genes assigned to only locus tag (blue), and the genes newly annotated by MeSH in this work (green). (B) The genes assigned to only gene name (red), the genes annotated by gene name and MeSH (green), and the genes newly annotated by MeSH in this work (blue).
USAGE
Simple R code for MeSH ORA. MeSH ORA for Homo sapiens can be performed by a simple R code.
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RefSeq ID to Entrez Gene ID : gene2refseq
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Taxonomy ID to Entrez Gene ID : gene2accession
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Locus tag to Entrez Gene ID : gene2accession
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Gene name to Entrez Gene ID : gene2accession
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Ensembl Gene ID to Entrez Gene ID : gene2ensembl
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GO ID to Entrez Gene ID : gene2go
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PubMed ID to Entrez Gene ID : gene2pubmed
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UniGene ID to Entrez Gene ID : gene2unigene
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STS ID to Entrez Gene ID : gene2sts
Each of the 120 organisms can be loaded in the same way, and the same analysis can be performed. The parameters for the MeSH ORA are defined on the 6th line. This procedure is the same as that used by other ORA packages such as GOstats [32]. Here, MeSH “D” (Chemicals and Drugs) category from the Gendoo dataset is set using a 0.05 threshold for QV (Q-value method) FDR adjustment. The function meshHyperGTest performes the MeSH ORA against the prepared parameters. The result is summarized in a table from which the user can retrieve additional information such as significant MeSH terms or corresponding Entrez Gene IDs.
We can also retrieve the documents of related studies seamlessly by using the save.pdf and save.abst functions. By these functions, PDF files can be downloaded onto a local machine, which saves the time spent on a PubMed search, allowing the researchers to spend more time on downstream data analysis involving biological interpretation (an abstract retrieval function will also be implemented by Bioconductor 3.1). The meshr package provides two types of documentation: manual pages to explain all functions and optional parameters and a vignette containing the R source-code, description, and figures. The user can refer to these documents and conduct further sophisticated analyses by combining this package with other Bioconductor packages.
Case-study
Here, we demonstrate the usability of our framework by re-analyzing past genome-wide studies. We used a DNA-microarray dataset for a calorie-restricted rat as a representative study of a major organism and the NGS dataset of Pseudomonas aeruginosa - a known drug-resistant bacterium - as a minor organism.
Case-study 1: Rattus norvegicus (Rat) as a major organism
Caloric restriction (CR) has been suggested to be associated with longevity. Chujo et al. found that life-long CR is related to the remodeling of white adipose tissue (WAT) by analyzing the gene expression of rats under CR conditions [67].
The author used 4 CR rats and 4 control rats and compared them by Affymetrix Rat Genome 230 2.0 Array (GEO Accession: GSE30668). DEGs were detected by two-way ANOVA (p < 0.001) using superNORM [68] normalization, principal component analysis and further evaluation by quantitative real-time RT-PCR. They used the GO (Biological Process) database to annotate 199 upregulated DEGs and 226 downregulated DEGs. 36 GO terms showed that the DEGs were involved in “lipid biosynthesis” or “inflammation”.
ORA with calorie-restricted rats. Enrichment analysis using Gendoo, gene2pubmed and GO. A (Anatomy), B (Organisms), C (Diseases), D (Chemicals and Drugs), G (Phenomena and Processes) of Gendoo, A, C, D, E (Analytical Diagnostic and Therapeutic Techniques and Equipment), I (Anthropology, Education, Sociology and Social Phenomena) of gene2pubmed, and BP (Biological Process), MF (Molecular Function), CC (Cellular Component) of GO. Only enriched terms are drawn by the tagcloud package (p < 0.05). The minus logarithm of p-values is used as weight and emphasized as the font size.
Well-known CR-related terms such as “Diabetes Mellitus, Experimental” (C of Gendoo and gene2pubmed, E of gene2pubmed), “Carbohydrate Metabolism” (G, Gendoo), “Cell Aging” (G, Gendoo), and “Blood Glucose” and “Insulin” (D, gene2pubmed) are detected. As reported by Chujo et al., mitochondrial biogenesis is also enhanced by CR. For example, in A and D (Gendoo), and CC (Gene Ontology), “Submitochondrial Particles”, “Mitochondria, Muscle”, “Mitochondria, Heart”, “Mitochondrial matrix”, “Mitochondrion”, “Mitochondrial pyruvate dehydrogenase”, and “Mitochondrial part” are actually enriched.
Moreover, by using ORA within a MeSH framework, we can retrieve additional information, including immune-related terms such as “Immunological Synapses” and disease names such as “Meningitis, Meningococcal”, “Spinal Neoplasms” (C, Gendoo), and health-related scientific terms such as “Physical Education and Training”. These terms are expected to help researchers to consider other hypotheses that cannot be constructed using only GO.
Case-study 2: Pseudomonas aeruginosa as a minor organism
Pseudomonas aeruginosa, a clinically important bacterium, is known for its innate antibiotic resistance. Gallagher et al. developed the Tn-seq methodology based on NGS and the amplification of single-strand circles carrying transposon junction sequences that contribute to drug resistance [69]. The authors focused on 28 genes identified by Tn-seq as exhibiting strong mutant hypersensitivity to tobramycin.
They annotated these genes by gene description (NCBI). Known mutations related to drug resistance occurred in some genes, including those encoding the MexXY-OprM efflux pump (ABC-transporter) and a potassium uptake transporter.
GO and MeSH annotations for antibiotic registance Pseudomonas aeruginosa : comparison of NCBI gene descriptions, GO, and MeSH
Locus | Symbol | ID | Description | Gene ontology | ||
|---|---|---|---|---|---|---|
BP | MF | CC | ||||
PA3303 | - | 882468 | (control) | - | - | - |
PA0392 | - | 878514 | Conserved hypothetical | - | - | - |
PA4077 | - | 878707 | Transcriptional regulator | - | - | - |
PA5199 | amgS | 880300 | Two-component sensor | Phosphorelay signal transduction system | Phosphorelay sensor kinase activity | - |
PA5366 | pstB | 881628 | Phosphate transport | Transport | - | Membrane (+1) |
PA5200 | amgR | 880301 | Two-component response regulator | Phosphate-containing compound metabolic process (+2) | Phosphorelay response regulator activity | - |
PA0016 | trkA | 879255 | Potassium uptake | Transport (+1) | Glutamyl-tRNA reductase activity (+1) | - |
PA4942 | hflK | 877755 | Protease subunit | Cytokinesis by binary fission (+2) | - | - |
PA3014 | faoA | 878680 | Fatty acid oxidation | Cellular amino acid metabolic process (+11) | Benzoylformate decarboxylase activity | - |
PA4398 | - | 881355 | Two-component sensor | Phosphate ion transport | - | - |
PA5528 | - | 877964 | Hypothetical | - | - | - |
PA1805 | ppiD | 878369 | Peptidyl-prolyl isomerase | Protein folding (+1) | Chorismate synthase activity | - |
PA3016 | - | 879098 | Hypothetical | - | - | - |
PA4223 | - | 880074 | Transport | Lipid transport | - | - |
PA4960 | - | 878558 | Amino acid metabolism | L-serine biosynthetic process | - | - |
PA0374 | ftsE | 883078 | Cell division | Cytokinesis (+2) | - | - |
PA3013 | foaB | 880523 | Fatty acid oxidation | Cellular amino acid metabolic process (+7) | Thioredoxin-disulfide reductase activity | - |
PA5471 | - | 877632 | Hypothetical | Translational termination | - | - |
PA0502 | - | 878654 | Biotin biosynthesis | Biotin biosynthetic process | - | - |
PA3194 | edd | 882909 | Carbohydrate metabolism | Generation of precursor metabolites and energy (+3) | Phosphoenolpyruvate carboxylase activity | - |
PA1775 | cmpX | 877590 | Cytoplasmic membrane protein | - | - | - |
PA0427 | oprM | 877851 | Multidrug efflux | transport (+1) | Porin activity (+1) | Membrane |
PA4222 | - | 880073 | Transport | Transport | - | ATP-binding cassette (ABC) transporter complex |
PA5369 | pstS | 880528 | Phosphate transport | - | - | - |
PA2018 | mexY | 878882 | Multidrug efflux | Transport (+1) | - | Membrane (+1) |
PA5285 | - | 878098 | Hypothetical | - | - | - |
PA2604 | - | 882310 | Conserved hypothetical | - | - | - |
PA2019 | mexX | 878839 | Multidrug efflux | Transport (+1) | - | - |
PA4050 | pgpA | 879074 | Phospholipid biosynthesis | Cellular lipid metabolic process | - | - |
GO and MeSH annotations for antibiotic registance Pseudomonas aeruginosa : comparison of NCBI gene descriptions, GO, and MeSH
Locus | MeSH | |||||
|---|---|---|---|---|---|---|
A, p=0.0064 | B, p=0 | C, p=0.12 | D, p=0 | E, 3.9E-9 | G, p=0 | |
PA3303 | - | - | - | - | - | - |
PA0392 | - | - | - | - | - | - |
PA4077 | - | - | - | - | - | - |
PA5199 | Periplasm (+7) | Bacillus subtilis (+9) | Chromosome Deletion | DNA, Bacteria (+88) | Gene Knockout (+39) | Alleles (+98) |
PA5366 | Cell Membrane (+1) | Escherichia coli (+5) | Chromosome Deletion | RNA, Bacterial (+33) | Cloning (+14) | Binding Sites (+31) |
PA5200 | Cell Membrane (+10) | Salmonella typhimurium (+11) | Urinary Tract Infections (+2) | 5’ Untranslated Regions (+115) | Amino Acid Substitution (+50) | Biofilm (+110) |
PA0016 | Cell Membrane (+8) | Enterobacteriaceae (+11) | Salmonella Infections | Acetates (+89) | Cell Fractionation (+17) | Drug Resistance, Microbial (+66) |
PA4942 | Cytoplasm (+10) | Bacillus subtilis (+10) | - | Adenosine Triphosphatases (+65) | Cloning, Molecular (+28) | Amino Acid Motifs (+73) |
PA3014 | Liver | Escherichia coli (+7) | - | Proteome (+58) | Methods (+15) | Kinetics (+37) |
PA4398 | - | - | - | - | - | - |
PA5528 | - | - | - | - | - | - |
PA1805 | Capsid (+4) | Escherichia coli (+1) | - | Sigma Factor (+31) | Mutagenesis (+13) | Cell Division (+32) |
PA3016 | - | - | - | - | - | - |
PA4223 | - | Pseudomonas aeruginosa | - | ATP-Binding Cassette Transporters (+4) | Gene Expression Profiling (+2) | Gene Expression Regulation, Bacterial (+4) |
PA4960 | - | Bacteriophage lamda | - | Alcohol Oxidoreductases (+35) | Autoradiography (+14) | Carbohydrate Metabolism (+38) |
PA0374 | Chromosomes (+4) | Escherichia coli (+2) | Chromosome Deletion | Magnesium (+42) | Culture Media (+11) | Operon (+50) |
PA3013 | Chromosomes, Bacterial (+2) | Swine (+5) | - | Acetyl Coenzyme A (+33) | Genetic Complementation Test (+8) | Promoter Regions, Genetic (+24) |
PA5471 | Ribosomes | Pseudomonas aeruginosa | - | Anti-Bacterial Agents (+8) | Microbial Sensitivity Test (+4) | Drug Resistance, Multiple (+10) |
PA0502 | - | - | - | - | - | - |
PA3194 | Cell-Free System (+2) | Salmonella typhimurium (+3) | Chromosome Aberrations | DNA, Bacterial (+45) | Spectrophotometry (+8) | Sequence Homology, Nucleic Acid (+28) |
PA1775 | - | - | - | - | - | - |
PA0427 | Brain (+6) | Animals, Newborn | Meningitis, Bacteria (+1) | Copper (+76) | Absorptiometry (+39) | DNA Damage (+67) |
PA4222 | - | - | - | - | - | - |
PA5369 | - | - | - | - | - | - |
PA2018 | Ribosomes | Escherichia coli (+1) | (+9) | Peptides (+9) | Transfection (+3) | Plasmids (+12) |
PA5285 | - | - | - | - | - | - |
PA2604 | Cytosol (+9) | Spheroplasts (+6) | - | Chaperonins (+31) | Statistics as Topic (+6) | Apoptosis (+25) |
PA2019 | - | Pseudomonas aeruginosa | - | Anti-Bacterial Agents (+4) | Electrophoretic Mobility Shift Assay | Promoter Regions, Genetic (+4) |
PA4050 | - | - | - | - | - | - |
GO and MeSH annotations for antibiotic registance Pseudomonas aeruginosa : comparison of NCBI gene descriptions, GO, and MeSH
Locus | MeSH | ||||
|---|---|---|---|---|---|
H, p=0.040 | J, p=0.021 | L, p=1.5E-7 | M, p=0.36 | N, p=0.061 | |
PA3303 | - | - | - | - | - |
PA0392 | - | - | - | - | - |
PA4077 | - | - | - | - | - |
PA5199 | Systems Biology (+1) | - | Computer Simulation (+3) | - | Temperature (+2) |
PA5366 | Photochemistry | - | Phylogeny (+3) | - | - |
PA5200 | Proteomics (+3) | Polystyrene (+1) | Base Sequence (+5) | - | Biomass (+3) |
PA0016 | Genetics, Microbial (+1) | Membranes, Artificial | Amino Acid Sequence (+3) | - | Sensitivity and Specificity (+1) |
PA4942 | - | - | Databases, Protein (+4) | - | Ultraviolet Rays (+2) |
PA3014 | - | Polyesters | Software (+4) | - | Temperature |
PA4398 | - | - | - | - | - |
PA5528 | - | - | - | - | - |
PA1805 | Genomics | - | Base Sequence (+1) | - | Hot Temperature |
PA3016 | - | - | - | - | - |
PA4223 | - | - | - | - | - |
PA4960 | Research | - | Computers (+4) | - | Cluster Analysis (+1) |
PA0374 | Computational Biology | - | Phylogeny (+4) | - | Temperature |
PA3013 | - | - | Software (+4) | - | - |
PA5471 | - | - | - | - | - |
PA0502 | - | - | - | - | - |
PA3194 | Molecular Biology (+1) | - | Molecular Sequence Data (+2) | - | Biomass |
PA1775 | - | - | - | - | - |
PA0427 | Crystallography (+2) | Liposomes (+2) | Base Sequence (+2) | Infant, Newborn | Environment |
PA4222 | - | - | - | - | - |
PA5369 | - | - | - | - | - |
PA2018 | - | - | Molecular Sequence Data | - | - |
PA5285 | - | - | - | - | - |
PA2604 | Statistics as Topic | - | Terminology as Topic (+4) | - | Statistics as Topic |
PA2019 | - | - | - | - | - |
PA4050 | - | - | - | - | - |
Conclusions
As shown in the case-studies described above, our framework can be a powerful tool for genome-wide studies. Due to the large number of categories included in MeSH, different interpretations are possible without a restriction to molecular biological terms. The MeSH ORA framework detects enriched MeSH terms from gene lists. Such results can seamlessly be converted into related PubMed documents. This function may support those researchers who spend much time searching through related literature, allowing them to concentrate on the biological interpretation involeved in the downstream steps.
We will also implement GSEA (gene set enrichment analysis) [70], SEA (simpler enrichment analysis) [71], or GSA (gene-set analysis) [72] functions using MeSH. In addition, the plot function will be implemented to visualize the results as a tagcloud, tree, or network mapped from enriched MeSH terms. All data will be semi-annually updated in every Bioconductor version. The data from MeSH and PubMed will be annually downloaded and the analysis of RBBH will also be performed regularly.
All R scripts used in this paper are provided in as Additional file 4.
Availability and requirements
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Project name: MeSH ORA Framework
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URLs:
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http://www.bioconductor.org/packages/release/data/annotation/html/MeSH.db.html
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http://www.bioconductor.org/packages/release/data/annotation/html/MeSH.AOR.db.html
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http://www.bioconductor.org/packages/release/data/annotation/html/MeSH.PCR.db.html
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http://www.bioconductor.org/packages/release/data/annotation/html/org.MeSH.Hsa.db.html
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http://bioconductor.org/packages/release/bioc/html/meshr.html
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http://bioconductor.org/packages/release/bioc/html/MeSHDbi.html
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Operating system: Platform independent
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Programming language: R v 3.1.0 or higher
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Other requirements: Bioconductor 2.14 or higher (save.abst function will be released by Bioconductor 3.1). Please also note that org.MeSH.XXX.db-type packages are renamed as MeSH.XXX.eg.db-type packages by Bioconductor 3.1.
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License: Artistic-2.0
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Any restrictions to use by non-academics: For non-profit use only
Declarations
Acknowledgements
We acknowledge PubMed and MeSH, particularly Kilbourne John who is head of the MeSH section in NLM. We also thank Margaret McGhee and Veronica Robinson Garrett for providing the licensed PubMed data. Some of the calculations were performed using the RIKEN Integrated Combined Cluster (RICC) and supercomputer system at the National Institute of Genetics (NIG) and Research Organization of Information and Systems (ROIS).
Authors’ Affiliations
References
- Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP. Summaries of affymetrix genechip probe level data. Nucleic Acid Res. 2003; 31:e15.View ArticlePubMedPubMed CentralGoogle Scholar
- Mardis ER. The impact of next-generation sequencing technology on genetics. Trends Genet. 2008; 24:134–41.View ArticleGoogle Scholar
- Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. Rna-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008; 18(9):1509–17.View ArticlePubMedPubMed CentralGoogle Scholar
- Wang L, Feng Z, Wang X, Wang X, Zhang X. Degseq: an r package for identifying differentially expressed genes from rna-seq data. BMC Bioinformatics. 2009; 26(1):136–8.View ArticleGoogle Scholar
- Tarazona S, García-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in rna-seq: a matter of depth. Genome Res. 2003; 21(12):2213–23.View ArticleGoogle Scholar
- Bourgon R, Gentleman R, Huber W. Independent filtering increases detection power for high-throughput experiments. PNAS. 2010; 107(21):9546–51.View ArticlePubMedPubMed CentralGoogle Scholar
- Tsai FJ, Yang CF, Chen CC, Chuang LM, Lu CH, Chang CT, et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in han chinese. PLOS Genet. 2010; 6(2):e1000847.View ArticlePubMedPubMed CentralGoogle Scholar
- Li M, Atmaca-Sonmez P, Othman M, Branham KEH, Khanna R, Wade MS, et al. Cfh haplotypes without the y402h coding variant show strong association with susceptibility to age-related macular degeneration. Nat Genet. 2008; 38(9):1049–54.View ArticleGoogle Scholar
- Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. Rna-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008; 18:1509–17.View ArticlePubMedPubMed CentralGoogle Scholar
- Auer PL, Doerge RW. A two-stage poisson model for testing rna-seq data. Stat Appl Genet Mol Biol. 2011; 10(1):1–26.Google Scholar
- Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010; 11:106.View ArticleGoogle Scholar
- Robinson MD, McCarthy DJ, Smyth GK. edger: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010; 26(1):139–40.View ArticlePubMedGoogle Scholar
- Hardcastle TJ, Kelly KA. bayseq: Empirical bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics. 2010; 11:422.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhou Y-H, Xia K, Wright FA. A powerful and flexible approach to the analysis of rna sequence count data. Bioinformatics. 2011; 27(19):2672–8.View ArticlePubMedPubMed CentralGoogle Scholar
- McCarthy DJ, Smyth GK. Testing significance relative to a fold-change threshold is a treat. Bioinformatics. 2009; 25(6):765–71.View ArticlePubMedPubMed CentralGoogle Scholar
- Feng J, Meyer CA, Wang Q, Liu JS, Shirley LX, Zhang Y. Gfold: a generalized fold change for ranking differentially expressed genes from rna-seq data. Bioinformatics. 2012; 28(21):2782–8.View ArticlePubMedGoogle Scholar
- Troyanskaya OG, Garber ME, Brown PO, Botstein D, Altman RB. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics. 2002; 18:1454–61.View ArticlePubMedGoogle Scholar
- Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004; 573:83–92.View ArticlePubMedGoogle Scholar
- Yamamoto H, Fujimori T, Sato H, Ishikawa G, Kami K, Ohashi Y. Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis. BMC Bioinformatics. 2014;15(51).Google Scholar
- Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. PNAS. 2001; 98:5116–21.View ArticlePubMedPubMed CentralGoogle Scholar
- Huang W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37(1):1–13.View ArticleGoogle Scholar
- Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: Current approaches and outstanding challenges. PLOS Comput. Biol.2012;8(2).Google Scholar
- Consortium TGO. Gene ontology: tool for the unification of biology. Nat Genet. 2000; 25:25–9.View ArticleGoogle Scholar
- Kanehisa M, Goto S. Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000; 28:27–30.View ArticlePubMedPubMed CentralGoogle Scholar
- Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, de Bono B, et al. Reactome knowledgebase of biological pathways and processes. Nucleic Acids Res. 2008; 37:619–22.View ArticleGoogle Scholar
- Caspi R, Altman T, Dale JM, Dreher K, Fulcher CA, Gilham F, et al. The metacyc database of metabolic pathways and enzymes and the biocyc collection of pathway/genome databases. Nucleic Acids Res. 2010; 38:473–9.View ArticleGoogle Scholar
- Nishimura D. Biocarta. Biotech Softw Internet Rep. 2001; 2:117–20.View ArticleGoogle Scholar
- Schriml LM, Arze C, Nadendla S, Chang YW, Mazaitis M, Felix V, et al. Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res. 2012; 40(D1):940–6.View ArticleGoogle Scholar
- Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (msigdb) 3.0. Bioinformatics. 2011; 27(12):1739–40.View ArticlePubMedPubMed CentralGoogle Scholar
- Hosack DA, Dennis GJ, Sherman BT, Lane HC, Lempicki PA. Identifying biological themes within lists of genes with ease. Genome Biol. 2003; 4:70.View ArticleGoogle Scholar
- Shahrour A, Diaz-Uriarte R, Dopazo J. Fatigo: a web tool for finding significant associations of gene ontology terms with groups of genes. Bioinformatics. 2004; 20:578–80.View ArticleGoogle Scholar
- Falcon S, Gentleman R. Using gostats to test gene lists for go term association. Bioinformatics. 2007; 23(2):257–8.View ArticlePubMedGoogle Scholar
- Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR. Genmapp, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet. 2002; 31:19–20.View ArticlePubMedGoogle Scholar
- Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, et al. Gominer: a resource for bilogical interpretation of genomic and proteomic data. Genome Biol. 2003; 4:28.View ArticleGoogle Scholar
- Zhong S, Storch KF, Lipan O, Kao MC, Weitz CJ, Wong WH. Gosurfer: a graphical interactive tool for comparative analysis of large gene sets in gene ontology space. Appl Bioinformatics. 2004; 3(4):261–4.View ArticlePubMedGoogle Scholar
- DAndrea D, Grassi L, Mazzapioda M, Tramontano A. Fidea: a server for the functional interpretation of differential expression analysis. Nucleic Acids Res. 2013; 41:84–8.View ArticleGoogle Scholar
- Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for rna-seq: accounting for selection bias. Genome Biol. 2010; 11(2):R14.View ArticlePubMedPubMed CentralGoogle Scholar
- Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A. Enrichnet: network-based gene set enrichment analysis. Bioinformatics. 2012; 28(18):451–7.View ArticleGoogle Scholar
- Draghici S, Khatri P, Bhavsar P, Shah A, Krawetz SA, A TM. Onto-tools, the toolkit of the modern biologist: Onto-express, onto-compare, onto-design, and onto-translate. Nucleic Acids Res. 2003; 31:3775–81.View ArticlePubMedPubMed CentralGoogle Scholar
- Maere S, Heymans K, Kuiper M. Bingo: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005; 21:3448–9.View ArticlePubMedGoogle Scholar
- Huang W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009; 37(1):1–13.View ArticleGoogle Scholar
- Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Comput. Biol. 2012; 8(2):e1002375.View ArticlePubMedPubMed CentralGoogle Scholar
- Glass K, Girvan M. Annotation enrichment analysis: An altenative method for evluating the functional propertives of gene sets. Sci Rep.2014;4(4191).Google Scholar
- Subramanian A, Tamayo P, Mootha V. K, Mukherjee S, Ebert B. L, Gillette M. A, et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. 2005; 102(43):15545–50.View ArticlePubMedPubMed CentralGoogle Scholar
- Tipney H, Hunter L. An introduction to effective use of enrichment analysis software. Hum Genomics. 2010; 4(3):202–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Nelson SJ, Schopen M, Savage AG, Schulman JL, Arluk N. The mesh translation maintenance system: structure, interface design, and implementation. Stud Health Technol Inform. 2004; 107:67–9.PubMedGoogle Scholar
- Nakazato T, Takinaka T, Mizuguchi H, Matsuda H, Bono H, Asogawa M. Biocompass: a novel functional inferance tool that utilizes mesh hierarchy to analyze groups of genes. In Silico Biol. 2007; 8:53–61.Google Scholar
- Nakazato T, Bono H, Matsuda H, Takagi T. Gendoo: functional profiling of gene and disease features using mesh vocabulary. Nucleic Acids Res. 2009; 37:166–9.View ArticleGoogle Scholar
- Sartor MA, Ade A, Wright Z, States D, Omenn GS, Athey B, et al. Metab2mesh: annotating compounds with medical subject headings. Bioinformatics. 2012; 28:1408–10.View ArticlePubMedPubMed CentralGoogle Scholar
- Jani SD, Argraves GL, Barth JL, Argraves WS. Genemesh: a web-based microarray analysis tool for relating differentially expressed genes to mesh terms. BMC Bioinformatics. 2010;11(166).Google Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approarch to multiple testing. J R Stat Soc B. 1995; 57:289–300.Google Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al. Bioconductor: open software development for computational biology and bioformatics. BMC Genome Biol. 2004; 5(10):R80.View ArticleGoogle Scholar
- Meyer LR, Zweig AS, Hinrichs AS, Karolchik D, Kuhn RM, Wong M, et al. The ucsc genome browser database: extensions and updates 2013. Nucleic Acids Res. 2012; 41:64–69.View ArticleGoogle Scholar
- Kawai J, Shinagawa A, Shibata K, Yoshino M, Itoh M, Ishii Y, et al. Functional annotation of a full-length mouse cdna collection. Nature. 2001; 409(6821):685–690.View ArticlePubMedGoogle Scholar
- Okazaki Y, Furuno M, Kasukawa T, Adachi J, Bono H, Kondo S, et al. Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cdnas. Nature. 2002; 420(6915):563–573.View ArticlePubMedGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez J. M, Terol J, Talon M, Robles M. Blast2go: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005; 21(18):3674–76.View ArticlePubMedGoogle Scholar
- Jones P, Binns D, Chang H, Fraser M, Li W, McAnulla C, et al. Interproscan 5: genome-scale protein function classification. Bioinformatics. 2014; 30(9):1236–40.View ArticlePubMedPubMed CentralGoogle Scholar
- Ye Y, Choi J, Tang H. Rapsearch: a fast protein similarity search tool for short reads. BMC Bioinformatics. 2011;12(159).Google Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990; 215(3):403–10.View ArticlePubMedGoogle Scholar
- Quinlan JR. C4.5: Programs for machine learning. Burlington, Massachusetts, US: Morgan Kaufmann Publishers Inc.; 1993.Google Scholar
- Storey JD. The positive false discovery rate: A bayesian interpretation and the q-value. Ann Stat. 2003; 31:2013–35.View ArticleGoogle Scholar
- Storey JD, Tibshirani R. Statistical significance for genomewide studies. PNAS. 2003; 100:9440–5.View ArticlePubMedPubMed CentralGoogle Scholar
- Efron B, Tibshirani R, Storey JD, Tusher V. Empirical bayes analysis of a microarray experiment. J Am Stat Assoc. 2001; 96:1151–60.View ArticleGoogle Scholar
- Efron B, Tibshirani R. Empirical bayes methods and false discovery rates for microarrays. Genetic Epidemiol. 2002; 23:70–86.View ArticleGoogle Scholar
- Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the r/bioconductor package biomart. Nat Protocols. 2009; 4:1184–91.View ArticlePubMedGoogle Scholar
- Durinck S, Moreau Y, Kasprzyk A, Davis S, Moor BD, Brazma A, et al. Biomart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics. 2005; 21:3439–40.View ArticlePubMedGoogle Scholar
- Chujo Y, Fujii N, Okita N, Konishi T, Narita T, Yamada A, et al. Caloric restriction-associated remodeling of rat white adipose tissue: effects on the growth hormone/insulin-like growth factor-1 axis, sterol regulatory element binding protein-1, and macrophage infiltration. Age (Dordr). 2013; 35(4):1143–1156.View ArticleGoogle Scholar
- Konishi T. Three-parameter lognormal distribution uniquitosusly found in cdna microarray data and its application to parametric data treatment. BMC Bioinformatics. 2004;5.Google Scholar
- Gallagher LA, Shendure J, Manoil C. Genome-scale identification of resistance functions in pseudomonas aeruginosa using tn-seq. mBio. 2011; 2:00315–10.View ArticleGoogle Scholar
- Aravind S, Pablo T, Vamsi KM, Sayan M, Benjamin LE, Michael AG, et al. A knowledge-based approach for interpreting genome-wide expression profiles. PNAS. 2005; 102(43):10.Google Scholar
- Irizarry RA, Wang C, Zhou Y, Speed TP. Gene set enrichment analysis made simple. Stat Methods Med Res. 2009; 18(6):565–75.View ArticlePubMedPubMed CentralGoogle Scholar
- Efron B, Tibshirani R. On testing the significance of sets of genes. Annu Appl Stat. 2007; 1(1):107–129.View ArticleGoogle Scholar
Copyright
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
