All GO annotations for unique Entrez Gene ids (a one-to-many mapping) were retrieved from the 9 May release of the gene2go file, also. About NCBI · Research at NCBI · NCBI News & Blog · NCBI FTP Site · NCBI on Facebook · NCBI on Twitter · NCBI on YouTube · Privacy Policy. External link. gene2GO — an R named vector mapping gene names to associated GO terms. The initialized object is run using Fisher classic analysis.

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The website is setup to use sensible defaults. If not, then the GO annotation database is also available. The default set of evidence codes gene2go youtube is gene2go youtube same as for the prebuilt GO based gene set collections provided at MSigDB. For many less well characterised species almost all GO term associations have the automated electronic annotation code 'IEA' and including all evidence codes is more appropriate in that case.

Using an externally hosted GO database is the easiest option since it requires no downloading and installation of GO term data, but can be slow. For the species included in the NCBI gene2go tables Appendix Agene2go is the cleanest source of data available in terms of consistent use of gene identifiers.

Each parent term can have multiple children, and each child term can have multiple parents. The first is identifying which genes from the organism in question are associated with which GO terms. The second gene2go youtube propagating these gene associations up towards the root term. For instance if a gene is associated with the 'calcium transport' molecular function, it should also be associated with the 'ion transport', and 'transport' functions.

These higher level associations are often not captured in the primary sources of gene association data, where only the most specific GO term membership is annotated, however it is crucial for detecting biologically relevant patterns in gene2go youtube sets. Gene set collections built in this way will likely contain multiple GO terms with identical gene associations.

Such duplicate gene sets can affect the accuracy of the key GSEA false discovery rate statistic and it is necessary that these are removed from the gene set collection. The description field in the output gene set file contains a list of ALL GO terms with that set of gene associations. The URL link field which can only reference one term contains a link to whichever gene2go youtube the GO terms has the shortest distance between it and the root term - in other words the most general of the terms associated with that gene set.

During analysis of the results from a GO based gene set analysis the experimenter is likely to want to home in on more specific terms that show statistically significant changes.

In this implementation a rough guide is provided by calculating how many terms exist between the GO term in question and the gene2go youtube of the ontology tree, taking the shortest path. This distance is shown in brackets at the end of each term name in the description field. A number of Perl libraries that are not part of the standard distribution need to be present. These are:. Some of these will likely gene2go youtube installed as standard with perl on your Linux distribution, others will need to be installed from the distribution repositories, or from CPAN.

On an Ubuntu This gene2go youtube either display help information or error indicating which required libraries are not present.

To test without having to install any databases, download additional files or access remote databases, you can use the cached version of the GO ontology contained in this directory along with the supplied E. Gene2go youtube this directory issue:. This should display a collection of E. If you have access to a GO gene2go youtube mirror at your institution or elsewhere you can test with it.

The example below uses the mirror at ebi. Similarly to use the ebi. Ensure you have MySQL server and command line installed and running. On an ubuntu GO2MSIG gene2go youtube the gene2go youtube installation is called 'mygo', the user is 'gouser' and the password is 'amigo'. Bring up a mysql command line prompt for a user which can create and populate databases. To gene2go youtube the 'gouser' user and assign access rights, issue:.

Unlike some of the gene association data available in the GO database, this data set has the advantage that it uses a consistent gene identifier, the Entrez gene ID.

In conjunction with the NCBI geneinfo table it is possible to generate gene sets for these species using consistently either the gene ID or the standard gene symbol. However unlike the GO MySQL database the gene2go and geneinfo tables are available only in the form of a tab separated text file, so it is necessary to generate the MySQL tables and load the data using the instructions below.

These create a database called 'bioannoation' which contains the two tables. Assuming you gene2go youtube already created the user 'gouser' above, to assign access rights for the user to access the bioannotation database issue:. Nikto2 centos load the data issue: Any warnings which occur can gene2go youtube displayed by issuing 'show warnings'. To generate a similar collection of E.

This will generate a collection gene2go youtube E. If you have also installed a MySQL based geneinfo table you can test this by generating the same gene set using the gene symbol as the identifier:.

In addition to the parameter values shown, those examples all use the default values for gene set maximum and minimum size cutoffs, required ontologies, and output file format. Program switches for these features are -maxgenes -mingenes -ontology and -format respectively. It is also possible to produce gene sets without propagating the gene associations from specific GO terms to their more general parent terms using the -nochild switch.

Details on usage of these switches can be gene2go youtube by issuing go2msig -help. Output from this is reproduced in Appendix B. By default the program uses the same subset of allowed evidence codes as the GO based gene set collections provided at MSigDB. For many less well characterised species almost all GO term associations have the automated electronic annotation code 'IEA' and so using the '-e all' switch as for the examples above would be advised in this case.

The user can supply a mapping file to translate identifiers used in the original association source to identifiers of the user's choice. The mapping file is a tab separated gene2go youtube value list hymn jp armii the key 1st column is the identifier as exists in the association source, and the value 2nd column is the identifier to be output in the final result.

If the same key exists multiple times in the map file with different values the original identifier will be expanded out into each value. By default, if an identifier in the association source is not represented in the mapping file, the original identifier gene2go youtube be output. One example utility here would be if the association source uses inconsistent identifiers.

Gene2go youtube for instance the user wishes to generate a collection of gene sets for Rhododoccus gene2go youtube RHA1 from the GO database. Gene2go youtube basic query would be:. The majority of gene2go youtube genes output are identified by an abstract ID of the form RO. However a small fraction are instead identified by a gene symbol.

Thus it would be ideal to provide a mapping file that could translate the small number of gene symbols in the gene sets to the standard gene ID format for compatibility with the annotation file when used in GSEA.

The annotation file from GEO itself lists a number of gene symbols and a basic mapping file can be extracted from this. More complete mapping would require some gene2go youtube of manual curation. Example files are available in the examples directory. The go2msig command would be:. If the -repress switch is set when a user mapping file is being used, gene2go youtube identifier in the association source that is not present in the mapping file is excluded from the gene set.

One example usage of this is generating gene sets using the Affymetrix E. The array contains probes made to the gene complement of 4 different E. The array annotation file maps GO terms to probe identifiers, and so if used in default fashion without a mapping file the gene sets will contain probe ids. In this case the gene sets need to contain Entrez gene ids, not probe ids. Because the array represents multiple species some of the probe ids will not correspond to genes for the particular E.

Using the 'repress' flag in conjunction with a mapping file that maps the probe ids to gene ids, probes to genes not present in the E. The examples directory contains an E.

You will need to uncompress this with:. Running the command without the -repress switch as below will map probe ids to Entrez gene IDs where possible, but will display probe ids for those probes without gene2go youtube E. The presence of both unmapped probe ids gene2go youtube Entrez gene ids is obvious in the output. Instead, running the command with the -repress switch will produce gene sets containing exclusively E. This is particularly useful if you wish to use a remote GO database for term source, but have a local gene association source such as a GAF file.

To build a local cache file use the gene2go youtube makecache switch and provide the cache file name gene2go youtube with -cachefile filename. To list the species available from the ncbi database:. The latter will list hundreds of thousands of species, so it's best to filter for those of interest, e. Where a term ID present in an association has been superceded by a new version determined from synonym information in the term source the GO gene2go youtube ID in the output gene sets will be replaced with the new version.

A warning message will be displayed: If the gene association data includes term IDs gene2go youtube are obsolete according to the current source of term information, or do not exist in the current source of term information then a warning message will also be displayed.

In this case the obsolete or nonexistent term will not be output in the final gene sets as it cannot be placed into the GO term hierarchy. Website updated 1 September

Gene set analysis, which translates gene lists into enriched functions, is among the most common bioinformatic methods. Yet few would advocate taking the results at face value.

Not only is there no agreement on the algorithms themselves, there is no agreement on how to benchmark them. In this paper, we evaluate the robustness and uniqueness of enrichment results as a means of assessing methods gene2go youtube where correctness is unknown. By providing a means of determining where enrichment analyses report non-specific and non-robust findings, we are able to assess where we can be confident in their use.

We find significant progress in recent bias correction methods for enrichment and provide our own software implementation. Our approach can be readily adapted to any pre-existing package.

As originally conceived, gene set analysis is a way to summarize rankings or groups of genes obtained from high-throughput experiments and as a tool for discovery 1 — 4. Broadly speaking, these methods look for statistical similarity between an experimentally derived gene set or a ranked list of genes and previously characterized gene sets e.

Running enrichment gene2go youtube on such data sets is now standard practice. Given the heavy reliance on these methods for hypothesis generation and experimental validation checks, it is important to improve our understanding of their benefits and limitations. As we will highlight, the central challenge in this analysis is how to manage and interpret results in light of gene set independence, or lack thereof. One of the key insights into the challenge of gene set analysis is that some genes are simply generally more likely to be annotated to any sets.

Such genes will appear in many sets. In the gene set analysis literature gene2go youtube property is often described in terms of overlap or annotation bias. In earlier work, we showed that the tendency for some genes to be frequently represented in GO is a gene2go youtube confound in gene network analysis 8. A useful element of our approach in that work was to gene2go youtube redundancy within GO in terms of the ability of a single list of gene gene2go youtube predict the membership of each gene set derived from GO and its annotation.

The degree to which a single list predicts all GO terms says how redundant GO is, which sets look to be most generic, and which genes contribute to those tendencies. Trivially, genes with many annotations would appear at the top of such a list because predicting them frequently will be correct across more GO groups.

In the gene set analysis context, because the redundancy and overlap in GO is often apparent when inspecting results, there have been a partons vite kaolin mp3 of efforts to improve the situation we use GO as our motivating example of an annotation scheme without loss of generality to alternatives.

Many approaches attempt to reduce the redundancy in GO either by trimming it down up front 9 — 13or adjusting the results of an analysis 14 — An implicit understanding of the undesirability of overlaps of gene sets gene2go youtube also present when analyses are limited to a single branch of the GO gene2go youtube e.

Such approaches serve the dual purposes of simplifying interpretation of enrichment results and diminishing multiple test correction penalties, thereby improving P -values. However, attempts to reduce redundancy inevitably involve a loss of information, especially in schemes like GO where the extent of overlaps is extreme 8. Another approach to correct for redundancy is through improving the statistical machinery underlying gene gene2go youtube analysis, e.

More commonly, enrichment approaches make post-hoc adjustments, following a basic strategy of reducing the impact of multifunctional genes. Some approaches take the view that differential annotation for genes reflects a bias in the annotations that needs to be corrected, but that the correction needn't depend on the experimental data on which gene set analysis is to be applied 18 The commonality we point to in the various approaches is that it is hard to know gene2go youtube they improve upon what is already done.

There is no strongly generalizable way to test the efficacy of these methods, as there are no gold standards. This is a problem likewise faced gene2go youtube any biologist in reading about and interpreting any results using any of these methods.

But we take the stance that gene2go youtube the gene set analysis method or the gene set annotations is fraught with difficulties. Instead, our approach is akin to methods intended to test gene2go youtube or overfitting, and is not a new form of enrichment analysis and thus can be applied to any gene set analysis method. We rely on two central heuristics, uniqueness and robustness, which relate multifunctionality to the properties possessed by well-conditioned problems.

Traditionally, well-conditioned problems are those that possess solutions unique and robust to minor data variation. For example, if enrichment output gene2go youtube identical to that produced by sets of genes that are present in many functions i. Likewise, an enrichment result should not hinge on the presence or absence of any given single gene.

Because we argue uniqueness and robustness are fundamental properties for the analysis to be meaningful, they will provide strong heuristic value gene2go youtube the interpretation of what would otherwise be a black box. In this paper, we further develop and explore our model for enrichment and particularly the problem of multifunctionality, focusing both on detailed examples and a large corpus of studies. We show that our approach improves the specificity of interpretation in enrichment analyses through an analysis across 17 commonly used enrichment methods.

We propose that measurements of the effects of multifunctionality should be routinely incorporated in such analyses. To this end, we provide user-friendly implementations of the methods in a graphical user interface as part of the ErmineJ software package 22 April ] We limited our analysis to lists of size 11—, which come from different publications, and often form pairs e.

We did not filter based on evidence codes and used all three domains of GO similar results were obtained using just biological process or molecular function. Except where noted, we considered GO groups that had between 10 and genes. There were GO terms meeting this criterion. GO was constructed as above, using the OBO file go. Further to parsing the role of properties within the existing GO and its annotations, we generated four novel versions of GO gene2go youtubeencompassing an alternate conceptualization of how an ontology, annotations to it and methods exploiting the two, interact.

Ortho-GO alters GO by performing dimension reduction on the original matrix of propagated GO annotations, yielding new genes sets that are closer to independent but retain the original tendencies of pairs of genes to be co-annotated. Weigh-GO discards binary membership, such that gene2go youtube gene is weighted based on set annotation specificity.

Local-GO is a more targeted version of GO, where we select a gene2go youtube of interest, and pick non-overlapping GO terms to gene2go youtube test. In this case, the annotation sets are held constant, but the ontology is tweaked to only include a subset of groups.

To generate and assess this, we pick a random function within GO to be of interest and then iteratively pick new functions based on the gene2go youtube Jaccard overlap with the remainder, stopping at pavitra bandham songs or local functions local-GO and local-GO, respectively. We considered two basic types of algorithms. The second is based on ranks without setting a threshold.

For this purpose gene2go youtube used a method based hugo playstation 1 the AUROC 27the same as the method mentioned above to measure multifunctionality of a GO group but using the experimentally-derived ranking.

ErmineJ implements several additional methods, including the resampling methods described by 2 and a GSEA-inspired method that uses precision-recall analyses rather than modified Kolmogorov—Smirnov statistics, in which the mean average precision mera 16 ka dola to the area under the precision-recall curve is calibrated by random sampling to obtain a null distribution.

The challenge is identifying an appropriate stopping point. Our algorithm is motivated by finding a point agir fumar para esquecer skype which the enrichment results are maximally sensitive to the removal of the most multifunctional gene. Intuitively, if some gene sets are only enriched due to overlaps, as we remove overlapping genes, those si sboccia il pageant yahoo sets will eventually fall away.

This gene2go youtube point will be reflected spa music collection shirley cason a rapid alteration in the most significantly related gene sets, similar to the phenomenon shown in Figure 3C.

A formal description of the gene removal algorithm and a schematic is given in the supplement Supplementary Data. For methods that use a full ranking of genes, we developed an approach using regression. For the ROC-based method 27the appropriate regression was unweighted linear regression of the genes scores against the gene multifunctionality scores; the original gene scores are replaced by the Studentized residuals of this regression.

Gene2go youtube note that some methods, such as GSEA, use the full ranking but behave more gene2go youtube precision-recall curves than ROCs, in that they put much more emphasis on highly ranked genes. In this gene2go youtube unweighted regression is gene2go youtube. While not investigated gene2go youtube part of our analysis reported here, a regression-based correction for the precision-recall method is implemented in ErmineJ 3.

This variability is what determines the expected contribution of a hit to the aggregate variability in the area under the precision-recall curve. Gene2go youtube relationships, organized by disease ontology DO terms, were obtained from Phenocarta 29 [date: April ].

ErmineJ implements multifunctionality analysis as well as the unweighted and weighted regression correction algorithms. For the case studies reported here, ErmineJ analyses were limited to the biological process GO aspect, for terms containing 20— genes. Gene lists for case studies were extracted from data presented in the original reports or Supplementary Data.

The gene lists we discuss are based on the identifiers we could match to official gene symbols in our database, so may not exactly match the lists reported by the authors. The data gene2go youtube used for gene2go youtube case studies are available in the online supplement http: We selected 17 common methods that perform varying forms of gene set enrichment and correction procedures accessed between Dec and April For the gene2go youtube part, gene2go youtube methods rely on a statistical test to determine which gene sets are significant and some method of enrichment correction.

Here, we focus on methods specifically designed for GO. We ran each method with the same default parameters, and when we could, used the same background input.

The GO annotations file also varied as some methods had set their annotation file, and others allowed the user to specify it. For consistency, we attempted to use the same GO version when possible. Because we could not directly control for the number of GO terms used, we attempted to control for this by comparing the fraction of GO terms returned, instead of totals. However, we did not wish to penalize methods, so we continued to compare all results, even if some GO terms were missing between methods.

The total set of GO terms with gene annotations was then almost 16K, and for each case study methods reported between and terms. We did not limit the results to a particular GO category and excluded IEA annotations as is commonly done for purely gene2go youtube assessments, since IEA annotations are themselves algorithmically determined. To calculate the functions different methods are likely to return for most hit lists, we performed GO enrichment analysis, using a list of the top multifunctional genes derived from human GO annotations as of Decfor the 17 different methods, and variations of a few of these methods, including Gene2go youtube and a basic gene set enrichment implementation hypergeometric test.

We calculated the number of GO terms returned as significant for this list gene2go youtube genes and how these terms and their P -values correlated between methods. We chose a P -value threshold of 0. Some methods return all tested values, while others only the significant terms they found enriched.

Most methods perform their own multiple gene2go youtube test corrections, and when able, we specified for Benjamini—Hochberg. All these analyses were similarly repeated for mouse GO annotations. For the uniqueness assessment, we took each case study and compared the enrichment results gene2go youtube the multifunctionality results, first by calculating the average multifunctionality of the GO terms returned as enriched, and also comparing the overlap of results from the previous multifunctionality enrichment, species specific.

We then performed a robustness analysis using gene2go youtube same case studies. We then calculated the overlap between the enrichment results returned for each method, as a measure of stability. We also then once again compared how multifunctional the results were once we removed the most multifunctional genes. Additional information gene2go youtube data files for many of the analyses and scripts are available online at http: In this paper, we evaluate the effect of multifunctional genes on enrichment results.

We start gene2go youtube outlining our motivation and illustrating the impact of multifunctional genes gene2go youtube our model of uniqueness and robustness. We move on to providing specific examples in four case studies.


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