GeneTrail2 is able to read various input file formats through which the user can provide measurement data or categories that should be analyzed. In general, GeneTrail2 will try to automatically detect the meta-data of the uploaded data. This means it attempts to detect the used data format, identifier type, and organism the data was derived from. If errors arise during this step, it is important to understand which input types are supported by GeneTrail2.
Thus, in the following we discuss the expected input formats and the assumptions GeneTrail2 makes about their contents.
nameof such an entity as it is used in some database such as Ensembl, UniProt, or NCBI Gene.
Identifier listsThe simplest way to provide input data to GeneTrail2 is to upload a list of identifiers. Identifier lists can contain both: a, typically short, list of
relevantentities or a, typically long, list of entities sorted by relevance.
GDA SCN3A SCN3B RPLP2 GFER SNORA68 SNORA65 PIP5KL1 BTBD1 RPLP0 BTBD2 BTBD3 ...
Identifier level scores
Similarly to identifier lists, score lists can be provided in a text based format containing one identifier per
line. The difference to identifier lists is that a score, a numerical value measuring the
relevance of the
entity, is provided in an additional column. Both columns are separated by a whitespace, preferably by a tab
GDA 0.05501 SCN3A -0.017374 SCN3B 0.33427200000000046 RPLP2 -0.10048799999999997 GFER 0.08075766666666603 SNORA68 0.2532145 SNORA65 -0.289492 PIP5KL1 0.267125 BTBD1 -0.824291000000001 RPLP0 0.050174750000000046 BTBD2 -0.424771999999999 BTBD3 0.267594 RPLP1 -0.1359804999999995 ATP6 -0.2206155 ...
GeneTrail2 provides support for directly analyzing matrices containing high-throughput measurements. These can be normalized expression values obtained from microarray or RNA-seq experiments or protein abundances from mass-spectrometry runs. Additionally we offer rudimentary support for analyzing count data obtained via RNA-seq.
Measurements can be uploaded as a plain text, tab-separated matrix. Optionally, the first column of the file contains names for each of the contained samples. Each subsequent row contains the measurement data for one identifier in all samples. Thus each row except the first starts with an identifier followed by N numerical values, where N is the number of samples.
Sample1 Sample2 Sample3 GeneA 0.1 4.3 2.3 GeneB 3.2 -1.2 1.1 GeneC 2.7 9.1 0.3 ...The advantage of uploading matrices of measurements is, that sample-based (sometimes called phenotype-based) permutation schemes can be used to determine p-values.
Microarray dataA major use case of GeneTrail2 is the analysis of microarray data. For this experimental platform, well established normalization pipelines exist that usually generate normal or log-normal distributed expression values. GeneTrail2 can directly work with this kind of data and offers a range of statistics that can be used to derive scores from expression matrices.
RNA-seq data usually comes in the form of count data. This means, that for each transcript and sample the number of reads that were mapped to the transcript is reported. The distribution of this data is fundamentally different to the distribution of microarray data, and hence new methods for the analysis of count data have been developed. GeneTrail2 offers some basic support for directly analyzing count data. For this purpose it uses the DESeq2 , edgeR , and RUVSeq  R packages that can be used to compute scores from count data.
Note that currently for count data, no sample-based permutations can be performed due to the prohibitive runtime of the score computation process.
OthersData from other experimental platforms can also be used in GeneTrail2. Here, however, it is up to the user to select an appropriate scoring scheme.
CategoriesWhile GeneTrail2 offers a large collection of categories that have been derived from a number of third-party databases (see List of categories), it can be desirable to create custom categories that should be checked for enrichment. Examples would be potential targets of a transcription factor that have been identified by a Chip-seq experiment. For specifying categories GeneTrail2 uses the Gene Matrix transposed (GMT) format . In the GMT format every line represents a category. The first column corresponds to the name of the category, the second column to an optional description or source url. The following columns contain the members of the category. Each member occupies exactly one column. GeneTrail2 assumes, that the columns are tab-separated.
CategoryA http://test.url/A GeneA GeneB GeneC GeneD CategoryB http://test.url/B GeneA GeneD CategoryC http://test.url/C GeneD GeneE GeneH ...An additional description of the format can be found here.
Besides the list of relevant entities, the ORA method requires a second list of identifiers which represents the universe of identifiers that can be detected by an experiment. The input format is the same as for identifier lists.
GeneTrail2 does not recognize my score list exported from Excel
MS Excel is a popular tool for managing biological datasets. However, there are some pitfalls especially when it
comes to interoperability with other tools. It can happen that Excel reformats gene identifiers as dates. For
example the gene
Apr1 is routinely recognized as April the first. Please make sure, that no such
conversions have taken place before exporting your data from Excel.
For more information see also Zeeberg et al. .
- Global functional profiling of gene expression Genomics Elsevier (View online)
- Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol (View online)
- edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics Oxford Univ Press (View online)
- Normalization of RNA-seq data using factor analysis of control genes or samples Nature biotechnology Nature Publishing Group (View online)
- GSEA-P: a desktop application for Gene Set Enrichment Analysis Bioinformatics Oxford Univ Press (View online)
- Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics BMC bioinformatics BioMed Central Ltd (View online)