RegulatorTrail 1.1

A web service for the identification of key transcriptional regulators

Input data

RegulatorTrail is able to read various input file formats through which the user can provide measurement data , genomic regions or RTIs that should be analyzed. In general, RegulatorTrail 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 RegulatorTrail.

Thus, in the following we discuss the expected input formats and the assumptions bc RegulatorTrail makes about their contents.

As RegulatorTrail is able to process data not only from microarray experiments, but also from e.g. mass-spectrometry experiments, we use the term entity for talking about genes, protein, miRNA, etc. Similarly, we uses the term identifier whenever we mean the name of such an entity as it is used in some database such as Ensembl, UniProt, or NCBI Gene.

Identifier lists

The simplest way to provide input data to RegulatorTrail is to upload a list of identifiers. Identifier lists can contain both: a, typically short, list of relevant entities or a, typically long, list of entities sorted by relevance.
Different methods assume different properties for the input lists. For example the ORA method [1] requires a list of relevant entities. Methods such as the Kolmogorov-Smirnov statistics assume that the identifier list is sorted by relevance. Do not use a list prepared for one method to compute enrichments of the second kind.
The input format for identifier lists recognized by RegulatorTrail is a simple text file containing exactly one identifier per line: 
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 character. 

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
...
If possible prefer score lists to identifier lists. A score list can be used in any scenario an identifier list can be used in and is much less likely to run into the difficulties frequently encountered with the former.
Note, that RegulatorTrail does not check whether the scores follow a certain distribution or not. While most of the implemented methods work surprisingly well if their assumptions are violated, we recommend that the user chooses an appropriate analysis technique. To this end, the (unweighted) Kolmogorov-Smirnov test and the Wilcoxon test are non-parametric enrichment methods that do not require a specific score distribution.

Measurements

RegulatorTrail 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.

Analyzing data from high-throughput experiments is not just applying a statistical test to each row of the dataset. In practice, quality control, batch effect removal, and normalization must be performed carefully. The features offered by RegulatorTrail are provided for convenience and assume, that the data has been properly prepared!

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 data

A major use case of RegulatorTrail 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. RegulatorTrail 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

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. RegulatorTrail offers some basic support for directly analyzing count data. For this purpose it uses the DESeq2 [2], edgeR [3], and RUVSeq [4] 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.

The used packages perform some level of normalization. However, RegulatorTrail performs no quality control or proper batch effect removal. Just as with microarray data, the web service relies on normalized or at least well-behaved input data.

Others

Data from other experimental platforms can also be used in RegulatorTrail. Here, however, it is up to the user to select an appropriate scoring scheme.

BED files

Open-chromatin regions or histone marks, needed for an INVOKE analysis, can be uploaded in BED file format. In this format every line represents a region of interest. Each individual line contains at least three fields.

  • Chromosome
  • Start position of the region
  • End position of the region
chr1	180775	180925
chr1	181395	181545
chr1	273895	274045
chr1	629895	630045
chr1	633855	634005
...

An additional description of the format can be found here.

RTI file format

While RegulatorTrail already offers a large collection of RTIs, it can be desirable to upload custom data that is not yet included. For this purpose users can upload their own RTIs in a tab-delimited format. In this format every line represents a single RTI. The first column corresponds to the name of the regulator and the second column to the respective target gene. 

E2F5    PSMA2P1
E2F5    ZNF879
E2F5    OSMR-AS1
E2F5    CAMK1
E2F5    SPR
E2F5    ZNF700
E2F5    ZNF707
E2F5    CAMK4
E2F5    OR8A3P
E2F5    EDEM2
E2F5    ZC3H10
E2F5    RNF114
E2F5    ZC3H15

Troubleshooting

RegulatorTrail 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. [5].

Bibliography

  1. Draghici, Sorin and Khatri, Purvesh and Martins, Rui P. and Ostermeier, G. Charles and Krawetz, Stephen A. Global functional profiling of gene expression Genomics Elsevier (View online)
  2. Love, Michael I and Huber, Wolfgang and Anders, Simon Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol (View online)
  3. Robinson, Mark D and McCarthy, Davis J and Smyth, Gordon K edgeR: a Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics Oxford Univ Press (View online)
  4. Risso, Davide and Ngai, John and Speed, Terence P and Dudoit, Sandrine Normalization of RNA-seq data using factor analysis of control genes or samples Nature biotechnology Nature Publishing Group (View online)
  5. Zeeberg, Barry R and Riss, Joseph and Kane, David W and Bussey, Kimberly J and Uchio, Edward and Linehan, W Marston and Barrett, J Carl and Weinstein, John N Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics BMC bioinformatics BioMed Central Ltd (View online)