EMBnet.journal 18.1_14-21

Minimum Information About a Peptide Array Experiment (MIAPepAE)

Gordon Botha.jpgJudit Kumuthini.jpg

Gordon Botha, Judit Kumuthini*

Centre for Proteomic and Genomic Research (CPGR), Cape Town, South Africa

* corresponding author (jkumuthini@gmail.com)

Submited 17 November 2011; Accepted 29 June 2012; Published 15 October 2012

Competing Interests: none


Peptide-array screening is currently a well-established high-throughput technique with growing numbers of applications. Peptide-array technology is used for protein recognition, quantification of peptide expression levels, and detection of protein-protein interactions. The use of protein/peptide arrays in medical life science studies is becoming increasingly widespread. Their increased use in diagnostic applications and protein function profiling calls for a standardised set of guidelines to be followed by future experimenters to enable reproducible, high-quality data and accurate findings.

We aim to provide preliminary guidelines describing the Minimum Information About a Peptide-Array Experiment (MIAPepAE). We propose a checklist of data and meta-data that should accompany a peptide-array experiment, and invite fellow researchers in the field to collaborate in this effort to create a sustainable and coherent set of guidelines for the benefit of the protein/peptide-array research community. Although this article focuses on spotting peptide arrays, MIAPepAE is intended to be a work-in-progress to be adopted for other peptide-array types, such as in situ synthesised peptide arrays.


The use of protein/peptide arrays in medical life science studies is becoming increasingly widespread (Reimer et al., 2002; Wulfkuhle et al., 2003; Cretich et al., 2006). Broadly speaking, they are used for two main purposes: diagnostic applications (bio-markers or antibody detection) and protein function profiling. Peptide arrays are powerful diagnostic tools, as they allow both multiple analyses of identical samples and single-instance analyses of differential samples. For example, they have been applied to immune-response profiling experiments by measuring antibody-antigen interactions (Davies et al., 2005; Ingvarsson et al., 2008; Andresen and Grötzinger, 2009); they have also been instrumental in protein-function profiling studies (Katz et al., 2011), in part because they use very little sample material and can process many proteins in parallel (Haab, 2001), and partly also because they can quantify very low concentrations of protein (Korf et al., 2008) and take into account protein/peptide tertiary structures. Overall, peptide arrays are becoming pi-votal to protein studies, spurring developments in related fields.

The technology and methodology is steadily advancing, in terms of slide preparation (Kopf et al., 2005; Beyer et al., 2006) and sample preparation (Ghazani et al., 2006; Usui et al., 2006), and, in turn2, is leading bioinformaticians to develop new software tools (Li et al., 2005) and Web applications (Li et al., 2009). Specific statistical techniques for the analysis of peptide arrays have also been developed (Royce et al., 2006). Furthermore, high-throughput sequencing methods, such as real time PCR (Heid et al., 1996), have delivered an abundance of genomic and proteomic data for many species (Love et al., 1990; Blattner et al., 1997; Dean et al., 2002). With so much proteomic information and analysis tools available, it is inevitable that many more peptide-array experiments will be conducted in the foreseeable future.

Our aim is to provide preliminary guidelines for the Minimum Information About a Peptide-Array Experiment (MIAPepAE). We propose a checklist of data and meta-data that should accompany a peptide-array experiment, aiming to fulfill the following main objectives:

• MIAPepAE should provide authors, reviewers, editors and readers with the specifics required to critically evaluate, understand and reproduce a peptide-array experiment;

• MIAPepAE should provide sufficient information to aggregate/integrate similar experimental data, independently of the platform on which the experiment was performed;

• MIAPepAE should allow secondary data, such as clinical patient and epidemiology data, to be integrated, enabling the extraction of more meaningful information from peptide-array experiments.

We emphasise meta-data pertaining to the sample. Variation in preparation of protein/peptide samples and their assaying to the array slides can be a major contributor to experimental variation and, as such, warrants a focused effort toward the proposed guidelines.

In the interest of coherent and coordinated development of such guidelines, the project is registered on the MIBBI porta[1]. The MIBBI project is a collaboration between leaders in the biological and biomedical fields, acting as a meeting point for the coordination of minimum information guidelines and checklists (Taylor et al., 2008)

We have also based our checklist format on the guidelines for peptide-array experiments provided in the Minimum Information About a Proteomics Experiment (MIAPE) article (Taylor et al., 2007), and the Minimum Information About a Microarray Experiment (MIAME) article (Brazma et al., 2001b). The original MIAME checklist for microarray experiments has been 14-revised (see (Abeygunawardena, 2007)), and we have based our checklist for peptide-arrays on the revised checklist. Hence, we have drafted our checklist with the following main subjects: Raw Data, Final Processed Data for Set of Hybridisations, Sample Annotation and Experimental Factors, Experimental Design, Sufficient Annotation of Array Design, Essential Experimental and Data-Processing Protocols.

We endeavour to adhere to two criteria introduced by the MIAPE article: those of Sufficiency and Practicability. Sufficiency states that the minimum information requirements are constructed in such a way that the reviewer is able to “understand and critically evaluate the interpretation and conclusions”. The reader must also be able to support the findings. Practicability states that the incorporation of a minimum information requirement for a proteomic experiment need not be so taxing on the experimenters that its adoption is impaired.

The checklist is still under development and will undoubtedly undergo revision as more peptide array experiments are performed and more comments and suggestions from colleagues in the field are incorporated.

Key Concepts

Our approach towards the formulation of guidelines for a peptide-array experiment takes several key concepts into account. These need to be defined clearly before proceeding, as this is necessary to interpret our guidelines.

Microarray Nomenclature

We have compiled a nomenclature from previous definitions (Brazma et al., 2001a; Royce et al., 2006). The molecules bonded to the slide at the time of manufacture are termed probes. Any subsequent binding molecules are termed targets. A spot or feature is defined as a group of probes with identical sequences, concentrated at a known position on the microarray. A group of targets from the same biological entity is defined as a sample. One instance of the introduction of one or more samples to the array is known as probing. Finally, a series of probing to investigate a hypothesis is known as an experiment.

Unique Peptide

A unique peptide, as used in a peptide-array experiment, should conform to the following properties. It should have a unique identification number (ID) such as a National Centre for Biotechnology Information (NCBI) or a Protein Data Bank (PDB) number. If the peptide is synthe-tic, the full amino-acid sequence must be made available. A list of the protein(s) in which the peptide can be found should be given, including the starting position in the protein. The peptide length should be specified, and the overlap used when aligning the peptide to a protein. Finally, any unidentified/ambiguous amino acids within the peptide sequence must be noted.

Table 1. MIAPepAE checklist for authors, revie-wers and editors. All essential information (E) must be submitted with the manuscript. Desirable information (D) should be submitted if available.


Author (submitter), laboratory, contact information (e-mail, postal address), links (URL), citation


Typically, these are the data-files produced by microarray image-analysis software



Raw data-files provided

Native format


Type: e.g., image, binary data


The file matches the respective array design


Scanned image files for each slide


Data location



Normalised/Summarised data on which conclusions are based



Processed (normalised) data-files


Normalisation application: e.g., pin-to-pin, array-to-array, slide-to-slide, background correction


Normalisation method


The identifiers match the array annotation/location


Control(s) on which normalisation was based



Describes the key experimental variables in the experiment. Additional information regarding sample, such as storage conditions, preparation methods, etc., are of great importance.



Basic experimental factors (dose, time, disease state, treatment) provided for all samples


Additional sample information

Sample type


Sample storage condition


Sample dilution buffer


Sample name/annotation


Sample dilution used in the



Blocking agent


Detection antibody


Concentration of detection antibody


Hybridisation and washing conditions


Type of dye


Source organism (NCBI ta-xo-nomy)


Laboratory protocol for sample treatment (name, version,



Any post-printing processing, including cross-linking

Protein from which peptide was extracted (incl. ID) (NCBI/ UniProtKB/ SwissProt)


Peptide position in protein


Peptide overlap in protein alignment


Peptide conservancy


Peptide/protein sequence ID (NCBI/ UniProtKB /SwissProt)



Describes the basic way in which the experiment was set up. Associations between samples and raw data generated from using these samples are critical. Note that the representation of an experimental design is best done via a graphical representation. The MAGE-TAB spreadsheet template (see text) provides a simple format for encoding such graphs.



Experimental design description

Table showing (sample) - (raw-data file) associations


Essential relationships between sample and array biomaterial noted


Experiment variables: e.g., treated vs untreated



Identify which, if any, of the arrays are replicates


Identify whether replicates are technical/biological



Our guidelines aim to maximise the reproducibility of an experiment. They will also ease the interpretation of findings by peers, as a clear idea of experimental procedures will more effectively orient a reviewer.

Comparability and Re-usability

Another key concept that we want to capture in the guidelines is that of platform-independent comparability. Findings between studies can be compared effectively if standardised data formats are in place. Furthermore, if data are extracted in a concise and correct manner, they can be used in subsequent experiments. We feel that quality of data supersedes quantity of data, and using a concise method of data extraction from peptide arrays can greatly increase the experimenter’s ability to sort biological meaning from experimental error.


The specificity of an experiment, or an experiment in a more general case, measures the ability to correctly classify positive events. In a peptide-array context, this could measure the probability that a peptide/protein-binding event is in fact a specific binding, and hence biologically significant, and not due to a non-specific binding event or experimental error.


The crux of the guidelines is to enable correct quantification of spot intensity within an array. It is paramount that spot intensities are biologically significant readings and not the result of experimental variation. The correct quantification of experimental parameters lends itself to effective verification of findings. We aim to achieve this with the proposed guidelines.


We have provided a checklist for capturing essential information when conducting peptide-array experiments. By conforming to this checklist, experimenters will:

provide authors, reviewers, editors and readers with the specifics required to critically evaluate, understand and reproduce a peptide-array experiment. This will lead to more accurate conclusions and higher quality data;

be able to compare and combine experiments across different platforms, greatly enhancing the re-usability of the data;

be able to extract and combine meta-data from experiments that might bring to light interesting observations. In so doing, experimental data can be utilised fully to discover biologically relevant observations.

The MIAPepAE form/format contains most of the required fields and sections in one document type, and allows for continuous updating as procedural standards become apparent from discussions within the community. Certainly, as technological advancements are made, the guideline will be appropriately adjusted. The document is version controlled and is available on the MIBBI portal.

In the interests of speeding up adoption of the MIAPepAE checklist in peptide-array experiments, we urge experimenters to provide at least the essential fields in an electronic format with published data and articles. Only with other researchers’ input can the ease of conforming to the standards, and accuracy of field prioritisation within the checklist, be assessed. We do, however, note that, for the full benefits of the MIAPepAE guidelines to be reached, project conformity will have to be enforced at a higher level. Like other minimum information protocols, compliance can be required for: i) the publication of research articles (at journal level); ii) data submission to proprietary and public data repositories (at project and framework level); iii) funding and grant proposals (from funders); and possibly iv) encouragement from open-source project repositories.

We hope that the MIAPepAE guidelines are useful to data generators, data consumers and end users. This will, however, depend entirely on the willingness of the scientific community to adopt the guidelines and, more importantly, the willingness of fellow peptide-array experimenters to contribute to (and criticise) the development of the guidelines. In the end, the success of this project depends entirely on the community that it serves.


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