We assessed the performance of PeaKDEck by calling peaks in 10 DNaseI-seq datasets from the NCBI Short Reads Archive (Supplementary Information, Section S3).
The strength of this study of electronic problem list documentation was the large dataset from the clinical information system of a primary care network serving a diverse population.
The child dataset contains the information from these three questionnaires relevant to child health.
Detailed information regarding downloaded datasets from the TAIR microarray database is listed in Additional file 10.
A typical dataset from the GPRD contains information on a patient's sex, age, year of birth and registration details.
This is because, although the frequency domain method produces a normalised spectrum without assuming that the underlying bias is a strict 1/ f curve, (since the normalisation curve is modelled directly from the dataset), the phase information of the EEG signal is lost.
To provide a comprehensive dataset, the information was retrieved from two commercial and two freely available databases: ABDA [ 9], KEGG [ 18], SIDER [ 19], and DrugBank [ 20], whereas the latter represents the basis of the system.
To illustrate the Grape analyses, we use an RNA-Seq dataset from the Illumina Human Body Map (HBM) project (Additional information on the sample and the raw data can be obtained from http://www.ncbi.nlm.nih.gov/), with http://rnaseq.crg.es/project/ENCODE/tab/experiments/ as an example.
The main strength of the present study is the large dataset from the Swedish Medical Birth Registry, comprising routinely assembled information from antenatal health care clinics covering almost all pregnancies in the region.
In this study we describe an EST dataset from the bandicoot, Isoodon macrourus, providing information on the transcriptional profile of the bandicoot thymus and the opportunity for a genome wide comparison between the bandicoot and opossum, two distantly related marsupial species.
