Within ReadXplorer, an imported mapping dataset is called track.
Haplotype analyses including the top genotyped SNPs in the iCOGS fine-mapping dataset were performed in Haplostats (http://www.mayo.edu/research/labs/statistical-genetics-genetic-epidemiology/software).
The E-MAP dataset was already heavily pre-processed and custom-scored, as has been noted also in previous works [26], and therefore it was not expected that the matrix decomposition could provide marked improvements in this data.
A second map dataset was contributed by Prof. Juan F. Medrano and after size filtering (≥300 kb) it added another 1,064,919 Rmaps, bringing the total raw dataset up to 2,973,315 (439 X coverage, before alignment).
We run two additional PC Analyses in which Hap Map datasets were included (European datasets only in a first instance and then Hap Map datasets from European, African and Asian ancestry).
Two E-MAP datasets were used here, in which the mapping approach was applied to the genes involved either in the yeast early secretory pathway (Secretory-Map) [ 53] or in various aspects of chromosome biology (Chromosome-Map) [ 54].
For each position to which a probe pair was mapped, a dataset was generated consisting of all pairs mapping within a window of ± 30 bp, which defines the number of bases extending from the position being analyzed so that every probe in the 61-bp region was included in the signal and p-value analysis.
The intronic miRNAs in Baskerville et al.'s dataset and Barad et al.'s dataset were mapped to the host genes in Su's dataset, respectively.
Reads from this dataset were mapped using the single fragment WT pipeline.
All readseq reads of each dataset were mapped to the genome using TopHat [ 48] and called differential expression by Cuffdiff [ 49].
In order to obtain a quantitative transcriptome map, values from each dataset were linearized when provided as logarithms.
