Interactive streamgraph visualization of measles cases over the 20th Century

<p>These are the data files used to produce an interactive streamgraph visualization of the mean weekly number of measles cases over the 20th century in US states, highlighting the dramatic impact that introducung the measles vaccine had on the case rate from this dreadful disease.</p

Wild-type yeast gene read counts from 48 replicate experiment

<p>These data are RNA-seq read counts for 48 biological replicates of wild-type yeast.</p> <p>The reads were aligned to the Saccharomyces cerevisiae genome with Tophat and gene counts determined with htseq-count. Reference genome and gene annotations were taken from Ensembl.</p> <p>See the linked papers for more details and the source for the raw data.</p

SNF2 knock-out yeast gene read counts from a 48 replicate experiment

<p>These data are RNA-seq read counts for 48 biological replicates of SNF2 yeast knock-out.</p> <p>The reads were aligned to the Saccharomyces cerevisiae genome with Tophat and gene counts determined with htseq-count. Reference genome and gene annotations were taken from Ensembl.</p> <p>See the linked papers for more details and the source of the raw data.</p

Single molecule RNA sequencing on small tissue volumes yields tissue-specific insights into mechanistic pathways in atopic dermatitis

<p>Figures, data and source code for paper submission.</p

Metadata for a highly replicated two-condition yeast RNAseq experiment.

<p>This datafile contains metadata associated with the European Nucleotide Archive (ENA) entry ERP004763 (see below for link).</p> <p>The ENA entry has 672 fastq files which are from a two-condition 48 biological and 7 technical replicate experiment. However, the metadata are a little unclear at ENA. This file should help disambiguate the information.</p> <p>The experiment is described in two pre-prints on arXiv linked below and has been published in Bioinformatics.</p

DGE tool TPR/FPR performance with 2 replicates

This figure is an expansion on Figure 2 from our paper in RNA "How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?". The figure shows the comparison of the true positive rate (TPR) and false positive rate (FPR) performance for each of the DGE tools for extremely low-replication (n_r=2) RNA-seq data. As for Figure 2 in the paper, the TPRs & FPRs for each tool are calculated by comparing the mean number of true and false positive (TPs and FPs) calculated over 100 bootstrap iterations to the number of TPs and FPs calculated from the same tool using the full clean dataset (error-bars are 1 standard deviation). Although the TPRs and FPRs from each tool are calculated by comparing each tool against itself rather than a tool-independent ‘gold standard’ (albeit with the full clean dataset) the results are comparable across tools (see the paper for more details)

DGE tool TPR/FPR performance with 2-40 replicates: The Movie

This movie is an expansion on Figure 2 from our paper in RNA "How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?". The movie shows the comparison of the true positive rate (TPR) and false positive rate (FPR) performance for each of the DGE tools from extremely low-replication (n_r=2) to extremely high-replication (n_r=40) RNA-seq data. As for Figure 2 in the paper, the TPRs & FPRs for each tool are calculated by comparing the mean number of true and false positive (TPs and FPs) calculated over 100 bootstrap iterations to the number of TPs and FPs calculated from the same tool using the full clean dataset (error-bars are 1 standard deviation). Although the TPRs and FPRs from each tool are calculated by comparing each tool against itself rather than a tool-independent ‘gold standard’ (albeit with the full clean dataset) the results are comparable across tools (see the paper for more details)