SPAT: Searching for Poly(A) Tails in RNA-Seq de novo Assemblies

<p>A method for detecting alternative polyadenylation in RNA-Seq libraries using de novo assembly with ABySS. It will be presented at HiTSeq and ISMB 2013 by Anthony Raymond.</p

SNV concordance between tools for one read set (Sample 2).

<p>a) Number of variants in dbSNP (v137) plotted against number of variants called at various levels of depth. Depth begins on far right at 6 bp and each point represents increasing depth of 1 bp coverage. b) Overlap of known SNVs called c) Overlap of known non-synonymous SNVs called d) Overlap of SNVs called in COSMIC. All SNP calls were assessed at depth of 6. *BWA-MEM.</p

Alignment results of three paired end tag (PET) RNA-seq libraries.

<p>*BWA-MEM algorithm.</p><p>Comparison of alignment statistics and SNP concordance with dbSNP (v137) between JAGuaR, GSNAP, MapSplice2 and TopHat2. SNVs were identified with a minimum total coverage of 6 reads in order to maximize the number of SNVs for comparison while maintaining a minimum dbSNP concordance of 50% for all tools. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0102398#pone-0102398-g001" target="_blank">Figure 1</a> for ROC plot of Sample 2 (Figure S3 for Sample 1 and Figure S4 for Sample 3).</p

Execution Time.

<p>*BWA-MEM algorithm.</p><p>**BWA memory.</p><p>Comparison of execution time and memory usage. All tools were run on a node with 64 GB memory with no other applications running.</p

Simulation.

<p>*BWA-MEM algorithm.</p><p>All SNPs called at > = 6 bp depth.</p><p>Comparison of SNP calls between tools from a simulated dataset. PET synthetic reads were generated from a reference with 652,256 planted SNPs. These fastqs were aligned to hg19 (hg19+junctions with JAGuaR) and SNPs called. SNPs that were identified by from the alignment of at least one tool and which were in the list of planted SNPs, were considered as the filtered expected SNPs. The number of recovered SNPs are the number of SNPs out of the tool's total set that are seen in the expected list. All tools have a similar ratio. Calls from the MapSplice2 alignment show the highest number of SNPs that were not planted and calls from TopHat2 show the least.</p