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

Automated high throughput nucleic acid purification from formalin-fixed paraffin-embedded tissue samples for next generation sequence analysis

<div><p>Curation and storage of formalin-fixed, paraffin-embedded (FFPE) samples are standard procedures in hospital pathology laboratories around the world. Many thousands of such samples exist and could be used for next generation sequencing analysis. Retrospective analyses of such samples are important for identifying molecular correlates of carcinogenesis, treatment history and disease outcomes. Two major hurdles in using FFPE material for sequencing are the damaged nature of the nucleic acids and the labor-intensive nature of nucleic acid purification. These limitations and a number of other issues that span multiple steps from nucleic acid purification to library construction are addressed here. We optimized and automated a 96-well magnetic bead-based extraction protocol that can be scaled to large cohorts and is compatible with automation. Using sets of 32 and 91 individual FFPE samples respectively, we generated libraries from 100 ng of total RNA and DNA starting amounts with 95–100% success rate. The use of the resulting RNA in micro-RNA sequencing was also demonstrated. In addition to offering the potential of scalability and rapid throughput, the yield obtained with lower input requirements makes these methods applicable to clinical samples where tissue abundance is limiting.</p></div

Suitability of the FormaPure extracted RNA for FFPE strand-specific RNA-seq.

<p>(<b>A)</b> Strand-specific libraries were generated from four different FormaPure extracted human FFPE samples (FFPE A-D) and UHR fresh RNA. Two different total (DNase-treated) RNA input amounts were used (100 and 200 ng, respectively). Final library yield (nM) (left panel) and % duplicates (middle panel) as well as the distribution of aligned reads to various regions of the transcriptome (right panel) are shown graphically. These libraries were sequenced as a pool at PE75 bp. (<b>B</b>) Comparison of Qiagen and FormaPure extraction protocols using mouse FFPE scrolls. Final library yield (nM) (Left panel) and % duplicates, % aligned, and the distribution of aligned reads to various regions of the transcriptome (middle panel) as well as number of genes with 1x coverage (right panel) are shown graphically.</p

Automated high throughput FormaPure-based extraction protocol.

<p>(<b>A</b>) Work flow illustration of sample acquisition, upstream sample processing and extraction. Note that a separate high temperature incubation step is added to facilitate the reversal of remaining crosslinks. The upstream processes are manual in the original protocol whereas those steps are modified to be suitable for automation in the modified protocol. The in-house on-deck heating blocks were instrumental in rendering the lysis/deparaffinization steps automatable. Acquisition of samples in SBS format matrix tubes with their automated capping and decapping were also further measures that allowed the entire process to be amenable for automated liquid handling. (<b>B</b>) gDNA yield. Historical gDNA yield data from the Qiagen/High Pure protocol (Q; n = 142) using equivalent sizes of numerous FFPE samples of lymphoma origin was compared with that of the FormaPure protocol (F; n-91). (C) RNA yield. Comparison of the Qiagen-High Pure (Q-H), and FormaPure (F) protocols are shown. N = 142 for Q-H and N = 44 for F.</p