Large Scale Transcriptomics and Unique Molecular Identi˚ers … · 2017-11-21 · UMI-ready DNA-,...
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UMI-ready DNA-, RNA-, and small RNA-Seq Qiagen GeneRead® Number of SNPs called by variant calling workflow UMI-aware DNA-Seq Regular DNA-Seq Cluster plot on a 538 sample single-cell small-RNA-Seq study with UMIs. Improvement in SNP caller specificity on somatic DNA-Seq data with a UMI-aware workflow. Gene specific primer Target Common Sequence 11nt UMI Sequence 12 nt Read 1 Read 2 Archer Variant Plex® UMI Sequence 8 nt Target Common Sequence 13nt Gene specific primer Read 1 Read 2 Rubicon Thruplex® Stem Sequence 8-11 nt Stem Sequence 8-11 nt Target UMI 1 Sequence 6 nt UMI 2 Sequence 6 nt Read 1 Read 2 Bioo Scientific NextFlex® Target UMI 2 Sequence 8 nt UMI 1 Sequence 8 nt Read 1 Read 2 6000 4000 2000 0 Unique Molecular Identifiers, or UMIs, are short sequences (6-12bp) added to sequencing libraries prior to PCR amplification. UMIs can improve the accuracy of RNA-Seq quantification and lead to improved specificity in low-frequency variant calling for DNA-Seq. Strand NGS v3.1 implements a complete, end-to-end UMI workflow for DNA-, RNA-, and small-RNA-Seq. UMI Protocols with native support in Strand NGS. Strand NGS v3.1 also supports custom, user-specified UMI protocols. Confounding variable analysis for RNA- and small-RNA-Seq Legend, before-after PCA plots (left and center) and gene regression variance scatter (right), showing the effect of confounding variable analysis (CVA) on a 283 sample Pan cancer RNA-Seq study. The PCA plots show how CVA removes the effect of batch; the regression variance plot shows how CVA reduces the original variance, resulting from batch effect, on all genes. Confounding variable analysis (CVA) is a statistical approach to remove unwanted variance from datasets. Strand NGS v3.1 implements an intuitive interface based on Surrogate Variable Analysis (SVA), for CVA. Color by Batch Batch 03 Batch 04 Batch 02 Batch 01 Batch 05 Batch 06 Large Scale Transcriptomics and Unique Molecular Identifiers Support from Strand NGS v3.1
Transcript of Large Scale Transcriptomics and Unique Molecular Identi˚ers … · 2017-11-21 · UMI-ready DNA-,...
UMI-ready DNA-, RNA-, and small RNA-Seq
Qiagen GeneRead®
Number of SNPs called by variantcalling work�ow
UMI-aware DNA-Seq
Regular DNA-Seq
Cluster plot on a 538 sample single-cell small-RNA-Seqstudy with UMIs.
Improvement in SNP caller speci�cityon somatic DNA-Seq data with aUMI-aware work�ow.
Genespeci�cprimer Target
CommonSequence11nt
UMISequence12 nt
Read 1 Read 2
Archer Variant Plex®UMISequence8 nt Target
CommonSequence13nt
Genespeci�cprimer
Read 1 Read 2
Rubicon Thruplex®StemSequence8-11 nt
StemSequence8-11 ntTarget
UMI 1Sequence6 nt
UMI 2Sequence6 nt
Read 1 Read 2
Bioo Scienti�c NextFlex®
Target
UMI 2Sequence8 nt
UMI 1Sequence8 nt
Read 1 Read 2
6000
4000
2000
0
Unique Molecular Identi�ers, or UMIs, are short sequences (6-12bp) added to sequencing libraries prior to PCR ampli�cation. UMIs can improve the accuracy of RNA-Seq quanti�cation and lead to improved speci�city in low-frequency variant calling for DNA-Seq. Strand NGS v3.1 implements a complete, end-to-end UMI work�ow for DNA-, RNA-, and small-RNA-Seq.
UMI Protocols with native support in Strand NGS. Strand NGS v3.1 also supports custom, user-speci�ed UMI protocols.
Confounding variable analysis for RNA- and small-RNA-Seq
Legend, before-after PCA plots (left and center) and gene regression variance scatter (right), showing the e�ect of confounding variable analysis (CVA)on a 283 sample Pan cancer RNA-Seq study. The PCA plots show how CVA removes the e�ect of batch; the regression variance plot shows how CVA reduces
the original variance, resulting from batch e�ect, on all genes.
Confounding variable analysis (CVA) is a statistical approach to remove unwanted variance from datasets. Strand NGS v3.1 implements an intuitive interface
based on Surrogate Variable Analysis (SVA), for CVA.
Color by Batch
Batch 03
Batch 04
Batch 02
Batch 01
Batch 05
Batch 06
Large Scale Transcriptomicsand Unique Molecular Identi�ersSupport from Strand NGS v3.1
Correlation of gene expressionson a 283-sample
RNA-Seq Pan Cancer study.
Analysis of large-scale RNA-Seq and small-RNA-Seq data
Large-scale RNA and small-RNA-Seq. Strand NGS v3.1 contains enhancements for the analysis and visualization of hundreds of samples in RNA- and small-RNA-Seq. Highlights include a pleasingly-graded color scheme for publication quality plots; a gene body coverage diagram; the t-SNE plot as an alternative to PCA for clustering and classi�cation of large-scale data; and several other features.
t-SNE plot on a 931-sample single-cell study showing the separation between alpha and
beta-cells between birth and maturation. Gene body coverage on a RNA-Seq study,showing coverage bias towards the 3’ end
in certain samples.
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