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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