Sequence of Indices (Barcodes) Used for Multiplexing

QuantSeq libraries are intended for a high degree of multiplexing. With QuantSeq for Illumina up to 9,216 samples can be uniquely barcoded in one lane by using the up to 96 external i7 indices (7001-7096) included in the kit together with the 96 external i5 indices (5001-5096), which are part of the Lexogen i5 6 nt Dual Indexing Add-on Kit (Cat. No. 047).

pdf Lexogen i7 and i5 Index Sequences

Automated Data Analysis Pipeline

The QuantSeq data analysis pipeline has been implemented on the Bluebee® Genomics Platform and in the Partek Flow software, offering to every user, even without bioinformatics experience, the opportunity to analyze QuantSeq samples in a convenient and fast way.

The pipeline on the Bluebee® Genomics Platform is accessible through a complimentary code provided with each QuantSeq kit. Learn more and get started at https://www.bluebee.com/lexogen/.

Activation codes for additional runs can be purchased from Lexogen via the webshop.

The QuantSeq pipeline in the Partek Flow software is available for any Partek Flow user at http://www.partek.com/lexogen-quantseq-pipeline.

QuantSeq – FWD Data Analysis

The following analysis pipeline demonstrates a principal workflow.
The used tools are freely available and to a great extend part of the Galaxy platform1. All proprietary Software, for instance CLC workbench, should perform similarly.
The script examples uses mainly the bash internal for-loop; in Galaxy for instance, the single files have to be processed manually one by one.
Following software packages (and their dependencies) should be installed when following the given protocol:

  • Samtools
  • FastQC
  • bbmap
  • STAR aligner

Create a working directory and the subdirs fastq, qualitycheck and download the de-multiplexed fastq files into the fastq dir.
Let the fastq files be:

  • runID_control1_S1_L001_R1_001.fastq
  • runID_control2_S2_L001_R1_001.fastq
  • runID_treatment1_S3_L001_R1_001.fastq
  • runID_treatment2_S4_L001_R1_001.fastq

Let your STAR index files be in /data/star/human, the file truseq_rna.fa.gz is provided by bbmap and is here symbolically linked to /data/resources/truseq_rna.fa.gz.
The file /data/resources/polyA.fa.gz is a simple GNU-zipped fasta file containing a single entry with 18 ‘A’s.

#### use fastqc to check your data; please adjust the number of threads to your machine
fastqc --outdir qualitycheck --format fastq --threads 8 fastq/runID*

#### check the result with a browser
######## preparation for mapping
###go to fastq directory
cd fastq
### remove the adapter contamination, polyA read through, and low quality tails
for sample in runID*R1_001.fastq; do cat $i | bbduk.sh in=stdin.fq out=${i}_trimmed_clean ref=/data/resources/polyA.fa.gz,/data/resources/truseq_rna.fa.gz k=13 ktrim=r useshortkmers=t mink=5 qtrim=r trimq=10 minlength=20; done
### create symbolic links for better handling
### for-loops can be used according to name and structure
ln -s runID_control1_S1_L001_R1_001.fastq_trimmed_clean control1_R1.fastq
...
ln -s runID_treatment2_S4_L001_R12_001.fastq_trimmed_clean treatment2_R1.fastq

######### mapping
###############################
# create for each sample a folder in star_out/
cd ..
mkdir star_out
mkdir star_out/control1
mkdir star_out/control2
...

### run star

for sample in control1 control2 treatment1 treatment2 ; do \
STAR --runThreadN 8 --genomeDir /data/star/human --readFilesIn fastq/${sample}_R1.fastq \
--outFilterType BySJout --outFilterMultimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 \
--outFilterMismatchNmax 999 --outFilterMismatchNoverLmax 0.1 --alignIntronMin 20 \
--alignIntronMax 1000000 --alignMatesGapMax 1000000 --outSAMattributes NH HI NM MD
--outSAMtype BAM SortedByCoordinate --outFileNamePrefix star_out/${sample} ;\
done

#Indexed bam files are necessary for many visualization and downstream analysis tools
cd star_out
for bamfile in */starAligned.sortedByCoord.out.bam ; do samtools index ${bamfile}; done

#From this point any further analysis can be applied.

1Goecks, J, Nekrutenko, A, Taylor, J and The Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 2010 Aug 25;11(8):R86.