Sequence of Indices (Barcodes) Used for Multiplexing

SENSE libraries can be easily multiplexed. External barcodes allowing up to 9,216 samples to be uniquely indexed are available for multiplexing. Up to 96 i7 indices (i7 Index Plate) are included in the SENSE kit and additionally 96 external i5 indices are available in the Lexogen i5 6 nt Dual Indexing Add-on Kit (Cat. No. 047).

pdf Lexogen i7 and i5 Index Sequences

SENSE Total 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
  • FastX toolkit
  • tophat2

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_control1_S1_L001_R2_001.fastq
  • runID_control2_S2_L001_R1_001.fastq
  • runID_control2_S2_L001_R2_001.fastq
  • runID_treatment1_S3_L001_R1_001.fastq
  • runID_treatment1_S3_L001_R2_001.fastq
  • runID_treatment2_S4_L001_R1_001.fastq
  • runID_treatment2_S4_L001_R2_001.fastq

Let your bowtie2 index files be in /data/bowtie/human, your reference annotation in /data/reference_annotation/human

#### 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 starter sequence from the R1 read

for sample in runID*R1_001.fastq; do fastx_trimmer -f 10 -Q33 -i ${sample} -o ${sample}_trimmed ; done

### remove the stopper sequence from the R2 read

for sample in runID*R2_001.fastq; do fastx_trimmer -f 7 -Q33 -i ${sample} -o ${sample}_trimmed ; done

### remove low quality tails

for sample in *trimmed ;do fastq_quality_trimmer -t 10 -l 20 -Q33 -v -i ${sample} -o ${sample}_clean; 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_control1_S1_L001_R2_001.fastq_trimmed_clean control1_R2.fastq

...

ln -s runID_treatment2_S4_L001_R2_001.fastq_trimmed_clean treatment2_R2.fastq

######### mapping

###############################

# create for each sample a folder in tophat_out/

cd ..

mkdir tophat_out

mkdir tophat_out/control1

mkdir tophat_out/control2

...

### run tophat2

### denote, sometimes it is useful to run all control and treatment samples pooled and seperate the alignments afterwards.

### This will result in better junction detection.

tophat -o tophat_out/control1 -p 8 /data/bowtie/human/h_sapiens fastq/control1_R1.fastq fastq/control1_R2.fastq

tophat -o tophat_out/control2 -p 8 /data/bowtie/human/h_sapiens fastq/control2_R1.fastq fastq/control2_R2.fastq

tophat -o tophat_out/treatment1 -p 8 /data/bowtie/human/h_sapiens fastq/treatment1_R1.fastq fastq/treatment1_R2.fastq

tophat -o tophat_out/treatment2 -p 8 /data/bowtie/human/h_sapiens fastq/treatment2_R1.fastq fastq/treatment2_R2.fastq

#Indexed bam files are necassary for many visualization and downstream analysis tools

cd tophat_out

for bamfile in */accepted_hits.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.