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workflow graph QuantSeq 3' mRNA-Seq single-read

### Pipeline for Lexogen's QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina [Lexogen original documentation](https://www.lexogen.com/quantseq-3mrna-sequencing/) * Cost-saving and streamlined globin mRNA depletion during QuantSeq library preparation * Genome-wide analysis of gene expression * Cost-efficient alternative to microarrays and standard RNA-Seq * Down to 100 pg total RNA input * Applicable for low quality and FFPE samples * Single-read sequencing of up to 9,216 samples/lane * Dual indexing and Unique Molecular Identifiers (UMIs) are available ### QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina The QuantSeq FWD Kit is a library preparation protocol designed to generate Illumina compatible libraries of sequences close to the 3’ end of polyadenylated RNA. QuantSeq FWD contains the Illumina Read 1 linker sequence in the second strand synthesis primer, hence NGS reads are generated towards the poly(A) tail, directly reflecting the mRNA sequence (see workflow). This version is the recommended standard for gene expression analysis. Lexogen furthermore provides a high-throughput version with optional dual indexing (i5 and i7 indices) allowing up to 9,216 samples to be multiplexed in one lane. #### Analysis of Low Input and Low Quality Samples The required input amount of total RNA is as low as 100 pg. QuantSeq is suitable to reproducibly generate libraries from low quality RNA, including FFPE samples. See Fig.1 and 2 for a comparison of two different RNA qualities (FFPE and fresh frozen cryo-block) of the same sample. ![Fig 1](https://www.lexogen.com/wp-content/uploads/2017/02/Correlation_Samples.jpg) Figure 1 | Correlation of gene counts of FFPE and cryo samples. ![Fig 2](https://www.lexogen.com/wp-content/uploads/2017/02/Venn_diagrams.jpg) Figure 2 | Venn diagrams of genes detected by QuantSeq at a uniform read depth of 2.5 M reads in FFPE and cryo samples with 1, 5, and 10 reads/gene thresholds. #### Mapping of Transcript End Sites By using longer reads QuantSeq FWD allows to exactly pinpoint the 3’ end of poly(A) RNA (see Fig. 3) and therefore obtain accurate information about the 3’ UTR. ![Figure 3](https://www.lexogen.com/wp-content/uploads/2017/02/Read_Coverage.jpg) Figure 3 | QuantSeq read coverage versus normalized transcript length of NGS libraries derived from FFPE-RNA (blue) and cryo-preserved RNA (red). ### Current workflow should be used only with the single-end RNA-Seq data. It performs the following steps: 1. Separates UMIes and trims adapters from input FASTQ file 2. Uses ```STAR``` to align reads from input FASTQ file according to the predefined reference indices; generates unsorted BAM file and alignment statistics file 3. Uses ```fastx_quality_stats``` to analyze input FASTQ file and generates quality statistics file 4. Uses ```samtools sort``` and generates coordinate sorted BAM(+BAI) file pair from the unsorted BAM file obtained on the step 2 (after running STAR) 5. Uses ```umi_tools dedup``` and generates final filtered sorted BAM(+BAI) file pair 6. Generates BigWig file on the base of sorted BAM file 7. Maps input FASTQ file to predefined rRNA reference indices using ```bowtie``` to define the level of rRNA contamination; exports resulted statistics to file 8. Calculates isoform expression level for the sorted BAM file and GTF/TAB annotation file using GEEP reads-counting utility; exports results to file

https://github.com/datirium/workflows.git

Path: workflows/trim-quantseq-mrnaseq-se.cwl

Branch/Commit ID: 22880e0f41d0420a17d643e8a6e8ee18165bbfbf

workflow graph scatter-valuefrom-wf4.cwl#main

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/scatter-valuefrom-wf4.cwl

Branch/Commit ID: 48bd6c751aceef30614d9e43d91865980035781f

Packed ID: main

workflow graph kmer_ref_compare_wnode

https://github.com/ncbi/pgap.git

Path: task_types/tt_kmer_ref_compare_wnode.cwl

Branch/Commit ID: 733ab7198a66a0153d0f03c3022ab53c17325ff8

workflow graph wgs alignment with qc

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/alignment_wgs.cwl

Branch/Commit ID: a59a803e1809a8fbfabca6b8962a8ad66dd01f1d

workflow graph exome alignment and somatic variant detection

https://github.com/tmooney/cancer-genomics-workflow.git

Path: definitions/pipelines/somatic_exome_mouse.cwl

Branch/Commit ID: 0db1a5f1ceedd4416ac550787c27b99c87dbe985

workflow graph sc_atac_seq_process_and_analyze.cwl

https://github.com/hubmapconsortium/sc-atac-seq-pipeline.git

Path: steps/sc_atac_seq_process_and_analyze.cwl

Branch/Commit ID: 97586613d396c4389d013cc46e37fc7aa5974f28

workflow graph format_rrnas_from_seq_entry

https://github.com/ncbi/pgap.git

Path: task_types/tt_format_rrnas_from_seq_entry.cwl

Branch/Commit ID: 7f9cfcbda5998b164bd1d8f1f6006aefda0f47f3

workflow graph bact_get_kmer_reference

https://github.com/ncbi/pgap.git

Path: task_types/tt_bact_get_kmer_reference.cwl

Branch/Commit ID: ac387721a55fd91df3dcdf16e199354618b136d1

workflow graph Single-Cell Preprocessing Pipeline

Devel version of Single-Cell Preprocessing Pipeline ===================================================

https://github.com/datirium/workflows.git

Path: workflows/single-cell-preprocess.cwl

Branch/Commit ID: 7ae3b75bbe614e59cdeaba06047234a6c40c0fe9

workflow graph wgs alignment and tumor-only variant detection

https://github.com/tmooney/cancer-genomics-workflow.git

Path: definitions/pipelines/tumor_only_wgs.cwl

Branch/Commit ID: 0db1a5f1ceedd4416ac550787c27b99c87dbe985