snRNAseq project

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        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        About MultiQC

        This report was generated using MultiQC, version 1.4.dev0 (2ebab02)

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        snRNAseq project
        RNAseq of small nuclear RNAs

        PI
        PI NAME
        User
        CONTACT NAME
        Date
        2017-09-20
        Contact E-mail
        luca.cozzuto@crg.eu
        Application Type
        snRNA-seq
        Sequencing Platform
        HiSeq 2500 High Output V4

        Report generated on 2017-11-07, 13:35 based on data in: /nfs/software/bi/biocore_tools/git/nextflow/smallRNAseq/work/eb/25f8cddcba52164482fdc770b944b5

        Welcome! Not sure where to start?   Watch a tutorial video   (6:06)

        General Statistics

        Showing 4/4 rows and 6/9 columns.
        Sample Name% Dups% GCM Seqs% Trimmed% AlignedM Aligned
        C1_d0_test
        93.3%
        56%
        12.1
        97.8%
        39.1%
        2.7
        C1_d32_test
        95.8%
        50%
        15.0
        98.2%
        62.5%
        8.0
        C3_d0_test
        94.0%
        55%
        11.9
        97.9%
        49.3%
        3.4
        C3_d32_test
        95.6%
        51%
        16.5
        98.1%
        60.7%
        7.9

        Tool description

        Tool description This section describes the tools used during the analysis and their reference

        Tool version
        Reference
        FastQC v0.11.5
        Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc
        STAR_2.5.3a
        Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013 Jan 1;29(1):15-21. doi: 10.1093/bioinformatics/bts635. Epub 2012 Oct 25. PubMed PMID: 23104886; PubMed Central PMCID: PMC3530905
        skewer version: 0.2.2
        Jiang H, Lei R, Ding SW, Zhu S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics. 2014 Jun 12;15:182. doi: 10.1186/1471-2105-15-182. PubMed PMID: 24925680; PubMed Central PMCID: PMC4074385
        QualiMap v.2.2.1
        García-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Götz S, Tarazona S, Dopazo J, Meyer TF, Conesa A. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012 Oct 15;28(20):2678-9. doi: 10.1093/bioinformatics/bts503. Epub 2012 Aug 22. PubMed PMID: 22914218
        bedtools v2.26.0
        Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010 Mar 15;26(6):841-2. doi: 10.1093/bioinformatics/btq033. Epub 2010 Jan 28. PubMed PMID: 20110278; PubMed Central PMCID: PMC2832824
        samtools 1.4.1
        Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9. doi: 10.1093/bioinformatics/btp352. Epub 2009 Jun 8. PubMed PMID: 19505943; PubMed Central PMCID: PMC2723002

        FastQC (trimmed)

        This section of the report shows FastQC results after adapter trimming.

        Sequence Quality Histograms
        4
        0
        0

        The mean quality value across each base position in the read. See the FastQC help.

        Created with Highcharts 5.0.6Position (bp)Phred ScoreChart context menuExport PlotFastQC: Mean Quality Scores51015202530354045500510152025303540Created with MultiQC

        Per Sequence Quality Scores
        4
        0
        0

        The number of reads with average quality scores. Shows if a subset of reads has poor quality. See the FastQC help.

        Created with Highcharts 5.0.6Mean Sequence Quality (Phred Score)CountChart context menuExport PlotFastQC: Per Sequence Quality Scores0510152025303501000000200000030000004000000500000060000007000000Created with MultiQC

        Per Base Sequence Content
        0
        0
        4

        The proportion of each base position for which each of the four normal DNA bases has been called. See the FastQC help.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content
        0
        0
        4

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content. See the FastQC help.

        Created with Highcharts 5.0.6% GCCountChart context menuExport PlotFastQC: Per Sequence GC Content010203040506070809010001234567Created with MultiQC

        Per Base N Content
        4
        0
        0

        The percentage of base calls at each position for which an N was called. See the FastQC help.

        Created with Highcharts 5.0.6Position in Read (bp)Percentage N-CountChart context menuExport PlotFastQC: Per Base N Content051015202530354045500123456Created with MultiQC

        Sequence Length Distribution
        0
        4
        0

        The distribution of fragment sizes (read lengths) found. See the FastQC help.

        Created with Highcharts 5.0.6Sequence Length (bp)Read CountChart context menuExport PlotFastQC: Sequence Length Distribution05101520253035404550010000002000000300000040000005000000600000070000008000000Created with MultiQC

        Sequence Duplication Levels
        0
        0
        4

        The relative level of duplication found for every sequence. See the FastQC help.

        Created with Highcharts 5.0.6Sequence Duplication Level% of LibraryChart context menuExport PlotFastQC: Sequence Duplication Levels123456789>10>50>100>500>1k>5k>10k+020406080100Created with MultiQC

        Overrepresented sequences
        0
        0
        4

        The total amount of overrepresented sequences found in each library. See the FastQC help for further information.

        Created with Highcharts 5.0.6Percentage of Total SequencesChart context menuExport PlotFastQC: Overrepresented sequencesTop over-represented sequenceSum of remaining over-represented sequencesC1_d0_testC1_d32_testC3_d0_testC3_d32_test0%5%10%15%20%25%30%35%40%45%50%55%60%65%Created with MultiQC

        Adapter Content
        4
        0
        0

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. See the FastQC help. Only samples with ≥ 0.1% adapter contamination are shown.

        Created with Highcharts 5.0.6Position (bp)% of SequencesChart context menuExport PlotFastQC: Adapter Content51015202530350123456Created with MultiQC

        Skewer

        Skewer is an adapter trimming tool specially designed for processing next-generation sequencing (NGS) paired-end sequences.

        Created with Highcharts 5.0.6Read Length% of ReadsChart context menuExport PlotSkewer: Read Length Distribution after trimming05101520253035404550020406080100Created with MultiQC

        QualiMap

        QualiMap is a platform-independent application to facilitate the quality control of alignment sequencing data and its derivatives like feature counts.

        Genomic origin of reads

        Classification of mapped reads as originating in exonic, intronic or intergenic regions. These can be displayed as either the number or percentage of mapped reads.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. This allows mapped reads to be grouped by whether they originate in an exonic region (for QualiMap, this may include 5′ and 3′ UTR regions as well as protein-coding exons), an intron, or an intergenic region (see the Qualimap 2 documentation).

        The inferred genomic origins of RNA-seq reads are presented here as a bar graph showing either the number or percentage of mapped reads in each read dataset that have been assigned to each type of genomic region. This graph can be used to assess the proportion of useful reads in an RNA-seq experiment. That proportion can be reduced by the presence of intron sequences, especially if depletion of ribosomal RNA was used during sample preparation (Sims et al. 2014). It can also be reduced by off-target transcripts, which are detected in greater numbers at the sequencing depths needed to detect poorly-expressed transcripts (Tarazona et al. 2011).

        Created with Highcharts 5.0.6Chart context menuExport PlotQualimap RNAseq: Genomic OriginExonicIntergenicC1_d0_testC1_d32_testC3_d0_testC3_d32_test0102030405060708090100Created with MultiQC

        Gene Coverage Profile

        Mean distribution of coverage depth across the length of all mapped transcripts.

        There are currently three main approaches to map reads to transcripts in an RNA-seq experiment: mapping reads to a reference genome to identify expressed transcripts that are annotated (and discover those that are unknown), mapping reads to a reference transcriptome, and de novo assembly of transcript sequences (Conesa et al. 2016).

        For RNA-seq QC analysis, QualiMap can be used to assess alignments produced by the first of these approaches. For input, it requires a GTF annotation file along with a reference genome, which can be used to reconstruct the exon structure of known transcripts. QualiMap uses this information to calculate the depth of coverage along the length of each annotated transcript. For a set of reads mapped to a transcript, the depth of coverage at a given base position is the number of high-quality reads that map to the transcript at that position (Sims et al. 2014).

        QualiMap calculates coverage depth at every base position of each annotated transcript. To enable meaningful comparison between transcripts, base positions are rescaled to relative positions expressed as percentage distance along each transcript (0%, 1%, …, 99%). For the set of transcripts with at least one mapped read, QualiMap plots the cumulative mapped-read depth (y-axis) at each relative transcript position (x-axis). This plot shows the gene coverage profile across all mapped transcripts for each read dataset. It provides a visual way to assess positional biases, such as an accumulation of mapped reads at the 3′ end of transcripts, which may indicate poor RNA quality in the original sample (Conesa et al. 2016).

        Created with Highcharts 5.0.6Transcript Position (%)CoverageChart context menuExport PlotQualimap RNAseq: Coverage Profile Along Genes (total)0102030405060708090100050000100000150000200000250000300000Created with MultiQC

        STAR

        STAR is an ultrafast universal RNA-seq aligner.

        Alignment Scores

        Created with Highcharts 5.0.6# ReadsChart context menuExport PlotSTAR: Alignment ScoresUniquely mappedMapped to multiple lociMapped to too many lociUnmapped: otherC1_d0_testC1_d32_testC3_d0_testC3_d32_test01M2M3M4M5M6M7M8M9M10M11M12M13M14MCreated with MultiQC

        Gene Counts

        Statistics from results generated using --quantMode GeneCounts. The three tabs show counts for unstranded RNA-seq, counts for the 1st read strand aligned with RNA and counts for the 2nd read strand aligned with RNA.

           
        Created with Highcharts 5.0.6# ReadsChart context menuExport PlotSTAR: Gene CountsOverlapping GenesNo FeatureAmbiguous FeaturesMultimappingUnmappedC1_d0_testC1_d32_testC3_d0_testC3_d32_test01M2M3M4M5M6M7M8M9M10M11M12M13M14MCreated with MultiQC