snRNAseq of cells differentiating to kidney

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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 (f36d735)

        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 of cells differentiating to kidney
        RNAseq of small non coding RNAs from cells at different time points

        PI
        Nuria Montserrat
        User
        Carolina Tarantino
        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 2018-02-08, 12:43 based on data in: /nfs/users/bi/projects/external/Nuria_Montserrat/sequencing_analysis/Carolina_Tarantino/2018-02-07-microRNAs_kidney/analysis/work/8e/00bcba567c97121d871d7c35361651


        General Statistics

        Showing 10/10 rows and 7/11 columns.
        Sample Name% Dups% GCM Seqs% TrimmedM Aligned% AssignedM Assigned
        P1_d0_23305_CGATGT
        94.2%
        53%
        14.6
        96.7%
        8.1
        61.9%
        5.6
        P1_d12_23305_CAGATC
        96.6%
        47%
        13.3
        97.6%
        10.7
        89.4%
        9.8
        P1_d20_23305_GGCTAC
        96.7%
        48%
        20.2
        97.6%
        16.0
        86.0%
        14.3
        P1_d4_23305_TGACCA
        95.3%
        48%
        10.0
        97.4%
        7.4
        73.8%
        5.7
        P1_d9_23305_ACAGTG
        95.9%
        49%
        10.0
        97.4%
        7.3
        85.0%
        6.5
        P2_d0_23305_AGTCAA
        92.7%
        52%
        9.1
        96.7%
        5.0
        60.1%
        3.4
        P2_d12_Dnase_plus_23305_GAGTGG
        96.8%
        46%
        13.3
        97.6%
        11.3
        92.3%
        10.7
        P2_d20_23305_GTTTCG
        95.9%
        50%
        13.6
        97.5%
        10.3
        78.8%
        8.7
        P2_d4_23305_CCGTCC
        95.8%
        47%
        14.6
        97.2%
        11.8
        84.4%
        10.4
        P2_d9_23305_GTGAAA
        96.6%
        49%
        13.2
        97.7%
        9.4
        85.8%
        8.5

        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"
        bowtie 1.2.2
        'Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25. doi: 10.1186/gb-2009-10-3-r25. Epub 2009 Mar 4. PubMed PMID: 19261174; PubMed Central PMCID: PMC2690996.'
        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 v2.25.0
        "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"
        HTseq 0.8.0.
        'Anders S Pyl PT Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015 Jan 15;31(2):166-9. doi: 10.1093/bioinformatics/btu638. Epub 2014 Sep 25. PubMed PMID: 25260700; PubMed Central PMCID: PMC4287950'
        samtools 1.3.1
        "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"
        ShortStack version 3.8.5
        "Axtell MJ. ShortStack: comprehensive annotation and quantification of small RNA genes. RNA. 2013 Jun;19(6):740-51. doi: 10.1261/rna.035279.112. Epub 2013 Apr 22. PubMed PMID: 23610128; PubMed Central PMCID: PMC3683909"

        FastQC (trimmed)

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

        Sequence Quality Histograms

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

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        Per Sequence Quality Scores

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

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        Per Base Sequence Content

        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

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

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        Per Base N Content

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

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        Sequence Length Distribution

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

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        Sequence Duplication Levels

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

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        Overrepresented sequences

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

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        Adapter Content

        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.

        No samples found with any adapter contamination > 0.1%

        Skewer

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

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        Bowtie 1

        Bowtie 1 is an ultrafast, memory-efficient short read aligner.

        This plot shows the number of reads aligning to the reference in different ways.

        There are 3 possible types of alignment: Aligned: Read has only one occurence in the reference genome. Multimapped: Read has multiple occurence. * Not aligned: Read has no occurence.

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        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).

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        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).

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        HTSeq Count

        HTSeq Count is part of the HTSeq Python package - it takes a file with aligned sequencing reads, plus a list of genomic features and counts how many reads map to each feature.

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