From 4e28e226479f0346bc078c1490acf320816395ab Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Mon, 9 Sep 2024 10:39:12 +0000 Subject: [PATCH] Add changes for df90eb84c8d8b267cebd243b1681689ec2490989 --- 1- Library preparation.html | 84 ++++++++---------- 2- Sequencing technologies.html | 11 +-- 3- Quality Control and Preprocessing.html | 18 ++-- _images/fastq_format.png | Bin 204973 -> 102338 bytes _sources/1- Library preparation.rst.txt | 60 ++++++------- _sources/2- Sequencing technologies.rst.txt | 14 +-- ... Quality Control and Preprocessing.rst.txt | 20 ++--- searchindex.js | 2 +- 8 files changed, 94 insertions(+), 115 deletions(-) diff --git a/1- Library preparation.html b/1- Library preparation.html index 4e47f93..7e847ce 100644 --- a/1- Library preparation.html +++ b/1- Library preparation.html @@ -201,88 +201,77 @@

RNA library bias*source: https://doi.org/10.1016/j.yexcr.2014.01.008*

Sample Preservation and Isolation

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  1. Degradation of RNA:

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    1. Degradation of RNA:

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    +

    Tip

    Minimizing the sample processing and freezing and thawing cycles, ensures that RNA is preserved as best as possible.

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  3. RNA extraction:

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    1. RNA extraction:

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    Tip

    If possible use high concentrations of RNA samples or avoid TRIzol extraction altogether.

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Library Construction

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  1. Low-quality and/or low-quantity RNA samples:

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    1. Low-quality and/or low-quantity RNA samples:

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    Tip

    RNase H has been the best method for detecting low-qualityRNA and even could effectively replace the standard RNA-seq method based on oligo (dT). For low-quantity RNA,the SMART and NuGEN approaches had lower duplication rates and significantly decreased the necessary amount of starting material compared to other methods.

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  2. -
  3. mRNA enrichment bias: In eukaryotes enrich for polyadenylated RNA transcripts with oligo (dT) primers have shown that this method remove all non-poly (A) RNAs, such a reolication-dependant histones and lncRNAs (lacking of polyA),or incomplete mRNAs.

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    1. mRNA enrichment bias: In eukaryotes enrich for polyadenylated RNA transcripts with oligo (dT) primers have shown that this method remove all non-poly (A) RNAs, such a reolication-dependant histones and lncRNAs (lacking of polyA),or incomplete mRNAs.

    2. +
    +

    Tip

    Targeting rRNA as depletion method will not limit to only mRNA molecules, may capture more immature transcripts, leading to a complexity increase of sequencing data (also is more expensive). Subtractive hybridization using rRNA-specific probes as the method that introduced the least bias in relative transcript abundance,

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  5. RNA fragmentation bias: There are two major approaches of RNA fragmentation: chemical (using metal ions) and enzymatic (using RNase III). During this process could be introduced lenght biases or errors (propagated to later cycles).

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    1. RNA fragmentation bias: There are two major approaches of RNA fragmentation: chemical (using metal ions) and enzymatic (using RNase III). During this process could be introduced lenght biases or errors (propagated to later cycles).

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    Tip

    Studies have shown that methods that involve non specific restriction endonucleases indicate less sequence bias and have been shown to perform similarly to the physical methods. Also enzymatic methoda are easy to automate

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  7. Primer bias: During reverse transcription into cDNA by random hexamers can lead to deviation of nucleotide content of RNA sequencing reads, resulting in low complexity of RNA sequencing data.

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    1. Primer bias: During reverse transcription into cDNA by random hexamers can lead to deviation of nucleotide content of RNA sequencing reads, resulting in low complexity of RNA sequencing data.

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    Tip

    Could be avoid using the Illumina Genome Analyzer, which perform the reverse transcription directly on the flowcells, avoiding the PCR. Also has been proposed a bioinformatics tool in a reweighing scheme to adjust for the bias and make the distribution of the reads more uniform.

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  9. Adapter ligation bias: Adapter ligation introduces a significant but widely overlooked bias in the results of NGS small RNA sequencing.

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    1. Adapter ligation bias: Adapter ligation introduces a significant but widely overlooked bias in the results of NGS small RNA sequencing.

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    Tip

    As a solution, several groups propose to randomize the 3’ end of the 5’adapter and the 5’end of the 3’adapter. The strategy is based on the hypothesis that a population of degenerate adapters would average out the sequencing bias because the slightly different adapter molecules would form stable secondary structures with a more diverse population of RNAsequences - Reverse transcription bias: reverse transcriptases tend to produce false second strand cDNA throughDNA-dependent DNA polymerase. ActinomycinD, a compound that specifically inhibits DNA-dependent DNAsynthesis, has been proposed as an agent to eliminate antisense artifacts

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  11. Reverse Transcription: A known feature of reverse transcriptases is that they tend to produce false second strand cDNA through DNA-dependent DNA polymerase. This may not be able to distinguish the sense and antisense transcript and create difficulties for the data analysis.

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    1. Reverse Transcription: A known feature of reverse transcriptases is that they tend to produce false second strand cDNA through DNA-dependent DNA polymerase. This may not be able to distinguish the sense and antisense transcript and create difficulties for the data analysis.

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    Tip

    • The deoxyuridine triphosphate (dUTP) method, one of the leading cDNA-based strategies, can be specifically removed by enzymatic digestion

    • Another method is to synthesize the first strand of cDNA using labeled random hexamer primer and SSS using DNA-RNA template-switching primer

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    1. PCR amplification bias: main source of artifacts and base composition bias in the process of library construction:

      7.1. Extremely AT/GC-Rich: Fragments of GC-neutral can be amplified more than GC-rich or AT-rich fragments.

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      Tip

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    diff --git a/2- Sequencing technologies.html b/2- Sequencing technologies.html index a1dd0c0..d0a3eea 100644 --- a/2- Sequencing technologies.html +++ b/2- Sequencing technologies.html @@ -161,27 +161,18 @@

    FASTQ format and Phred quality score_images/fastq_format.png

    For each read, the information it’s divided in four lines:

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    1. Sequence identifier: starts with ‘@’ and contains information about the read. Such as the instrument, run ID, flow cell ID, lane, tile, x, y coordinates, and read number.

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    Note

    The @ symbol can not be used for count the number of reads, because it could also appear as a quality score symbol.

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      Sequence: the nucleotide sequence of the read.
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      1. Sequence: the nucleotide sequence of the read.

      2. Quality identifier: starts with ‘+’ and contains the same information as the sequence identifier. Or it may be empty and in some cases is used for metadata.

      3. Quality scores: the Phred quality score for each base in the read. The Phred quality score is a measure of the quality of the base call,

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    \[Q = -10 * log10(P)\]
    diff --git a/3- Quality Control and Preprocessing.html b/3- Quality Control and Preprocessing.html index 88c35d5..48b95d7 100644 --- a/3- Quality Control and Preprocessing.html +++ b/3- Quality Control and Preprocessing.html @@ -144,13 +144,15 @@

    FASTQC<
  13. Per sequence GC content: GC content distribution for all the reads in the file, and compared to a modelled normal distribution of human GC content (blue line).

    *Per Sequence GC Content FASTQC module* +
    +
  14. +

Danger

If the GC content is not close to the normal distribution, or more than one peak is found, this could indicate a contamination or a problem in the library preparation. Also, depending on the organism the GC content could vary, so if possible it’s good to know the GC content of the organism of interest previously and avoid compare it with the human modelled distribution.

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  1. Per Base N content: If the sequencer is unable to determine the base in a position, it will be represented as an ‘N’. This section shows the distribution of Ns in the reads.

    @@ -223,10 +225,10 @@

    Pre-processing -
  2. Trimmomatic <http://www.usadellab.org/cms/index.php?page=trimmomatic>

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  4. Cutadapt, only remove the adapaters (it needs to be used in combination with sickle), requires the adapter sequence to be known <https://cutadapt.readthedocs.io/en/stable/>

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  6. Sickle, remove low quality tail bases <https://github.com/najoshi/sickle>

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  8. fastp <https://github.com/OpenGene/fastp>

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  10. Trimmomatic http://www.usadellab.org/cms/index.php?page=trimmomatic.

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  12. Cutadapt, only remove the adapaters (it needs to be used in combination with sickle), requires the adapter sequence to be known https://cutadapt.readthedocs.io/en/stable/.

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  14. Sickle, remove low quality tail bases https://github.com/najoshi/sickle.

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  16. FASTP https://github.com/OpenGene/fastp.

  17. Fastp performs in all one the following corrections:

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Challenges and Solutions `_. diff --git a/_sources/2- Sequencing technologies.rst.txt b/_sources/2- Sequencing technologies.rst.txt index 953580d..e6e21ff 100644 --- a/_sources/2- Sequencing technologies.rst.txt +++ b/_sources/2- Sequencing technologies.rst.txt @@ -104,15 +104,16 @@ For Illumina Paired end sequencing, two FASTQ files are generated, one for each For each read, the information it's divided in four lines: - 1. Sequence identifier: starts with '@' and contains information about the read. Such as the instrument, run ID, flow cell ID, lane, tile, x, y coordinates, and read number. - - .. Note:: - The @ symbol can not be used for count the number of reads, because it could also appear as a quality score symbol. + 1. Sequence identifier: starts with '@' and contains information about the read. Such as the instrument, run ID, flow cell ID, lane, tile, x, y coordinates, and read number. + .. Note:: + The @ symbol can not be used for count the number of reads, because it could also appear as a quality score symbol. 2. Sequence: the nucleotide sequence of the read. - 3. Quality identifier: starts with '+' and contains the same information as the sequence identifier. Or it may be empty and in some cases is used for metadata. - 4. Quality scores: the Phred quality score for each base in the read. The Phred quality score is a measure of the quality of the base call, + + 3. Quality identifier: starts with '+' and contains the same information as the sequence identifier. Or it may be empty and in some cases is used for metadata. + + 4. Quality scores: the Phred quality score for each base in the read. The Phred quality score is a measure of the quality of the base call, .. math:: Q = -10 * log10(P) @@ -127,4 +128,3 @@ For each read, the information it's divided in four lines: - diff --git a/_sources/3- Quality Control and Preprocessing.rst.txt b/_sources/3- Quality Control and Preprocessing.rst.txt index 2e25ab3..afd4369 100644 --- a/_sources/3- Quality Control and Preprocessing.rst.txt +++ b/_sources/3- Quality Control and Preprocessing.rst.txt @@ -29,7 +29,7 @@ the extensions supported are: It is available for usage by CLI and GUI. It generates a html report for each file and its divided in the following Quality metrics modules: - 1. **Basic Statistics**: Display the information related with the file, number and lenght of the sequences, and overall %GC. + 1. **Basic Statistics**: Display the information related with the file, number and lenght of the sequences, and overall %GC. 2. **Per base sequence quality**: Shows how the quality score (y axis) varys throughout the sequence reads (x axis). For each position a BoxWhisker is displayed, the red line represents the median and the blue the mean. @@ -70,9 +70,9 @@ It is available for usage by CLI and GUI. It generates a html report for each fi :align: center :alt: *Per Sequence GC Content FASTQC module* - .. danger:: - If the GC content is not close to the normal distribution, or more than one peak is found, this could indicate a contamination or a problem in the library preparation. - Also, depending on the organism the GC content could vary, so if possible it's good to know the GC content of the organism of interest previously and avoid compare it with the human modelled distribution. + .. danger:: + If the GC content is not close to the normal distribution, or more than one peak is found, this could indicate a contamination or a problem in the library preparation. + Also, depending on the organism the GC content could vary, so if possible it's good to know the GC content of the organism of interest previously and avoid compare it with the human modelled distribution. 7. **Per Base N content**: If the sequencer is unable to determine the base in a position, it will be represented as an 'N'. This section shows the distribution of Ns in the reads. @@ -125,8 +125,8 @@ Also, other sources of contaminats could be checked: - PhiX: is a control used by Illumina to check the quality of the sequencing run (if the library is under or overloaded). - rRNA: in RNA-seq is a good control of rRNA depletion during library preparation. - - Lambda - - Vectors: to check that vectors used during library preprartion have not been amplified. + - Lambda: cloning vector. + - Vectors: other vectors used during library preprartion. - Adapters Example of a FASTQ-Screen report: @@ -155,10 +155,10 @@ the reads need to be pre-processed in order to get rid of them and improve quali Typical tools used for pre-processing are: - - Trimmomatic - - Cutadapt, only remove the adapaters (it needs to be used in combination with sickle), requires the adapter sequence to be known - - Sickle, remove low quality tail bases - - fastp + - Trimmomatic ``_. + - Cutadapt, only remove the adapaters (it needs to be used in combination with sickle), requires the adapter sequence to be known ``_. + - Sickle, remove low quality tail bases ``_. + - FASTP ``_. Fastp performs in all one the following corrections: diff --git a/searchindex.js b/searchindex.js index dd2aa4e..4dd71a0 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1 Library preparation": [[0, null]], "2 Main Sequencing Technologies": [[1, null]], "3 Quality Control and Preprocessing": [[2, null]], "4 Quality of the Mapping": [[3, null]], "About the course": [[4, null]], "BAM QC": [[3, "bam-qc"]], "DNA library bias": [[0, "dna-library-bias"]], "Dates, time, location": [[4, "dates-time-location"]], "FASTQ format and Phred quality score": [[1, "fastq-format-and-phred-quality-score"]], "FASTQ-Screen": [[2, "fastq-screen"]], "FASTQC": [[2, "fastqc"]], "Illumina": [[2, "illumina"]], "Learning objectives": [[4, "learning-objectives"]], "Library Construction": [[0, "library-construction"]], "Library preparation": [[0, "id1"]], "Long read sequencing (Nanopore)": [[1, "long-read-sequencing-nanopore"]], "Main Causes of poor quality data": [[0, 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