{% extends "base.html" %} {% block content %}

Sample: {{ project }}
Type: {{ type }}
Pipeline: {{ pipeline_name }}

Input
| Others

{{ dataset }}

The following table gives some basic statistics about the raw data before any filtering. The A, C, G, T, N rows report the percentage of each bases in the overall sequences. The GC content is provided in percentage as well.

{{ sample_stats }}
{% if sample_image_r1 %}

The following figure(s) gives the average quality (red line) of raw reads (500,000 at max). The x-axis being the length of the reads. The yellow enveloppe gives the variation of the quality (1 standard deviation).

Click on the image to jump to a full FastQC report.

Fig1: R1 reads
{% endif %} {% if sample_image_r2 %}
Fig2: R2 reads
{% endif %}
{# ================================================================= PHIX #} {% if cfg['bwa_mem_phix'] and cfg['bwa_mem_phix']['do'] == True %}

Phix content
| Details
| FastQC

{{ phix_section }}

We mapped the raw reads on a reference (see config file). The reads mapped are removed and the unmapped reads are kept. Here below are some statistics about the mapped and unmapped reads.

The A, C, G, T, N rows report the percentage of each bases in the overall sequences

{{ phix_stats }}
{% endif %} {# ============================================================ ADAPTERS #} {% if cfg['adapter_removal'] and cfg['adapter_removal']['do'] %}

Adapters
| Cutadapt report
| Details
| FastQC

Reads statistics after trimming and adapter removal. Again, the A, C, G, T, N rows report the percentage of each bases in the overall sequences

{{ cutadapt_stats1 }} The final FastQ files without adapters have the following characteristics: {{ cutadapt_stats2 }}
{% endif %} {# ============================================================= OUTPUT #} {% if output %}

Output

{{ output }}
{% if output_image_r1 %}

The following figure(s) gives the average quality (red line) of raw reads (500,000 at max). The x-axis being the length of the reads. The yellow enveloppe gives the variation of the quality (1 standard deviation).

Click on the image to jump to a full FastQC report.

Fig1: R1 reads
{% endif %} {% if output_image_r2 %}
Fig2: R2 reads
{% endif %}
{% endif %} {# ============================================================ TAXONOMY #} {% if cfg['kraken'] %}

Taxonomic content
Details

Overview of the Taxonomic content of the filtered reads.

The taxonomic analysis is performed with Kraken and the database {{ kraken_database }}. The analysis is performed with a Kmer approach. The details about the database itself are available in the Sequana documentation. The taxonomic analysis should give a good idea of the content of the FastQ files but should be used as a sanity check. Indeed, species absent from the database won't be detected; close species may be detected instead. Besides, be aware that closely related species may not be classified precisely.

The following clickable image is a simplified version (only genus are shown) of an interactive and more detailled version based on Krona. Finally, note that the unclassified species in the pie plot may correspond to species not present in the data base or adapters (if not removed).


{{ kraken_html_table }}
{% endif %} {# ==================================================================== DAG #} {% if workflow %}

Workflow

{% include 'workflow.html' %}
{% endif %} {# ======================================================== software Details #} {% if snakemake_stats %}

Running Stats
collapse/expand

{% endif %} {% if dependencies %}

Dependencies
collapse/expand

Python dependencies (Pypi) {{ dependencies }}
Dependencies downloaded from bioconda requirements
{% endif %} {% endblock %}