Raw Vs Filtered Reads for Mapping to Assembly
Pre-processing of sequencing reads
Once sequencing reads are obtained from the sequencing auto, they need to exist pre-processed for farther assay. This footstep includes the quality control of initial reads and read trimming that includes removing adapter sequences, filtering out low quality reads and trimming reads off depression quality base pairs.
QC of sequencing reads
To assess the quality of sequencing data, we will use the programs FastQC and Fastq Screen.
FastQC calculates statistics about the composition and quality of raw sequences, while Fastq Screen looks for possible contaminations.
mkdir QC $RUN fastqc resources/A549_25_3chr10_*.fastq.gz -o ./QC/ Started analysis of A549_25_3chr10_1.fastq.gz Approx 5% complete for A549_25_3chr10_1.fastq.gz Approx ten% consummate for A549_25_3chr10_1.fastq.gz Approx 15% complete for A549_25_3chr10_1.fastq.gz Approx 20% complete for A549_25_3chr10_1.fastq.gz ... Approx 85% complete for A549_25_3chr10_2.fastq.gz Approx 90% complete for A549_25_3chr10_2.fastq.gz Approx 95% complete for A549_25_3chr10_2.fastq.gz Analysis consummate for A549_25_3chr10_2.fastq.gz We can brandish the results with a browser; e.g., Firefox, for each file individually or all files with i control:
firefox QC/A549_25_3chr10_1_fastqc.html firefox QC/*.html 
Below is an example of a poor quality dataset. Equally you can see, the average quality drops dramatically towards the 3'-end.
FastQ Screen requires a number of databases to be installed. Note, the program is included in the Docker and Singularity images but the databases are not. A number of databases prepared by the authors of the program can be downloaded past using the post-obit control (DO Non LAUNCH Information technology IN THE Course because it will take a while!!! But you will need to do information technology when completing the Final project):
$RUN fastq_screen --get_genomes This volition download Bowtie indexes for 11 genomes (arabidopsis, drosophila, East. coli, human, lambda, mouse, mitochondria, phiX, rat, worm and yeast) and 3 collection of sequences (adapters, vectors, rRNA). The files volition be downloaded in the FastQ_Screen_Genomes binder. The file fastq_screen.conf will be likewise downloaded in this folder. To employ the tool, you volition have to modify the fastq_screen.conf by providing the full path to the Bowtie2 executable (/usr/local/bin/bowtie2 if yous utilise our singularity image) and total paths to the downloaded folders with genome index files. Here we testify where to change the executables:
# This is a configuration file for fastq_screen ########### ## Bowtie # ########### ## If the bowtie binary is not in your PATH then you can ## gear up this value to tell the program where to find it. ## Uncomment the line below and gear up the appropriate location ## #BOWTIE /usr/local/bin/bowtie/bowtie BOWTIE2 /usr/local/bin/bowtie2 ... FastQ Screen runs bank check on a random subset of 100,000 reads (that can be changed using option –subset).
To execute FastQ Screen:
$RUN fastq_screen --conf FastQ_Screen_Genomes/fastq_screen.conf \ resources/A549_0_1chr10_1.fastq.gz \ --outdir ./QC/A549_0_1 Using fastq_screen v0.13.0 Reading configuration from 'fastq_screen.conf' Aligner (--aligner) non specified, simply Bowtie2 path and index files plant: mapping with Bowtie2 Adding database Man Adding database Mouse Adding database Rat Calculation database Drosophila Adding database Worm Calculation database Yeast Adding database Arabidopsis Adding database Ecoli Adding database rRNA Adding database MT Adding database PhiX Adding database Lambda Adding database Vectors Adding database Adapters Using 7 threads for searches Selection --subset set to 100000 reads Processing A549_0_1chr10_1.fastq.gz Counting sequences in A549_0_1chr10_1.fastq.gz Making reduced sequence file with ratio 711:i ... Beneath is an example of the FastQ Screen results for A549_0_1_1.fastq.gz which nosotros prepared.
wget https://biocorecrg.github.io/RNAseq_course_2019/precomp_res/A549_0_1_fastq_screen.tar.gz tar -zvxf A549_0_1_fastq_screen.tar.gz A549_0_1_fastq_screen/ A549_0_1_fastq_screen/A549_0_1chr10_1_screen.html A549_0_1_fastq_screen/A549_0_1chr10_1_screen.txt A549_0_1_fastq_screen/A549_0_1chr10_1_screen.png mv A549_0_1_fastq_screen QC firefox QC/A549_0_1_fastq_screen/A549_0_1chr10_1_screen.html 
Initial processing of sequencing reads
Before mapping reads to the genome/transcriptome or performing a de novo assembly, the reads has to be pre-processed, if needed, equally follows:
- Demultiplex by index or barcode (it is normally washed in the sequencing facility)
- Remove adapter sequences
- Trim reads by quality
- Discard reads by quality/ambiguity
- Filter reads by k-mer coverage (recommended for the de novo assembly)
- Normalize chiliad-mer coverage (recommended for the de novo assembly)
Equally shown before, both the presence of low quality reads and adapters are reported in the fastqc output.
Adapters are usually expected in small RNA-Seq because the molecules are typically shorter than the reads, and that makes an adapter to be present at 3'-end. Let'due south run FastQC on a fastq file for small-scale RNA-Seq.
$RUN fastqc resources/subsample_to_trim.fq.gz -o ./QC Started analysis of subsample_to_trim.fq.gz Approx five% complete for subsample_to_trim.fq.gz Approx 10% complete for subsample_to_trim.fq.gz Approx xv% complete for subsample_to_trim.fq.gz Approx 20% complete for subsample_to_trim.fq.gz Approx 25% complete for subsample_to_trim.fq.gz ... 
EXERCISE
Once you lot got the FastQC report (in a higher place), how to figure out the sequence(south) of the adapter(due south) that needs to exist trimmed?
There are many tools for trimming reads and removing adapters, such as Trim Galore!, Trimmomatic, Cutadapt, skewer, AlienTrimmer, BBDuk, and the near recent SOAPnuke and fastp.
Permit's use skewer to trim the Illumina pocket-sized RNA 3' adapter.
$RUN skewer resources/subsample_to_trim.fq.gz -ten TGGAATTCTCGGGTGCCAAGG -o QC/subsample_to_trim .--. .-. : .--': :.-. `. `. : `'.' .--. .-..-..-. .--. .--. _`, :: . `.' '_.': `; `; :' '_.': ..' `.__.':_;:_;`.__.'`.__.__.'`.__.':_; skewer v0.2.2 [Apr 4, 2016] Parameters used: -- 3' end adapter sequence (-x): TGGAATTCTCGGGTGCCAAGG -- maximum error ratio allowed (-r): 0.100 -- maximum indel mistake ratio allowed (-d): 0.030 -- minimum read length allowed after trimming (-50): 18 -- file format (-f): Sanger/Illumina i.8+ FASTQ (auto detected) -- minimum overlap length for adapter detection (-thousand): 3 Thu Apr 18 17:51:18 2019 >> started |=================================================>| (100.00%) Thu Apr 18 17:51:25 2019 >> washed (6.789s) 1000000 reads candy; of these: 30171 ( 3.02%) short reads filtered out after trimming past size control 2220 ( 0.22%) empty reads filtered out after trimming by size control 967609 (96.76%) reads available; of these: 958360 (99.04%) trimmed reads bachelor after processing 9249 ( 0.96%) untrimmed reads available after processing log has been saved to "QC/subsample_to_trim-trimmed.log". Nosotros can wait at the read distribution after the trimming of the adapter past inspecting the log-file or re-launching FastQC.
$RUN fastqc QC/subsample_to_trim-trimmed.fastq -o QC Started analysis of subsample_to_trim-trimmed.fastq Approx 5% consummate for subsample_to_trim-trimmed.fastq Approx 10% complete for subsample_to_trim-trimmed.fastq Approx 15% complete for subsample_to_trim-trimmed.fastq Approx xx% complete for subsample_to_trim-trimmed.fastq Approx 25% complete for subsample_to_trim-trimmed.fastq Approx 30% complete for subsample_to_trim-trimmed.fastq ... 
EXERCISE
Allow'southward explore the tool skewer in more than item, using "skewer –help" control.
- Which parameter indicates the minimum read length allowed after trimming? And what is its default value?
- Which parameter indicates the threshold on the average read quality to be filtered out?
- Using skewer filter out reads in "subsample_to_trim.fq-trimmed.fastq" that have boilerplate quality below 30 and trim them on 3'-end until the base of operations quality is reached 30. How many reads were filtered out and how many remained?
Raw Vs Filtered Reads for Mapping to Assembly
Source: https://biocorecrg.github.io/RNAseq_course_2019/qc.html