Quality Control Preprocessing Talk

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Quality control and data pre-processing

Contents

  • Data formats
– Fasta and Fastq formats
– Sequence quality encoding
  • Quality Control (QC)
– Evaluation of sequence quality
– Quality control tools
– Addressing QC with FastQC
– Typical artifacts and sequence filtering

Data formats

  • Text-based formats
  • If not compressed, it can be huge
  • Almost every programming language has a parser

Fformat.png

Data formats

Fastq Format: Sequence quality encoding

Ascii.png

Sequence Data Format

Raw sequence data format (Flat/Binary files)

Processed sequence data format (Flat files)

  • Column separated files containing genomic features

and their chromosomal coordinates.

  • Different files
  • GFF and GTF
  • BED

GFF

  • Column separated file format contains features located at chromosomal locations
  • Not a compact format
  • Several versions

– GFF 3 most currently used – GFF 2.5 is also called GTF (used at Ensembl for describing gene features)

GFF3 file example

##gff-version 3
##sequence-region ctg123 1 1497228
ctg123 . gene 1000 9000 . + . ID=gene00001;Name=EDEN
ctg123 . TF_binding_site 1000 1012 . + . Parent=gene00001
ctg123 . mRNA 1050 9000 . + . ID=mRNA00001;Parent=gene00001
ctg123 . mRNA 1050 9000 . + . ID=mRNA00002;Parent=gene00001
ctg123 . mRNA 1300 9000 . + . ID=mRNA00003;Parent=gene00001
ctg123 . exon 1300 1500 . + . Parent=mRNA00003
ctg123 . exon 1050 1500 . + . Parent=mRNA00001,mRNA00002
ctg123 . exon 3000 3902 . + . Parent=mRNA00001,mRNA00003
ctg123 . exon 5000 5500 . + . Parent=mRNA00001,mRNA00002,mRNA00003
ctg123 . exon 7000 9000 . + . Parent=mRNA00001,mRNA00002,mRNA00003
ctg123 . CDS 1201 1500 . + 0 ID=cds00001;Parent=mRNA00001
ctg123 . CDS 3000 3902 . + 0 ID=cds00001;Parent=mRNA00001
ctg123 . CDS 5000 5500 . + 0 ID=cds00001;Parent=mRNA00001
ctg123 . CDS 7000 7600 . + 0 ID=cds00001;Parent=mRNA00001
ctg123 . CDS 1201 1500 . + 0 ID=cds00002;Parent=mRNA00002
ctg123 . CDS 5000 5500 . + 0 ID=cds00002;Parent=mRNA00002
ctg123 . CDS 7000 7600 . + 0 ID=cds00002;Parent=mRNA00002
ctg123 . CDS 3301 3902 . + 0 ID=cds00003;Parent=mRNA00003
ctg123 . CDS 5000 5500 . + 2 ID=cds00003;Parent=mRNA00003
ctg123 . CDS 7000 7600 . + 2 ID=cds00003;Parent=mRNA00003
ctg123 . CDS 3391 3902 . + 0 ID=cds00004;Parent=mRNA00003
ctg123 . CDS 5000 5500 . + 2 ID=cds00004;Parent=mRNA00003
ctg123 . CDS 7000 7600 . + 2 ID=cds00004;Parent=mRNA00003
Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9
"seqid" "source" "type" "start" "end" "score" "strand" "phase" "attributes"

GFF graphicalGFF

representation structure

Sascha.png

GFF3 can describes the representation of a protein-coding gene (From Sascha Steinbiss' genomte-tools suite of programs: http://genometools.org/)

BED

  • Created by UCSC Genome team
  • Contains similar information to the GFF, but optimized for viewing in the UCSC genome browser
- Essentially about features and ranges.
  • BIG BED, optimized for next gen data – essentially a binary version
– It can be displayed at UCSC Web browser (even several Gbs !!)

Quality Control

Evaluation of sequence quality

  • Primary tool to assess sequencing
  • Evaluating sequences in depth is a valuable approach to assess how reliable our results will be.
  • QC determines posterior filtering
- Any filtering decision will affect downstream analysis.
  • QC must be run after every critical step.

Quality control tools

Fastx.png

Ngsqc.png

Quality control tools

Fastqc.png

Quality Control

  • Addressing QC with FastQC
- Basic stats
- Per base sequence quality
- Per read sequence quality
- Per base sequence content
- Per base GC content
- Per sequence GC content
- Per base N content
- Sequence length distribution
- Duplicate sequences
- Overrepresented sequences
- Overrepresented k-mers

Examples on web:

- Good quality:

http://www.bioinformatics.babraham.ac.uk/projects/fastqc/good_sequence_short_fastqc.html

- Bad quality:

http://www.bioinformatics.babraham.ac.uk/projects/fastqc/bad_sequence_fastqc.html

Per base sequence quality

Pbsqg.png Good data = Consistent high quality along the read

Addressing QC with FastQC

Per base sequence quality Pbsqb.png

Bad data = Quality decreases towards the end of the read and High variance

Addressing QC with FastQC

Per sequence quality scores

Pbsqb.png

Good data = most reads are high-quality sequences
Bad data = Distribution with bi-modalities

Per tile sequence quality 1

Ptsq.png

Good data = Blue all over
Bad data = Presence of hot colours

Per tile sequence quality 2

Ptsq2.png

Problems in some tiles
Filtering of reads with Q30 on 90% of the read

Per base sequence content

Pbsc.png

Good data = smooth over the read
Bad data = Sequence position bias and adapter contamination

Per base GC content

Pbgcc.png

Good data = smooth over the read
Bad data = Sequence position bias and adapter contamination

Per sequence GC content

Psgcc.png

Good data = Normal distribution, Distribution fits with expected, Organism dependent
Bad data = Distribution doesn’t fit with expected. Possibility of contamination

Per sequence N content

Psnc.png

Good data
Bad data = There are peaks of Ns per base position.

Sequence duplication levels

Sdl.png

Good data
Bad data = High number of duplicates. Indicates some kind of enrichment bias.
  • Note:
- Only few sequences are used to make this judgment.
- For RNASeq, higher number of duplicated sequences are expected.

Overrepresented sequences and k-mer content

Ovrep.png

  • Exact same sequences too many times
  • PCR primers, Adapters, etc.
  • Note:
- Sometimes this is expected

Sequence Filtering 1

  • It is important to remove bad quality data as our confidence on downstream analysis will be improved.

Sf.png

Sequence Filtering 2

Sf2.png

  • Mean quality
  • Read length after trimming
  • Percentage of bases above a quality threshold
  • Adapter trimming

Sequence filtering tools

  • Fastq-mcf
- https://code.google.com/p/ea-utils/wiki/FastqMcf
  • Cutadapt
- https://code.google.com/p/cutadapt/
  • SeqTK
- https://github.com/lh3/seqtk
  • Trimmomatic
- http://www.usadellab.org/cms/?page=trimmomatic)

Next

Practical sequence filtering session