1. Diseño Experimental y Controles de Calidad
Introducción a NGS
Diseño Experimental y Controles de Calidad
Antonio Rueda

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NGS technologies 2

3. Library preparation

4. Amplification

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02/04/12
Sequencing
The identity of each base of a cluster is read off from sequential images
From Michael Metzker, 5

6. Concepts
Lecturas (reads): Son cada una de las secuencias que lee el secuenciador, cada una de las posiciones de la read se leyó en un ciclo del secuenciación. Para cada posición se reporta una base y un valor de calidad.
Cada read tiene un identificador único.

7. Concepts Calidad de secuenciación (Base quality):
Todos los secuenciadores asumen errores durante el proceso de secuenciación, se reporta por cada base secuenciada un valor de calidad.

8. Concepts
Mapeo: Consiste en colocar cada read en su lugar en el genoma de referencia. Aunque existen otros formatos, el formato SAM/BAM es el más usado

9. Concepts
Cobertura (coverage): Llamamos coverage al número de veces que se lee cada una de las posiciones. Cuando analizamos el coverage, debemos tener en cuenta dos puntos muy importantes:
El coverage. Usaremos el parámetro, coverage medio para medirlo. Debe ser suficiente para dar confianza a la zona secuenciada
El coverage debe ser uniforme en todas las zonas que queremos secuenciar, usaremos el % de posiciones cubiertas y la desviación típica para cuantificarlo.

10. Concepts
Detección de variantes(Variant Calling): Consiste en la búsqueda de diferencias en las reads con respecto al genoma de referencia.
Referencia

11. Experimental Design Resequencing Mutation calling
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Resequencing
Mutation calling
Profiling
Genome annotation
RNA-seq /Transcriptomics
Quantitative
Descriptive
Alternative splicing
miRNA profiling
De novo sequencing
Exome sequencing
Targeted sequencing
ChIP-seq /Epigenomics
Protein-DNA interactions
Active transcription factor binding sites
Histone methylation
Copy number variation
Metagenomics
Metatranscriptomics 11

12. DNA sequencing - 1 Whole GENOME Resequencing Need reference genome
Variation discovery

13. DNA sequencing - 2 Whole GENOME “de novo” sequencing
Uncharacterized genomes with no reference genome available
Known genomes where significant structural variation is expected
Long reads or mate-pair libraries. Sequencing mostly done by Roche 454 and also Illumina
Assembly of reads is needed: Computational intensive
E.g. Genome bacteria sequencing

14. DNA sequencing - 3 Whole EXOME Resequencing E.g. Human exome
Need reference genome
Available for Human and Mouse
Variation discovery on ORFs
2% of human genome (lower cost)
85% disease mutation are in the exome
Need probes complementary to exons
Nimblegen
Agilent
E.g. Human exome

15. DNA sequencing - 4 Targeted Resequencing Custom genes panel sequencing
Capture of specific regions in the genome
Custom genes panel sequencing
Allows to cover high number of genes related to a disease
E.g. Disease gene panel
Low cost and quicker than capillary sequencing
Multiplexing is possible
Need custom probes complementary to the genomic regions
Nimblegen
Agilent

16. Introduction to NGS Technologies
Transcriptomics - 1
RNA-Seq
Sequencing of mRNA
rRNA depleted samples
Very high dynamic range
No prior knwoledge of expressed genes
Gives information about (richer than microarrays)
Differential expression of known or unknown transcripts during a treatment or condition
Isoforms
New alternative splicing events
Non-coding RNAs
Post-transcriptional mutations or editing
Gene fusions
2 Oct, 2013
Introduction to NGS Technologies

17. Introduction to NGS Technologies
Transcriptomics - 1
RNA-Seq
Sequencing of mRNA
Detecting gene fusions
Introduction to NGS Technologies

18. Common Problems Signal Errors.
Intensity signal Error(454, Ion-Torrent).

19. Common Problems Diploid and Polyploidy Genomes:
Error or Heterozygous Variant??!!!!
USE COVERAGE!!

20. Common Problems Polymerase Errors(All platforms, excluding Solid)
Ligase Errors (Solid)
Mapping Errors
Variant Calling Errors
Human Error
Maybe, Quality Control is needed!!

21. Comparison Roche 454 Illumina SOLiD Long fragments Low throughput
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Comparison
Roche 454
Illumina
SOLiD
Long fragments
Low throughput
Expensive
Poly nts errors
De novo sequencing
Amplicon sequencing
Metagenomics
RNASeq
Short fragments
High throughput
Cheap
GC bias
Resequencing
De novo sequencing
ChipSeq
RNASeq
MethylSeq
Short fragments
High throughput
Cheap
Color-space
Resequencing
ChipSeq
RNASeq
MethylSeq 21

22. Similar to all NGS platforms Pipeline & LOTS of DATA
DNA Sample
NGS Instrument
Data
Library Preparation
Sequencing
Data Analysis
NGS is relatively cheap but think what you want to answer, because the analysis will not do magic

23. Basic steps NGS data processing
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QC and
read cleaning 23

24. Basic steps NGS data processing
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QC and
read cleaning
Mapping 24

25. Basic steps NGS data processing
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QC and
read cleaning
Mapping
DNA
Binding site 25

26. Práctica Descargar programas:
FastQC
Qualimap
Descargar ficheros (Página de la asignatura):
Fastq file
Bam file

27. Quality Control: Raw Data
Number of input reads
Base Quality
Reads Quality
Biases
Software: FastQC

28. Transcript quantification
Where are we?
Sequence processing
Mapping
Variant calling
Transcript quantification
Variant annotation
DE analysis
miRNA prediction

29. (inversed integer value)
Fastq format
Fastq format “ is a fasta with qualities”:
Header line (like fasta but starting with
Sequence (string of nucleotides)
“+” and sequence ID (optional)
Quality values of sequence encoded as a single byte ASCII code
File extension: .fastq
Sequence quality encoding
Base quality must be encoded in just 1 byte!
Each base has a corresponding quality value: quality in position n is related to base in position n
Encoding procedure:
@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT +
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65
Phred transformation
(inversed integer value)
Error probability
ASCII encoding

30. Fastq format. Sequence quality encoding
Phred transformation
(inversed integer value)
Error probability
ASCII encoding
Prob. of incorrect base call
Phred
quality
Score
Base
call
accuracy
1 in 10 10
90%
1 in 100 20
99%
1 in 1000 30
99.9%
1 in 10000 40
99.99%
1 in 50
99.999%
Phred + 33
Sanger [0,40], Illumina 1.8 [0,41], llumina 1.9 [0,41]
Phred + 64
Illumina 1.3 [0,40], Illumina 1.5 [3,40]

31. Per base sequence quality
Good quality
Good data
Consistent
High quality along the read
Reasonable quality
Poor quality
Shows an overview of the range of quality values across all bases at each postion in the fastq file
The central red line is the median value
The yellow box represents the inter-quartile range (25-75%)
The upper and lower whiskers represent the 10% and 90% points
The blue line represents the mean quality

32. Per base sequence quality
Good
Bad data
High variance
Quality decreases towards the end of the read
Reasonable
Poor

33. Per sequence quality scores
Allows to see if a subset of sequences have universally low quality values
Low quality reads
Good data
Most of the reads are high-quality sequences
Bad data
Distribution with bi-modalities

34. Per base sequence content
Good data
Smooth over the read
Bad data
Sequence position bias
Library contamination (overrep. sequence)?
Plots the proportion of each base position in a file for which each of the four normal DNA bases has been called -> little/ no difference between different bases in a random library
The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other

35. Per base GC content
Good data
Smooth over the read
Bad data
Sequence position bias
Library contamination (overrep. sequence)?
Plots the GC content of each base position in a file -> little / no difference between the different bases (random library)
The overall GC content should reflect the GC content of the underlying genome

36. Per sequence GC content
Good data
Normal distribution
Distribution fits with expected
Organism dependent
Bada data
Distribution does not fit with expected
Library contamination?
Measures the GC content across the whole length sequence in a file and compares it to a modelled normal distribution of GC content

37. Per base N content
Good data
Bada data
There are N bias per base position
Plots the percentages of base calls at each position for which an N was called
It’s not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence

38. Sequence length distribution
Some sequencers generate sequence fragments of uniform length
Some sequencers output reads of different length (for example, Roche 454)

39. Sequence duplication levels
Bada data
High number of duplicates
May indicate some kind of enrichment bias (eg PCR over amplification)
Good data
Low level of duplication
May indicate a very high level of coverage in the target sequence
In transcriptomics, it is expected higher number of duplicated sequences
In genomics, it is expected a low number of duplicated sequences

40. Overrepresented sequences and K-mer content
Exact same sequences too many times…
Is that a problem? It depends….
PCR primers, adapters,…

41. Typical artifacts
Sequence adapters

42. Typical artifacts
Platform dependent

43. Sequence Filtering
It is important to remove bad quality data -> our confidence on downstream analysis will be improved

44. Sequence Filtering Sequence filtering: Mean quality Read length
Read length after trimming
Percentage of bases above a quality threshold
Adapter trimming
Adapter reads
Minimum quality threshold

45. Sequence Filtering Sequence filtering tools
Fastx-toolkit (
Galaxy (
SeqTK (
Cutadapt ( ….

46. Transcript quantification
Where are we?
Sequence processing
Mapping
Variant calling
Transcript quantification
Variant annotation
DE analysis
miRNA prediction

47. Quality Control: Mapping Data
Coverage
Mapped Reads
Uniformity
Biases
Software: BamQC

48. Qualimap Example

49. Quality Control: Capture Data
Sensitivity
Specificity
Uniformity
Biases
Software: NGScat

50. ngsCAT Example