NGS, Cancer and Bioinformaticsrssf.i2bc.paris-saclay.fr/transfert/IFSBM/IFSBM_intro_NGS-YB.pdf · NGS and Oncology 5/3/2015 Yannick Boursin NGS is now widely used as: • A research

5/3/2015 Yannick Boursin

NGS, Cancer and Bioinformatics

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NGS and Clinical Oncology

• NGS in hereditary cancer genome testing• BRCA1/2 (breast/ovary cancer)

• XPC (melanoma)

• ERCC1 (colorectal cancer)

• NGS for personalized cancer treatment• Clinical trials: MOSCATO (GR), SAFIR (GR), SHIVA (Curie), …

• Ipilimumab (anti-CTLA4), Nivolumab (anti-PD1), Trastuzumab (anti-HER2), Cetuximab (anti-EGFR)

• Detection of chimeric transcripts• Chronic Myeloid Leukemia: Philadelphia chromosome (BCR/ABL)

• Non-Small-Cell Lung Cancer: EML4-ALK

5/3/2015 Yannick Boursin 2

NGS and Oncology


NGS is now widely used as:• A research tool to screen a large amount of cancer samples

NGS and Oncology

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07-09th April 2014 NGS and Bioinformatics

NGS is now widely used as:

• A research tool to screen a large amount of cancer samples

• A clinical/diagnosis tool in daily practice

These projects require dedicated bioinformatics integration project to access and analyses this huge amount of data


These projects require dedicated bioinformatics integration project to access and analyses this huge amount of data.

NGS and Oncology

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NGS is now widely used as:

• A research tool to screen a large amount of cancer samples


These projects require dedicated bioinformatics integration project to access and analyses this huge amount of data

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Why do we need computers for NGS

Sequencing data size evolution Needs to address

• Store PetaBytes of data (1 PB is1000 TB).

• Share data around the world through networks

• Analyze huge amounts of data with complex algorithms


Bioinformatics and Oncology

• Problem: finding, extracting, and presenting relevantinformations.

• Partial solution: designingworkflows in order to ease data analysis.


Interdisciplinary collaboration


Bioinformatics acts as a hubs between the different fields. Trust between partners is needed, training is needed as well for efficient understanding.

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Standard Workflow for NGS Analysis


A typical NGS workflow

Step 1: Quality Check and improvements





NGS Data: what do they look like ?


A raw data file (.fastq, .sff, .fa, .csfasta/.qual)with millions of short reads of the same size (SOLiD, HiSeq) or readsof different size (Ion PGM/Proton)

Enhanced view of the reads in a fastq file

FASTQ format


• 1 sequence = 1 read = 4 lines in the file

• First line = sequence identifier

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FASTQ format


• Fourth line = Quality

• ASCII encoded (Reduce the file size)

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Sequence quality encoding


Why looking at sequencing quality ?


• Quality of data is very important for various downstream analyses:• Sequence assembly or mapping• Variants detection• Gene expression studies•...

• Quality of data = poor• Try to find a reason• Can we correct/improve the quality ? • May lead to erroneous conclusions

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Quality controls on raw reads: which metricsto check ?


Mainly:• Quality score per base and over the reads

But also: • Read length distribution• Sequence content per base and % of GC• Kmers content• Overrepresented sequences• Duplicated reads

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Quality scores


• Per base (Box Whisker type plot)-> to see wether base calls falls into low quality(commonly towards the end of a read)

• Per sequence (mean quality distribution)-> to see if a subset of your sequences have universallylow quality values

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Quality scores


Quality scores





Reads cleaning: removing bad quality bases

• After QC, we need to remove bad quality entities.

• This is often done by scanning reads with a sliding window algorithm.


Read-ends trimming by a quality trimming algorithm. In red: bad quality bases. In blue: good quality bases.

Reads cleaning: adapters removal


• An adapter is a small piece of known DNA located at the end of the reads• Adapters roles:

• Hang read to the sequencer flowcell• Allows a specific PCR enrichment of reads having adapter • Use in multiplex sequencing (samples in mix)

• Available tools to trim adapters: • Cutadapt• Trimmomatic• RmAdapter

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In blue: adapters. In orange: informative part of the read.




Step 2: Short Reads Alignment





Reads alignment - Vocabulary


Reference Genome : The reference genome is a known sequence, supposed to be as close as possible to the input genome, and whichis used as an anchor to organize the single reads information.Alignment : (mapping) The reads alignment aims at transformingthe single reads information in an organized and reduced set of information. Giving each read a genomic position.Mismatch : Incoherence between two nucleotidesGap : Bridge within the read alignment (i.e. small Insertion/deletion)Indels : Insertion/Deletion into the reference genomeMappability : Uniqueness of a region (repeated region = lowmappability, unique region = good mappability)

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Reads alignment – Two strategies


The reads alignment aims at transforming the single readsinformation in an organized and reduced set of information.

Two strategies can be applied :

- De novo Reads AssemblyUsed when no reference genome are available. It aims atreconstructing long scaffolds from single reads information.

- Alignment on a Reference GenomeThe reads are directly compared to a known reference genome.

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Alignment on a reference genome


The reference genome is a known sequence, supposed to be as close as possible to the input genome, and which isused as an anchor to organize the single reads information.

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Alignment of reads against reference genome



The reference genome is a known sequence, supposed to be as close as possible to the input genome, and which isused as an anchor to organize the single reads information.

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Alignment on a reference genome - Challenges


New alignment algorithms must address the requirements and characterics of NGS reads

– Millions of reads per run (30x of genome coverage)– Reads of different size (35bp - 200bp)– Different types of reads (single-end, paired-end, mate-pair, etc.)– Base-calling quality factors– Sequencing errors ( ~ 1%)– Repetitive regions– Sequencing organism vs. reference genome– Must adjust to evolving sequencing technologies and data formats

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Alignment on a reference genome – Bioinformaticstools


Finding the best alignment - Rational


Given a reference and a set of reads, report at least one “good” local alignment for each read if one existsWhat is “good”? For now, we concentrate on: – Fewer mismatches is better

– Failing to align a low-quality base is better than failing to align a high-quality base

Based on a scoring system, i.e. score for a match (1), MM penalty (3), gap open penalty (5), gap extension penalty (2). The best alignmentis the one with the highest score.

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Alignment key parameters - Repeats


Approximately 50% of the human genome is comprised of repeats

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Alignment key parameters - Repeats


Close proximity with genes : intergenic and intragenic positions

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BRCA2: a mosaic of repeated regions

Alignment key parameters – Repeats – 3 strategies


-1- Report only unique alignment-2- Report best alignments and randomly assign readsacross equaly good loci-3- Report all (best) alignments

Treangen T.J. and Salzberg S.L. 2012. Nature review Genetics 13, 36-46

-1- Report only unique alignment -2- Report best alignments and randomly assign reads across equaly good loci -3- Report all (best) alignments

A B A B A B

-1- -2- -3-


Alignment Key Parameters Repeats – Three strategies

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Treangen T.J. and Salzberg S.L. 2012. Nature review Genetics 13, 36-46

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Key points

• The alignment is a crucial step of the NGS analysis.

• The reference genome has to be carefully chosen.

• The mappability of the region of interest has to be takken intoaccount (primer design).

• The scoring method has to be chosen accordingly to the sequencing error rate and the quality of the raw reads.

• The alignment parameters have to be set properly.

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Limitations of Alignment Tools


Even if we have now some nice tools to align reads on a reference genome, several issues are still important :

- Homopolymer mapping- Efficiently align small indels- Alignment on several genomes- Alignment on repeted sequences - ...

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Alignment formats


• A lot of formats exists:

• SAM• BAM• ELAND (Illumina specific) • MAQ map• …

SAM and BAM are now the standard for aligned data

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SAM format


• SAM for Sequence Alignment Map• Tabulated text file• 1 line per read• Each line is composed of 11 fields (minimum)

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SAM format


SAM format


• Second field can be used for quick sort of file

• With Samtools (command line) and –f et –F options • Useful webpage:

• http://broadinstitute.github.io/picard/explain-flags.html

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BAM format


• BAM for Binary Alignment/Map• Correspond to SAM format compressed as BGZF • Reduce by 5 fois the size of the alignment file• Not directly readable as SAM format• Require Samtools• Best format for alignment file sharing• Couples with an index file (BAI)• Avoid a sequential read of the complete file

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QC 3 : Which metric to check ?


In practice, how to validate my alignment ?

Be aware of the mapping strategy usedLook at simple descriptive statistics

– Number of aligned reads– Coverage/Depth– Mapping quality– Number of normal/abnormal pairs for paired-end

data–...

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NGS Analysis : How can I work with my NGS data ?


• Difficult on personal computer (lack of ressources)• 1 alignement = 4 processors + 15gb Ram (to multiply by the number of samples)• Impossible to open files into sofwares like text editor• Need a very large storage capacity• Data backup administration• Applications server connected to a computing cluster and storage array:

• Commercials solution (CLC Bio, NextGene, ...) • Galaxy server:

https://galaxy.gustaveroussy.fr/galaxyprod

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After sequencing : Data analysis


Main challenges :

• The rapid evolution of the high-throughput technologies

• The rapid evolution of the bioinformatics solutions

• The rapid evolution of the biological/medical knowledge

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Data analysis

Chimerictranscript search

Alternative transcripts study

Differentialexpression study

Methylation study Detection of genomic variants

Detection of copy-number variation


Chimeric transcripts


Does the tumoral cells express any chimeric transcript ?

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History of the bcr-abl fusion

Alternative transcripts


Differential expression


Are there genesthat would bestronglyexpressed in one kind of tumor that are not in the otherkind ?

Can we group tumorsaccording to their expression profiles ?

Clustering differential expression in breast tumours.

Methylome


Is there any difference between DNA methylation in tumors and in normal cells ?

How does methylation promotes cancer ?

Detection of copynumber variations


Are there any copy-number alteration (gain or loss of chomosomal regions, amplifications …) that could explain tumorigenesis ?

Copynumber variations in cancer. MYC and KRAS are amplified.

Detection of genomic variants


Are there mutational events that are specific to the tumoral genome ? Could the tumorigenesis be explained by those ? Is there any drug targeting those mutations ?

Pancreas adenocarcinoma: from normal cells to tumoral cells

Limitations: Detection of genomic variants


Between 1.4 and 8.9 % of the variants are technology specific

Limitations: Detection of genomic variants


Common genomic variants between different variant callers

Conclusion

• Nowadays, NGS is widely used in cancer centers in order to categorize cancers and link patients with personalized treatments (Precision Medicine)

• NGS is also used in cancer research, in order to discover new oncogenetic mechanisms, to understand the way a treatment works, to link biological and genetical characters …

• Due to technical issues using NGS might not answer your questions. It is important to know that the technique is limited:

• A) by the question you asked at first. If a cancer cannot be explained by mutational events, it might be explained by other mechanisms. But still, sometimes, nothing is to be found in data.

• B) by technical issues. Sequencers and softwares are prone to errors. Statistically, there will be at least one error for any analysis. You can often limit the effects of this limitations by making biological and technical replicates.



Paired-end mapping


• Insert-size checking

• % of "All Good"= both reads in the pair have aligned• "the pair is properly aligned" meaning that they mapped within a

proper distance from each other• % of "All Bad" = neither the read nor its mate mapped• % of Only one read maps = only one read in a pair is mapped

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Alignment key parameters – Using single or paired-end reads ?


The type of sequencing (i.e. single or paired-end reads) is oftendriven by the application.Exemple : Finding large indels, genomic rearrangements, ...However, in most of the case, the pair information can improve the mapping specificity- Single-end alignment – repeated sequence

- Paired-end alignment – unique sequence

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NGS Toolkit : SAMtools


http://samtools.sourceforge.net/samtools.shtmlInteracting with SAM/BAM format

SAMTools provides the following commands :view : tansform and filter SAM or BAM datasort : sort a BAM file per genomic location or nameindex : creates a new index file that allows fast look-up of

data in a (sorted) SAM or BAMmpileup : SNVs/indel detectionrmdup : remove duplicated readsflagstat : compute statistics on the SAM/BAM file ...

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http://samtools.sourceforge.net/samtools.shtml

NGS Toolkit : BEDTools


http://code.google.com/p/bedtools/

• Address common genomics tasks such as finding feature overlaps and computing coverage.• Can manage BED, GFF/GTF, VCF and SAM/BAM• Unix-like command• Fast• All intersections or annotations tasks can be done with BEDTools

Quinlan AR and Hall IM, 2010. BEDTools: a flexible suite of utilities for comparinggenomic features. Bioinformatics. 26, 6, pp. 841–842.

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http://code.google.com/p/bedtools/

How to visualise data ?


IGV : Integrative Genome Viewer

http://www.broadinstitute.org/igv/

JAVA application (local version) Annotation available on the Broad server Batch command lineSupport a lot of different file formats (Variants visualization)Easy to useLimited in term of annotations

Screencast: How to use IGV (french) https://www.youtube.com/watch?v=Wx3zHYK0cNg,

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http://www.broadinstitute.org/igv/

https://www.youtube.com/watch?v=Wx3zHYK0cNg,

How to visualise data ?


UCSC Genome Browser

http://genome.ucsc.edu/

Hundred of annotation data Hundred of public (ENCODE) profils Tables functionsFully online (session) Can be difficult to upload big data files (new format: bigBED, bigWIG, etc.)

Screencast: How to use the UCSC genome browser (french) https://www.youtube.com/watch?v=VPeoeJebdFM,

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http://genome.ucsc.edu/

https://www.youtube.com/watch?v=VPeoeJebdFM,

Sequence length distribution


• Sequencers generates:• either sequence fragments of uniform length• or reads of wildly varying lengths.

Helps to identify and remove reads with abnormal length.

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Sequence length distribution


Sequence content


• Proportion of each base position for which A,C, G, T has been called

• GC content of each base position-> in random librairies = a little to no difference betweenthe different bases

• N content per base-> If a sequencer is unable to make a base call withsufficient confidence

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Sequence content


Over-represented sequences


• The sequences that are highly duplicated in your library, as well as any primer and/or adapter dimers that werepresent in the original library.

• Run A:• Sequence:

GACTCGGCAGCATCTCCATCCAAACTTTTCATTTCTGCTTTTAAAGGAAA • Count: 37• Pourcentage 0.1%

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Duplicate reads


• Different reads which have the same sequence• A duplicate could be PCR effect or reading same

fragment twice or come from enrichment• Reads which align to the identical location on the

reference

• Remove duplicates ? It depends of the application. • Exemple: for targeted sequencing, you do not

want duplicates to be removed

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Duplicate reads


Data analysis

Motif search Chimeric transcriptsearch

Microbiota study Alternatetranscript search

Differentialexpression study

Methylation study Detection of genomic variants

Detection of copy-number variation


Motif search


How does my protein interacts with DNA ?

Chimeric transcripts


Are there any chimeric transcripts ?

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Microbiota


What kind of species grows in the human gut ? Could thosespecies beassociated withtumorigenesis ?

Alternative transcripts


Are there any differences between normal cell and tumoral cells regardingsplicing events ?

Differential expression


Are there genesthat would bestronglyexpressed in one kind of tumor that are not in the otherkind ?

Can we group tumorsaccording to their expression profiles ?

Methylome


Is there anydifferencebetween DNA methylation in tumors and in normal cells ?

Detection of copynumber variations


Are there any copy-number alteration (gain or loss of chomosomal regions, amplifications …) that could explain tumorigenesis ?

Detection of genomic variants


Are there mutational events that are specific to the tumoral genome ? Could the tumorigenesis be subrogated to that ?

Quality controls on raw reads : lets start aftersequencing


A raw read is characterized by three parameters:• Its length• Its sequence• Per-base-in-sequence quality

Raw reads

NGS, Cancer and Bioinformaticsrssf.i2bc.paris-saclay.fr/transfert/IFSBM/IFSBM_intro_NGS-YB.pdf · NGS and Oncology 5/3/2015 Yannick Boursin NGS is now widely used as: • A research

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