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Genome alteration detection using high throughput data
The document discusses genomic alterations identified through sequencing data, focusing on structural variations such as copy number variations (CNV), insertions, inversions, and translocations. It highlights various detection methods for these alterations, including array comparative genomic hybridization and next-generation sequencing (NGS) techniques, while emphasizing the advantages of NGS for precise characterization. The necessity for more robust algorithms is also noted, as current methods yield variable results in genomic alteration detection.
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Genome alteration detection using high throughput data
- 1. Analyzing Genomic Alteration in humangenome using sequencing data Samarth Kulshrestha National Institute of Biomedical Genomics Kalyani, W.B, India
- 2. Genomic alteration 2 Structural Variations(SV) are defined as genomic alterations that involves DNA segments with a length of >1Kb. There are different categories of genomic alterations: ● Copy number variation(CNV): This class includes Copy number deletion and Copy number duplication of genomic segment. ● Insertion: The sequence of one or more nucleotides added between two adjacent nucleotides in the sequence ● Inversion: A DNA segment that is reversed in the orientation with respect to other segments. ● Translocation: A process of exchange of chromosomal segment within the chromosome (Intrachromosomal) or with other chromosomes (Interchromosomal). Other complex alterations includes recently discovered complex rearrangement phenomenon “Chromothripsis”
- 3. 3 doi: https://doi.org/10.1182/blood-2006-06-030858 Genomic variation(A) A single nucleotide polymorphism (SNP) occurs as a result of a single base substitution at an individual site in the DNA sequence (B) A deletion is the loss/absence of DNA sequence. (C) An inversion is a rearrangement causing a segment of DNA to be present in reverse orientation. (D) A copy number variant (CNV) is a segment of DNA that is 1 kb or larger and is present at a variable copy number in comparison with a reference genome. CNVs can be either deletion variants where there is loss of copy number relative to the reference sequence or multicopy duplications where there is gain of copy number relative to the reference sample. (E) A segmental duplication is a segment of DNA at least 1 kb in size that occurs in 2 or more copies per haploid genome, with the different copies sharing at least 90% sequence identity.
- 4. Copy number variation(CNV) Copy Number Variation (CNV) is a prevalent form of genetic variation that leads to abnormal number of copies of large genomic region in a cell. Copy number variation play an important role in pathogenesis and progression of Cancer and variety of other human disorders. CNV length varies from 1 Kb (Kilobases) to several Mb (Megabases). 4
- 5. CNV Disease Association Nature2011 Nature 2014 Nature 2009 5
- 6. Nature 2010 n=313126 histological types Nature 2013 n=4934 11 cancer types 6
- 7. CNV detection usingsequencing data 7 CELL 2013 Sample=811 Nature comm 2015 Sample=1070 Genome Medicine 2013 Sample=25
- 8. CNV Detection Technologies DifferentCNV detection methods are available based on: 1. Array CGH (Comparative Genomic Hybridization) 1. SNP Chips low spatial resolution, low throughput 3. Sequencing data Precise characterization of breakpoints 8
- 9. Strategies to identifystructural alteration (CNV) 9 Read-depth approach (RD) Paired-end mapping (PEM) Split read approach (SR)
- 10. CNV Detection usingLarge-Scale genome sequencing Methods for sequencing based CNV detection Analysis Method Alteration category Pair-end or Mate-pair mapping Deletion, Duplication, Insertion, Inversion, Translocation Split-read mapping Deletion, Duplication, Insertion, Inversion, Translocation Read depth Deletions and duplication 10
- 11. Methods for sequencingbased CNV detection Analysis Method Alteration category Pair-end or Mate-pair mapping Deletion, Duplication, Insertion, Inversion, Translocation Split-read mapping Deletion, Duplication, Insertion, Inversion, Translocation Read depth Deletions and duplication 11 CNV Detection using Large-Scale genome sequencing
- 12. Read depth Approachto detect CNV using NGS data 12 Higher Depth indicates Amplification events
- 13. 13 Read depth approachdetect CNV by investigating read densities in genomic regions. doi: 10.1002/0471142905.hg0719s75. Figure: Y axis represents read-count for tumor (red dots) and normal (green dots) data and X represents genome coordinates. Vertical purple bars are the genomic regions with variations. Average read-count for the given image is 100, when this count is ~ 150 in tumor (red dots), there is a somatic gain event, when read counts are ~50, there is a chance of somatic loss.
- 14. Log2ratio plots 14 logRratio Chromosome Amplification Deletion Example plotshowing genomic alteration. Black dots are log2ratios of sample/reference read counts Deletion Amplification
- 15. Strategies to identifygenomic alteration 15 Read-depth approach (RD) Paired-end mapping (PEM) Split read approach (SR)
- 16. 1) Concordant reads:have span size within the range of expected fragment size and consistent orientation of read pairs with respect to reference. 2) Discordant reads: have unexpected span size/inconsistent orientation of read pairs. Concordantreads Discordantreads Greater Mapping distance Lower Mapping distance Expected Mapping distance Read Orientation Useful for genomic alteration detection A) B) C) Deletion Signature Different read category (paired-end) R1 R2 Inversion Signature
- 17. Strategies to identifygenomic alteration 17 Read-depth approach (RD) Paired-end mapping (PEM) Split read approach (SR)
- 18. 18 Split-read Split reads areincompletely mapped reads Reference genome Mapped reads
- 19. Read Depth basedtools SegSeq CNV-seq BIC-seq ReadDepth CMDS CNVnator PEM/SR based tools BreakDancer PEMer Pindel GASV VariationHunter DELLY Combinatorial Methods CNVer GASVPro Genome STRiP SVDetect inGAP-sv FASTQ BAM SAM Pileup 19 Input format Available tools for CNV detection (Genome/Exome)
- 20. Tools for CNVdetection using exome data Tools Input requirement Control-FREEC SAM/BAM CoNIFER BAM XHMM BAM ExomeDepth BAM ExomeCNV BAM/pileup VarScan2 BAM/pileup 20 *Control-FREEC detect CNV for both the datasets (Genome and Exome)
- 21. Highly variable CNVcalls 21 It has been found that current available genomic alteration detection algorithms are producing highly variable results, limited in their performance and more robust algorithms are needed. Genome medicine 2016 Oxford 2015
- 22. Array vs Sequencing 22 Microarraysare generally unable to resolve breakpoints at the single-base-pair level. Microarrays offer a distinct advantage in terms of throughput and cost. The most important benefit of NGS technologies is that it is possible to discover a multitude of variant classes with a single sequencing experiment. Sequencing data offers different approaches: 1) Read-depth 2) Pair-end 3) Split read strategies to detect genomic alteration at base pair level resolution. ARRAY Sequencing
- 23. 23 Drop in coverage Deletionevent in IGV doi:10.1038/npjgenmed.2016.26 Figure: A deletion event visualization using paired- end reads in an IGV interface. All gray color reads are concordant reads with no mapping abnormalities while red color reads are discordant deletion supportive read pairs with abnormal read mapping. There is a coverage drop in the region too which supports deletion event. SV detection using discordants read pairs
- 24.
- 25. CNV Data Visualization 25 IGVInterface Circos Plot (Integrative Genomics Viewer)
- 26. IGV 26 Different approaches forIGV visualization: ● Genomic coordinate based chr:Start-Stop ● Gene based Any amplified/deleted gene name ● HeatMap Gene based: Read density Heat Map
- 27. Somatic Amplification event 27 Tumor BAM Normal BAM SomaticAmplification event visualization through IGV Read density Higher read density for Tumor data when compared to Normal data in a given window of ~17kb for an amplification event indicates possibility of somatic event IGV visualisation based on Gene based or coordinate based search
- 28. Somatic Deletion event 28 Readdensity Lower read density for Tumor data when compared to Normal data in a given window of ~28kb for a deletion event indicates possibility of somatic deletion event Somatic Amplification event visualization through IGV Tumor BAM Normal BAM IGV visualisation based on Gene based or coordinate based search
- 29. File require forHeat map visualization= segmentation file generated from CNV caller FORMAT of .seg file is : pat_ID chr start stop ratio p113 chr1 10001 810000 -0.1616 p113 chr1 810001 1700000 -0.7475 p113 chr1 1700001 2251000 -0.6318 p113 chr1 2251001 2290000 -1.1409 p113 chr1 2290001 2694000 -0.6104 p113 chr1 2694001 4119000 -0.4411 p113 chr1 4119001 4814000 -0.2095 . . p119 chr8 70240001 70702000 0.3316 p119 chr8 70702001 71018000 -0.0404 p119 chr8 71018001 .seg file extension determines Copy number data IGV Heatmap Visualization
- 30. 30 Load BICseq.seg file usinggiven path. .seg file should contains logRratio information of all patients
- 31. 31 Data Type DefaultGraph Type Default Data Range Default Colors Copy number Heatmap -1.5 - 1.5 Blue to red Gene Expression Heatmap -1.5 - 1.5 Blue to red DNA Methylation Heatmap 0 to 1 (methylation score) Green . . . Different Data type and display option in IGV
- 32. 32 Red: Amplification Blue: Deletion SampleID Chromosomes IGV heatmap visualization of CNV events generated by CNV caller
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- 34. Thanks for yourkind attention 34
Editor's Notes
- #5 Redon, R., et al. Global variation in copy number in the human genome. Reference for point 3 ) A copy number variation map of the human genome
- #9 Agilent, Nimblegen: ArrayCGH SNP chips: Affymetrix, Illumina Sequencing : Sequencing data Precise characterization of breakpoints Chip Image: Affymetrix Inc. has unveiled its Genome-Wide Human SNP Array 6.0. The single microarray measures more than 1.8 million markers for genetic variation, enabling more powerful whole-genome association studies by genotyping more markers from more individuals. The array contains more than 900,000 single-nucleotide polymorphisms (SNPs) and more than 946,000 nonpolymorphic probes for the detection of copy number variation
- #13 Agilent, Nimblegen: ArrayCGH SNP chips: Affymetrix, Illumina Sequencing : Chip Image: Affymetrix Inc. has unveiled its Genome-Wide Human SNP Array 6.0. The single microarray measures more than 1.8 million markers for genetic variation, enabling more powerful whole-genome association studies by genotyping more markers from more individuals. The array contains more than 900,000 single-nucleotide polymorphisms (SNPs) and more than 946,000 nonpolymorphic probes for the detection of copy number variation
- #14 A survey of copy-number variation detection tools based on high-throughput sequencing data. Xi R1, Lee S, Park PJ.
- #15 LogRratio:
- #20 Some tools require case-control both the data for CNV detection Some tools use multiple samples as input Reference: Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives
- #22 Add Evaluation of somatic copy number estimation tools for whole-exome sequencing data (check for image)
- #23 http://www.nature.com/nrg/journal/v12/n5/full/nrg2958.html
- #27 CNV visualization using IGV interface: (Heat Map + patient specific)