rna-seq from a bioinformatics perspective · fusion transcripts snv / indels novel transcripts....
TRANSCRIPT
![Page 1: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/1.jpg)
RNA-seq from a bioinformatics perspective
Harmen van de WerkenErasmus MC;
Cancer Computational Biology Center (CCBC)
![Page 2: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/2.jpg)
Outlook
RNA seq software + RNA-seq courses
Alternative splicing &Promoters
IntroductionRNAseq data
Differential expression
Read-Through & Fusion Transcripts
SNV / InDels
Novel Transcripts
![Page 3: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/3.jpg)
Table 6-1 Molecular Biology of the Cell (© Garland Science 2008)
![Page 4: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/4.jpg)
THE HUMAN GENOME▪ Consensus ~ 22,500 protein-coding genes
~ 9,000 long non-coding RNAs ~ 2,500 – 3,000 small RNAs
▪ miTranscriptome1 ~ 91,013 genes ~ 58,648 lncRNA genes
1Iyer MK et al. Nature Genetics 47, 199–208 (2015)
Transcriptomics of (Cancer) Tissue
![Page 5: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/5.jpg)
Common mRNA-seq Workflow
Dry labBioinformatics
Wetlab
![Page 6: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/6.jpg)
(c)DNA Next Generation Sequencing (NGS)
ThermoFisher Ion Torrent
Personal Genome Machine (PGM)
PACBio RS IIIllumina HiSeq 2000
![Page 7: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/7.jpg)
Illumina HiSeq 2000
![Page 8: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/8.jpg)
Illumina Sequencing
![Page 9: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/9.jpg)
Ion Torrent Platform
![Page 10: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/10.jpg)
RNAseq Data Analysis
Alternative splicing &PromotersRNAseq data
Differential expression
Read-Through & Fusion Transcripts
SNV / InDels
Novel Transcripts
![Page 11: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/11.jpg)
Detecting Single Nucleotide Variants and small indels
![Page 12: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/12.jpg)
Errors occur at each stage
Primary Analysis- Incorrect base calling- Homopolymer errors- Phasing
![Page 13: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/13.jpg)
Errors occur at each stage
Secondary AnalysisRead mapping- Incorrect ref. Sequence- Pseudogenes- Indels- Complex variants
![Page 14: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/14.jpg)
Errors occur at each stage Secondary AnalysisVariant calling
Variant Calling filters are heuristics; therefore, they will generate falsenegatives and positives and are best applied as soft filters.
![Page 15: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/15.jpg)
Errors occur at each stage
Tertiary Analysis
- Incorrect gene annotation
- Contamination in reference Databases.
![Page 16: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/16.jpg)
![Page 17: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/17.jpg)
False Negative: c.2237_2259del,insCCAACAAGGAAEGFR
![Page 18: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/18.jpg)
False Negative BRAF p.V600R; False Positives BRAF p.V600G & p.V600M
![Page 19: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/19.jpg)
RNAseq Data Analysis
Alternative splicing &PromotersRNAseq data
Differential expression
Read-Through & Fusion Transcripts
SNV / InDels
Novel Transcripts
![Page 20: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/20.jpg)
Differential Gene Expression mRNA-seq Workflow
![Page 21: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/21.jpg)
Fig1. FastQC report on Base Quality of position and overrepresented sequences
@SRR001666.1 071112_SLXA-EAS1_s_7:5:1:817:345
GGGTGATGGCCGCTGCCGATGGCGTCAAATCCCACC
+SRR001666.1 071112_SLXA-EAS1_s_7:5:1:817:345
IIIIIIIIIIIIIIIIIIIIIIIIIIIIII9IG9IC
Fig2. Fastq format of one read
![Page 22: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/22.jpg)
mRNA-seq alignment
Courtesy: Wikipedia
![Page 23: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/23.jpg)
Alignment to transcriptome
Alignment to reference genome
![Page 24: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/24.jpg)
RNA-Seq - Alignment
Alignment algorithms need:• Reference sequence• Transcriptome database (optional)
Algorithms commonly used for RNA-Seq alignment:• Tophat • STAR• HISAT2
![Page 25: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/25.jpg)
Visualization of NGS Transcriptomics and Genomics data
![Page 26: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/26.jpg)
RNA-Seq - Alignment/QC
![Page 27: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/27.jpg)
RNA-Seq - Stranded
![Page 28: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/28.jpg)
Differential expression
Rakesh Kaundal et al.
![Page 29: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/29.jpg)
Normalization of RNA-seq
Total count (TC): Gene counts are divided by the total number of mapped readsUpper Quartile (UQ): Very similar in principle to TC, the total counts are replaced by the upper quartile of countsMedian (Med): Also similar to TC, the total counts are replaced by the median counts Trimmed Mean of M-values (TMM): This normalization method is implemented in the edgeR Bioconductor package (Robinson et al., 2010).Quantile (Q): First proposed in the context of microarray data, this normalization method consists in matching distributions of gene counts across lanes.
Reads Per Kilobase per Million mapped reads (RPKM): This approach quantifies gene expression from RNA-Seq data by normalizing for the total transcript length and the number of sequencing reads.
![Page 30: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/30.jpg)
Reduce Dimensions PCA /QC
Figure 1 Principal components Analysis (PCA) of a multivariate Gaussian distribution. PCA is a linear algorithm. It will not be able to interpret complex polynomial relationship between features.
Figure 2. Principal Component Analysis (PCA) of Colon and Ovarian Cancer cell lines.
![Page 31: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/31.jpg)
Reduce Dimensions t-SNEFigure 1.t-Stochastic Neighbor Embedding (t-SNE) is a non-linear algorithm of Colon and Ovarian Cancer cell lines.
![Page 32: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/32.jpg)
Clustering Analysis/ QC
Fig 1: Example Hierarchical Clustering.Example of hierarchical clustering: clusters are consecutively merged with the most nearby clusters. The length of the vertical dendogram-lines reflect the nearness. (Jansen et al.)
![Page 33: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/33.jpg)
Clustering Analysis/ QC
Figure 1.Hierarchical clustering of Colon and Ovarian Cancer cell lines.
![Page 34: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/34.jpg)
Differential expression DESeq2
A common difficulty in the analysis of read count data is the strong variance of Log Fold Change (LFC) estimates for genes with low read count.
![Page 35: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/35.jpg)
Differential expression of genes
Test Differentially gene expression with correction for multiple testing
![Page 36: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/36.jpg)
Gene Set Enrichment Analysis: GO and KEGG database
![Page 37: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/37.jpg)
Gene Set Enrichment Analysis: GO and KEGG database
![Page 38: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/38.jpg)
RNAseq Data Analysis
Alternative splicing &PromotersRNAseq data
Differential expression
Read-Through & Fusion Transcripts
SNV / InDels
Novel Transcripts
![Page 39: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/39.jpg)
Fusion Gene Detection
Fig1 RNA-seq mapping of short reads over exon-exon junctions, it could be defined a Trans or a Cis event. (wikipedia)
![Page 40: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/40.jpg)
Fusion Gene Detection
![Page 41: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/41.jpg)
Fusion Gene Detection
![Page 42: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/42.jpg)
Fusion Catcher Tool
Fusion Catcher outperforms other toolsby using multiple Aligners
● Bowtie● Bowtie2● BLAT● STAR
![Page 43: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/43.jpg)
RNAseq Data Analysis
Alternative splicing &PromotersRNAseq data
Differential expression
Read-Through & Fusion Transcripts
SNV / InDels
Novel Transcripts
![Page 44: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/44.jpg)
RNA-seq de novo Assembly
● Define the whole transcriptome without a reference.
● Trinity
![Page 45: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/45.jpg)
RNA-seq Analysis Software
![Page 46: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/46.jpg)
RNA-seq Molmed Courses
● Basic Course on 'R' ● Galaxy for NGS ● Workshop Ingenuity Pathway Analysis (IPA) + CLC
Workbench / Ingenuity Variant Analysis● Gene expression data analysis using R: How to make
sense out of your RNA-Seq/microarray data
![Page 47: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/47.jpg)
Take Home Message
Think before you start
![Page 48: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/48.jpg)
Thank you for your attention
Hematology
Mathijs A. SandersRemco Hoogenboezem
CCBC
Job van RietWesley van de Geer
![Page 49: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/49.jpg)
[email protected]://ccbc.erasmusmc.nl
@ErasmusMC_CCBC
Harmen van de Werken
Cancer Computational Biology Center (CCBC)
![Page 50: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/50.jpg)
●
![Page 51: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/51.jpg)
●
● Negative Binomial● Models count as ‘binomial successes until a set number of failures’ which better fits
the RNA-Seq fragment generation (limited reagent)● Allows/captures the ‘overdispersion’ seen in RNA-Seq experiments.
![Page 52: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/52.jpg)
Outline••
••
••
••
••••
•
![Page 53: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/53.jpg)
RNA-Seq - Why•••••••
![Page 54: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/54.jpg)
RNA-Seq - Differential splicing
•
![Page 55: RNA-seq from a bioinformatics perspective · Fusion Transcripts SNV / InDels Novel Transcripts. Table 6-1 Molecular Biology of the Cell (© Garland Science 2008) THE HUMAN GENOME](https://reader034.vdocuments.net/reader034/viewer/2022050413/5f8a488bc6b2883c7c2e381a/html5/thumbnails/55.jpg)
Conclusion•
•••• ••••
•••