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June 25-26, Workshop
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- 2. T-BioInfo is designedfor processing, analysis and integration of multi-omics data. The platform is used in multiple research groups to extract meaningful insights from large multi-omics datasets. Our current effort expands to education, by enabling more people to extract meaningful, data-driven insights from omics datasets with biomedical applications. To learn more about the platform and it’s research and educational features, follow the highlighted links . T-bio.info | edu.t-bio.info | server.t-bio.info 2
- 3. 3 LBRN Summer Program June1 Program Announcement August 15 Independent Projects
- 4. Part 1: RNA-Seq Processing fromraw reads to a table of expression 4
- 5. 5 Processing NGS Data(RNA-seq) 1. Pre-processing: • AdaptorTrimming • PCR duplicates 2. Alignment: • Aligning reads to exons • Exon Junctions • GTF updates 3. Quantification • Table of Expression • NormalizationTechniques 4. Differential Gene Expression • Student’sT-test • P-value • False Discovery Rate (FDR) • Fold Change
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- 8. Modeling Precision Medicine MachineLearning forTranscriptomics Data: Extracting Meaningful insights from high-throughput biomedical data. 8
- 9. Clinical Subtypes MolecularSubtypes 9
- 10. Diagnosis, Prognosis, ResponsetoTreatment 10 Survival prediction Treatment Selection OncotypeDXPAM50
- 11. Daemen et al.,2013, “Modeling precision treatment of breast cancer”: an analysis of over 70 different Breast Cancer cell lines and over 90 different therapeutic agents. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r110 Sample 1 Sample 2 Sample 3 Sample 4 gene 1 4 3 3 7 gene 2 6 5 5 8 gene 3 6 6 6 6 gene 4 1 2 1 2 gene 5 9 10 1 5 gene 6 12 4 0 5 gene 7 1 7 9 8 gene 8 4 8 3 10 11
- 12. 12 Cell line Transcriptiona l subtype 21MT1Basal 21NT Basal 21PT Basal HCC1143 Basal HCC1569 Basal HCC1806 Basal HCC1937 Basal HCC1954 Basal HCC3153 Basal HCC70 Basal JIMT1 Basal MX1 Basal SUM149PT Basal SUM229PE Basal BT549 Claudin-low HCC1395 Claudin-low HCC38 Claudin-low HS578T Claudin-low MDAMB231 Claudin-low SUM1315MO2 Claudin-low 600MPE Luminal AU565 Luminal BT474 Luminal BT483 Luminal CAMA1 Luminal EFM192A Luminal EFM192B Luminal EFM192C Luminal HCC1419 Luminal HCC1428 Luminal HCC202 Luminal LY2 Luminal MCF7 Luminal MDAMB134VI Luminal MDAMB175VII Luminal MDAMB361 Luminal MDAMB453 Luminal SKBR3 Luminal SUM225CWN Luminal SUM52PE Luminal T47D Luminal T47D_KBluc Luminal UACC812 Luminal UACC893 Luminal ZR751 Luminal ZR7530 Luminal ZR75B Luminal 184A1 Normal-like 184B5 Normal-like MCF10A Normal-like MCF10F Normal-like MCF12A Normal-like
- 13. 13 MCF10A (Normal Like)HCC38 (Claudin Low)
- 14. Files we willuse in this session 14
- 15. 15 .fastq, .fq .fa.gtf, .gff GeneralTransfer FormatRAW READS (Read Sequence File)
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- 17. Part 1: RNA-Seq Processing fromraw reads to a table of expression 17
- 18. 18 RNA TRANSCRIPTION 1. DNAis transcribed into RNA 2. RNA is transformed into mRNA 3. RNA is translated into proteins
- 19. 19 GTF file FASTQ file Mappingreads: Genes and Isoforms
- 20. 20 Unmapped reads Mapped reads Mappingon Junctions
- 21. 21 Introns vs. Exons 1.Exons from different genes spliced together 2. Highly conserved exons from different genes 3. Introns sometimes are left ! What is in an intron 1. Contains Information on how to combine exons 2. Includes species markers (nucleotide sequences) 3. Non-coding regions (microRNAs, repeats) Gene Fusion 1. Chromosome aberration 2. Gene fusion (terminator region mutation) Exceptions to RNA TRANSCRIPTION
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- 23. RNA-Seq: technical overview .…TCTGAAACAATGCTTCAATCTAACTTATCATTCATTGGGA…. Genome 23 GeneA Gene B Gene C Transcr. ATranscript A Transcr. ATranscript C
- 24. 24 .…TCTGAAACAATGCTTCAATCTAACTTATCATTCATTGGGA….Gene A GeneB Gene C Transcr. ATranscript A Transcr. ATranscript C Reads RNA-Seq: overview
- 25. 25 .…TCTGAAACAATGCTTCAATCTAACTTATCATTCATTGGGA….Gene A GeneB Gene C Transcr. ATranscript A Transcr. ATranscript C Reads RNA-Seq: some details 1. Shattering 2. Adapters ligation 3. PCR amplification 4. “Reading”
- 26. Preprocessing: • Adapters removalplus additional • Removing PCR duplicates 26 Quantification of expression levels Mapping • Mapping on the set of known transcripts • Mapping on genome (and potential identification of novel transcripts) • Combined strategy RNA-Seq: overview
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- 28. 28 RNA-Seq: basic (andfastest) pipeline
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- 30. 30 RNA-Seq: extended pipeline(detail)
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- 32. 32 ExpressionTable (FPKM orTPM) SampleName Gene ID What is this number?
- 33. Standard Measures ofRNA Quantification: • Counts • FPKM – fragments per kilobase per million mapped reads: Number of reads mapped on the gene ((total number of mapped reads – in millions) x (gene length in kilobases)) • TPM – transcripts per million For one sample TPMg = C x FPKMg, where C is selected in such a way that sum of all million. Constants C are different for different samples. 33
- 34. Linear scale vsLog-scale Relative differences are biologically more meaningful than absolute. are simplified if a log-scaling is performed: Log-scaled measure = log2 (linear-scale measure + shift) For relatively large values: difference equal to 1 in log-scale is a 2x difference in linear scale; difference equal to 3 in log-scale is a 8x difference in linear scale. etc; difference equal to -1 in log-scale is a 2x difference in linear scale, but in the opposite direction. 34
- 35. Comparison: the roleof preprocessing 35 High expression can be affected by pre-processing steps like PCR-clean and “Trimmomatic”
- 36. Preprocessing: • Adapters removalplus additional • Removing PCR duplicates 36 Quantification of expression levels Mapping • Mapping on the set of known transcripts • Mapping on genome (and potential identification of novel transcripts) • Combined strategy RNA-Seq: overview
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- 39. BREAK 39 Q&A Error Correction –CORAL, ECHO, RACER, eMER Different Mappers – HiSat,TopHat, STAR, BWA Other Sections: • Differential Expression – CuffDiff, EDGER, DESEQ • Segmentation - BinS
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- 43. Interpretation Annotating and InterpretingGene Expression 43
- 44. Gene annotation: ENSGto Gene Symbols plus GO 44
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Editor's Notes
- #11 HER2 – human epidermal factor receptor (encoded by the ERBB2 gene) HER2 is a member of the human epidermal growth factor receptor (HER/EGFR/ERBB) family. Amplification or over-expression of this oncogene has been shown to play an important role in the development and progression of certain aggressive types of breast cancer. In recent years the protein has become an important biomarker and target of therapy for approximately 30% of breast cancer patients. Herceptin