2018首届-高通量测序后期数据分析系统学习实操班

主要内容：

1. DNA测序技术的进化

a) 第一代测序技术：Sanger测序原理

b) 第二代测序技术：Illumina，454, Ion Torrent原理

c) 第三代测序技术：PacBio, Hellicos原理

d) 第四代测序技术: Oxford NanoPore原理

e) 其他技术Hybridization based methods (NabSys)

2. High throughput Sequencing for various biological problems （应用高通量测序技术解决各种生物学问题）

2.1 RNA Transcription （RNA转录）

1. Chromatin Isolation by RNA Purification (ChIRP-Seq)

2.  Global Run-on Sequencing (GRO-Seq)

3. Ribosome Profiling Sequencing (Ribo-Seq)

4. RNA Immunoprecipitation Sequencing (RIP-Seq)

5. High-Throughput Sequencing of CLIP cDNA library (HITS-CLIP)

6. Crosslinking and Immunoprecipitation Sequencing (CLIP-Seq)

7. Photoactivatable Ribonucleoside–Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP)

8. Individual Nucleotide Resolution CLIP (iCLIP)

9. Native Elongating Transcript Sequencing (NET-Seq)

10. Targeted Purification of Polysomal mRNA (TRAP-Seq)

11. Crosslinking, Ligation, and Sequencing of Hybrids (CLASH-Seq)

12. Parallel Analysis of RNA Ends Sequencing (PARE-Seq)

13. Genome-Wide Mapping of Uncapped Transcripts (GMUCT)

14. Transcript Isoform Sequencing (TIF-Seq)

15. Paired-End Analysis of TSSs (PEAT)

2.2. RNA Structure （RNA结构解析）

1. Selective 2’-Hydroxyl Acylation Analyzed by Primer Extension Sequencing (SHAPE-Seq)

2. Parallel Analysis of RNA Structure (PARS-Seq)

3. Fragmentation Sequencing (FRAG-Seq)

4. CXXC Affinity Purification Sequencing (CAP-Seq)

5. Alkaline Phosphatase, Calf Intestine-Tobacco Acid Pyrophosphatase Sequencing (CIP-TAP)

6. Inosine Chemical Erasing Sequencing (ICE)

7. m6A-Specific Methylated RNA Immunoprecipitation Sequencing (MeRIP-Seq)

2.3.  Low-Level RNA Detection, Digital RNA Sequencing （微量RNA检测，数字RNA测序）

1. Whole-Transcript Amplification for Single Cells (Quartz-Seq)

2. Designed Primer–Based RNA Sequencing (DP-Seq)

3. Switch Mechanism at the 5’ End of RNA Templates (Smart-Seq)

4. Switch Mechanism at the 5’ End of RNA Templates Version 2 (Smart-Seq2)

5. Unique Molecular Identifiers (UMI)

6. Cell Expression by Linear Amplification Sequencing (CEL-Seq)

7. Single-Cell Tagged Reverse Transcription Sequencing (STRT-Seq)

2.4.  Low-Level DNA Detection（微量DNA检测）

1. Single-Molecule Molecular Inversion Probes (smMIP)

2. Multiple Displacement Amplification (MDA)

3. Multiple Annealing and Looping–Based Amplification Cycles (MALBAC)

4. Oligonucleotide-Selective Sequencing (OS-Seq)

5. Duplex Sequencing (Duplex-Seq)

2.5.  DNA Methylation（DNA甲基化）

1. Bisulfite Sequencing (BS-Seq)

2. Post-Bisulfite Adapter Tagging (PBAT)

3. Tagmentation-Based Whole Genome Bisulfite Sequencing (T-WGBS)

4. Oxidative Bisulfite Sequencing (oxBS-Seq)

5. Tet-Assisted Bisulfite Sequencing (TAB-Seq)

6. Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq)

7. Methylation-Capture (MethylCap)

8. Methyl-Binding-Domain–Capture (MBDCap)

9. Reduced-Representation Bisulfite Sequencing (RRBS-Seq)

2.6.  DNA-Protein Interactions（DNA和蛋白质互作）

1. DNase l Hypersensitive Sites Sequencing (DNase-Seq)

2. MNase-Assisted Isolation of Nucleosomes Sequencing (MAINE-Seq)

3. Chromatin Immunoprecipitation Sequencing (ChIP-Seq)

4. Formaldehyde-Assisted Isolation of Regulatory Elements (FAIRE-Seq)

5. Assay for Transposase-Accessible Chromatin Sequencing (ATAC-Seq)

6. Chromatin Interaction Analysis by Paired-End Tag Sequencing (ChIA-PET)

7. Chromatin Conformation Capture (Hi-C/3C-Seq)

8. Circular Chromatin Conformation Capture (4-C or 4C-Seq)

9. Chromatin Conformation Capture Carbon Copy (5-C)

2.7.  Sequence Rearrangements（序列重排）

1. Retrotransposon Capture Sequencing (RC-Seq)

2. Transposon Sequencing (Tn-Seq)

3. Translocation-Capture Sequencing (TC-Seq)

3. Data analysis （part 1）：data pre-processing（数据分析第一部分，数据前处理）

3.1 evaluation of data quality 数据质量评估

Data format，fasta，fastq，quality value，gff3

3.2 Data cleanup数据清洗

Quality filter, trimmer, clipper

4. Data analysis （part 2）：reference free analyses，（数据分析第二部分，无参转录组分析）

4.1 Trinity de novo transcriptome assembly

4.2 Analysis of Differential Expressed Gene （DEGs）

4.3 Abundance estimation using RSEM

4.4 Differential expression analysis using EdgeR

4.5 Explore the results (cummerbund)

4.6 MA plot, Volcano plot, False Discovery Rate (FDR)

4.7 hierarchical two-way clustering, pairwise sample-distance, gene expression profiles.

5 Data analysis （part 3）：reference based analyses，（数据分析第三部分，有参转录组分析）

5.1 Mapping reads to the reference (tophat)

5.2 Assemble mapped reads (cufflinks)

5.3 Merge sample-specific assemblies (cuffmerge)

5.4 Analysis of Differentially Expressed Gene （DEGs）

5.5 Identify DEGs (cuffdiff)

5.6 Explore the results (cummerbund)

6 Data analysis （part 4）：from gene list to gene function，（数据分析第四部分，基因功能注释）

6.1 File format for annotation information: GFF3

6.2 Annotation

6.3 Homology search (BLAST+/SwissProt/Uniref90)

6.4 Protein domain identification (HMMER/PFAM)

6.5 Protein signal peptide and transmembrane domain prediction (singalP/tmHMM)

6.6 Comparing to currently curated annotation databases (EMBL Uniprot eggnog/GO)

6.7 Enrichment analysis using DAVID

6.8 Gene name batch viewer

6.9 Gene functional classification

6.10 Functional annotation chart

6.11 Functional annotation clustering

Lab（上机实验）

Lab1: Connection to cloudlab using Putty

Lab2: File transfer between cloudlab and local computer using filezilla

Lab3: Linux commands

Lab4: Reads quality evaluation: fastqc

Lab5a: Reads quality control: fastx tool kit

Lab5b: Processing the mapping file: samtool

Lab6: Reference free analysis: Tuxedo package

Lab7: Reference based analysis: Trinity package

Lab8: Annotation: Trinnotate

Lab9: Enrichment analysis using DAVID

-会议门票-

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