RNA-Seq Workshop AChemS Sunil K Sukumaran Monell Chemical Senses Center Philadelphia

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1 RNA-Seq Workshop AChemS 2017 Sunil K Sukumaran Monell Chemical Senses Center Philadelphia

2 Benefits & downsides of RNA-Seq Benefits: High resolution, sensitivity and large dynamic range Independent of prior knowledge (in contrast to predesigned probes in microarray analysis). Unravel previously inaccessible complexities. Downside: Data analysis is not straightforward; methods continue to evolve. Cost

3 Types of RNA-Seq analysis Gene expression analysis Single cell RNA-Seq (scrna-seq) Small RNA-Seq (mirna-seq) Analysis of RNA-protein/RNA-RNA-interaction

4 Goals of typical RNA-Seq analysis Identify expressed genes and transcripts Quantify gene expression in different conditions or tissues (differential expression). Identify novel transcripts and genes (de novo assembly) Alternative splicing Novel transcribed genes Transcriptome from non-model organisms

5 Comparison of sequencing platforms 1 st Gen 2 nd Gen 3 rd Gen

6 Overview of Illumina RNA-Seq

7 Sequencing strategies Which library preparation protocol to use? How many replicates? What is the optimal library size (sequencing depth)? Paired end or single end? Which data analysis pipeline to use?

8 Not all types of RNA encode information The bulk (~95%) of cellular RNA is rrna and trna.

9 Quality and quantity of input RNA High quality RNA is preferred, but many times not available. Needle biopsies, Laser microdissection and formalin fixed paraffin embedded samples yield low integrity RNA. The amount of RNA may be low by necessity or by design (e.g. scrna-seq).

10 mrna has to be selectively enriched polya Selection RNase H = Magnetic bead Ribo-Zero

11 Stranded libraries are better! Stranded libraries preserve information on the strand of origin of the transcript Helpful when overlapping antisense transcripts occur in a genomic region (~19% of genes in human genome!) e.g. Mouse Gng13 and Chtf18 genes.

12 How many replicates? Considerations Include: Technical variability of RNA-Seq protocol. The intrinsic biological variability. The desired statistical power. Multiple samples can be sequenced in the same lane (multiplexing). Prepare all replicate libraries at once, to avoid batch effects.

13 Sequencing mode and length Paired end preferred for de novo transcriptome assembly and isoform level analysis Single end sequencing sufficient for gene expression studies Illumina sequencer read lengths vary from bp. Longer reader length= better mappability.

14 Library size Only a subset of the genome is transcribed The dynamic range of gene expression is huge Reliable detection of genes expressed at lower levels need bigger library size. scrna seq needs lower depth Tools such as Scotty and RNASeqPower can help calculate optimum library size and # replicates based on pilot data. The ENCODE consortium guidelines: es.html

15 RNA-Seq Library preparation

16 Library specific index sequences allow pooling multiple libraries ~ 6 libraries are pooled per lane for typical RNA-Seq 100 s of libraries are pooled for scrna-seq.

17 Digital RNA-Seq uses barcodes to correct PCR bias Proc Natl Acad Sci U S A Jan 24;109(4): Particularly useful when many cycles of PCR amplification are used (e.g scrna-seq)

18 Illumina Sequencing

19 From sequence to biological insights Reads Mapping FASTQ Files QC by FastQC/R To genome/transcriptome/de novo Expression quantification Summarize read counts : EM/union of exons QC by RSeQC Differential Expression Analysis Gene/transcript level Functional Interpretation Enriched pathways/go terms, integration with other data Biological Insights & hypothesis

20 FASTQ file format FASTQ format is used by modern sequencers. Bundles a FASTA sequence and its quality data. Line1: Sequence identifier Line2: Raw sequence Line3: meaningless, may repeat sequence identifier Line4: quality values for the sequence (!=lowest, ~ GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT +!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

21 Sequencing QC, using FastQC Basic information (total reads, sequence length, etc.) Per base sequence quality Overrepresented sequences GC content Duplication level Etc.

22 FastQC report

23 Per base sequence quality

24 Overrepresented Sequences Adapter

25 Challenges in RNA-Seq read alignment How to correctly align short reads to the parent gene? Theoretically, the chances of a 100 bp read occurring more than once in a genome is infinitesimally small (4 100 = ~1.6*10 60, compared to the size of mammalian genome, ~3*10 9 ). But repeat elements, such as conserved regions in gene families and overlapping antisense genes abound in the genome. About 1/3 of RNA-Seq reads span exon-exon junctions!

26 Are you awake?

27 The shredded book analogy for short read alignment Adapted from a lecture by Michael Schatz, JHU

28 Nature Methods. 10, (2013)

29 de Bruijn Graph assembly Repeat sequences make correct reconstruction and quantification difficult.

30 Overview of read mapping and transcript identification Model organisms (Reference sequence available) RNA-Seq Reads Non-model organisms (poor or no reference sequence) RNA-Seq reads Splice aware mapper TopHat, STAR, HISAT Ungapped mapper BWA, Bowtie De novo assembler Trinity StringTie Cufflinks Align to genome Align to transcriptome Identify all transcripts EM algorithm Cufflinks, RSEM With GTF Analyze known transcripts Union of exons FeatureCounts W/O GTF Discover novel transcripts RSEM, Kallisto Analyze known transcripts Align to de novo transcriptsome Analyze BWA, Bowtie RSEM Kallisto

31 Alignment and annotation files SAM is a text based file format for storing sequences aligned to a reference sequence. Consists of header (read names) and alignment sections (mandatory). Alignment section has 11 mandatory fields specifying alignment information BAM files are compressed forms of SAM files. GTF, GFF and BED files contain annotations of features such as the cordinates of genes, transcripts and exons.

32 Genome browsers Sashimi Plot Web based: UCSC Desktop: IGV

33 Taste cell and tissue isolation for RNA-Seq analysis A B C Before cutting After cutting Type III Salt Type III Sour T1R3GFP (Sweet/Umami) GustGFP (Mostly bitter) GADGFP (Type III) Lgr5GFP (Stem) Circumvallate Fungiform 33

34 Mapping QC Percentage of reads properly mapped or uniquely mapped Among the mapped reads, the percentage of reads in exon, intron, and intergenic regions. Splice junctions 5' or 3' bias Etc Popular software include RseqQC and RNAseqQC.

35 Read mapping to gene features

36 Splice junction saturation

37 Taste cells express many novel isoforms and genes 42%-45% of the splice junctions in taste libraries are either completely or partially novel. But these novel splice junctions were rarely used (<5%). Taste and olfactory tissue is barely represented in public gene annotation efforts.

38 Normalized read mapping intensity Gene body coverage 100 Bulk taste libraries Single cell libraries 0 Normalized Distance along transcript 5 ->3 (%)

39 Motivation for re-annotating the taste transcriptome Not all transcripts are fully annotated, even in human and mouse Transcriptomes are annotated from well studied tissues by RefSeq and Gencode. The 3 and 5 UTRs of genes are poorly annotated This causes problems for 3 end sequencing Especially problematic for scrna-seq

40 Strategies vary for model organisms

41 And non-model organisms

42 Methods for transcriptome Assembly Reference-based assembly De novo assembly Martin J.A. and Wang Z., Nat. Rev. Genet. (2011) 12:

43 Transcriptome assembly when reference genome is available

When reference genome and transcriptome are available Bioinformatics (2011) 27 (17): 2325-2329.

44 When reference genome and transcriptome are available Bioinformatics (2011) 27 (17): Reference annotation based transcriptome assembly (RABT assembly) leverages existing gene annotations for discovering novel transcripts. Appropriate for model organisms.

45 Strategy for RABT assembly of taste RNA-Seq data Reference annotation based transcriptome assembly using cufflinks and Stringtie packages of taste bud libraries Results from the two workflows were combined. Non coding, pre-mrna and transcripts containing premature stop codons were removed. Potential coding transcripts were functionally annotated. More info: Poster # 520

46 Many novel genes and isoforms of known genes were identified in the taste buds Transcript types De novo Gene annotations Identical to known * Novel Intronic 115 Novel isoforms of known genes Novel intergenic Transcripts 1649 Novel antisense transcripts 303 *Out of a total of transcripts in Gencode M7

47 Improved bitter taste receptor gene annotations Blue= de novo model, red = refseq model 23/35 Tas2r genes are multi-exonic. Ten of them were verified by RT-PCR using cdna from taste tissue

48 Novel isoform of known genes: e.g. Chromogranin A

49 Improved mouse OR gene annotations A B 913 (73.1%) OR and 246 (45.9%) VR genes had extended gene Models. The de novo models are more sensitive at detecting OR gene expression (B). A : From PLoS Genet Sep 4;10(9):e B: From Scientific Reports 5, Article number: (2015) doi: /srep18178

50 Thanks for your attention! Many figures and slides in this presentations came from publications, presentations, web pages etc. I am grateful to the authors for making them available.

Transcriptome analysis

Statistical Bioinformatics: Transcriptome analysis Stefan Seemann seemann@rth.dk University of Copenhagen April 11th 2018 Outline: a) How to assess the quality of sequencing reads? b) How to normalize