Transcript reconstruction
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- Isabella Dixon
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1 Transcript reconstruction
2 Summary I Data types, file formats and utilities Annotation: Genomic regions Genes Peaks bedtools Alignment: Map reads BAM/SAM Samtools Aggregation: Summary files Wig (UCSC) TDF (IGV)
3 Summary II Data process Short read alignment (Bowtie, BWA) Making the genome searchable: Hashing/BW Seed an extend (hashing) vs suffix searches (BW) New aligners are mix Spliced aligners (TopHat, STAR, GSNAP) Map read fragments then strung them Choosing the fragment size Avoiding biases using information (junctions) Quantifying (RSEM/Cufflinks) Read/Isoform assignment Normalization procedures Differential expression (DESeq/EdgeR/Cufflinks)
4 Summary III Using a graphical user interface Galaxy for knowledgable users who are not comfortable with UNIX All tools available Not great for many samples
5 Todays topics Assemble transcripts from RNA-Seq (theory) Processing large number of samples using predesigned pipelines.
6 Scan distribution, an old problem Is the observed number of read counts over our region of interest high? Given a set of Geiger counts across a region find clusters of high radioac7vity Are there 7me intervals where assembly line errors are high? Scan distribu7on α=0.05 α FWER =0.05 Thankfully, the Scan Distribu-on computes a closed form for this distribu7on. ACCOUNTS for dependency of overlapping windows thus more powerful! Poisson distribu7on
7 Scan distribution for a Poisson process The probability of observing k reads on a window of size w in a genome of size L given a total of N reads can be approximated by (Alm 1983): where P (k w, N, L) 1 F p (k 1 w)e k w k (T w)p (k 1 w) P (k 1 w) is the Poisson probability of observing k 1 counts given an expected count of w and F p (k 1 w) is the Poisson probability of observing k 1 or fewer counts given an expectation of w reads The scan distribu/on gives a computa/onally very efficient way to es/mate the FWER
8 By utilizing the dependency of overlapping windows we have greater power, while still controlling the same genome-wide false positive rate.
9 Transcript reconstruction problem as a segmentation problem RNA (1000 b) AAAAAAA Genome ( bp) 100s bp 10s kb Challenges: Genes exist at many different expression levels, spanning several orders of magnitude. Reads originate from both mature mrna (exons) and immature mrna (introns) and it can be problema7c to dis7nguish between them. Reads are short and genes can have many isoforms making it challenging to determine which isoform produced each read.
10 Scripture: Genome-guided transcriptome reconstruction Sta/s/cal segmenta/on of chroma/n modifica/ons uses con/nuity of segments to increase power for interval detec/on RNA-Seq If we know the connec/vity of fragments, we can increase our power to detect transcripts
11 Longer (76) reads increased number of junction reads intron Exon junc/on spanning reads provide the connec/vity informa/on.
12 The power of spliced alignments Protein coding gene with 2 isoforms Read coverage Exon- exon junc7ons Alterna7ve isoforms Aligned read Gap
13 Statistical reconstruction of the transcriptome Step 1: Align Reads to the genome allowing gaps flanked by splice sites genome Step 2: Build an oriented connec7vity graph using every spliced alignment and orien7ng edges using the flanking splicing mo7fs The connec/vity graph connects all bases that are directly connected within the transcriptome
14 Statistical reconstruction of the transcriptome Step 3: Iden7fy segments across the graph Step 4: Find significant segments
15 Can we identify enriched regions across different data types? Short modifica7on P Long modifica7on P Discon7nuous data P Are we really sure reconstruc/ons are complete?
16 RNA-Seq data is incomplete for comprehensive annotation Library construc/on can help provide more informa/on. More on this later
17 If there is no reference genome! Genome independent methods
18 Rayan Chikhi lecture Cufflinks Sripture Abyss Trinity Velvet Garber et al, Nature Methods 2011
19 Assembly approach
20 Assembly approach But this challenging already with DNA and RNA has many different challenges
21 The Trinity approach: Localize Decompose all reads into overlapping Kmers (25- mers) Iden7fy seed kmer as most abundant Kmer, ignoring low- complexity kmers. Extend kmer at 3 end, guided by coverage. G GATTACA 9 A T C Briah Haas
22 The Trinity approach: Localize GATTACA 9 G G C C A T 1 0 T A 1 5 C T G A Briah Haas
23 The Trinity approach: Localize A 5 G 4 A 6 GATTACA 9 A 7 Report con7g:.aagattacaga. Remove assembled kmers from catalog, then repeat the en7re process. Briah Haas
24 Trinity approach: Assemble RNA- Seq reads Greedy assembly Group similar con7gs Local de- Bruijn graphs Parse and filter graph into Transcripts key: localize the assembly problem
25 Pros and cons of each approach Transcript assembly methods are the obvious choice for organisms without a reference sequence. Genome-guided approaches are ideal for annotating highquality genomes and expanding the catalog of expressed transcripts and comparing transcriptomes of different cell types or conditions. Hybrid approaches for lesser quality or transcriptomes that underwent major rearrangements, such as in cancer cell. More than 1000 fold variability in expression leves makes assembly a harder problem for transcriptome assembly compared with regular genome assembly. Genome guided methods are very sensitive to alignment artifacts.
26 RNA-Seq transcript reconstruction software Assembly Oasis (velvet) Trans- ABySS Genome Guided Cufflinks Scripture Trinity
27 Differences between Cufflinks and Scripture Scripture was designed with annota7on in mind. It reports all possible transcripts that are significantly expressed given the aligned data (Maximum sensi8vity). Cuffllinks was designed with quan7fica7on in mind. It limits reported isoforms to the minimal number that explains the data (Maximum precision).
28 Maximum sensitivity vs. maximal precision b Branch point 1 Branch point 2 Transcript graph Maximal set Minimal possible set 1 Minimal possible set 2
29 Differences between Cufflinks and Scripture - Example Annota7on Ehbp1l1 T4SCRPTR.chr T4SCRPTR.chr Scripture T4SCRPTR.chr T4SCRPTR.chr T4SCRPTR.chr T4SCRPTR.chr T4SCRPTR.chr T4SCRPTR.chr Cufflinks T T T T T T T T T Alignments
30 Comparing reconstructions CPU Hours Total Memory Genes fully reconstructed Mean isoforms per reconstruc/on Mean fragments per known annota/on Number of fragments predicted Cufflinks G 5, ,856 Scripture G 6, ,922 Trans- Abyss G 4 3, ,117,238 Many of the bogus locus and isoforms are due to alignment ar/facts Garber et al, Nature Methods 2011
31 Why so many isoforms Annota7on Reconstruc7ons Every such splicing event or alignment ar/fact doubles the number of isoforms reported Longer reads (already possible) will reduce the uncertainty and possibili/es
32 Reconstruction comparison Too much of a good thing is not handled well by most reconstruc/on methods
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