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====== Lecture Notes for April 19, 2010 ====== | ====== Lecture Notes for April 19, 2010 ====== | ||
- | Need to fill in any ** INCOMPLETE ** portions | ||
- | |||
- | {{:lecture_notes:20100419_banana_slug_seminar.ppt|Daniel's Powerpoint}} | ||
===== Presentation ===== | ===== Presentation ===== | ||
+ | ** {{:lecture_notes:20100419_banana_slug_seminar.ppt|Assembling and comparing genomes or transcriptomes using de Bruijn graphs}} ** \\ | ||
+ | Daniel Zerbino, Marcel Schulz, Wendy Lee, Ewan Birney and David Haussler | ||
+ | |||
==== Overview ==== | ==== Overview ==== | ||
* De Bruijn graphs | * De Bruijn graphs | ||
Line 10: | Line 10: | ||
* Practical considerations | * Practical considerations | ||
* Extensions to Velvet | * Extensions to Velvet | ||
+ | |||
==== De Bruijn graphs ==== | ==== De Bruijn graphs ==== | ||
- | Overlap-Layout-Consensus (traditional) | + | |
+ | === Overlap-Layout-Consensus (traditional) === | ||
* Traditional assemblers: Phrap, Arachne, Celera, etc. | * Traditional assemblers: Phrap, Arachne, Celera, etc. | ||
* Short reads: Edena | * Short reads: Edena | ||
- | * Generally more expensive computationally | + | * Generally more expensive computationally. |
- | * Overlaps only allow clean right-to-left and left-to-right overlaps | + | * Overlaps only allow clean right-to-left and left-to-right overlaps. |
* Transitivity | * Transitivity | ||
- | * Need to remove the redundant connections | + | * Need to remove the redundant connections. |
* Well studied graph theory problem | * Well studied graph theory problem | ||
- | Comparison of different assembly techniques | + | * Use whole-read alignments |
- | * Greedy hash table searches (~2007) | + | |
- | * Some examples: SSAKE, SHARCGS, VCAKE | + | === Quick Overlap-Layout-Consensus (OLC) example === |
- | * ** INCOMPLETE ** | + | * Filter out all the reads that are identical or self-contained in another read. Only left-to-right, etc. are allowed. |
- | * De Bruijn graph assemblers (~2009) | + | * Compute all their overlaps. |
- | * Currently the most common for NGS: Euler, ALLPATHS, Velvet, ABySS, SOAPdenovo | + | * Get many transitive redundant connections. |
- | * ** INCOMPLETE ** | + | * Many algorithms are available for transitive reduction. |
- | * Can throw in reads of different lengths with no problems | + | * You obtain simplified paths through the reads. |
- | In Velvet: | + | * Get 4 big chunks of reads from the example. |
- | * Errors that are created in assembly create tips (spurs) that can be easily removed | + | * Hard to distinguish which reads belong to which copy of the repeat. |
- | * When finding bubbles, remap the branch with the lower coverage back onto the branch with the higher coverage (as not to lose the information) | + | * This is how contigs were first built. |
- | * When encountering loops, ** INCOMPLETE ** | + | * Have to compare all reads against each other. |
- | So what's the difference? | + | * Quadratic. This has become difficult. |
+ | * Correction: quadratic behavior can be reduced to about O(n log n) by doing a clustering before aligning: just like in several fast search techniques, you only attempt to align sequences that share seed hits to relatively short k-mers (much shorter than those needed for de Bruijn graphs, since they only need to reduce the number of hits, not get unique locations in the genome). --- //[[karplus@soe.ucsc.edu|Kevin Karplus]] 2010/04/24 07:51// | ||
+ | * Next generation sequencing reads are short, and overlaps between reads are short. They are getting a little longer with newer technology. | ||
+ | * OLC method uses all information in the read at least. | ||
+ | |||
+ | === Comparison of different assembly techniques === | ||
+ | |||
+ | == Greedy hash table searches (~2007) == | ||
+ | * Some examples: SSAKE, SHARCGS, VCAKE | ||
+ | * Much faster & leaner than the previous | ||
+ | * Less robust to variation and noise | ||
+ | * Essentially simplified de Bruijn graph assemblers | ||
+ | * Build a dictionary of words in the data set. | ||
+ | * His example is 4mer, but typically 20mer and longer. | ||
+ | * For each, create a node in the hash table. | ||
+ | * Big dataset. Typically a binary marker to say if present. | ||
+ | * Sometimes at branch have two possible extensions. | ||
+ | * Greedy approach essentially de Bruijn graph with local info only. In this structure you could have many paths through. | ||
+ | * Can throw in reads of different lengths with no problems. | ||
+ | |||
+ | == De Bruijn graph assemblers (~2009) == | ||
+ | * Currently the most common for NGS: Euler, ALLPATHS, Velvet, ABySS, SOAPdenovo | ||
+ | * Pavel Pevzner popularized in paper from 2001 | ||
+ | * Why do we use them? | ||
+ | * High through-put, cheap, short reads with errors | ||
+ | * Basically a compromise between two previous approaches: | ||
+ | * Just a k-mer hash | ||
+ | * ... but processed globally | ||
+ | * Graph construction and correction is similar in all assemblers. | ||
+ | * ABySS handles parallel implementation. | ||
+ | |||
+ | === Working with de Bruijn graphs in Velvet === | ||
+ | |||
+ | == Fixing errors == | ||
+ | * Errors that are created in assembly create spurs/tips that can be easily removed. | ||
+ | * When finding bubbles, remap the branch with the lower coverage back onto the branch with the higher coverage (as not to lose the information). | ||
+ | * Loops can represent tandem repeats. | ||
+ | |||
+ | == Creating de Bruijn graphs == | ||
+ | * First, concatenate the simple graphs without branches into nodes which compact the non-branching runs into a single item. | ||
+ | * Biochemistry of short reads means errors tend to be near the ends. | ||
+ | * If you have errors in the reads in the second half, it creates millions of tips, but they are at least easy to remove later. | ||
+ | * Each read is a path through the graph. The head of the read is still kept, so we're not losing the read placement information. | ||
+ | * We are still left with bubbles. | ||
+ | * Possibly accumulation of overlapping spurs/tips. More often variation (e.g. SNPs or variation). | ||
+ | * Would like to keep that information where possible. | ||
+ | * Could also be a long read. But we don't want to cut the long read in the middle. | ||
+ | * Now we have loops that goes back on itself (not a bubble). | ||
+ | |||
+ | === So what's the difference? === | ||
* Algebraic difference: | * Algebraic difference: | ||
* Reads in the OLC methods are atomic (i.e. an arc) | * Reads in the OLC methods are atomic (i.e. an arc) | ||
* Reads in the DB graph are sequential paths through the graph | * Reads in the DB graph are sequential paths through the graph | ||
* …this leads to practical differences | * …this leads to practical differences | ||
- | * DB graphs allow for greater variety in overlap ** INCOMPLETE ** | + | * DB graphs allow for greater variety of overlaps |
- | * ** INCOMPLETE ** | + | * Overlaps in the OLC approach require a global alignment, not just a shared k-mer |
+ | * OLC methods gets error correction from long reads, while DB graphs requires more work to correct. | ||
+ | |||
+ | === Simulations: Tour Bus - error === | ||
+ | * Compare 5mb chunk from 4 species: //E. coli//, yeast, //C. elegans//, human | ||
+ | * Increased coverage from 0 to 50x | ||
+ | * N50 on y-axis | ||
+ | * First there is exponential growth as expected, but then the complexity causes trouble unless you have other kinds of information. | ||
+ | * Not using paired-reads information | ||
+ | * You see that the human is a little more complex than //E. coli//. | ||
==== Repeat resolution ==== | ==== Repeat resolution ==== | ||
- | Scaling up the hard way: chromosome X | + | |
+ | === Scaling up the hard way: chromosome X === | ||
* 548 million Solexa reads were generated from a flow-sorted human X chromosome. | * 548 million Solexa reads were generated from a flow-sorted human X chromosome. | ||
* Fit in 70GB of RAM | * Fit in 70GB of RAM | ||
- | * Many contigs: 898,401 contigs | + | * Numerous short contigs |
- | * Shirt contigs: 260bp N50 (however max of 6,956bp) | + | * Many contigs: 898,401 contigs |
+ | * Shirt contigs: 260bp N50 (however max of 6,956bp) | ||
* Overall length: 130Mb | * Overall length: 130Mb | ||
+ | * How to handle repeats? | ||
* Moral: there are engineering issues to be resolved but the complexity of the graph needs to be handled accordingly. | * Moral: there are engineering issues to be resolved but the complexity of the graph needs to be handled accordingly. | ||
- | * Reduced representation (Margulies et al.). | + | |
- | * Combined re-mapping and //de novo// sequencing (Cheetham et al., Pleaseance et al.). | + | == Handling complexity of the graph == |
- | * Code parallelization (Birol et al., Jeffrey Cook). | + | * Reduced representation (Margulies et al.). |
- | * Improved indexing (Zamin Iqbal and Mario Caccamo). | + | * Reduced representation using restriction enzymes and gel to cut the problem into tractable bits. |
- | * Use of intermediate remapping (Matthias Haimei). | + | * Cut chunks from the gel and then sequenced the gel slice separately or at least in different lanes. |
- | Rock band: Using long and short reads together | + | * Then the assembly problem is smaller and more localized instead of whole genome. |
+ | * Combined re-mapping and //de novo// sequencing (Cheetham et al., Pleaseance et al.). | ||
+ | * Map to reference or similar genome, then re-map locally some stuff that is unique. | ||
+ | * MAQ (Mapping and Assembly with Quality) and BWA (Burrows-Wheeler Alignment Tool) only allow 3 or 4 mismatches. | ||
+ | * Can see reads not mapping in a small area with too many SNPs or breakpoint rearrangments. | ||
+ | * Code parallelization (Birol et al., Jeffrey Cook). | ||
+ | * Only ABySS has published yet | ||
+ | * Improved indexing (Zamin Iqbal and Mario Caccamo). | ||
+ | * Fit whole genome shotgun onto 200GB | ||
+ | * Use of intermediate remapping (Matthias Haimei). | ||
+ | * Wanted to parallelize the second level work of building scaffolds, but those are mostly engineering issues. | ||
+ | |||
+ | === Repeats in a de Bruijn graph === | ||
+ | * The de Bruijn graph for a small organism looks like long-ish contigs, but still makes a hair-ball. | ||
+ | |||
+ | === Rock band: Using long and short reads together === | ||
* If all of the paths along the short reads move from one long read (e.g. 100-1kb) to another long read, then you know the sequence from the short reads | * If all of the paths along the short reads move from one long read (e.g. 100-1kb) to another long read, then you know the sequence from the short reads | ||
- | Different approaches to repeat resolution | + | * Long reads which are mapped seamlessly into the graph. |
- | * Theoretical: spectral graph analysis | + | * 35bp reads 50x, some long reads (100-1kb) 0-20x |
- | * Equivalent to a Principle Component Analysis or a heat conduction model | + | * Played with the length |
- | * Relies on a (massive) matrix diagonialization | + | * The longer your long reads are, the more bridgeable it is and the longer the contigs. |
- | * Comprehensive: all data integrated at once | + | * Someone might ask: "Why not just assemble the long reads?" |
- | * Traditional scaffolding | + | |
- | * E.g. Arachne, Celera, BAMBUS | + | === Different approaches to repeat resolution === |
- | * Heuristic approach similar to ** INCOMPLETE ** | + | |
- | * In NGS assemblers: | + | == Theoretical: spectral graph analysis == |
- | * Euler: for each pair of reads, find all possible paths from one read to another | + | * Equivalent to a Principle Component Analysis or a heat conduction model |
- | * ABySS: Same as above, but the read-pairs are bundled into node-to-node connections to reduce calculations | + | * Find what is the vector that threads its way |
- | * ALLPATHS: Same as above, but the search is limited to localized clouds around pre-computed scaffolds | + | * Relies on a (massive) matrix diagonialization |
- | * Using the differences in ** INCOMPLETE ** | + | * Comprehensive: all data integrated at once |
- | * ** INCOMPLETE ** | + | * Takes all the data at once in one huge matrix, not too affected by noise. No one has actually tried this approach as it requires too much computation. |
- | Peeble: Handling paired-end reads | + | |
+ | == Traditional scaffolding == | ||
+ | * E.g. Arachne, Celera, BAMBUS | ||
+ | * Heuristic approach similar to that used in traditional overlap layout-consensus contigs | ||
+ | * The traditional approach is contigs with paired-end reads. | ||
+ | * Find chains connected by contig reads. | ||
+ | * Build a big graph of pairwise connections, simplify, extract obvious linear components | ||
+ | * Your coverage is huge. | ||
+ | * Each base pair has to handle 1000 coverage read-pairs. Long reads separated by a mess in the middle. Check possible paths. Find which are acceptable. | ||
+ | * Find all the reads that start and end on the two long reads. Then consider them all together, much more efficient. | ||
+ | * 5% or more of mate-pairs can be wrong | ||
+ | * Hopefully will be able to filter them out. | ||
+ | * In Velvet use a likelihood approach | ||
+ | * Have a distance estimate | ||
+ | * Coverage of A and B, how many would we expect to see? If we see too few, we discard the connection. | ||
+ | |||
+ | == In NGS assemblers: == | ||
+ | * Euler: for each pair of reads, find all possible paths from one read to another | ||
+ | * ABySS: Same as above, but the read-pairs are bundled into node-to-node connections to reduce calculations | ||
+ | * ALLPATHS: Same as above, but the search is limited to localized clouds around pre-computed scaffolds | ||
+ | * ALLPATHS did better using small localized problems of a single small scaffold for parallelization. | ||
+ | |||
+ | == Using the differences between insert length == | ||
+ | * The Shorty algorithm uses the variance between read pairs anchored on a common contig on k-mer | ||
+ | * Projected image of the other ends creates possibility of assembling a smaller set, the projection from a node. | ||
+ | |||
+ | === Peeble: Handling paired-end reads === | ||
* Pick one unique node, follow their paths | * Pick one unique node, follow their paths | ||
+ | * Velvet used the reverse idea, the projections to a node | ||
* Some of them are unique, pick out those nodes | * Some of them are unique, pick out those nodes | ||
+ | * Hit the nodes in the vicinity of the starting contig, got distance information on the immediate vicinity. | ||
+ | * Uses a greedy algorithm. Find way to next unique node. | ||
+ | * Having a more-precise insert size could help | ||
+ | * Velvet did a size assuming 10% distribution | ||
+ | * Solexa might have been different | ||
+ | * SOLiD up to 2k, Solexa just 300 | ||
+ | * It is difficult to have enough DNA of a narrow range. | ||
+ | * Don't love barcoding because it uses a lot of reagents. | ||
+ | |||
==== Practical considerations ==== | ==== Practical considerations ==== | ||
- | Colorspace | + | |
+ | === Colorspace === | ||
* Di-base encoding has a 4 letter alphabet, but very different behavior to sequence space | * Di-base encoding has a 4 letter alphabet, but very different behavior to sequence space | ||
+ | * Double-encoded files use ACGT. | ||
+ | * Be careful not to confuse it! | ||
* Different rules for complementarity | * Different rules for complementarity | ||
+ | * Just reverse, don't complement them. | ||
* Direct conversion to sequence space is simple but wasteful | * Direct conversion to sequence space is simple but wasteful | ||
- | * One error messes up all the remaining bases | + | * One error messes up all the remaining bases. This is a huge loss, so do all in colorspace and make a complete assembly, then finally convert to base calls later. |
* Conversion must therefore be done at the very end of the process, when the reads are aligned | * Conversion must therefore be done at the very end of the process, when the reads are aligned | ||
- | * You can then use the transition rules to detect errors | + | * You can then use the transition rules to detect errors. Typically it detects the error after 1 or 2 bp, then corrects. |
- | Different error models | + | * SOLiD has developed tools |
+ | |||
+ | === Different error models === | ||
* When using different technologies, you have to take into account different technologies | * When using different technologies, you have to take into account different technologies | ||
* Trivial when doing OLC assembly | * Trivial when doing OLC assembly | ||
- | * Much more tricky when doing De Bruijn graph assemble, since k-mers are not assigned to reads | + | * Much more tricky when doing de Bruijn graph assemble, since k-mers are not assigned to reads |
* Different assemblers had different settings (e.g. Newbler) | * Different assemblers had different settings (e.g. Newbler) | ||
- | Pre-filtering the reads | + | * 454 had homopolymer issues in flow-space. |
+ | * Illumina or ABI are different. | ||
+ | * Makes it hard to handle errors. | ||
+ | * Especially in de Bruijn graphs. Applying the correction rules becomes much more complex. | ||
+ | * Newbler may be better for 454. | ||
+ | |||
+ | === Pre-filtering the reads === | ||
* Some assemblers have in-built filtering of the reads (e.g. Euler) but not a generality | * Some assemblers have in-built filtering of the reads (e.g. Euler) but not a generality | ||
* Efficient filtering of low quality bases can cut down on the computational cost (memory and time) | * Efficient filtering of low quality bases can cut down on the computational cost (memory and time) | ||
+ | * User-specific, data-space specific solutions | ||
+ | * No tried and true consistent approach | ||
* Beware that some assemblers require reads of identical length | * Beware that some assemblers require reads of identical length | ||
+ | |||
==== Extensions to Velvet ==== | ==== Extensions to Velvet ==== | ||
- | Oases: //de novo// transcriptome assembly (How to study mRNA reads which do not map) | + | ** Oases: //de novo// transcriptome assembly ** \\ |
- | De Brujin graphs ~ splice graphs | + | How to study mRNA reads which do not map |
- | * Pavel Pevzner mentioned that De Bruijn graphs are more or less like splice graphs | + | |
- | Oases: Process | + | === De Brujin graphs ~ splice graphs === |
+ | * People are using next generation sequencing generally because it's cheaper. | ||
+ | * Would have just one path except for alternate splicing. | ||
+ | * Pavel Pevzner mentioned in 2002 that de Bruijn graphs are equivalent to splice graphs, so we can apply those algorithms developed for splice graphs to de Bruijn graphs. | ||
+ | |||
+ | === Oases: Process === | ||
* Create clusters of contigs: | * Create clusters of contigs: | ||
* Connecting reads | * Connecting reads | ||
* Connecting read pairs | * Connecting read pairs | ||
+ | * The genetic locus should create a connected locus, cluster of contigs. | ||
+ | * Sometimes one cluster gets broken into two, but works pretty well. | ||
* Re-implement traditional algorithms to run on graphs, instead of a reference genome | * Re-implement traditional algorithms to run on graphs, instead of a reference genome | ||
* Greedy transitive reduction (Myers, 2005) | * Greedy transitive reduction (Myers, 2005) | ||
* Motif searches | * Motif searches | ||
* Dynamic assembly of transcripts (Lee, 2003) | * Dynamic assembly of transcripts (Lee, 2003) | ||
- | Oases: Output | + | * find the path of highest weight |
+ | |||
+ | === Oases: Output === | ||
* Full length transcripts - handles alternative splicing. | * Full length transcripts - handles alternative splicing. | ||
+ | * With skipped exon and one artifact. | ||
* Transcripts from different genes - handles varying coverage levels. | * Transcripts from different genes - handles varying coverage levels. | ||
* Alternative splicing events: when possible, distinguished between common AS events. | * Alternative splicing events: when possible, distinguished between common AS events. | ||
- | Oases: Results: | + | * Find donor and acceptor sites on or around junctions. |
- | * ** INCOMPLETE ** | + | |
- | Oases: results of Drosophila PE-reads | + | === Oases: Results === |
- | * The number of transcripts and loci are in the ballpark of what they were expecting | + | * Benchmarked within the RGASP competition |
- | Mapping vs. //de novo// | + | * Already 100+ users, some reporting very promising results: |
+ | * N50 = 1886 bp (Monica Britton, UC Davis) | ||
+ | * 99.8% of aligned transcripts against unigene (Micha Bayer - Scottish Crop Research Institute) | ||
+ | |||
+ | === Oases: results of Drosophila PE-reads === | ||
+ | * The number of transcripts and loci are in the ballpark of what they were expecting. | ||
+ | * rl 36, k21 ins size 200 | ||
+ | * Map to genome ~90% | ||
+ | |||
+ | === Oases: examples === | ||
+ | * Found an unannotated gene with 7 exons | ||
+ | * Assembler has no notion of the reference genome, but still found one example that mapped very well. | ||
+ | |||
+ | === Mapping vs. de novo === | ||
* Mapping | * Mapping | ||
- | * Many tools available | + | * Many good mature tools available |
* Easy to compute | * Easy to compute | ||
- | * Simpler to analyze | + | * Simpler to analyze, comes with genetic coords |
* //De novo// | * //De novo// | ||
+ | * Harder than mapping | ||
+ | * May require special hardware | ||
* No reference needed | * No reference needed | ||
* Handles novel structures without //a priori// | * Handles novel structures without //a priori// | ||
- | Columbus: Velvet module | + | * How to get the best of both worlds? |
+ | |||
+ | === Columbus: Velvet module === | ||
* Solution: the reference-based de Bruijn graph | * Solution: the reference-based de Bruijn graph | ||
- | * Preserves the structure of the reference sequences | + | * Just throw in the reference genome as long reads. |
- | * Integrates alignment information (SAM, BAM file) | + | * Chucking in long reads and getting short contigs. |
- | * Edits, breaks up, reconnects ** INCOMPLETE ** | + | * Preserves the structure of the reference sequences. Not allowed to overlap with self. |
+ | * What they wanted really was to start from a reference genome, then handle it as a little insertion. | ||
+ | * Integrates alignment information (SAM, BAM file with BWA, Bowtie) | ||
+ | * Edits, breaks up, reconnects, and extends the "alignment contigs" as necessary | ||
+ | * Applications | ||
+ | * Comparative transcriptome assembly | ||
+ | * Cancer RNAseq | ||
+ | * Comparative assembly | ||
+ | |||
+ | === De Bruijn graph convention for next generation sequencing === | ||
+ | * For transcriptome, using k=21. | ||
+ | * Can use reads as short as 25. | ||
+ | * How many X coverage needed? | ||
+ | * How many genes do you want to catch? | ||
+ | * With genome, want long inserts generally. | ||
+ | * With transcriptome, short inserts are more useful. They have been using 200bp tiny stuff. | ||
+ | |||
+ | ===== After presentation ===== | ||
+ | |||
+ | Kevin on Wednesday will be talking about: | ||
+ | * Use SOLiD data for ordering and orientation of //Pog// contigs | ||
+ | * Special purpose scripts | ||
+ | * The data was so noisy |