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Use of spark for proteomic scoring seattle presentation
- 1. Use of Spark for Proteomic Scoring Steven M. Lewis PhD Institute for Systems Biology EMBL Uninett http://tinyurl.com/qgtzhkw
- 2. Abstract Tandem mass spectrometry has proven to be a powerful tool for proteomic analysis. A critical step is scoring a measured spectrum against an existing database of peptides and potential modifications. The details of proteomic search are discussed. Such analyses stain the resources of existing machines and are limited in the number of modifications that can be considered. Apache Spark is a powerful tool for parallelizing applications. We have developed a version of Comet - a high precision scoring algorithm and implemented it on a Spark cluster. The cluster outperforms single machines by a factor of greater than ten allowing searched which take 8 hours to be performed in under 30 minutes. Equally important, search speed scales with the number of cores allowing further speed ups or increases in the number of modifications by adding more computing power. The considerations required to run large jobs in parallel will be discussed.
- 3. This is a war story It describes a large problem The approaches to parallelize it The problems encountered The tools developed to solve them
- 4. How did I get into this? A few years ago I developed a Hadoop application to to protein search It was a good - reasonably big problem We published a paper I got a note from Gurvinder Singh at Uninett a Norwegian cloud provider asking if I was interested in implementing what I did in Spark
- 5. Consider a Protein MTRRSRVGAGLAAIVLALAAVSAAAPIAGAQ SAGSGAVSVTIGDVDVSPANPTTGTQVLITPS INNSGSASGSARVNEVTLRGDGLLATEDSLG RLGAGDSIEVPLSSTFTEPGDHQLSVHVRGL NPDGSVFYVQRSVYVTVDDRTSDVGVSART TATNGSTDIQATITQYGTIPIKSGELQVVSDGR IVERAPVANVSESDSANVTFDGASIPSGELVI RGEYTLDDEHSTHTTNTTLTYQPQRSADVAL TGVEASGGGTTYTISGDAANLGSADAASVRV NAVGDGLSANGGYFVGKIETSEFATFDMTVQ ADSAVDEIPITVNYSADGQRYSDVVTVDVSGA SSGSATSPERAPGQQQKRAPSPSNGASGGG LPLFKIGGAVAVIAIVVVVVRRWRNP It is a string of Amino Acids (20) designated by one letter
- 6. Digestion ●Trypsin breaks proteins after arginine (R) or lysine (K) except when followed by proline (P) MTRSVGAGLAAIVLALAAVSAARPIARGAQ SAGSGAVSVKTIGDVDVSPANPTTGTQVL Cleaves to: MTR SVGAGLAAIVLALAAVSAARPIAR GAQSAGSGAVSVK TIGDVDVSPANPTTGTQVL
- 7. Tandem Mass Spec Proteomics Proteins are digested into Peptides (fragments) Run through a column to separate them and analyzed in a Mass Spectrometer to yield a spectrum. A database of known proteins is searched for the best match
- 8. Basics of Tandem Mass Spectrometry http://en.wikipedia.org/wiki/Tandem_mass_spectrometry
- 9. Measured Spectrum From Kinter and Sherman
- 10. Proteomic Search So you went into the lab Prepared a sample Ran it through a Tandem Mass Spec Collected Thousands of spectra Now we need a Search a Database of Proteins to find matches
- 11. Protein Database ● Search Starts with a list of proteins ○ Read From Uniprot ○ Parsed from a known genome ○ Supplied by a researcher ● Protein Databases for Humans are around 20 million amino Acids ● For search you add the same number of decoy (false) proteins ● Multiorganism databases may run 500 MB Moral - databases are fairly big
- 12. Protein Database Fasta File >sp|Q58D72|ATLA1_BOVIN Atlastin-1 OS=Bos taurus GN=ATL1 PE=2 SV=2 MAKNRRDRNSWGGFSEKTYEWSSEEEEPVKKAGPVQVLVVKDDHSFELDETALNRILLSEAVRDKEVVAVSVAGA FRKGKSFLMDFMLRYMYNQESVDWVGDHNEPLTGFSWRGGSERETTGIQIWSEIFLINKPDGKKVAVLLMDTQGT FDSQSTLRDSATVFALSTMISSIQVYNLSQNVQEDDLQHLQLFTEYGRLAMEETFLKPFQSLIFLVRDWSFPYEFSY GSDGGS >sp|Q58D72_REVERSED|ATLA1_BOVIN Atlastin-1 OS=Bos taurus GN=ATL1 PE=2 SV=2-REVERSED MKKKESQETSESKPAPFAQHYLHRHTAAASYLKYLAENTSGQDWLAAAVQDIVAGLERYEGSYRIYAWTCLTILTL MIMNCLSAIIDLGIFGTVGAIVYTIFIVVFLTAPTRAAHFINKSDNHKIYQIYLEDIETELQQLYRRSFEEGGMKKVGRFL KVSEEKLELHKTQLDNPALFPKDGGCIEEMKKNYTDKATAVAALNNAEATAQLMSKPHPLEEGQYIKIYAKFYEVLG RCTIKN >tr|Q58D73|Q58D73_BOVIN Chromosome 20 open reading frame 29 OS=Bos taurus GN=C20orf29 PE=2 SV=1 MVHAFLIHTLRAAKAEEGLCRVLYSCFFGAENSPNDSQPHSAERDRLLRKEQILAVARQVESMYQLQQQACGRHA VDLQPQSSDDPVALHEAPCGAFRLAPGDPFQEPRTVVWLGVLSIGFALVLDTHENLLLVESTLRLLARLLLDHLRLL VPGGANLLLRADCIEGILTRFLPHGQLLFLNDQFVQGLEKEFSAAWSH >tr|Q58D73_REVERSED|Q58D73_BOVIN Chromosome 20 open reading frame 29 OS=Bos taurus GN=C20orf29 PE=2 SV=1-REVERSED … And so on for the next 20-500 mb
- 13. Protein Database ● Starting with a database ● These are digested in silico to produce peptides ● Modifications may be added to produce a list of peptides to search ● Every potential modification roughly doubles the search space IAM[15.995]S[79.966]GS[79.966]S[79.966]S AIYVR RGNTVLKDLK IEFLNEAS[79.966]VMK 1360.63272 TVRAKQPSEK
- 15. Digestion ●Trypsin breaks proteins after arginine (R) or lysine (K) except when followed by proline (P) MTRSVGAGLAAIVLALAAVSAARPIARGAQ SAGSGAVSVKTIGDVDVSPANPTTGTQVL Cleaves to: MTR SVGAGLAAIVLALAAVSAARPIAR GAQSAGSGAVSVK
- 16. Well … Almost ●Sometimes cleavages are missed ●Sometimes breaks occur in other places ●Some amino acids are modified chemically ●Samples may be labeled with isotopes to distinguish before and after proteins All these changes can push the number of scored peptides from hundreds of thousands to tens of millions or more
- 17. Finding Fragments ● http://db.systemsbiology.net:8080/proteomicsToolkit/FragIonServlet.html LGAGDSIEVP B ion Y ion LGAGDSIEVP LGAGDSIEVP LGAGDSIEVP LGAGDSIEVP LGAGDSIEVP LGAGDSIEVP LGAGDSIEVP LGAGDSIEVP LGAGDSIEVP
- 18. Theoretical and Measured Spectra B ION Y ION
- 19. Cross Correlation measured=215.36 …. measured=310.17 measured=312.76 measured=312.76 theory=312.18 measured=319.31 measured=344.22 … measured=354.19 theory=356.17 measured=355.16 theory=356.17 measured=356.08 theory=356.17 measured=355.16 measured=356.08 … measured=431.21 measured=442.03 measured=442.03 theory=440.24 measured=443.43 … measured=942.79 theory=944.5
- 20. Score is a weighted sum of matching peaks in the correlation ●Scoring is done against all peptides with a similar MZ to the measured spectrum ●The output is the best scoring peptide and a few of the "runner ups" NOTE In a typical Experiment only 15-25% of spectra will be identified with peptide in the database These are used to identify proteins
- 21. Why is this a Big Data Problem The human body has about 20K Proteins Usually for quality control there is a ‘Decoy’ for every protein There are optional modifications with increase peptides by a factor of 2 A smaller Sample will have about 50 M peptides - 900 M with larger database and more modifications A large run is about 100 K spectra The search space is proportional to peptides * spectra
- 23. Political Concerns To sell the answer to biologists we must copy a well known algorithm. This means translating the code to Java from C++ and accepting the algorithm’s data structures and memory requirements
- 24. Binning 50,000 spectra * 2,000,000,000 peptides is a VERY large number Fortunately all pairs do not have to be scored - Spectra are measured with precursor mass peptides have a mass - only peptides and spectra in a specific mass range (bin) - need be compared On modern high precision instruments the bin is about 0.03 Dalton This reduces the number of pairs to score 2000 million - on a Small sample we score 128 million pairs at about 500 microsec per scoring
- 25. Binning Bins put all peptides and spectra with a specific MZ range into groups Spectra are put in several bins Bins can be subdivided for scoring Bins hold N Spectra and K peptides Currently there are tens of thousands of bins Scoring fails in larger Bins due to excess GC time
- 27. Debugging and Performance This involves taking an unfamiliar problem running on an unfamiliar platform Questions Which operations are taking most time? How many times is each function called? Are functions balanced across machines on the cluster? When a small number of cases fail how can you instrument them
- 28. Did it work the first time? Hell No After it stopped crashing and did well on a trivial problem a base sample took 30 hours to run on the cluster - Way longer than on a single machine!!! - issues - data not like familiar test data - Hadoop Input format bug
- 29. Spark Accumulators Accumulators are like counters but much more powerful. Accumulators can track any object supporting add and zero methods
- 30. Sample Code to Accumulate a Set of Strings public class SetAccumulableParam implements AccumulatorParam<Set<String>>, Serializable { public Set<String> addAccumulator(final Set<String> r, final Set<String> t) { HashSet<String> ret = new HashSet<String>(r); ret.addAll(t); return ret; } public Set<String> addInPlace(final Set<String> r1, final Set<String> r2) { return addAccumulator(r1,r2); } public Set<String> zero(final Set<String> initialValue) { return initialValue; } }
- 31. Sample Accumulator Use JavaSparkContext ctx = new JavaSparkContext(sparkConf); // make an accumulator final Accumulator<Set<String>> wordsUsed = ctx.accumulator(new HashSet<String>(), new SetAccumulableParam()); JavaRDD<String> lines = ctx.textFile(args[0]); // read lines JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() { public Iterable<String> call(String s) { List<String> stringList = Arrays.asList(s.split(" ")); wordsUsed.add(new HashSet<String>(stringList)); // accumulate words return stringList; } }); … Finish word count
- 32. Function Accumulators Functions extend AbstractFunctionBase all reporting code in base class Functions implement doCall not call Calls are wrapped for timing and statistics Data Gathered total calls total time times executed on each MAC address
- 33. Sample Instrumented Function public static class ChooseBestScanScore extends AbstractLoggingFunction2<IScoredScan, IScoredScan, IScoredScan> { @Override public IScoredScan doCall(final IScoredScan v1, final IScoredScan v2) throws Exception { ISpectralMatch match1 = v1.getBestMatch(); ISpectralMatch match2 = v2.getBestMatch(); (match1.getHyperScore() > match2.getHyperScore()) ? v1 : v2; } } CombineCometScoringResults totalCalls:69M totalTime:29.05 sec machines:15 variance 0.058
- 34. Running Job
- 35. Improving Performance Fix bugs in Hadoop Format for large files Find most time spent in scoring Use a Parquet database to store digestion Discover that repartition is cheaper than expensive operations Smart partitioning to balance work in partitions Use more partitions for larger jobs
- 36. Smart Partitioning a bin is a set of spectra and peptides that score together Bin sizes vary by orders of magnitude Scoring puts pressure on memory Bin sizes can be counted before scoring step Partitioning puts larger bins in separate partitions puts multiple smaller bins in the same partition
- 37. Performance ● A Larger test test took 4 hours on a single machine ● On a small 15 node cluster it took ○ 69 minutes real time ○ Used 41 hours of cpu time ○ Scored 2100 million peptides ○ generated 605 million peptides ○ with 4 potential modifications ○ 95% of the time we find the same top peptides as Comet
- 38. Summary Proteomic Search is a large data problem involving scoring a large number of spectra against an even larger number of candidate peptides. In the future the complexity will increase with more spectra and more modifications adding more peptides Spark is a parallel execution environment allowing search to be performed on a cluster Performance is superior to existing tools and can be improved by increasing the size of the cluster
- 39. Code Part 1 // Read Spectra RDD<IMeasuredSpectrum> spectraToScore = SparkScanScorer.getMeasuredSpectra(scoringApplication); // Condition Spectra RDD<CometScoredScan> cometSpectraToScore = spectraToScore.map(new MapToCometSpectrum(comet)); // Assign bins to spectra PairRDD<BinChargeKey, CometScoredScan> keyedSpectra = handler.mapMeasuredSpectrumToKeys(cometSpectraToScore); // read Proteins RDD<IProtein> proteins = readProteins(jctx); // Digest to peptides RDD<IPolypeptide> digested = proteins.flatMap(new DigestProteinFunction(app)); // map to bins PairRDD<BinChargeKey, IPolypeptide> keyedPeptides = digested.flatMapToPair(new mapPolypeptidesToBin(application, usedBins));
- 40. Code Part 2 // Now collect the contents of spectra and peptide bins PairRDD<BinChargeKey, Tuple2<Iterable<CometScoredScan>, Iterable<HashMap<String, IPolypeptide>>>> binContents = keyedSpectra.cogroup(keyedPeptides); // do scoring RDD< IScoredScan> scores = binContents.flatMap(new ScoreSpectrumAndPeptideWithCogroup(application)); // combine spectrum scoring RDD< IScoredScan> cometBestScores = handler.combineScanScores(scores); // write results as a single file consolidator.writeScores(cometBestScores);
- 41. Proteomic Search PseudoCode RDD<Spectrum> spectra = readSpectra(); // mydata.mzXML RDD<Proteins> proteins = readDatabase(); // uniprot_swiss.fasta RDD<Peptides> peptides= digest(proteins );
- 42. THESE ARE UNUSED SLIDES DON’T GO HERE
- 43. Consider a Protein - a collection of Amino Acids MTRRSRVGAGLAAIVLALAAVSAAAPIAGAQ SAGSGAVSVTIGDVDVSPANPTTGTQVLITPS INNSGSASGSARVNEVTLRGDGLLATEDSLG RLGAGDSIEVPLSSTFTEPGDHQLSVHVRGL NPDGSVFYVQRSVYVTVDDRTSDVGVSART TATNGSTDIQATITQYGTIPIKSGELQVVSDGR IVERAPVANVSESDSANVTFDGASIPSGELVI RGEYTLDDEHSTHTTNTTLTYQPQRSADVAL TGVEASGGGTTYTISGDAANLGSADAASVRV NAVGDGLSANGGYFVGKIETSEFATFDMTVQ ADSAVDEIPITVNYSADGQRYSDVVTVDVSGA SSGSATSPERAPGQQQKRAPSPSNGASGGG LPLFKIGGAVAVIAIVVVVVRRWRNP
- 44. Protein Database Digest Measured Spectra Normalize Add Modifications MZ Bin Fragments in one bin MZ Bin Spectra put in multiple bins Cross Product Score all pairs Hadoop Input Filter (and write) FlatMap PairFlatMap Hadoop Input Sort Spark Operations Map
- 45. What is Spark Spark is a Framework for parallel execution Spark works well on Hadoop clusters (also has a local mode for testing) Spark is less formal than Map-Reduce and multiple operations can run locally
- 46. Protein Database Digest Measured Spectra Normalize Add Modifications MZ Bin Fragments in one bin MZ Bin Spectra put in multiple bins Cross Product Score all pairs Sort by Spectra Report Best Fits All operations are on a 15 node Spark Cluster and are performed in parallel with lazy execution Most time is spent in the Score all Pairs Step
- 47. Multi Stage Mass Spec From Kinter and Sherman
- 48. A Protein is a Collection of Amino Acids ●Each (of 20) Amino acid is indicated by a letter ●Assume we have a sample with a number of proteins. ●Assume that we can list the possible proteins in the sample. ●Tandem Mass Spectrometry is similar to shotgun genomics