Bioinformatics t8-go-hmm wim-vancriekinge_v2013


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Gene Ontologies, Prediction & HMM

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Bioinformatics t8-go-hmm wim-vancriekinge_v2013

  1. 1. FBW 3-12-2013 Wim Van Criekinge
  2. 2. Gene Prediction, HMM & ncRNA What to do with an unknown sequence ? Gene Ontologies Gene Prediction Composite Gene Prediction Non-coding RNA HMM
  3. 3. UNKNOWN PROTEIN SEQUENCE LOOK FOR: • Similar sequences in databases ((PSI) BLAST) • Distinctive patterns/domains associated with function • Functionally important residues • Secondary and tertiary structure • Physical properties (hydrophobicity, IEP etc)
  4. 4. BASIC INFORMATION COMES FROM SEQUENCE • One sequence- can get some information eg amino acid properties • More than one sequence- get more info on conserved residues, fold and function • Multiple alignments of related sequencescan build up consensus sequences of known families, domains, motifs or sites. • Sequence alignments can give information on loops, families and function from conserved regions
  5. 5. Additional analysis of protein sequences • transmembrane regions • signal sequences • localisation signals • targeting sequences • GPI anchors • glycosylation sites • hydrophobicity • amino acid composition • molecular weight • solvent accessibility • antigenicity
  6. 6. FINDING CONSERVED PATTERNS IN PROTEIN SEQUENCES • Pattern - short, simplest, but limited • Motif - conserved element of a sequence alignment, usually predictive of structural or functional region To get more information across whole alignment: • Profile • HMM
  7. 7. PATTERNS • Small, highly conserved regions • Shown as regular expressions Example: [AG]-x-V-x(2)-x-{YW} – [] shows either amino acid – X is any amino acid – X(2) any amino acid in the next 2 positions – {} shows any amino acid except these BUT- limited to near exact match in small region
  8. 8. PROFILES • Table or matrix containing comparison information for aligned sequences • Used to find sequences similar to alignment rather than one sequence • Contains same number of rows as positions in sequences • Row contains score for alignment of position with each residue
  9. 9. HIDDEN MARKOV MODELS (HMM) HMM • An HMM is a large-scale profile with gaps, insertions and deletions allowed in the alignments, and built around probabilities • Package used HMMER ( • Start with one sequence or alignment HMMbuild, then calibrate with HMMcalibrate, search database with HMM • E-value- number of false matches expected with a certain score • Assume extreme value distribution for noise, calibrate by searching random seq with HMM build up curve of noise (EVD)
  10. 10. Sequence
  11. 11. Gene Prediction, HMM & ncRNA What to do with an unknown sequence ? Gene Ontologies Gene Prediction HMM Composite Gene Prediction Non-coding RNA
  12. 12. What is an ontology? • An ontology is an explicit specification of a conceptualization. • A conceptualization is an abstract, simplified view of the world that we want to represent. • If the specification medium is a formal representation, the ontology defines the vocabulary.
  13. 13. Why Create Ontologies? • to enable data exchange among programs • to simplify unification (or translation) of disparate representations • to employ knowledge-based services • to embody the representation of a theory • to facilitate communication among people
  14. 14. Summary • Ontologies are what they do: artifacts to help people and their programs communicate, coordinate, collaborate. • Ontologies are essential elements in the technological infrastructure of the Knowledge Age •
  15. 15. The Three Ontologies •Molecular Function — elemental activity or task nuclease, DNA binding, transcription factor •Biological Process — broad objective or goal mitosis, signal transduction, metabolism •Cellular Component — location or complex nucleus, ribosome, origin recognition complex
  16. 16. DAG Structure Directed acyclic graph: each child may have one or more parents
  17. 17. Example - Molecular Function
  18. 18. Example - Biological Process
  19. 19. Example - Cellular Location
  20. 20. AmiGO browser
  21. 21. GO: Applications • Eg. chip-data analysis: Overrepresented item can provide functional clues • Overrepresentation check: contingency table – Chi-square test (or Fisher is frequency < 5)
  22. 22. Gene Prediction, HMM & ncRNA What to do with an unknown sequence ? Web applications Gene Ontologies Gene Prediction HMM Composite Gene Prediction Non-coding RNA
  23. 23. Computational Gene Finding Problem: Given a very long DNA sequence, identify coding regions (including intron splice sites) and their predicted protein sequences
  24. 24. Computational Gene Finding Eukaryotic gene structure
  25. 25. Computational Gene Finding • There is no (yet known) perfect method for finding genes. All approaches rely on combining various “weak signals” together • Find elements of a gene – coding sequences (exons) – promoters and start signals – poly-A tails and downstream signals • Assemble into a consistent gene model
  26. 26. Genefinder
  27. 27. GENE STRUCTURE INFORMATION - POSITION ON PHYSICAL MAP This gene structure corresponds to the position on the physical map
  28. 28. The Active Zone limits the extent of analysis, genefinder & fasta dumps A blue line within the yellow box indicates regions outside of the active zone The active zone is set by entering coordinates in the active zone (yellow box) GENE STRUCTURE INFORMATION - ACTIVE ZONE This gene structure shows the Active Zone
  29. 29. Change origin of this scale by entering a number in the green 'origin' box GENE STRUCTURE INFORMATION - POSITION This gene structure relates to the Position:
  30. 30. Boxes are Exons, thin lines (or springs) are Introns GENE STRUCTURE INFORMATION - PREDICTED GENE STRUCTURE This gene structure relates to the predicted gene structures
  31. 31. Find the open reading frames The triplet, non-punctuated nature of the genetic code helps us out 64 potential codons 61 true codons 3 stop codons (TGA, TAA, TAG) Random distribution app. 1/21 codons will be a stop Any sequence has 3 potential reading frames (+1, +2, +3) Its complement also has three potential reading frames (-1, -2, -3) 6 possible reading frames GAAAAAGCTCCTGCCCAATCTGAAATGGTTAGCCTATCTTTCCACCGT E K K A K K P L S A L S Q P C S N P E L I M K * V W N G S L L A * S Y P F L I H S F R T P P
  32. 32. There is one column for each frame Small horizontal lines represent stop codons GENE STRUCTURE INFORMATION - OPEN READING FRAMES This gene structure relates to Open reading Frames
  33. 33. They have one column for each frame The size indicates relative score for the particular start site GENE STRUCTURE INFORMATION - START CODONS This gene structure represents Start Codons
  34. 34. Computational Gene Finding: Hexanucleotide frequencies • Amino acid distributions are biased e.g. p(A) > p(C) • Pairwise distributions also biased e.g. p(AT)/[p(A)*p(T)] > p(AC)/[p(A)*p(C)] • Nucleotides that code for preferred amino acids (and AA pairs) occur more frequently in coding regions than in non-coding regions. • Codon biases (per amino acid) • Hexanucleotide distributions that reflect those biases indicate coding regions.
  35. 35. Gene prediction Generation of datasets (Ensmart@Ensembl): Dataset 1 ( consists of >900 coding regions (DNA): Dataset 2 ( consists of >900 non-coding regions Distance Array: Calculate for every base all the distances (in bp) to the same nucleotide (focus on the first 1000 bp of the coding region and limit the distance array to a window of 1000 bp) Do you see a difference in this “distance array” between coding and noncoding sequence ? Could it be used to predict genes ? Write a program to predict genes in the following genomic sequence ( What else could help in finding genes in raw genomic sequences ?
  36. 36. The grey boxes indicate regions where the codon frequencies match those of known C. elegans genes. the larger the grey box the more this region resembles a C. elegans coding element GENE STRUCTURE INFORMATION - CODING POTENTIAL This gene structure corresponds to the Coding Potential
  37. 37. blastn (EST) For raw DNA sequence analysis blastx is extremely useful Will probe your DNA sequence against the protein database A match (homolog) gives you some ideas regarding function One problem are all of the genome sequences Will get matches to genome databases that are strictly identified by sequence homology – often you need some experimental evidence
  38. 38. The blue boxes indicate regions of sequence which when translated have similarity to previously characterised proteins. To view the alignment, select the right mouse button whilst over the blue box. GENE STRUCTURE INFORMATION - SEQUENCE SIMILARITY This feature shows protein sequence similarity
  39. 39. The yellow boxes represent DNA matches (Blast) to C. elegans Expressed Sequence Tags (ESTS) To view the alignment use the right mouse button whilst over the yellow box to invoke Blixem GENE STRUCTURE INFORMATION - EST MATCHES This gene structure relates to Est Matches
  40. 40. New generation of programs to predict gene coding sequences based on a non-random repeat pattern (eg. Glimmer, GeneMark) – actually pretty good Borodovsky et al., 1999, Organization of the Prokaryotic Genome (Charlebois, ed) pp. 11-34
  41. 41. Computational Gene Finding • CpG islands are regions of sequence that have a high proportion of CG dinucleotide pairs (p is a phoshodiester bond linking them) – CpG islands are present in the promoter and exonic regions of approximately 40% of mammalian genes – Other regions of the mammalian genome contain few CpG dinucleotides and these are largely methylated • Definition: sequences of >500 bp with – G+C > 55% – Observed(CpG)/Expected(CpG) > 0.65
  42. 42. This column shows matches to members of a number of repeat families Currently a hidden markov model is used to detect these GENE STRUCTURE INFORMATION - REPEAT FAMILIES This gene structure corresponds to Repeat Families
  43. 43. This column shows regions of localised repeats both tandem and inverted Clicking on the boxes will show the complete repeat information in the blue line at the top end of the screen GENE STRUCTURE INFORMATION - REPEATS This gene structure relates to Repeats
  44. 44. Exon/intron boundaries
  45. 45. Computational Gene Finding: Splice junctions • Most Eukaryotic introns have a consensus splice signal: GU at the beginning (“donor”), AG at the end (“acceptor”). • Variation does occur in the splice sites • Many AGs and GTs are not splice sites. • Database of experimentally validated human splice sites: tml
  46. 46. The Splice Sites are shown 'Hooked' The Hook points in the direction of splicing, therefore 3' splice sites point up and 5' Splice sites point down The colour of the Splice Site indicates the position at which it interrupts the Codon The height of the Splices is proportional to the Genefinder score of the Splice Site GENE STRUCTURE INFORMATION - PUTATIVE SPLICE SITES This gene structure shows putative splice sites
  47. 47. Gene Prediction, HMM & ncRNA What to do with an unknown sequence ? Web applications Gene Ontologies Gene Prediction HMM Composite Gene Prediction Non-coding RNA
  48. 48. Towards profiles (PSSM) with indels – insertions and/or deletions • Recall that profiles are matrices that identify the probability of seeing an amino acid at a particular location in a motif. • What about motifs that allow insertions or deletions (together, called indels)? • Patterns and regular expressions can handle these easily, but profiles are more flexible. • Can indels be integrated into profiles?
  49. 49. Hidden Markov Models: Graphical models of sequences • Need a representation that allows specification of the probability of introducing (and/or extending) a gap in the profile. continue A C D E F .1 .05 .2 .08 .01 Gap A C D E F .04 .1 .01 .2 .02 delete Gap A C D E F .2 .01 .05 .1 .06
  50. 50. Hidden Markov Chain • A sequence is said to be Markovian if the probability of the occurrence of an element in a particular position depends only on the previous elements in the sequence. • Order of a Markov chain depends on how many previous elements influence probability – 0th order: uniform probability at every position – 1st order: probability depends only on immediately previous position. • 1st order Markov chains are good for proteins.
  51. 51. Marchov Chain for DNA
  52. 52. Markov chain with begin and end
  53. 53. Markov Models: Graphical models of sequences • Consists of states (boxes) and transitions (arcs) labeled with probabilities • States have probability(s) of “emitting” an element of a sequence (or nothing). • Arcs have probability of moving from one state to another. – Sum of probabilities of all out arcs must be 1 – Self-loops (e.g. gap extend) are OK.
  54. 54. Markov Models • Simplest example: Each state emits (or, equivalently, recognizes) a particular element with probability 1, and each transition is equally likely. Begi n Emit 1 Emit 4 End Emit 2 Emit 3 Example sequences: 1234 234 14 121214 2123334
  55. 55. Hidden Markov Models: Probabilistic Markov Models • Now, add probabilities to each transition (let emission remain a single element) Begi n 0.5 0.5 Emit 1 0.25 0.1 Emit 2 0.9 0.75 Emit 4 1.0 End 0.8 Emit 3 0.2 • We can calculate the probability of any sequence given this model by multiplying p(1234) = 0.5 * 0.1 * 0.75 * 0.8 = 0.03 p(14) = 0.5 * 0.9 = 0.45 p(2334)= 0.5 * 0.75 * 0.2 * 0.8 = 0.06
  56. 56. Hidden Markov Models: Probablistic Emmision • If we let the states define a set of emission probabilities for elements, we can no longer be sure which state we are in given a particular element of a sequence Begi n 0.5 0.5 A (0.8) B(0.2) 0.25 0.1 B (0.7) C(0.3) 0.9 0.75 C (0.1) D (0.9) 1.0 End 0.8 C (0.6) A(0.4) BCCD or BCCD ? 0.2
  57. 57. Hidden Markov Models • Emission uncertainty means the sequence doesn't identify a unique path. The states are “hidden” Begi n 0.5 0.5 A (0.8) B(0.2) 0.25 0.1 B (0.7) C(0.3) 0.9 0.75 C (0.1) D (0.9) 1.0 End 0.8 C (0.6) A(0.4) 0.2 • Probability of a sequence is sum of all paths that can produce it: p(bccd) = 0.5 * 0.2 * 0.1 * 0.3 * 0.75 * 0.6 * 0.8 * 0.9 + 0.5 * 0.7 * 0.75 * 0.6 * 0.2 * 0.6 * 0.8 * 0.9 = 0.000972 + 0.013608 = 0.01458
  58. 58. Hidden Markov Models
  59. 59. Hidden Markov Models: The occasionally dishonest casino
  60. 60. Hidden Markov Models: The occasionally dishonest casino
  61. 61. Use of Hidden Markov Models • The HMM must first be “trained” using a training set – Eg. database of known genes. – Consensus sequences for all signal sensors are needed. – Compositional rules (i.e., emission probabilities) and length distributions are necessary for content sensors. • Transition probabilities between all connected states must be estimated. • Estimate the probability of sequence s, given model m, P(s|m) – Multiply probabilities along most likely path (or add logs – less numeric error)
  62. 62. Applications of Hidden Markov Models • HMMs are effectively profiles with gaps, and have applications throughout Bioinformatics • Protein sequence applications: – MSAs and identifying distant homologs E.g. Pfam uses HMMs to define its MSAs – Domain definitions – Used for fold recognition in protein structure prediction • Nucleotide sequence applications: – Models of exons, genes, etc. for gene recognition.
  63. 63. Hidden Markov Models Resources • UC Santa Cruz (David Haussler group) – SAM-02 server. Returns alignments, secondary structure predictions, HMM parameters, etc. etc. – SAM HMM building program (requires free academic license) • Washington U. St. Louis (Sean Eddy group) – Pfam. Large database of precomputed HMM-based alignments of proteins – HMMer, program for building HMMs • Gene finders and other HMMs (more later)
  64. 64. Example TMHMM Beyond Kyte-Doolitlle …
  65. 65. HMM in protein analysis • mb99.handouts/KK185FP.html
  66. 66. Hidden Markov model for gene structure Contents (red arcs): • 5’ UTR (J5’) • initial exon (EI) • exon (E) • intron (I) • final exon (EF) • single exon (ES) • 3’ UTR (J3’) • • • Signals (blue nodes): • begin sequence (B) • start translation (S) • donor splice site (D) • acceptor splice site (A) • stop translation (T) • end sequence (F) A representation of the linguistic rules for what features might follow what other features when parsing a sequence consisting of a multiple exon gene. A candidate gene structure is created by tracing a path from B to F. A hidden Markov model (or hidden semi-Markov model) is defined by attaching stochastic models to each of the arcs and nodes.
  67. 67. Classic Programs for gene finding Some of the best programs are HMM based: • GenScan – • GeneMark – Other programs • AAT, EcoParse, Fexeh, Fgeneh, Fgenes, Finex, GeneHacker, GeneID-3, GeneParser 2, GeneScope, Genie, GenLang, Glimmer, GlimmerM, Grail II, HMMgene, Morgan, MZEF, Procrustes, SORFind, Veil, Xpound
  68. 68. Hidden Markov Models: Gene Finding Software • A Semi-Markov Model GENSCAN not to be confused with GeneScan, a commercial product – Explicit model of how long to stay in a state (rather than just self-loops, which must be exponentially decaying) • Tracks “phase” of exon or intron (0 coincides with codon boundary, or 1 or 2) • Tracks strand (and direction)
  69. 69. Conservation of Gene Features 100% 95% 90% 85% 80% 75% 70% 65% 60% 55% 50% aligning identity Conservation pattern across 3165 mappings of human RefSeq mRNAs to the genome. A program sampled 200 evenly spaced bases across 500 bases upstream of transcription, the 5’ UTR, the first coding exon, introns, middle coding exons, introns, the 3’ UTR and 500 bases after polyadenylatoin. There are peaks of conservation at the transition from one region to another.
  70. 70. Composite Approaches • Use EST info to constrain HMMs (Genie) • Use protein homology info on top of HMMs (fgenesh++, GenomeScan) • Use cross species genomic alignments on top of HMMs (twinscan, fgenesh2, SLAM, SGP)
  71. 71. Gene Prediction: more complex … 1. 2. 3. 4. Species specific Splicing enhancers found in coding regions Trans-splicing …
  72. 72. Length preference 5’ ss intcomp branch 3’ ss
  73. 73. RNA genes Besides the 6000 protein coding-genes, there is: 140 ribosomal RNA genes 275 transfer RNA gnes 40 small nuclear RNA genes >100 small nucleolar genes Contents-Schedule ? pRNA in 29 rotary packaging motor (Simpson et el. Nature 408:745-750,2000) Cartilage-hair hypoplasmia mapped to an RNA (Ridanpoa et al. Cell 104:195-203,2001) The human Prader-Willi ciritical region (Cavaille et al. PNAS 97:14035-7, 2000)
  74. 74. miRNA genes RNA genes can be hard to detects UGAGGUAGUAGGUUGUAUAGU C.elegans let-27; 21 nt (Pasquinelli et al. Nature 408:86-89,2000) Often small Sometimes multicopy and redundant Often not polyadenylated (not represented in ESTs) Immune to frameshift and nonsense mutations No open reading frame, no codon bias Often evolving rapidly in primary sequence
  75. 75. Lin-4 • Lin-4 identified in a screen for mutations that affect timing and sequence of postembryonic development in C.elegans. Mutants reiterate L1 instead of later stages of development • Gene positionally cloned by isolating a 693-bp DNA fragment that can rescue the phenotype of mutant animals • No protein found but 61-nucleotide precursor RNA with stem-loop structure which is processed to 22-mer ncRNA • Genetically lin-4 acts as negative regulator of lin-14 and lin-28 • The 3’ UTR of the target genes have short stretches of complementarity to lin-4 • Deletion of these lin-4 target seq causes unregulated gof phenotype • Lin-4 RNA inhibits accumulation of LIN-14 and LIN-28 proteins although the target mRNA
  76. 76. Let-7 (Pasquinelli et al. Nature 408:86-89,2000) Let-7 (lethal-7) was also mapped to a ncRNA gene with a 21nucleotide product The small let-7 RNA is also thought to be a post-transcriptional negative regulator for lin-41 and lin-42 100% conserved in all bilaterally symmetrical animals (not jellyfish and sponges) Sometimes called stRNAs, small temporal RNAs
  77. 77. Two computational analysis problems • Similarity search (eg BLAST), I give you a query, you find sequences in a database that look like the query (note: SW/Blat) – For RNA, you want to take the secondary structure of the query into account • Genefinding. Based solely on a priori knowledge of what a “gene” looks like, find genes in a genome sequence – For RNA, with no open reading frame and no codon bias, what do you look for ?
  78. 78. Context-free grammers Basic CFG “production rules” S S S S -> -> -> -> aS Sa aSu SS A CFG “derivation” S -> aS
  79. 79. Context-free grammers Basic CFG “production rules” S S S S -> -> -> -> aS Sa aSu SS A CFG “derivation” S -> aS S -> aaS
  80. 80. Context-free grammers Basic CFG “production rules” A CFG “derivation” S S S S S -> aS S -> aaS S -> aaSS -> -> -> -> aS Sa aSu SS
  81. 81. Context-free grammers Basic CFG “production rules” A CFG “derivation” S S S S S S S S -> -> -> -> aS Sa aSu SS -> -> -> -> aS aaS aaSS aagScuS
  82. 82. Context-free grammers Basic CFG “production rules” A CFG “derivation” S S S S S S S S -> -> -> -> aS Sa aSu SS -> -> -> -> aS aaS aaSS aagScuS
  83. 83. Context-free grammers Basic CFG “production rules” S S S S -> -> -> -> aS Sa aSu SS A CFG “derivation” S S S S S -> -> -> -> -> aS aaS aaSS aagScuS aagaSucugSc
  84. 84. Context-free grammers Basic CFG “production rules” S S S S -> -> -> -> aS Sa aSu SS A CFG “derivation” S S S S S S S -> -> -> -> -> -> -> aS aaS aaSS aagScuS aagaSucugSc aagaSaucuggScc aagacSgaucuggcgSccc
  85. 85. Context-free grammers Basic CFG “production rules” S S S S -> -> -> -> aS Sa aSu SS A CFG “derivation” S S S S S S S S S S S S -> -> -> -> -> -> -> -> -> -> -> -> aS aaS aaSS aagScuS aagaSucugSc aagaSaucuggScc aagacSgaucuggcgSccc aagacuSgaucuggcgSccc aagacuuSgaucuggcgaSccc aagacuucSgaucuggcgacSccc aagacuucgSgaucuggcgacaSccc aagacuucggaucuggcgacaccc
  86. 86. Context-free grammers Basic CFG “production rules” S S S S -> -> -> -> aS Sa aSu SS A CFG “derivation” S S S S S S S S S S S S -> -> -> -> -> -> -> -> -> -> -> -> aS aaS aaSS aagScuS aagaSucugSc aagaSaucuggScc aagacSgaucuggcgSccc aagacuSgaucuggcgSccc aagacuuSgaucuggcgaSccc aagacuucSgaucuggcgacSccc aagacuucgSgaucuggcgacaSccc aagacuucggaucuggcgacaccc
  87. 87. Context-free grammers Basic CFG “production rules” S S S S -> -> -> -> aS Sa aSu SS A CFG “derivation” S S S S S S S S S S S S -> -> -> -> -> -> -> -> -> -> -> -> U C G U C*G A A*U G*C A U C A A G G G C * * * C C C A aS aaS aaSS aagScuS aagaSucugSc aagaSaucuggScc aagacSgaucuggcgSccc aagacuSgaucuggcgSccc aagacuuSgaucuggcgaSccc aagacuucSgaucuggcgacSccc aagacuucgSgaucuggcgacaSccc aagacuucggaucuggcgacaccc
  88. 88. The power of comparative analysis • Comparative genome analysis is an indispensable means of inferring whether a locus produces a ncRNA as opposed to encoding a protein. • For a small gene to be called a protein-coding gene, one excellent line of evidence is that the ORF is significantly conserved in another related species. • It is more difficult to positively corroborate a ncRNA by comparative analysis but, in at least some cases, a ncRNA might conserve an intramolecular secondary structure and comparative analysis can show compensatory base substitutions. • With comparative genome sequence data now accumulating in the public domain for most if not all important genetic systems, comparative analysis can (and should) become routine.
  89. 89. Compensatory substitutions that maintain the structure UU G C U A G 5’ A G C A U C UCGAC 3’
  90. 90. Evolutionary conservation of RNA molecules can be revealed by identification of compensatory substitutions
  91. 91. …………
  92. 92. • Manual annotation of 60,770 full-length mouse complementary DNA sequences, clustered into 33,409 „transcriptional units‟, contributing 90.1% of a newly established mouse transcriptome database. • Of these transcriptional units, 4,258 are new protein-coding and 11,665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome.
  93. 93. Function on ncRNAs
  94. 94. ncRNAs & RNAi
  95. 95. Therapeutic Applications • • Shooting millions of tiny RNA molecules into a mouse’s bloodstream can protect its liver from the ravages of hepatitis, a new study shows. In this case, they blunt the liver’s selfdestructive inflammatory response, which can be triggered by agents such as the hepatitis B or C viruses. (Harvard University immunologists Judy Lieberman and Premlata Shankar) In a series of experiments published online this week by Nature Medicine, Lieberman’s team gave mice injections of siRNAs designed to shut down a gene called Fas. When overactivated during an inflammatory response, it induces liver cells to self-destruct. The next day, the animals were given an antibody that sends Fas into hyperdrive. Control mice died of acute liver failure within a few days, but 82% of the siRNA-treated mice remained free of serious disease and survived. Between 80% and 90% of their liver cells had incorporated the siRNAs.