Multiple Sequence Alignment Theory and Applications

Transcription

1 Mahidol University Objectives SMI512 Molecular Sequence alysis Multiple Sequence lignment Theory and pplications Lecture 3 Pravech jawatanawong, Ph.. pravech.aja@mahidol.edu epartment of Microbiology Faculty of Science Mahidol University fter class, students should be able to: explain the definition and concept of multiple sequence alignment (MS) explain the concept and algorithm of progressive alignment explain the concept and algorithm of lustalw program explain the concept and algorithm of MUSL program explain the concept and algorithm of T-offee program explain the different of several MS algorithms Review of the ynamic Programming Review of the ynamic Programming G Global lignment 0 1 Local lignment match = 1 mismatch = 1 gap = 1 G

2 Problem of Using Pairwise lignment Multiple Sequence lignment (MS) seq 1 TGTGTG seq 2 TT-T--G seq 1 TGTGTG seq 3 -TTTG--G seq 2 TT-TG seq 3 -TTTGG good for comparing of only two sequences hard to understand and interpret the alignment results when a number of sequences are >2 less evolutionary meaning seq 1 TGTGTG seq 2 TT-T--G seq 3 -TTTG--G most useful object in sequence analysis mid 1980s, MS was generated by hand because dynamic programming (at that time) were slow when applied to >3 sequences idea arrangement of the homologous residues (nucleotide or amino acid) in the same column provides more biological information than pairwise sequence alignment, such as region(s) where the similarity come from evolutionary informative marker conserved area in the sequence or sequence signature Some Major pproaches for MS Optimal Global Sequence lignment Optimal Global Sequence lignment Progressive Global lignment onsistency-based scheme based on global sequence alignment under dynamic programming algorithm the optimum alignment is the alignment with the best score among several alignment pattern strongly depends on gap penalty tricks requires a high computation intensive power a few program uses this approach

3 ynamic Programming for MS ynamic Programming for MS concepts Mi,j,k = maximum M(i-1,j-1,k-1) + δ(vi, wj, uk) M(i-1,j-1,k) + δ(vi, wj, ) M(i-1,j,k-1) + δ(vi,, uk) M(i,j-1,k-1) + δ(, wj, uk) M(i-1,j,k) + δ(vi,, ) M(i,j-1,k) + δ(, wj, ) M(i,j,k-1) + δ(,, uk) M(i-1,j,k-1) M(i-1,j-1,k-1) M(i-1,j,k) M(i,j,k-1) M(i,j-1,k-1) cube diagonal (match/mismatch) indel in one sequence indel in two sequences S S V S S sequence 1 sequence 2 sequence 3 VSS S S seq 1 V S - S seq 2 - S - seq S M(i,j-1,k) M(i,j,k) MS Program MS Package for Multiple Sequence lignment lejandro Schäffer ioinformatics in Medical Genetics Group MS package still available in I web site command line that is run under UIX OS require a huge memory 8 sequences, 500 bases maximum each (the web version)

4 Progressive Global lignment Profile lignment perform multiple-pairwise sequence alignment in a series of these three steps calculation of alignment scores for all possible combination of pairwise sequence in an alignment set build a from from the matrix of alignment scores align those sequence by the order suggested from the become the most common method for multiple sequence alignment used in several software allows to implement some variety in the algorithm to speed up the process and use less memory example of programs used this algorithm the lustral series and MUSL treat multiple sequences as one alignment unit align two unit of alignments (multiple sequence each) by dynamic programming once gap needs to add, then, add gaps to all sequences in the same alignment unit TTTTGTGTGTTTTTTT TTTTGTGGTGTTTTTT TTGTGTGTTGGGTTTTT TTGTGGTGTTTGGGTTTTT TTTTGTGTGTTT----TTTT TTTTGTGGTGTTT----TTT TT----GTGTGTTGGGTTTTT TT----GTGGTGTTTGGGTTTTT Progressive lignment once a gap, always a gap lustal Family dynamic programming pairwise sequences alignment profile alignment (dynamic programming) multiple sequences alignment lustal was published by Thompson, et al. in 1994 (24 years ago) lustalw (W stands for weight because the program weight sequences differently) is a commandline version lustalx is GUI (graphic user interface) version was released in series with the same algorithm both lustalw and lustalx were obsoleted (cannot handle the complex genetic events), but the algorithm is good for understanding the MS algorithm generated a, then,do a progressive alignment based on that unaligned sequences pairwise sequences alignment dynamic programming modified from: otredame, Higgins, Heringa, 2000

5 lustal lgorithm: step-by-step lustal lgorithm: step-by-step calculate similarity scores for all possible pairs of sequences and generate a similarity matrix building a based on eighbor-joining (J) algorithm pick up the most similar pair of sequences and do pairwise alignment create a consensus sequence for profile alignment unaligned sequences similarity matrix most similar sequences -- pairwise sequence alignment lustal lgorithm: step-by-step lustal lgorithm: step-by-step pick up the next similar pair of sequences and do pairwise alignment again create a consensus sequence for the profile alignment pick up the next closely related pair of sequences and do pairwise alignment again create a consensus sequence for the profile alignment second most similar sequences -- pairwise sequence alignment next most similar sequences -- - pairwise sequence alignment

6 lustal lgorithm: step-by-step lustal Program redo the progressive alignment with the profile alignment until no more pair of alignment left Pro uses less memory than other programs last pair of alignments pairwise sequence alignment ons less accurate or scalable than modern programs gap penalty tricks strongly depends on the first alignment (order of the input sequences can change the alignment) lustal Omega MUSL Program the latest version of lustal family significantly increase in scalability and run-time the output alignments are validated by several benchmarks both online (via I) and standalone versions available for several OS platforms MUltiple Sequence omparison by Log-xpectation (MUSL) was published by dgar R, et al. in 2004 step I: progressive alignment step II: improve progressive alignment step III: refinement very easy command line improved speed and accuracy

7 lgorithm behind MUSL T-offee Program Tree-based onsistency Objective nction For alignment valuation otredame, et al. (2000) construct 2 libraries: global alignment (lustalw) and local alignment (Lalign) combination of 2 libraries and perform a progressive alignment the gaps in the previous steps cannot be shifted later T-offee Program T-offee lgorithm Modified from otredame. (2002) Phamacogenomics 3:1-14.

8 T-offee Primary Library T-offee xtended Library input sequences Seq GRFIL TH LST FT T Seq GRFIL TH VRY FST T Seq TH FT T Seq GRFIL TH LST FT T Seq GRFIL TH LST F-T T Seq GRFIL TH VRY FST T Seq GRFIL TH LST FT T Seq TH ---- FT T Seq GRFIL TH ---- FST T Seq GRFIL TH VRY FST T Seq TH F-T T Seq GRFIL TH VRY FST T Seq TH ---- F-T T Primary library: collection of global/local pairwise alignments Seq GRFIL TH LST FT T Seq GRFIL TH LST F-T T Seq GRFIL TH VRY FST T Seq GRFIL TH LST FT T Seq TH ---- FT T Seq GRFIL TH ---- FST T Seq GRFIL TH VRY FST T Seq TH F-T T Seq GRFIL TH VRY FST T Seq TH ---- F-T T Seq GRFIL TH LST FT T Seq GRFIL TH LST FT T Seq GRFIL TH VRY FST T Seq GRFIL TH LST FT T Seq TH FT T Seq GRFIL TH LST FT T Seq GRFIL TH LST F-T T Seq GRFIL TH ---- FST T Modified from otredame. (2002) Phamacogenomics 3:1-14. Modified from otredame. (2002) Phamacogenomics 3: offee Sum of pairs score (SP score) hba_horse VLSKTVKWSKVGGHGYGLRMFLGFPTTKTYFPHF-LShba_human VLSPKTVKWGKVGHGYGLRMFLSFPTTKTYFPHF-LShbb_horse VQLSGKVLLWKV--VGGLGRLLVVYPWTQRFFSFGLS hbb_human VHLTPKSVTLWGKV--VVGGLGRLLVVYPWTQRFFSFGLST glb5_petma PIVTGSVPLSKTKIRSWPVYSTYTSGVILVKFFTSTPQFFPKFKGLTT myg_phyca VLSGWQLVLHVWKVVGHGQILIRLFKSHPTLKFRFKHLKT lgb2_luplu GLTSQLVKSSWFIPKHTHRFFILVLIPKLFSFLKGTS *: : : *.. :.: * : * :. hba_horse ----HGSQVKHGKKVGLTLVGHL-----LPGLSLSLHHKLRVPVFKL hba_human ----HGSQVKGHGKKVLTVHV-----MPLSLSLHHKLRVPVFKL hbb_horse PGVMGPKVKHGKKVLHSFGGVHHL-----LKGTFLSLHKLHVPFRL hbb_human PVMGPKVKHGKKVLGFSGLHL-----LKGTFTLSLHKLHVPFRL glb5_petma QLKKSVRWHRIIVVSMT--KMSMKLRLSGKHKSFQVPQYFKV myg_phyca MKSLKKHGVTVLTLGILKKKGH------HLKPQSHTKHKIPIKYLF lgb2_luplu VP--QPLQHGKVFKLVYIQLQVTGVVVTTLKLGSVHVSKG-VHFPV..:: *. :.. *. : :. hba_horse LSHLLSTLVHLPFTPVHSLKFLSSVSTVLTSKYR hba_human LSHLLVTLHLPFTPVHSLKFLSVSTVLTSKYR hbb_horse LGVLVVVLRHFGKFTPLQSYQKVVGVLHKYH hbb_human LGVLVVLHHFGKFTPPVQYQKVVGVLHKYH glb5_petma LVITVG GFKLMSMIILLRSY myg_phyca ISIIHVLHSRHPGFGQGMKLLFRKIKYKLGYQG lgb2_luplu VKILKTIKVVGKWSLSWTIYLIVIKKM--- : :.:..... :.... i j seq... seq... seq... seq... seq... based on LOSUM 62 position i score = 10 x S(,) = 10 x 6 = 60 position j score = [3 x S(,)] + [6 x S(,)] + [1 x S(,)] = [3 x 6] + [6 x 3] + [1 x 9] = 9 urrent Opinion in Structural iology

9 omparison of some lignment Programs omparison of some lignment Programs increase sequence length + low and high indel frequency values PU time spent by each program Modified from uan PS, et al. (2006) M ioinfomatics doi: / Modified from uan PS, et al. (2006) M ioinfomatics doi: / Review Review TRS TRS in in Genetics Genetics Vol.19 Vol.19 o.6 o.6 June June Manual lignment by Visual Inspection Manual lignment by Visual Inspection (a) (a) (a) (a) Guide Guide tree tree taxon taxon good when sequences are not too different uses brain the most powerful and trainable compare to all tools allows direct integration of additional data, such as domain structure allows to use intuitive biological models that are too complicate to implement into any model F G H I J K subjective unscalable (b) Sequence addition order p p Pl My Rh Kt Kt osema osema spergillus. spergillus. Plasmodium.3 Plasmodium.3 ricetulus.2 ricetulus.2 Homo Homo rosophila.9 elegans.133 Spombe thaliana.40 discoideum. Porphyra.316 Tbrucei.1021 Leishmania QFGLFSPIRSSVLIR--YPTLG--VPKSGLVGHFGHILVK QFGLFSPIRSSVLIR--YPTLG--VPKSGLVGHFGHILVK QFGLFSPIKRMSVVHV--YPTMQRQRPRTKGLPGHFGHILT QFGLFSPIKRMSVVHV--YPTMQRQRPRTKGLPGHFGHILT LGVLPIIKKISVIV--VIYKG--FPRGGLYPGHFGHILK LGVLPIIKKISVIV--VIYKG--FPRGGLYPGHFGHILK QFGVLSPLKRMSVTGGIKYPTT--GGRPKLGGLPGHFGHILK QFGVLSPLKRMSVTGGIKYPTT--GGRPKLGGLPGHFGHILK QFGVLSPLKRMSVTGGIKYPTT--GGRPKLGGLPGHFGHILK QFGVLSPLKRMSVTGGIKYPTT--GGRPKLGGLPGHFGHILK QFGILSPIRRMSVTGGVQFTM--GGRPKLGGLPGHFGHILK QFGILGPIKRMSVH--VFPVY--GKPKLGGLPGHFGHLLK QFGILSPIRSMSVK--IFPTMSGQRPRVGGLPGHFGHILK QFGILSPIRQMSVIH----VHSTTKGKPKVGGLPGHFGYLLK PGHFGHILK PGHFGFILK QFIFKRQIKSYVLVHKSY----QSGPGHFGYIL QFVFKQIKYKIIHKSYHG----QPVRGGIPGHFGYVL osema spergillus. Spombe Plasmodium.3 ricetulus.2 Homo rosophila.9 elegans.133 thaliana.40 discoideum. Porphyra.316 Tbrucei.1021 Leishmania QFGLFSPIRSSVL--IRYPTL--GVPKSGLVGHFGHILVK QFGLFSPIKRMSVVH--VYPTMQRQRPRTKGLPGHFGHILT QFGILSPIRSMSVK--IFPTMSGQRPRVGGLPGHFGHILK LGVLPIIKKISV--IVVIYK--GFPRGGLYPGHFGHILK QFGVLSPLKRMSVTGGIKYPTT--GGRPKLGGLPGHFGHILK QFGVLSPLKRMSVTGGIKYPTT--GGRPKLGGLPGHFGHILK QFGILSPIRRMSVTGGVQFTM--GGRPKLGGLPGHFGHILK QFGILGPIKRMSVH--VFPVY--GKPKLGGLPGHFGHLLK QFGILSPIRQMSVIH--VHSTT--KGKPKVGGLPGHFGYLLK PGHFGHILK PGHFGFILK QFIFKRQIKSYVL--VHKSY--QSGPGHFGYIL QFVFKQIKYKI--IHKSY--HGQPVRGGIPGHFGYVL (b) taxon Step 1 + +F Step 2 + Step 3 + Step 4 I + J F + G IJ + K FG + H + FGH p Pl My Rh Kt Kt TRS in Genetics Step 5 FGH + IJK TRS TRS in in Genetics Genetics Fig Refining Refining an an alignment. alignment. (a) (a) The The raw raw output output from from a a lustalx lustalx alignment alignment of of Fig. rpb1 sequences, sequences, which which predicts predicts six six insertion/deletion insertion/deletion events events (boxed), (boxed), some some of of rpb1 which are are blatantly blatantly inconsistent inconsistent with with known known taxonomy. taxonomy. (b) (b) The The refined refined alignment alignment which makes much much better better evolutionary evolutionary sense, sense, because because it it shows shows only only two two insertion insertion events events makes