Multiple Sequence Alignment: Methods and Applications, Study notes of Chemistry

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Multiple Sequence Alignment BME 110: CompBio Tools Todd Lowe April 28, 2009 Original Slides: Carol Rohl Multiple Sequence Alignment •  Multiple sequence alignment (MSA) is one of the most important bioinformatics tools •  Many applications require accurate MSAs •  PSI-BLAST •  Family and domain classification •  Pattern identification •  Structure prediction •  secondary structure •  fold recognition •  Phylogeny •  Full-genome alignments in browsers Progressive Alignment 1.  Calculate global pair-wise alignments for all pairs •  Needleman and Wunsch 2.  Use pairwise alignment scores to calculate a guide tree describing the distance between all pairs of sequences 3.  Align the sequences progressively •  Start with the two most closely related sequences •  Add in sequences in order of increasing distance •  ClustalW uses this method ClustalW Example •  Input: 5 sequences detected by BLASTp using human SNAP-25 as a query •  Default parameters, output order: input >sp_P13795 MAEDADMRNELEEMQRRADQLADESLESTRRMLQLVEESKDAGIRTLVMLDEQGEQLERIEEGMDQINKD MKEAEKNLTDLGKFCGLCVCPCNKLKSSDAYKKAWGNNQDGVVASQPARVVDEREQMAISGGFIRRVTND ARENEMDENLEQVSGIIGNLRHMALDMGNEIDTQNRQIDRIMEKADSNKTRIDEANQRATKMLGSG >gi_31242623 MPAAAPPAENGAAVPKTELQELQMKQQQVVDESLDSTRRMLALCEESTEVGMRTIVMLDEQGEQLDRIEE GMDQINADMREAEKNLSGMEKCCGICVLPCNKSASFKEDDGTWKGNDDGKVVNNQPQRVMDDRNGLGPQA GYIGRITNDAREDEMEENMGQVNTMIGNLRNMALDMGSELENQNRQIDRINRKGDSNATRIAAANERAHD LLK >gi_3822409 MPTTAEPAQENGAPRSELQELQLKAGQVTDETLESTRRMLALCEESKEAGIRTLVALDDQGEQLERIEEN MDQINADMKEAEKNLTGMEKFCGLCVLPWNKSAPFKENEDAWKGNDDGKVVNNQPQRVMDDGSGLGPQGG YIGRITNDAREDEMEENVGQVNTMIGNLRNMAIDMGSELENQNRQIDRIKNKAEM >gi_39593308 MSARRGAPGGQRHPRPYAVEPTVDINGLVLPADMSDELKGLNVGIDEKTIESLESTRRMLALCEESKEAG IKTLVMLDDQGEQLERCEGALDTINQDMKEAEDHLKGMEKCCGLCVLPWNKTDDFEKNSEYAKAWKKDDD GGVISDQPRITVGDPTMGPQGGYITKITNDAREDEMDENIQQVSTMVGNLRNMAIDMSTEVSNQNRQLDR IHDKAQSNEVRVESANKRAKNLITK >gi_32567202 MSGDDDIPEGLEAINLKMNATTDDSLESTRRMLALCEESKEAGIKTLVMLDDQGEQLERCEGALDTINQD MKEAEDHLKGMEKCCGLCVLPWNKTDDFEKTEFAKAWKKDDDGGVISDQPRITVGDSSMGPQGGYITKIT NDAREDEMDENVQQVSTMVGNLRNMAIDMSTEVSNQNRQLDRIHDKAQSNEVRVESANKRAKNLITK Input Formats •  FASTA format •  Download from NCBI, ExPASy, EBI, Pfam … •  Sequence names should be •  Unique •  15 characters or less •  Comprised of only A-Z,a-z,0-9 and _ (Do not use #$%@|*!:;. or spaces) Progressive Alignment •  Greedy algorithm •  Breaks problem up into smaller problems •  Finds best solution to each small problem •  Combine solutions to get answer to whole problem •  Not necessarily the global answer •  Doesn’t use all information in solving sub-problems •  Suboptimal answers for small problems may combine to give a better overall answer •  Gaps: once created, they stay as part of alignment for rest of alignment iterations ClustalW Alignment CLUSTAL W (1.82) multiple sequence alignment sp_P13795 ---MAEDAD------------------------MRNELEEMQRRADQLADESLESTRRML 33 gi_31242623 MPAAAPPAENG-------------------AAVPKTELQELQMKQQQVVDESLDSTRRML 41 gi_3822409 MPTTAEPAQE--------------------NGAPRSELQELQLKAGQVTDETLESTRRML 40 gi_39593308 MSARRGAPGGQRHPRPYAVEPTVDINGLVLPADMSDELKGLNVGIDEKTIESLESTRRML 60 gi_32567202 MSGDDDIPEG---------------------------LEAINLKMNATTDDSLESTRRML 33 . *: :: . ::*:****** sp_P13795 QLVEESKDAGIRTLVMLDEQGEQLERIEEGMDQINKDMKEAEKNLTDLGKFCGLCVCPCN 93 gi_31242623 ALCEESTEVGMRTIVMLDEQGEQLDRIEEGMDQINADMREAEKNLSGMEKCCGICVLPCN 101 gi_3822409 ALCEESKEAGIRTLVALDDQGEQLERIEENMDQINADMKEAEKNLTGMEKFCGLCVLPWN 100 gi_39593308 ALCEESKEAGIKTLVMLDDQGEQLERCEGALDTINQDMKEAEDHLKGMEKCCGLCVLPWN 120 gi_32567202 ALCEESKEAGIKTLVMLDDQGEQLERCEGALDTINQDMKEAEDHLKGMEKCCGLCVLPWN 93 * ***.:.*::*:* **:*****:* * :* ** **:***.:*..: * **:** * * sp_P13795 KLKSSDA---YKKAWGNNQDG-VVASQPARVVDEREQMAISGGFIRRVTNDARENEMDEN 149 gi_31242623 KSASFKE---DDGTWKGNDDGKVVNNQPQRVMDDRNGLGPQAGYIGRITNDAREDEMEEN 158 gi_3822409 KSAPFKE---NEDAWKGNDDGKVVNNQPQRVMDDGSGLGPQGGYIGRITNDAREDEMEEN 157 gi_39593308 KTDDFEKNSEYAKAWKKDDDGGVISDQPRITVGDPT-MGPQGGYITKITNDAREDEMDEN 179 gi_32567202 KTDDFEK-TEFAKAWKKDDDGGVISDQPRITVGDSS-MGPQGGYITKITNDAREDEMDEN 151 * . :* ::** *: .** .:.: :. ..*:* ::******:**:** sp_P13795 LEQVSGIIGNLRHMALDMGNEIDTQNRQIDRIMEKADSNKTRIDEANQRATKMLGSG 206 gi_31242623 MGQVNTMIGNLRNMALDMGSELENQNRQIDRINRKGDSNATRIAAANERAHDLLK-- 213 gi_3822409 VGQVNTMIGNLRNMAIDMGSELENQNRQIDRIKNKAEM------------------- 195 gi_39593308 IQQVSTMVGNLRNMAIDMSTEVSNQNRQLDRIHDKAQSNEVRVESANKRAKNLITK- 235 gi_32567202 VQQVSTMVGNLRNMAIDMSTEVSNQNRQLDRIHDKAQSNEVRVESANKRAKNLITK- 207 : **. ::****:**:**..*:..****:*** *.: Interleaved Formats •  Most common output formats for MSAs are interleaved: •  MSF, ASN, BLAST query-anchored formats •  All sequences are stacked up, and chopped into blocks of ~60 residues •  Easy for humans to read, but difficult to edit •  Tools for converting formats are available on the web •  EMBOSS tool for conversion (squizz_convert) Sequence Logos •  Logos are another useful visualization of alignments that allow conserved positions to be easily picked out. •  Multiple tools available on the web or can be downloaded: •  http://weblogo.berkeley.edu Tcoffee •  Makes a library of pair-wise global and several local alignments •  Tries to find a multiple alignment that has best consensus with all alignments in the library. •  Still a progressive algorithm •  Slower, but usually a bit better than ClustalW Other Uses of MSA Servers •  ClustalW can refine an alignment •  If sequences are aligned when submitted, this info is used. •  Tcoffee can •  Combine alignments •  Evaluate alignment quality •  Use structural information if available Strategies •  Visually inspect alignment and try eliminating sequences that seem problematic. •  Avoid sequences with long insertions and/or terminal extensions •  “Orphan” sequences (highly divergent members of a family) usually don’t disrupt alignment because they’re the last to be aligned. Collections of MSAs •  Domain and family collection databases not only have sequences grouped by domain/family, but also have MSAs that were used for classification. •  Example: Pfam http://pfam.janelia.org/