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The universe of biological sequence analysis Word/pattern recognition- Identification of restriction enzyme cleavage sites AGGCGGAACGGGTACTGGACGTACTAGGCGAGGCGATCTAGCGAGGGCATGTTGATGGCG Sequence alignment methods CGGTTAGCGAGCTACTATCGGGGGGCGAGCTTATTGGGCGGGGCGGACTATGGGCTGGCG ATGAAAAAGAAAACAACACTTAGCGAGGAGGACCAGGCTCTGTTTCGCCAGTTGATGGCG GGGACTCGCAAGATTAAGCAGGACACGATTGTCCACCGACCGCAGCGTAAAAAAATCAGC GAAGTGCCGGTGAAACGCTTGATCCAGGAGCAGGCTGATGCCAGCCATTATTTCTCCGAT GAGTTTCAGCCGTTATTAAATACCGAAGGTCCGGTGAAATATGTTCGCCCGGATGTCAGC CATTTTGAGGCGAAGAAACTGCGCCGTGGCGATTATTCGCCGGAGTTGTTTTTGGATTTA CACGGTCTGACGCAGCTGCAGGCCAAGCAGGAACTGGGGGCGTTGATTGCCGCCTGCCGC PstI GAGTTGCCCTGATAAGGGTACTATTACGGACGAGTCATCTTATGCGGAGCGATTAGGGCG GGAGCGGTTTTTAGGGCGTTTTTGGCGGCCCCCTATCTATGCAGCACGAGCGACTATGCC The universe of biological sequence analysis - prediction of exon structure CGCCGAGGATGGCCGTCATGGCGCCCCGAACCCTCCTCCTGCTACTCTTGGGGGCCCTGG MetAlaProArgThrLeuLeuLeuLeuLeuLeuGlyAlaLeuAlaExon 1 CCCTGACCCAGACCTGGGCGGGTGAGTGCGGGGTCGTGGGGAAACCGCCTCTGCGGGGAG LeuThrGlnThrTrpAlaGly AAGCAAGGGGCCCGCCCGGCGGGGACGCAGGACCCGGGTAGCCGCGCCGGGAGGAGGGTC Pairwise alignment GGGTGGGTCTCAGCCACTCCTCGCCCCCAGGCTCCCACTCCATGAGGTATTTCACCACAT SerHisSerMetArgTyrPheThrThrSer CCGTGTCCCGGCCCGGCCGCGGGGAGCCCCGCTTCATCGCCGTGGGCTACGTGGACGACA C G A T A G C A T G A T G T C T ValSerArgProGlyArgGlyGluProArgPheIleAlaValGlyTyrValAspAspThrExon 2 C G A C A G C A T - A T G T C T CGCAGTTCGTGCGGTTTGACAGCGACGCCGCGAGCCAGAGGATGGAGCCGCGGGCACCGT * * * * * * * * * * * * * * GlnPheValArgPheAspSerAspAlaAlaSerGlnArgMetGluProArgAlaProTrp GGATAGAGCAGGAGGGGCCGGAGTATTGGGACCTGCAGACACGGAATGTGAAGGCCCAGT IleGluGlnGluGlyProGluTyrTrpAspLeuGlnThrArgAsnValLysAlaGlnSer CACAGACTGACCGAGCGAACCTGGGGACCCTGCGCGGCTACTACAACCAGAGCGAGGCCG GlnThrAspArgAlaAsnLeuGlyThrLeuArgGlyTyrTyrAsnGlnSerGluAla GTGAGTGACCCCGGCCCGGGGCGCAGGTCACGACCTCTCATCCCCCACGGACGGGCCGGG 1 Prediction of function Why sequence alignments ? Database of sequences Sequence to be investigated • Prediction of function • Protein family analysis • Comparative genomics • Phylogeny / Evolutionary history • Genome sequencing: Seq. with known function • Assembly • Alignment to reference genome We have a ‘new’ sequence. It is similar to a previously known sequence? We can test by alignment whether it is similar to a sequence with known function. If it is we can assign a possible function to our new sequence Protein family analysis Comparative genomics - reveals biologically significant regions of the genome 2 Pairwise alignment CGATAGCATGATGTCT Pairwise alignment CGATAGCATGATGTCT dotplot C dotplot C G G A A C C A A G G C C A A T T A A T G T T G C T T C T CGATAGCATGATGTCT *** ***** ****** CGACAGCAT-ATGTCT Pairwise alignment CGAT AGCAT GAT GT CT Pairwise alignment CGAT AGCAT GAT GT CT dotplot C dotplot C G G A A C C A A G G C C A A T T A A T T G G T T C C T T CGATAGCATGATGTCT CG-----ATAGCATGATGTCT *** ***** ****** ** *** ***** CGACAGCAT-ATGTCT CGACAGCATA------TGTCT ++----- +++- ---- - ++ + + + +2221222222222222 + +- + ++++- + ++ ++ + = 25 2 2 2 22 22 22 2 222 2 2222222 = -2 3 More sophisticated scoring of More sophisticated scoring of protein sequence alignments protein sequence alignments Each amino acid change has a Each amino acid change has a characteristic probability characteristic probability A G L C E | | | | | A A L C D substitution matrix 4+ 0+4 +9+2 =19 Local and global alignments Frequently used methods in sequence analysis A that are based on sequence alignment B BLAST - searches in databases for sequence similarity Local alignment ClustalW - multiple alignment of sequences | | | | | | | | | | Global alignment A | | | | | | | | | | | | | | B 4 BLAST Searching databases for sequence similarity - traditional alignment method too slow BLAST - Basic Local Alignment Search Tool A query sequence (DNA or protein) is tested against all sequences in a database (DNA or protein) , i.e the FASTA, 1988 query is aligned to all the database William Pearson sequences. Final output is a list of the best matching database sequences. BLAST, 1990 David Lipman Stephen Altschul BLAST output Searching databases for sequence similarity BLASTP 2.2.9 [May-01-2004] Reference: Altschul, Stephen F., Thomas L. Madden, Alejandro A. Schaffer, - shortcuts of BLAST Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res. 25:3389-3402. Query= lcl|SRP54_MOUSE (P14576) Signal recognition particle 54 kDa protein (SRP54) "word hit" (504 letters) Improvement MAK I QGLGKRY Database: swissprot of speed as 197,228 sequences; 71,501,181 total letters compared to local M * Searching..................................................done alignment algorithm: A * K ** Score E * Initial search is L * Sequences producing significant alignments: (bits) Value for word hits. Q SRP54_MOUSE (P14576) Signal recognition particle 54 kDa protein ... 959 0.0 * SRP54_PONPY (Q5R4R6) Signal recognition particle 54 kDa protein ... 958 0.0 G SRP54_MACFA (Q4R965) Signal recognition particle 54 kDa protein ... 958 0.0 * Word hits are then ** SRP54_HUMAN (P61011) Signal recognition particle 54 kDa protein ... 958 0.0 A SRP54_CANFA (P61010) Signal recognition particle 54 kDa protein ... 958 0.0 extended in either * SRP54_RAT (Q6AYB5) Signal recognition particle 54 kDa protein (S... 957 0.0 L * SRP54_GEOCY (Q8MZJ6) Signal recognition particle 54 kDa protein ... 794 0.0 direction. SR542_LYCES (P49972) Signal recognition particle 54 kDa protein ... 565 e-161 G * * SR543_ARATH (P49967) Signal recognition particle 54 kDa protein ... 560 e-159 K SR542_HORVU (P49969) Signal recognition particle 54 kDa protein ... 558 e-158 ** ... R ... * SRPR_MOUSE (Q9DBG7) Signal recognition particle receptor alpha s... 99 3e-20 Y SRPR_HUMAN (P08240) Signal recognition particle receptor alpha s... 99 3e-20 * SRPR_YEAST (P32916) Signal recognition particle receptor alpha s... 98 7e-20 5 BLAST output, cont. Expect value (E) sp|Q9I3P8.1|FLHF_PSEAE RecName: Full=Flagellar biosynthesis prot... 57 3e-07 sp|Q44758.1|FLHF_BORBU RecName: Full=Flagellar biosynthesis prot... 55 2e-06 Parameter that describes the number of hits one can sp|Q01960.1|FLHF_BACSU RecName: Full=Flagellar biosynthesis prot... 53 4e-06 "expect" to see just by chance when searching a sp|O28980.1|Y1289_ARCFU RecName: Full=Uncharacterized protein AF... 39 0.064 database of a particular size. Essentially, the E value sp|B9LKC1.1|CYSC_CHLSY RecName: Full=Adenylyl-sulfate kinase; Al... 38 0.21 sp|Q12U80.1|RADB_METBU RecName: Full=DNA repair and recombinatio... 37 0.29 describes the random background noise that exists for sp|A5D014.1|ACCD_PELTS RecName: Full=Acetyl-coenzyme A carboxyla... 35 0.93 sp|Q03T56.1|RSMA_LACBA RecName: Full=Ribosomal RNA small subunit... 35 1.2 matches between sequences. For example, an E value of sp|Q1I2K4.1|CYSC_PSEE4 RecName: Full=Adenylyl-sulfate kinase; Al... 35 1.6 sp|Q38V22.1|RSMA_LACSS RecName: Full=Ribosomal RNA small subunit... 34 1.8 1 assigned to a hit can be interpreted as meaning that in sp|A1U3X8.1|CYSC_MARAV RecName: Full=Adenylyl-sulfate kinase; Al... 34 2.3 sp|A6TD42.1|CYSC_KLEP7 RecName: Full=Adenylyl-sulfate kinase; Al... 34 2.9 a database of the current size one might expect to see 1 sp|P63890.2|CYSC_SALTI RecName: Full=Adenylyl-sulfate kinase; Al... 34 2.9 ... match with a similar score simply by chance. This means that the lower the E-value, or the closer it is to "0" the more "significant" the match is. High Scoring Pair (HSP) High Scoring Pair (HSP) Query: 1 MVLADLGRKITSALRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAI 60 >SRPR_MOUSE (Q9DBG7) Signal recognition particle receptor alpha subunit MVLADLGRKITSALRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAI (SR-alpha) (Docking protein alpha) (DP-alpha) Sbjct: 1 MVLADLGRKITSALRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAI 60 Length = 636 Query: 61 DLEEMASGLNKRKMIQHAVFKELVKLVDPGVKAWTPTKGKQNVIMFVGLQGSGKTTTCSK 120 Score = 99.0 bits (245), Expect = 3e-20 DLEEMASGLNKRKMIQHAVFKELVKLVDPGVKAWTPTKGKQNVIMFVGLQGSGKTTTCSK Identities = 68/313 (21%), Positives = 143/313 (45%), Gaps = 31/313 (9%) Sbjct: 61 DLEEMASGLNKRKMIQHAVFKELVKLVDPGVKAWTPTKGKQNVIMFVGLQGSGKTTTCSK 120 Query: 14 LRSLSNATIINEEVLNAMLKEVCTALLEADVNIKLVKQLRENVKSAIDLEEMASGLNKRK 73 Query: 121 LAYYYQRKGWKTCLICADTFRAGAFDQLKQNATKARIPFYGSYTEMDPVIIASEGVEKFK 180 L+ L + ++ E + ++L ++ L+ +V + QL E+V + ++ + M + LAYYYQRKGWKTCLICADTFRAGAFDQLKQNATKARIPFYGSYTEMDPVIIASEGVEKFK Sbjct: 322 LKGLVGSKSLSREDMESVLDKMRDHLIAKNVAADIAVQLCESVANKLEGKVMGTFSTVTS 381 Sbjct: 121 LAYYYQRKGWKTCLICADTFRAGAFDQLKQNATKARIPFYGSYTEMDPVIIASEGVEKFK 180 Query: 74 MIQHAVFKELVKLVDPGVKAW-------TPTKGKQNVIMFVGLQGSGKTTTCSKLAYYYQ 126 Query: 181 NENFEIIIVDTSGRHKQEDSLFEEMLQVSNAIQPDNIVYVMDASIGQACEAQAKAFKDKV 240 ++ A+ + LV+++ P + + + V+ F G+ G GK+T +K++++ NENFEIIIVDTSGRHKQEDSLFEEMLQV+NAIQPDNIVYVMDASIGQACEAQAKAFKDKV Sbjct: 382 TVKQALQESLVQILQPQRRVDMLRDIMDAQRRQRPYVVTFCGVNGVGKSTNLAKISFWLL 441 Sbjct: 181 NENFEIIIVDTSGRHKQEDSLFEEMLQVANAIQPDNIVYVMDASIGQACEAQAKAFKDKV 240 Query: 127 RKGWKTCLICADTFRAGAFDQLK-------------QNATKARIPFYGSYTEMDPVIIAS 173 Query: 241 DVASVIVTKLDGHAKGGGALSAVAATKSPIIFIGTGEHIDDFEPFKTQPFISKLLGMGDI 300 G+ + DTFRAGA +QL+ ++ + + + D IA DVASVIVTKLDGHAKGGGALSAVAATKSPIIFIGTGEHIDDFEPFKTQPFISKLLGMGDI Sbjct: 442 ENGFSVLIAACDTFRAGAVEQLRTHTRRLTALHPPEKHGGRTMVQLFEKGYGKDAAGIAM 501 Sbjct: 241 DVASVIVTKLDGHAKGGGALSAVAATKSPIIFIGTGEHIDDFEPFKTQPFISKLLGMGDI 300 6 BLAST output – revealing orthologs and paralogs BLASTP 2.2.9 [May-01-2004] The two kinds of protein evolutionary relationship Reference: Altschul, Stephen F., Thomas L.
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    Processing data of BLAST, clustering and ordering Student: Elena Bushmanova, Bioinformatics Institute, SPbSU Advisor: Pavel Dobrynin, Dobzhansky Center Saint-Petersburg, 2013 Goal: Creating a program, which allows ● to form single units of overlapping hits ● to rank the received units according to the coordinates on the chromosome ● and get some statistics Problem: To increase of the accuracy and efficiency of assembling of the genome on the basis of the reference. Other tools: • SyMAP • ABACAS • Arachne • MAUVE • LASTZ • RACA SyMAP – very long time to analyze only one pair of genomes. But this is only one program that currently used for reference assisted assembly of large genomes; ABACAS – very good program, but working only with bacteria or other small (20mbp) genomes; Arachne – we tried to adapt this assembler for Sanger sequences to assemble contigs but failed; MAUVE – not trivial output, only small genome, work slowly; LASTZ – great tool for alignment of large genomes, but working not very fast, and also quite complicated pipeline for genome assembly ; RACA – in theory one of the best ref assisted assembler, but still only in theory (no publications that used it). Algorithm • Processing: makeblastdb -in ref_all_chr.fsa.txt -dbtype nucl -title yeast -out yeast blastn -query Kyokai7_NRIB_2011_BABQ01000000.fsa -db yeast -outfmt 6 -out data.txt • Selection • Sorting • Checking: Q - 1000 ≤ S ≤ Q + 1000 • Splicing • Statistics Data Input data: yeast_blastn.txt: input list of fragments query_id, subject_id, identity, alignment_length, mismatches, gap_opens, q.start, q.end, s.start, s.end, e_value, bit_score Output data: outputList.txt: output list of fragments unusedList.txt: list of duplicate fragments In_statistics.txt, Out_statistics.txt: - total number of fragments and percent of unique fragments; - average, median, maximum and minimum count of the same id fragments; - similar characteristics of the number of matches.
  • Genomic Variants Among Threatened Acropora Corals

    Genomic Variants Among Threatened Acropora Corals

    G3: Genes|Genomes|Genetics Early Online, published on March 26, 2019 as doi:10.1534/g3.119.400125 Genomic variants among threatened Acropora corals Authors: Sheila. A. Kitchen*†, Aakrosh Ratan*‡, Oscar C. Bedoya-Reina§**, Richard Burhans††, Nicole D. Fogarty‡‡, $$, Webb Miller††, Iliana B. Baums† * Authors contributed equally to the manuscript Author Affiliations: † 208 Mueller Lab, Biology Department, The Pennsylvania State University, University Park PA 16802 [email protected], [email protected] ‡ Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia, Charlottesville VA 22908 [email protected] § MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, South Parks Road, Oxford OX1 3PT, UK [email protected] ** MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK. †† Centre for Comparative Genomics and Bioinformatics, Pennsylvania State University, University Park, PA 16802, USA [email protected], [email protected] ‡‡ Department of Marine and Environmental Sciences, Nova Southeastern University, Fort Lauderdale, FL 33314, [email protected] $$ Secondary Affiliation: Nicole D. Fogarty, Department of Biology and Marine Biology, University of North Carolina Wilmington, Center for Marine Science, 5600 Marvin K Moss Lane, Wilmington, NC 28409, USA; Data Accession numbers: NCBI SRA accession numbers are SRR7235977-SRR7236038. Kitchen et al. 1 © The Author(s) 2013. Published by the Genetics Society of America. Running Title: Genomic variants among threatened corals Keywords: coral, Caribbean, single nucleotide polymorphism, population genomics, Galaxy Corresponding Author (§§): I.B Baums, 208 Mueller Lab, Biology Department, The Pennsylvania State University, University Park PA 16802, Fax +1 814 865 9131, [email protected] Article Summary We provide the first comprehensive genomic resources for two threatened Caribbean reef- building corals in the genus Acropora.