DOCSLIB.ORG

Sequence Alignment Methods Pairwise Alignment

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Sequence Alignment Methods Pairwise Alignment 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.
Load more

Recommended publications
  • Gene Prediction: the End of the Beginning Comment Colin Semple
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by PubMed Central http://genomebiology.com/2000/1/2/reports/4012.1 Meeting report Gene prediction: the end of the beginning comment Colin Semple Address: Department of Medical Sciences, Molecular Medicine Centre, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK. E-mail: [email protected] Published: 28 July 2000 reviews Genome Biology 2000, 1(2):reports4012.1–4012.3 The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2000/1/2/reports/4012 © GenomeBiology.com (Print ISSN 1465-6906; Online ISSN 1465-6914) Reducing genomes to genes reports A report from the conference entitled Genome Based Gene All ab initio gene prediction programs have to balance sensi- Structure Determination, Hinxton, UK, 1-2 June, 2000, tivity against accuracy. It is often only possible to detect all organised by the European Bioinformatics Institute (EBI). the real exons present in a sequence at the expense of detect- ing many false ones. Alternatively, one may accept only pre- dictions scoring above a more stringent threshold but lose The draft sequence of the human genome will become avail- those real exons that have lower scores. The trick is to try and able later this year. For some time now it has been accepted increase accuracy without any large loss of sensitivity; this deposited research that this will mark a beginning rather than an end. A vast can be done by comparing the prediction with additional, amount of work will remain to be done, from detailing independent evidence.
    [Show full text]
  • Reconstructing Contiguous Regions of an Ancestral Genome
    Downloaded from www.genome.org on December 5, 2006 Reconstructing contiguous regions of an ancestral genome Jian Ma, Louxin Zhang, Bernard B. Suh, Brian J. Raney, Richard C. Burhans, W. James Kent, Mathieu Blanchette, David Haussler and Webb Miller Genome Res. 2006 16: 1557-1565; originally published online Sep 18, 2006; Access the most recent version at doi:10.1101/gr.5383506 Supplementary "Supplemental Research Data" data http://www.genome.org/cgi/content/full/gr.5383506/DC1 References This article cites 20 articles, 11 of which can be accessed free at: http://www.genome.org/cgi/content/full/16/12/1557#References Open Access Freely available online through the Genome Research Open Access option. Email alerting Receive free email alerts when new articles cite this article - sign up in the box at the service top right corner of the article or click here Notes To subscribe to Genome Research go to: http://www.genome.org/subscriptions/ © 2006 Cold Spring Harbor Laboratory Press Downloaded from www.genome.org on December 5, 2006 Methods Reconstructing contiguous regions of an ancestral genome Jian Ma,1,5,6 Louxin Zhang,2 Bernard B. Suh,3 Brian J. Raney,3 Richard C. Burhans,1 W. James Kent,3 Mathieu Blanchette,4 David Haussler,3 and Webb Miller1 1Center for Comparative Genomics and Bioinformatics, Penn State University, University Park, Pennsylvania 16802, USA; 2Department of Mathematics, National University of Singapore, Singapore 117543; 3Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA; 4School of Computer Science, McGill University, Montreal, Quebec H3A 2B4, Canada This article analyzes mammalian genome rearrangements at higher resolution than has been published to date.
    [Show full text]
  • Duplication, Deletion, and Rearrangement in the Mouse and Human Genomes
    Evolution’s cauldron: Duplication, deletion, and rearrangement in the mouse and human genomes W. James Kent*†, Robert Baertsch*, Angie Hinrichs*, Webb Miller‡, and David Haussler§ *Center for Biomolecular Science and Engineering and §Howard Hughes Medical Institute, Department of Computer Science, University of California, Santa Cruz, CA 95064; and ‡Department of Computer Science and Engineering, Pennsylvania State University, University Park, PA 16802 Edited by Michael S. Waterman, University of Southern California, Los Angeles, CA, and approved July 11, 2003 (received for review April 9, 2003) This study examines genomic duplications, deletions, and rear- depending on details of definition and method. The length rangements that have happened at scales ranging from a single distribution of synteny blocks was found to be consistent with the base to complete chromosomes by comparing the mouse and theory of random breakage introduced by Nadeau and Taylor (8, human genomes. From whole-genome sequence alignments, 344 9) before significant gene order data became available. In recent large (>100-kb) blocks of conserved synteny are evident, but these comparisons of the human and mouse genomes, rearrangements are further fragmented by smaller-scale evolutionary events. Ex- of Ն100,000 bases were studied by comparing 558,000 highly cluding transposon insertions, on average in each megabase of conserved short sequence alignments (average length 340 bp) genomic alignment we observe two inversions, 17 duplications within 300-kb windows. An estimated 217 blocks of conserved (five tandem or nearly tandem), seven transpositions, and 200 synteny were found, formed from 342 conserved segments, with deletions of 100 bases or more. This includes 160 inversions and 75 length distribution roughly consistent with the random breakage duplications or transpositions of length >100 kb.
    [Show full text]
  • BIOINFORMATICS ISCB NEWS Doi:10.1093/Bioinformatics/Btp280
    Vol. 25 no. 12 2009, pages 1570–1573 BIOINFORMATICS ISCB NEWS doi:10.1093/bioinformatics/btp280 ISMB/ECCB 2009 Stockholm Marie-France Sagot1, B.J. Morrison McKay2,∗ and Gene Myers3 1INRIA Grenoble Rhône-Alpes and University of Lyon 1, Lyon, France, 2International Society for Computational Biology, University of California San Diego, La Jolla, CA and 3Howard Hughes Medical Institute Janelia Farm Research Campus, Ashburn, Virginia, USA ABSTRACT Computational Biology (http://www.iscb.org) was formed to take The International Society for Computational Biology (ISCB; over the organization, maintain the institutional memory of ISMB http://www.iscb.org) presents the Seventeenth Annual International and expand the informational resources available to members of the Conference on Intelligent Systems for Molecular Biology bioinformatics community. The launch of ECCB (http://bioinf.mpi- (ISMB), organized jointly with the Eighth Annual European inf.mpg.de/conferences/eccb/eccb.htm) 8 years ago provided for a Conference on Computational Biology (ECCB; http://bioinf.mpi- focus on European research activities in years when ISMB is held inf.mpg.de/conferences/eccb/eccb.htm), in Stockholm, Sweden, outside of Europe, and a partnership of conference organizing efforts 27 June to 2 July 2009. The organizers are putting the finishing for the presentation of a single international event when the ISMB touches on the year’s premier computational biology conference, meeting takes place in Europe every other year. with an expected attendance of 1400 computer scientists, The multidisciplinary field of bioinformatics/computational mathematicians, statisticians, biologists and scientists from biology has matured since gaining widespread recognition in the other disciplines related to and reliant on this multi-disciplinary early days of genomics research.
    [Show full text]
  • Open Thesisformatted Final.Pdf
    The Pennsylvania State University The Graduate School The Huck Institutes of the Life Sciences COMPUTATIONAL APPROACHES TO PREDICT PHENOTYPE DIFFERENCES IN POPULATIONS FROM HIGH-THROUGHPUT SEQUENCING DATA A Dissertation in Integrative Biosciences in Bioinformatics and Genomics by Oscar Camilo Bedoya Reina 2014 Oscar Camilo Bedoya Reina Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2014 i The dissertation of Oscar Camilo Bedoya Reina was reviewed and approved* by the following: Webb Miller Professor of Biology and Computer Science and Engineering Dissertation Advisor Chair of Committee Ross Hardison T. Ming Chu Professor of Biochemistry and Molecular Biology George Perry Assistant Professor of Anthropology and Biology Kamesh Madduri Assistant Professor of Computer Science and Engineering Peter Hudson Willaman Professor of Biology Head of the Huck Institutes of the Life Sciences *Signatures are on file in the Graduate School iii ABSTRACT High-throughput sequencing technologies are changing the world. They are revolutionizing the life sciences and will be the foundation of a promising century of innovations. In recent years, the development of new sequencing technologies has dramatically decreased the cost of genome sequencing. Less than twenty years ago, sequencing the human genome cost 3 billion dollars, and took about a decade to complete. Today, high-quality 30X full-genome coverage can be obtained in just one day for US$ 5,000, while sequencing just the ~21,000 human genes to the same depth costs only about US$ 500. The latter is sufficient for detecting most of the rare variants, along with other sources of genetic variability such as indels, copy- number variations, and inversions that are characteristic of complex diseases.
    [Show full text]
  • ENCODE Analysis Working Group and Data Analysis Centre Rick Myers
    ENCODE Analysis Working Group and Data Analysis Centre Rick Myers Ewan Birney Motivation for mandated DAC y Genesis from the experience of the pilot project y Everyone looking at the ceiling when a key piece of annoying analysis needs to happen y A set of people who are funded to ensure that critical integrative analysis occurs (consistently and timely) y In no way exclusive y Everyone is invited in analysis y DAC should fit around things which are happening at the consortium level y Porous (no distinction expected between DAC members and other consortium members) except… y …the cleaning of the Aegean stables moment (eg, creating repeat libraries, consistently remapping everyone’s chip-seq data) y Interplay with DCC deliberate (trade off where things occur) y When there are too many things on the DAC to-do list - ask AWG to prioritise. AWG Participates in Rick Myers discussion Chair of AWG Birney BickelBickel Project Manager Haussler EBI (Ian Dunham) Bickel Directed Analysis Methods development EBI UCSC Yale BU EBI UCSC Yale BU U. Wash Penn Berkeley U. Wash Penn Berkeley DAC - federated, embedded y Ewan Birney/Paul Flicek/Ian Dunham (EBI)- comparative genomics, short read technology methods y Mark Gerstein (Yale) - chip-seq, link to genes/transcripts, link to modENCODE, P y Zhiping Weng (BU) - chip-chip, chip-seq, motif finding, bayesian analysis y Ross Hardison/Webb Miller (PSU) - comparative genomics, regulatory regions y Jim Kent/David Haussler (UCSC) - comparative genomics, DCC y Peter Bickel (UC Berkeley) - statistician y Bill Nobel (UW) - machine learning - HMMs, change point analysis, wavelets, SVMs New analysis tasks from AWG or community Results Provided Triage and Back to AWG Initial prioritisation Converting Priortisation Active ad hoc of all projects tasks analysis to by AWG handled pipelines by EDAC AWG prioritisation EDAC suggest pipelining tasks Experimental Data exploration, DCC group, in house Normalisation, coordination methods Sanity checking Feedback to AWG and expt.
    [Show full text]
  • 439: PALM: Probabilistic Area Loss Minimization for Protein Sequence
    PALM: Probabilistic Area Loss Minimization for Protein Sequence Alignment Fan Ding*1 Nan Jiang∗1 Jianzhu Ma2 Jian Peng3 Jinbo Xu4 Yexiang Xue1 1Department of Computer Science, Purdue University, West Lafayette, Indiana, USA 2Institute for Artificial Intelligence, Peking University, Beijing, China 3Department of Computer Science, University of Illinois at Urbana-Champaign, Illinois, USA 4Toyota Technological Institute at Chicago, Illinois, USA Abstract origin LRP S Protein sequence alignment is a fundamental prob- lem in computational structure biology and popu- L lar for protein 3D structural prediction and protein homology detection. Most of the developed pro- A grams for detecting protein sequence alignments Match are based upon the likelihood information of amino Insertion at S Insertion at T acids and are sensitive to alignment noises. We S: S _ L _ A present a robust method PALM for modeling pair- gt T: _ L R P _ wise protein structure alignments, using the area S: _ pred S L A distance to reduce the biological measurement 1 T: _ R PL noise. PALM generatively learn the alignment of S: __ _ S L A pred __ _ two protein sequences with probabilistic area dis- 2 T: LPR tance objective, which can denoise the measure- ment errors and offsets from different biologists. Figure 1: Illustration of protein sequence alignment and the During learning, we show that the optimization is area distance. (Bottom) The task is to align two amino acids computationally efficient by estimating the gradi- sequences S and T , where one amino acid from sequence ents via dynamically sampling alignments. Empiri- S can be aligned to either one amino acid from sequence cally, we show that PALM can generate sequence T (match), or to a gap (insertion, marked by “−”).
    [Show full text]
  • Comparative Genomics
    17 Aug 2004 15:49 AR AR223-GG05-02.tex AR223-GG05-02.sgm LaTeX2e(2002/01/18) P1: IKH 10.1146/annurev.genom.5.061903.180057 Annu. Rev. Genomics Hum. Genet. 2004. 5:15–56 doi: 10.1146/annurev.genom.5.061903.180057 Copyright c 2004 by Annual Reviews. All rights reserved First published online as a Review in Advance on April 15, 2004 COMPARATIVE GENOMICS Webb Miller, Kateryna D. Makova, Anton Nekrutenko, and Ross C. Hardison The Center for Comparative Genomics and Bioinformatics, The Huck Institutes of Life Sciences, and the Departments of Biology, Computer Science and Engineering, and Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania; email: [email protected], [email protected], [email protected], [email protected] KeyWords whole-genome alignments, evolutionary rates, gene prediction, gene regulation, sequence conservation, Internet resources, genome browsers, genome databases, bioinformatics ■ Abstract The genomes from three mammals (human, mouse, and rat), two worms, and several yeasts have been sequenced, and more genomes will be completed in the near future for comparison with those of the major model organisms. Scientists have used various methods to align and compare the sequenced genomes to address critical issues in genome function and evolution. This review covers some of the major new insights about gene content, gene regulation, and the fraction of mammalian genomes that are under purifying selection and presumed functional. We review the evolution- ary processes that shape genomes, with particular attention to variation in rates within genomes and along different lineages. Internet resources for accessing and analyzing the treasure trove of sequence alignments and annotations are reviewed, and we dis- cuss critical problems to address in new bioinformatic developments in comparative genomics.
    [Show full text]
  • The Myth of Junk DNA
    The Myth of Junk DNA JoATN h A N W ells s eattle Discovery Institute Press 2011 Description According to a number of leading proponents of Darwin’s theory, “junk DNA”—the non-protein coding portion of DNA—provides decisive evidence for Darwinian evolution and against intelligent design, since an intelligent designer would presumably not have filled our genome with so much garbage. But in this provocative book, biologist Jonathan Wells exposes the claim that most of the genome is little more than junk as an anti-scientific myth that ignores the evidence, impedes research, and is based more on theological speculation than good science. Copyright Notice Copyright © 2011 by Jonathan Wells. All Rights Reserved. Publisher’s Note This book is part of a series published by the Center for Science & Culture at Discovery Institute in Seattle. Previous books include The Deniable Darwin by David Berlinski, In the Beginning and Other Essays on Intelligent Design by Granville Sewell, God and Evolution: Protestants, Catholics, and Jews Explore Darwin’s Challenge to Faith, edited by Jay Richards, and Darwin’s Conservatives: The Misguided Questby John G. West. Library Cataloging Data The Myth of Junk DNA by Jonathan Wells (1942– ) Illustrations by Ray Braun 174 pages, 6 x 9 x 0.4 inches & 0.6 lb, 229 x 152 x 10 mm. & 0.26 kg Library of Congress Control Number: 2011925471 BISAC: SCI029000 SCIENCE / Life Sciences / Genetics & Genomics BISAC: SCI027000 SCIENCE / Life Sciences / Evolution ISBN-13: 978-1-9365990-0-4 (paperback) Publisher Information Discovery Institute Press, 208 Columbia Street, Seattle, WA 98104 Internet: http://www.discoveryinstitutepress.com/ Published in the United States of America on acid-free paper.
    [Show full text]
  • A Novel Approach for Predicting Protein Functions by Transferring Annotation Via Alignment Networks Warith Djeddi, Sadok Ben Yahia, Engelbert Mephu Nguifo
    A novel approach for predicting protein functions by transferring annotation via alignment networks Warith Djeddi, Sadok Ben Yahia, Engelbert Mephu Nguifo To cite this version: Warith Djeddi, Sadok Ben Yahia, Engelbert Mephu Nguifo. A novel approach for predicting protein functions by transferring annotation via alignment networks. 2019. hal-02070419 HAL Id: hal-02070419 https://hal.archives-ouvertes.fr/hal-02070419 Preprint submitted on 17 Mar 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A novel approach for predicting protein functions by transferring annotation via alignment networks Warith Eddine Djeddi1, Sadok Ben Yahia1;2∗ and Engelbert Mephu Nguifo3∗ 1University of Tunis El Manar, Faculty of Sciences of Tunis, LR11ES14, Capmus Universitaire 2092, Tunis, Tunisia 2Tallinn University of Technology, Department of Software Science, Akadeemia tee 15a, 12618 Tallinn, Estonia and 3 University Clermont Auvergne, CNRS, LIMOS, F-63000 CLERMONT-FERRAND, FRANCE ∗Corresponding author: [email protected], [email protected] Abstract One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge protein-protein interactions for many model species, it becomes an opportunity to computational methods to elucidate protein func- tion based on many strategies.
    [Show full text]
  • Обработка Результатов BLAST, Chaining, Netting
    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.
    [Show full text]
  • 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.
    [Show full text]