Gene Finding Strategies - Department of Biological Science
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1. if available use the window s own File menu Exit choice or Close or Cancel or OK button Introduction What sort of information can be determined from a genomic sequence Easy restriction digests and associated mapping e g software like the Wisconsin Package s Map MapSort and MapPlot Harder fragment assembly e g Week Four s GCG FAS and genome mapping like packages from the University of Washington s Genome Center http www genome washington edu Phred Phrap Consed http www phrap org and SegMap and The Institute for Genomic Research s http Avww tigr org Lucy and Assembler programs Very hard gene finding and sequence annotation This will be the topic of today s tutorial and is a primary focus of current genomics research Easy forward translation to peptides Hard again genome scale comparisons and analyses How are encoded proteins recognized in uncharacterized eukaryotic genomic DNA Translating from all translational start codons to all nonsense chain terminating stop codons in every frame produces a list of ORFs Open Reading Frames but which of them if any actually code for proteins And this only works in organisms without exons and introns Three general solutions to the gene finding problem can be imagined 1 all genes have certain regulatory signals positioned in or about them 2 all genes by definition contain specific code patterns an2. since I m guaranteeing you that all exons will be in the forward direction on these sequences This would not be the case in a real lab setting Run FindPatterns to discover all of the occurrences of the start and poly A patterns in your sequence You may not find any in which case you could rerun the program allowing one mismatch However this could bring in false positives so beware Especially pay attention to any mismatches found within the ATG start codon obviously it s a false positive if that s where a mismatch is located If you find any valid occurrences of the Kozak or poly A pattern check out the find output file It will list the pattern used the location of the pattern in your sequence and show any mismatches if you allowed them Also if your FindPatterns results looks promising use your Output Manager Add to Editor button and specify Overwrite old with new to add the new found feature annotation in the new rsf file to your FAS consensus sequence in the open Editor My search with my example genomic elongation factor sequence did not find any valid Kozak or poly A patterns Nothing was found in my example with zero mismatches and then when increased the mismatch level to one a Kozak pattern came up but its mismatch was in the start codon and poly A sites were everywhere 18 We ll use FindPatterns once more today to look for one dimensional signals However this time we ll
3. 563 578 Preferred region center between 1 and 5 Optimized cut off value 81 4 SG 23 0 0 38 0 15 24 18 SA 16 0 95 9 25 22 15 17 SU 45 0 5 26 43 24 33 33 SC 16 100 0 27 31 39 28 32 Total 303 303 303 303 303 303 303 303 CONSENSUS sequence to a certainty level of 63 percent at each position Cap Csn Length 8 October 7 1992 11 53 Type N Check 2736 1 KCABHYBY Finally McLauchlan et al s 1985 eukaryotic terminator weight matrix follows Possible eukaryotic termination signal region Base freguencies according to McLauchlan et al 1985 N A R 13 1347 1368 found in about 2 3 s of all eukaryotic gene sequences SG 19 81 9 94 14 10 11 19 SA 13 9 3 3 4 0 11 13 SU 51 9 89 3 79 61 56 47 SC 17 1 0 0 3 29 21 21 Total 70 70 70 70 70 70 70 70 CONSENSUS sequence to a certainty level of 68 percent at each position Terminator Csn Length 8 October 7 1992 14 25 Type N Check 2895 1 BGTGTBYY You can find as many of any of these sites in a DNA sequence as you want by running the list sizes as large as you want Most will be false positives None are a guarantee of coding potential only a possibility Not all genes have all or any of these sites in a biologically active state How is all of this sorted out There s got to be more so what else is there Content approaches Strategies for finding coding regions based on the content of the DNA itself I ve discussed many of the pitfalls of signal searching In general the
4. Navigating the Image The Gaicme Comparison Browser is being developed at the Gnome Center of Wisconsin to Several Haterobacterial genome datasets are curently available Clicking on the Genome Comparison Browser icon above provides Inwarmaps of all availble data Additionally the list belowcan be used to selecta particular visualisation The interactie circular diag ere developed to provide an interface to the browser begining from a global overviewof the circular diromosome Mac users please ead 0157 7 lisormap of 0157 H7 EDL933 genes ramactive circle diagoan fi globalviewof 0157 H7 EDLO33 chromosome pestis Livearmap of Yersinia pestis diagram for Yersinia p ges is global viewof Yersinia pestis genes on dense _hide s dense s hide Map Contigs Assembly Gap Coverage BAC End Pairs E ne paw Zz and the Ensembl project at the Sanger Center for Biolnformatics http Avww ensembl org 5 Netscape Ensembl Human Genome Server ContigView 9 Back i eo Print Security Shep A ve ength 1 Oooo Eve arceri QE What s Related E Looation tp ewe nse org Ho Ensembi e Human Contig View Home Human a What sNew a BLAST a ExpotDsta a Download a Disease Browser a Docs a e g U34879 AP000869 contigview Zhighlight NM_DO2T The Wellcome Trust gt Sanger Institute Find Sequence 3
5. continuous function so that each function defines an individual reading frame An open reading frame display accompanies each panel with start codons represented as vertical lines rising above each box and stop codons shown as lines falling below the reading frame boxes Do not use the ORF display for exon discovery but the stop codon portion of it may be helpful Rare codon choices are again shown for each frame now hash marks below the reading frames One must realize however that not all genes show particularly high codon usage preference This is especially true of 22 genes that are only weakly expressed Therefore you must as always carefully interpret your results and use as many sources of information as possible Homology inference with FrameAlign This part will be too easy because our Project Molecules are all very well studied Just imagine how difficult it would be if we couldn t find any close homologues We would only have the previous types of analyses to go on But for now we ll do it the easy way by aligning our genomic sequence to its exact protein counterpart Therefore temporarily switch to your xterm window do not yet exit SeqLab Change directory over to last week s database searching subdirectory and look at more the FastX output file Write down the top hit i e that pairwise alignment with the lowest E value that is relevant the very most similar entry to your FAS consensus from the Swiss Prot protein sequence
6. database Now return to your SeqLab session and load that sequence into the display by using the File Add Sequences From Databases button Remember that you need to specify both the database and the sequence name or accession code for this to work For instance typed sw ef11_ human under Database Specification in the Database Browser window for my example you ll use your own Project Molecule Swiss Prot entry Press Add to Main Window and then Close the Database Browser You ll now have your FAS consensus sequence and the new Swiss Prot entry one on top of the other in your SeqLab Editor Select both entries and then go to the Functions Pairwise Comparisons menu and pick FrameAlign For FrameAlign to work this way that is to use it for aligning more than one exon to a protein sequence you need to change some of its parameters Therefore press the Options button and change from the default local alignment to global alignment It also helps to tell FrameAlign Don t penalize gap extensions longer than about 12 or so This way there s minimal penalty for jumping over the introns Were the similarity between the protein and genomic sequence not so high then reducing all of the gap penalties may also be required but since we re dealing with things that should be nearly 100 identical that won t be an issue Close the O
7. have it look through David Ghosh s Transcription Factor Sites database 1990 Relaunch FindPatterns through the Windows menu Leave the Search Set as it is but press the Patterns button to change the patterns from the previous search Press Pattern Data File in the Pattern Chooser window and then replace the File Chooser specification in the Filter text box with genmoredata tfsites dat Press the Filter button and then select the file displayed tfsites dat and then press the OK button The Pattern Chooser window will update to show the new patterns Close its window Back in the main FindPatterns program window be sure that save matches as features in is still selected and that you are still using the OneStrand option If you allowed a mismatch in the previous search be sure to use the Options window to set it back to zero This is really important as even with zero mismatches you re going to get a ton of hits from this pattern database got 433 with my example sequence The top file displayed will be your new findpatterns rsf file that annotates all of transcription factor site locations Don t bother trying to read it just close it But do use the Output Manager Add to Editor button and then specify Overwrite old with new to add the new found feature annotation onto your FAS consensus sequence Also use the Outp
8. use the command line option data _datafile_name Name Offset Pattern Overhang Documentation Pribnow 1 TTGACwx 15 21 TAtAaT 0 Hawley amp McClure 1983 As mentioned above another signal that can be looked for in a similar fashion is the prokaryote Shine Dalgarno translational initiation ribosome binding site AGG GAG GGA x 6 9 ATG Stormo et al 1982 However the prokaryote patterns won t do us much good on eukaryotic sequences An impressive eukaryotic transcription factor consensus sequence database TFSites Dat Ghosh 1990 and 2000 is available in the GCG logical directory location GenMoreData tfsites dat Using this database is the same idea as looking for protein motifs in Bairoch s PROSITE Dictionary however with TFSites we are not looking for signatures that identify function or structure rather we are looking for signatures that identify the binding of various cataloged transcription factors to DNA FindPatterns can look for these type of sites But always beware of the inherent problem of one dimensional approaches they are usually not discriminatory enough i e in addition to finding the true positive sites they find lots of false positives as well A Better Way Two dimensional weight matrix signal searching and GCG s FitConsensus Just as Profile Analysis provides a more robust method of searching for protein sequence similarities FitConsensus provides a more robust nucleotide searching technique with a matrix appr
9. 0 SA 30 40 64 9 0 0 61 67 9 16 39 24 SU 20 7 13 12 0 100 7 11 5 63 22 27 SC 30 44 11 6 0 0 2 9 2 12 20 28 Total 140 140 140 140 140 140 140 140 140 140 137 137 CONSENSUS sequence to a certainty level of 75 percent at each position Length 12 bp 11 JUL 83 13 34 Check 6055 1 12 VMWKGTRRGW HH Even though this is a standard GCG sequence format file suitable as input anywhere you are asked for a sequence the FitConsensus program reads the matrix not the sequence Notice the location of the 100 GT requirement the splice cut occurs right before this not four bases away at the beginning of the matrix Next the acceptor csn file CONSENSUS of Acceptor Dat IVS Acceptor Splice Site Sequences from Stephen Mount NAR 10 2 459 472 figure 1 page 460 Intron ih Exon 52 24 19 22 17 20 37 29 18 22 32 SG 15 22 10 10 10 6 7 9 7 5 5 24 1 0 SA 15 10 10 15 6 15 11 19 12 3 10 25 4 100 sT 52 44 50 54 60 49 48 45 45 57 58 30 31 0 SC 18 25 30 21 24 30 34 28 36 35 27 21 64 0 ooo foe Total 114 114 115 127 127 127 128 128 128 130 131 131 131 131 131 131 131 131 CONSENSUS sequence to a certainty level of 75 0 percent at each position Length 18 February 15 1989 16 05 Check 3343 1 BBYHYYYHYY YDYAGVBH Here the problem of the cut site not being congruent with the beginning of the matrix is even worse it s fifteen bases away from the absolute AG This can easily cause misinterpretation of the results be careful Let s lo
10. 1987a Sequence Analysis in Molecular Biology Treasure Trove or Trivial Pursuit Academic Press Inc San Diego CA von Heijne G 1987b SIGPEP A sequence database for secretory signal peptides Protein Sequences amp Data Analysis 1 41 42 26
11. 3 57 40 14 21 21 21 17 20 SU 8 79 9 96 8 31 2 31 8 12 8 13 16 19 18 SC 37 12 0 3 0 0 1 1 11 35 38 33 30 28 26 Total 389 389 389 389 389 389 389 389 389 389 389 389 389 389 389 CONSENSUS sequence to a certainty level of 61 percent at each position Tata Csn Length 15 October 4 1992 17 12 Type N Check 715 1 STATAWAWRS SSSSS Next the GC box This consensus may relate to the binding of transcription factor Sp1 and occurs anywhere from 164 to 1 on the DNA sequence Eukaryotic promoter GC Box region Base freguencies according to Philipp Bucher 1990 J Mol Biol 212 563 578 Preferred region motif within 164 to 1 Optimized cut off value 88 SG 18 41 56 75 100 99 0 82 81 62 70 13 19 40 SA 37 35 18 24 0 1 20 17 0 29 8 0 7 15 SU 30 12 23 0 0 0 18 1 18 9 15 27 42 37 SC 15 11 2 0 0 0 62 0 1 0 6 61 31 9 Total 274 274 274 274 274 274 274 274 274 274 274 274 274 274 CONSENSUS sequence to a certainty level of 67 percent at each position Gc Csn Length 14 October 7 1992 13 46 Type N Check 7852 1 WRKGGGHGGR GBYK Next the cap signal The cap is a structure at the 5 end of eukaryotic mRNA introduced after transcription by linking the 5 end of a guanine nucleotide to the terminal base of the mRNA and methylating at least the additional guanine the structure is 7M G5 ppp Np The signal pattern is centered about 2 Eukaryotic promoter Cap region Base freguencies according to Philipp Bucher 1990 J Mol Biol 212
12. BSC4933 5936 5 Intro to Biolnfo Lab 6 BSC4933 5936 Introduction to Bioinformatics Laboratory Section Tuesdays from 3 45 to 5 45 PM Gene Finding Strategies Week Six Tuesday September 30 2003 Author and Instructor Steven M Thompson How are coding sequences recognized in genomic DNA After the sequencing s done and the fragments have all been assembled and preliminary database searches have been run what s next What more can we learn about a nucleotide sequence Searching by signal versus searching by content i e transcriptional and translational regulatory sites and exon intron splice sites versus nonrandomness codon usage and homology inference Understanding the concepts and limitations of the methods and differentiating between the approaches Steve Thompson Biolnfo 4U 2538 Winnwood Circle Valdosta GA USA 31601 7953 stevet bio fsu edu 229 249 9751 GCG is the Genetics Computer Group part of Accelrys Inc a subsidiary of Pharmacopeia Inc producer of the Wisconsin Package for sequence analysis 2003 Biolnfo 4U Nucleic Acid Characterization Recognizing Coding Sequences Standard disclaimer write these tutorials from a lowest common denominator biologist s perspective That is only assume that you have fundamental molecular biology knowledge but are relatively inexperienced regarding computers As a consequence of this they are written quite explicitly Therefore if yo
13. CodonPreference Gribskov et al 1984 CodonPreference additionally plots the compositional bias of the third position of each codon Bibb et al 1984 You must specify the codon usage table appropriate for your situation with either program Remember however that for most eukaryotic genomic sequences only the first exon will actually have a start codon Therefore Frames is generally more appropriate for sequences without exons such as cDNA or prokaryotic data For it to be of any help with sequences with exons and introns run it with the Show all start and stop signals not just open frames All option With genomic data this option allows us to see whether intron exon structure 11 and or sequencing errors may be responsible for the interruption of ORFs One advantage of Frames is it shows you both forward and reverse translation frames Homology inference Similarity searching methods can be particularly powerful for inferring gene location by homology These can often be the most informative of any of the gene finding techniques especially now that so many sequences have been collected and analyzed Sequence similarity search and alignment routines e g the Wisconsin Package programs Motifs the BLAST and FastA family of programs Compare and DotPlot Gap and BestFit and FrameAlign and FrameSearch can all be a huge help in this process But this too can be misleading and seldom gives exact start and stop positions unless you fi
14. Hs Mm UCSC vs MGD 466 00 cM Idb3 Hs Mm Map Information 2 romasonne 1 my Cytogenetic 1p36 13 p36 12 HUGO Markers Chr 1 xoan me Chr 1 WH6413 mw Chr WMAF 850 STS NCBI Reference Sequences RefSeq E http www nobinim nih gov aly se 4 SO ES A including the Genome DataBase http gdbwww gdb org and the excellent Genome Browser at the University of California Santa Cruz http genome ucsc edu O Netscape UCSCHuman Genome Browservs P6 Back Reload Home Search Netscape Print Security Shop Location J http Zgenome chgBSposition NM_002167 Ga What s Related UCSC Genome Browser on Aug 6 2001 Freeze move lt lt lt f lt lt gt Jb bee Jeoomin 15x S10 200m out 15x 3x 10 position et 6 602 size 1538 pixel width 620 jump 26312000 2312500 Bands Localized by FISH Mapp ing ciones Foston Chromosome B Genetic blue and Radiation Hybrid Tack Maps 13 er Gap Locat ions an Gene Prea Human mRNAs from Genbank X69111 D28449 Human ESTS Spliced 515 n eee Alignment of Tigr Gene Index T Tigr Gene Index Nonhuman mRNA stl Exof ish Ecor From Clone Over laps Je nucleotide Polymorphisms lt over lap SNPs I Ic le m Random Reads gle Nucleotide Polymorphisns Random sn Repeat ing Elements Repeatta
15. W ID3_HUMAN annotation human_id3 C m annotation human_id3 c m i annotation human_id3 annotation human_id3 annotation human_id3 annotation human_id3 annotation human_id3 annotation human_id3 annotation human_id3 annotation human_id3 pos 1614 col 1614 human_id3 gt Translation where to start and stop At least forward translation is nearly universal The genetic code is almost the same across all of life And even for those weird situations like some mitochondrial and ciliate genomes precompiled alternate translation tables are available But what about precursor versus mature proteins and signal peptides and other post translational processing mechanisms How can we tell just what makes up a mature protein Many matters complicate the process As we ve seen exons and introns in most Eukaryota can be especially confusing but prokaryotes and organelles have their own problems too One that concerns all genes is after you do translate the entire thing whether it has a signal peptide portion and how to tell which is what A database of pre protein signal peptides is available through Gunnar von Heijne for just this type of analysis 1987b The Wisconsin Package program SPScan incorporates von Heijne s method and can be run with a prokaryote gram negative or positive switch to change from the default eukaryote search matrix It is remarkably a
16. ace user with your account name gt ssh X user mendel csit fsu edu Do not issue this command on MS Windows SSH XWin32 Preliminary preparations Change your directory cd from home to last week s subdirectory List that directory 1s and check out the files left over from last week s tutorial Look through them more and remove rm any that you don t want to save Be sure to save your BLAST and FastX output files from last week we ll be using them later on in the semester Also save your FAS consensus sequence that was used as a query last week Next change directory back to your home directory create a subdirectory mkdir for this week s tutorial data and then change directory into it After you ve taken care of these file maintenance chores launch SeqLab with the following command but remember with SSH XWin32 you need to launch xclock s first gt seqlab amp Next it would probably be helpful to again change your SeqLab working directory to your present location so that everything you do today will automatically be saved in your new directory rather than last week s directory Do this with SeqLab s Options Preferences Working Dir button Now verify that you are in SeqLab s Main List Mode and start a new list to contain this week s data Therefore select New List from the File menu and give your new list an appropri
17. age provides a Web hyperlink portal to a wealth of information general description homologous sequences mapping location OMIM and PubMed associations oz Netscape LocusLink Report p Back Forward Reload Home Search Netscape Ima Print Security Shop Stop ia ous PARS 7www nobi nim nih gov Locusl ink LoeRpt ogi GEN what s Related LocusLink SJ Osplay Bnet Organism All rs a ae _____ Mew Hs ID3 one of 1 Losi Save All Loci gm n OON I I REMON a RET i art Tick to Display MRNA Genomic Aignments Gpanning 1860 bps ca IB Homo sapiens Official Gene Symbol and Name HGNC 108 inhibitor of DNA binding 3 dominant negative helix loop helix protein LoousID 3399 Overview R Protecme Summary Member of the Id helix loop helix farily of proteins inhibits DNAbinding of E2Acontaining complexes Locus Type gene with protein product function known or inferred Product inhibitor of DNA binding 3 dominant negative helix loop helix protein Alternate Symbols HEIR 1 Alias Inhibitor of DNA binding 3 dominant negative helix loop helix Function Submit GeneRIF all Pubs Gere Ortdogy Term Eviderce Source Pub developmental processes P Proteome Pm transcription co repressor E Proteome p Other Ontologies Term Evidence Sarce Pub Inhibitor or repressor E Proteome pm Relationships 2 Mouse Homology Maps NCBI vs MGD 4 66 00 cM I3
18. ate name It s not essential to use the file name extension list but it s a good idea Check OK You should now be in List Mode with an empty window Go to the File menu and select Add Sequences From Sequence Files Use the Directories column to move from your present directory over to Week 17 four s subdirectory and then replace the text in the Filter text box with the name or a wildcard specification that will identify your FAS consensus sequence used as the query last week Press the Filter button and then select the correct entry Press the Add button to add it into your new empty list file and then Close the Add Sequences window Select the sequence in your new list and switch Mode to Editor One dimensional signal searching with FindPatterns Begin your Project Molecule s FAS consensus gene finding investigation with a simple one dimensional start and poly A signal search FindPatterns allows you to type individual patterns in or you can specify data files as we did in the primer discovery tutorial We ll begin by looking for Kozak s 1984 eukaryotic start consensus and Proudfoot and Brownlee s 1976 poly A adenylation signal Launch FindPatterns from the Gene Finding and Pattern Recognition Functions menu Press the Search Set button and then the Add Main List Selection button in the new window Sel
19. ate names that make sense to you SeqLab will close Log out of your current UNIX session on Mendel and exit the X software on the workstation that you are sitting at Homework assignment Submit your consensus sequence to an appropriate World Wide Web gene finding site as discussed in the introduction Do not use X for this as the copying and pasting between Mendel and your workstation will generally be simpler in a standard ssh terminal window After comparing all of the results from this tutorial and from the WWW site query above with reality as predicted by FrameAlign tell me what programs found the real exons in your consensus sequence want to know where each exon lays and which predictions correctly identified each As with last week s homework type up a simple table with these answers For instance use something like the following imaginary values Exon 1 Exon 2 Exon 3 etc Reality 632 to 701 838 to 929 etc FindPatterns TFSites TATA at 598 poly A at 3896 Kozak ATG at 632 FitConsensus TATA at 578 acceptor at 823 donor at 934 donor at 1235 etc ccaat at 454 donor at 694 etc terminator at 4987 etc TestCode high probability low probability medium high etc CodonPreference high probability high probability etc WWW what happened Conclusion You have been exposed to a perplexing variety of techniques for the identification and analysis of protein coding regions in genomic DNA As in all molecular and biological computer a
20. ccurate Beyond just finding genes Genome scale analyses So locating genes within uncharacterized genomes is a huge matter but what about comparing and analyzing genome scale sequences megabases at a time What resources are available for that 14 Unfortunately much traditional sequence analyses software such as the Wisconsin Package break down when asked to analyze sequence datasets of this order Along these lines for your information the Wisconsin Package s restrictions as of version 10 3 allow individual sequences to be a maximum of 350 Kb in length longer entries are cut into overlaps in the local database though SeqLab can display longer sequences and therefore cope with some genome scale analyses The MSF file format can hold up to 500 sequences RSF can hold much more only limited by system memory This allows programs such as HmmerAlign to produce multiple sequence alignment output larger than 500 sequences PileUp itself can handle a sequence alignment up to 7 000 characters long including gaps Input sequences are restricted to a length of 5 000 characters by default The overall surface of comparison is restricted to 2 250 000 with the default program a bit more than all the residues or bases plus all the gaps in the alignment Alternative executables are provided with the Package for allowing 10 000 15 000 and 20 000 character input though these executables are usually not scrip
21. cophytes fungi myxomycetes protists 3 be experimentally defined or sufficiently similar to one defined as such 4 be biologically functional 5 be available in the current EMBL release 6 be distinct from other promoters in the database EPD Release 73 January 2003 has 2997 total promoter entries References Bibb M J Findlay P R and Johnson M W 1984 The relationship between base composition and codon usage in bacterial genes and its use for the simple and reliable identification of protein coding sequences Gene 30 157 166 Bucher P 1990 Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences Journal of Molecular Biology 212 563 578 Bucher P 1995 The Eukaryotic Promoter Database EPD EMBL Nucleotide Sequence Data Library Release 42 Postfach 10 2209 D 6900 Heidelberg Fickett J W 1982 Recognition of Protein Coding Regions in DNA Sequences Nucleic Acids Research 10 5303 5318 Genetics Computer Group GCG Copyright 1982 2003 Program Manual for the Wisconsin Package version 10 3 http Awww accelrys com products gcg_wisconsin_package index html Accelrys a wholly owned subsidiary of Pharmacopeia Inc San Diego California U S A Ghosh D 2000 Object oriented transcription factors database ooTFD Nucleic Acids Research 28 308 310 Ghosh D 1990 A relational database of transcription factors Nucleic Aci
22. d 3 many genes have already been sequenced and recognized in other organisms so we can infer function and location by homology if our new sequence is similar enough to an existing sequence All of these principles can be used to help locate the position of genes in DNA and are often known as searching by signal searching by content and homology inference respectively Homology inference can be very helpful but what happens in the case where no similar proteins can be found in the databases and even if homologues can be found discovering exon intron borders and UTRs 5 and 3 untranslated regions can be very difficult If you have cDNA available then you can try to align it to the genomic in order to ascertain where the genes lay but even this can be quite difficult and cDNA libraries are not always available No one method is absolutely reliable but one seldom has the luxury of knowing the complete amino acid sequence to the protein of interest and simply translating DNA until the correct pieces fall out This is the only method that would be 100 positive Since we are usually forced to discover just where these pieces are especially with genomic DNA computerized analysis becomes invaluable DNA needs to be very special in order to encode genes It must have regulational switches to turn things on and off and most eukaryotic DNA must have signals that indicate the beginnings and ends of exons and intr
23. dow 200 bp February 9 2003 11 59 Moot O61 PO HtA Hateoott 1 I i KO eHe EESSI I BD CHD Bei HS gt bd AD GH Hdd gt Hdd lS ti ee OD Ide RD lt td DDD as Hl SHS BOU IS BO GeEt Hd caSote ME Bl RH Ot gt SO oa te e tlode Tes 2 1 PEA iat ne aT 0 1 000 2 000 3 000 4 000 Fit In Out Crosshairs Print Close l page 1 of 1 Help 20 The plot is divided into three regions The top and bottom areas predict coding and noncoding regions respectively to a confidence level of 95 while the middle area claims no statistical significance Diamonds and vertical bars above the graph denote potential stop and start codons respectively One limitation of this program is it is not designed to detect coding regions shorter than 200 base pairs hence the default 200 bp window size No claim is made for significance with windows less than the default 200 therefore smaller exons may be missed Launch Frames from the same menu as the others next As discussed in the introduction Frames generally isn t that helpful for eukaryotic genomic DNA but works great for anything without introns To use Frames in a manner that is helpful with introns press the Options button First off notice that the default codon frequency table comes for E coli not what we need for any of out Project Molecules Therefore press the Codon Frequency Table button and choose the m
24. ds Research 18 1749 1756 25 Gribskov M and Devereux J editors 1992 Sequence Analysis Primer W H Freeman and Company New York N Y U S A Gribskov M Devereux J and Burgess R R 1984 The codon preference plot graphic analysis of protein coding sequences and prediction of gene expression Nucleic Acids Research 12 539 549 Hawley D K and McClure W R 1983 Compilation and analysis of Escherichia coli promoter sequences Nucleic Acids Research 11 2237 2255 Kozak M 1984 Compilation and analysis of sequences upstream from the translational start site in eukaryotic mRNAs Nucleic Acids Research 12 857 872 McLauchen J Gaffrey D Whitton J and Clements J 1985 The consensus sequences YGTGTTYY located downstream from the AATAAA signal is required for efficient formation of mRNA 3 termini Nucleic Acid Research 13 1347 1368 Mount S M 1982 A catalogue of splice junction sequences Nucleic Acids Research 10 459 472 Praz V P rier R C Bonnard C and Bucher P 2002 The Eukaryotic Promoter Database EPD new entry types and links to gene expression data Nucleic Acids Research 30 322 324 Proudfoot N J and Brownlee G G 1976 3 noncoding region in eukaryotic messenger RNA Nature 263 211 214 Stormo G D Schneider T D and Gold L M 1982 Characterization of translational initiation sites in E coli Nucleic Acids Research 10 2971 2996 von Heijne G
25. e more appropriate choice for most genomic eukaryotic sequences After that s done we can see that there are many potentially translated stretches so what What can be done with them how can we turn them into potential genes Signal searching Signal searches look for transcriptional and translational features Typical signals to look for are promoter and terminator consensus sequences and repeat regions GCG provides a searching program named Terminator for looking for terminator sites in prokaryotic rho independent cases however promoter signals from both prokaryotes and eukaryotes are so varied that they do not have a canned search for them An impressive eukaryotic transcription factor consensus sequence database has been assembled though and prokaryotic promoter sequences are fairly well characterized We can utilize the Wisconsin Package program FindPatterns to look for these type of sites within our sequence GCG also provides the ability to find short consensus patterns based on a family of related sequences using weight matrix analysis with the programs Consensus and FitConsensus These can be used to form and search for specific promoters or other signals based on known sequences Also many termination sites are accompanied by inverted repeats and enhancer sequences are often strong direct repeats because of these points the GCG programs StemLoop and Repeat as well as dotplot procedures may be helpful Start sites Transcri
26. e upstream of it End sites Transcriptional terminator and attenuator sequences can help identify gene ends as do the chain termination nonsense stop codons The GCG program Terminator will find about 95 of all prokaryotic factor independent terminators This is great odds for any computer algorithm even its namesake Arnold Schwarzenegger would have a hard time matching this But that s only for prokaryotes The sequence YGTGTTYY has been reported as a eukaryotic terminator consensus McLauchlan et al 1985 this is the consensus from the weight matrix listed below and the poly A adenylation signal AAUAAA is well conserved Proudfoot and Brownlee 1976 However exceptions can be found especially in some ciliated protists and due to eukaryote suppresser tRNAs The GCG programs StemLoop and Repeat may also provide some regulatory insight since many eukaryotic terminators have hairpin structures associated with them and some enhancer sequences contain strong direct repeats It s all quite complicated Nothing is as simple as it could be in biology and most signal searches even a sophisticated two dimensional approach like Terminator find too many false positives in other words they are not discriminatory enough Just like Schwarzenegger in T2 a few innocents always manage to get in the way All of these types of signals can help us recognize coding sequences however realize the inherent problems of consensus searches A major p
27. ect your FAS consensus sequence in this week s new list in the List Chooser window and then press Add and then Close Also Close the Build FindPattern s Search Set window Next press the Patterns button in the FindPatterns program window to get a Pattern Chooser for FindPatterns window Press Create New in the Pattern Chooser window This will produce another new window Create or Modify Item in its Pattern text box type Kozak s consensus pattern cc A g ccAUGg The use of upper and lower case letters is unnecessary and only indicates which positions are strongly conserved Give the pattern a name used Kozak and fill in a comment that makes sense Press Add and then Close the pattern editor window Repeat the Create New procedure with the poly A signal AAUAAA Close the Pattern Chooser window after specifying the two patterns The FindPatterns main program window should now show that you are using your chosen entry and your selected patterns Select the checkbox next to save matches as features in the default RSF file name is fine Next press the Options button and then push in the checkbox next to search only the top strand of nucleotide sequences in the FindPatterns Options window and then Close the Options window We are taking advantage of this OneStrand option to reduce complexity
28. gr soripts CHR2 webmam mumplot2v ntmt02 amp D amt L 1008 Mycobacterium tuberculosis Ha7Rv lab strain J Mycobacterium tuberculosis CDC1551 a Mocs Myconoctersam tuberculosis Ha7ey P Moun Mycaborter sum tuberculosis COCISSI Mouseover options Activate option and mou to display infocration show alignment into C show gene into fuberouasis COCISS1 vs Myocbacterium tuberoucele HI7Ru lab strain ification Took 2019010 Zz 4 i 4 4m bed Ee cordrates mml peo Your Project Molecular System choices Your Project Molecules are again listed Please maintain using the same one as in all the previous tutorials _ higher plant ribulose bisphosphate carboxylase oxygenase small subunit only 2 vertebrate P21 ras proto oncogene transforming protein 3 vertebrate basic fibroblast growth factor 4 fungal Cu Zn superoxide dismutase Week 6 Tutorial A Real Life Project Oriented Approach Gene Finding Strategies Activate and or log on to the computing workstation you are sitting at and then log onto Mendel with an X tunneled ssh session Remember that we do this on the Conradi PC s with the combination SSH and XWin32 Review the Biology Computing Facility Help pages if you ve forgotten how If using an xterm window on Mac OSX or UNIX Linux then issue the following command the X has to be capitalized and repl
29. i El chromosome 1 Cha L t x aE EE l ex El overview Chr 1 band P36 15 J iv We DNACcontigs Markers Genes NOVEL LHNRPR LTCEAS LQ9NXH7 SE2F2 t103 NOVEL LTCEBSLGALE tFUCAL LNOVEL ki Lasctes EL Trove tgaazpe UCNR2 WON Loma1so49 1 tm UiNpune Crusip2 eaisaso 1 timati Trove tanasg oass72 Geo8 0159 64 tCAB40160 1 egend MES ENGEMEL PREDICTED GENES KNOWN mm ENSERDL PREDICTED GENES NOVEL Gene legend SES EMSL CURATED GENES J El Detailed view Jumptoche T bp reei7itto ta7ivert Ez Other tools allow detailed observations of genome scale alignments e g the Blattner Lab E coli Genome resources at the University of Wisconsin Madison Campus http Avww genome wisc edu shown top on the right opposite E coli genome Comparison Jinenr map comparing genomes of laboratory strain K 12 entershemombagic 0157 H7 strain EDLO33 and a gt Fer i sie 98 SP Es A And tools are even available for performing genome scale alignments on your own sequences A very good method for this objective is distributed by The Institute for Genomic Research http www tigr org in the MUMMER package It also has a Web graphical viewer interface QO Netscape MUMer plot and Gene Display OF oe ER Print Seowity Shop Step 05S rage ISEC E what Related T Netsite J http www tior org ti
30. lations however it tells us the strand and reading frame for the gene products 10 Non Randomness techniques GCG s TestCode The first technique relies solely on the base compositional bias of every third position base non randomness A truly random sequence does not show any type of pattern at all and is not characteristic of any coding sequence The TestCode algorithm can estimate the probability that any stretch of DNA sequence is either coding or noncoding It will not tell us the strand or reading frame however it does not require any a priori assumptions as it relies exclusively on a statistical evaluation of the sequence composition itself the nonrandomness of every third base This statistic is known as the period three constraint and was developed by James Fickett at Los Alamos 1982 Codon usage analysis Codon frequency tables GCG s Frames and CodonPreference The second content type of gene finding strategy utilizes the fact that different organisms have different codon usage preferences i e genomes use synonymous codons unequally in a phylogenetic fashion Codon usage frequency is not the genetic translation code the genetic code is nearly universal across all phylogenetic lines with some notable exceptions However not all lines use the same percentage of the various degenerate codons the same amount The manner in which different types of organisms utilize the available codons is usually tabulated into
31. m runs we ll have to do it manually There are no options in this program but it may help reduce confusion some by reducing the Number of fits to show from the default 40 down to 20 Press the Consensus table file button then use the File Chooser to specify genmoredata donor csn and then press the Filter button to display the file Select the donor consensus file in the Files window then press OK and then Run FitConsensus Repeat this procedure with the other six consensus files in GenMoredata 0k 66 66 tata csn cap csn ccaat csn 66 acceptor csn gc csn and terminator csn Use the Output Manager to give the output files names that make sense based on the consensus file that you used 19 I ll show my elongation factor example donor site consensus fit output here FITCONSENSUS of FAS consensus Using Consensus donor csn CONSENSUS from Splice site sequences from Stephen Mount NAR 10 2 459 472 List size 20 Average quality 37 80 February 9 2003 10 21 position 605 1243 1722 1784 1802 1889 2268 2670 2784 2904 2963 frame 2 1 3 2 2 2 3 3 3 3 2 quality 61 08 53 00 52 92 48 25 48 08 49 00 48 33 53 58 48 17 52 75 47 83 position 3042 3248 3337 3458 3537 3572 3592 4173 4444 frame 3 2 1 2 3 2 1 3 1 quality 47 17 51 17 53 17 46 92 48 25 49 92 46 83 49 08 47 83 Note that the output includes the position frame and quality of each ma
32. nalyses the more you understand the chemical physical and biological systems involved the better your chance of success in analyzing them Certain strategies are inherently more appropriate to others in certain circumstances Making these types of subjective discriminatory decisions and utilizing all of the available options so that you can generate the most practical data for evaluation are two of the most important take home messages that can offer Several general references are available in this field many provide extensive weight matrices for consensus pattern searches Naturally each would have to be tailored into the format correct for whichever matrix searching program you might be using They also all describe many of the factors involved and the 24 constraints used in content type algorithms Sequence Analysis Primer by Gribskov and Devereux 1992 is a dated but still good starting point Supplemental information Phillipp Bucher s Eukaryotic Promoter Database EPD 1995 and 2002 http Awww epd isb sib ch index html Bucher has assembled an extensive list of eukaryotic promoter regions compiled from the EMBL database His database includes a user s manual the sequence information itself and an independent journal abstracted data reference section for each entry In order to be included in EPD an entry must l be recognized by eukaryotic RNA POL II 2 be active in eukaryotes excludes phy
33. nd an extremely close homologue Combined gene inference methods available on the Internet An additional source of information that should not be ignored is the Internet Several powerful World Wide Web servers have been established that can be a huge help with gene finding analyses Most of these servers combine many of the methods previously discussed but they consolidate the information and often combine signal and content methods with homology inference in order to ascertain exon locations Many use powerful neural net or artificial intelligence approaches to assist in this difficult decision process A very nice bibliography on computational methods for gene recognition has been compiled at Rockefeller University http linkage rockefeller edu wli gene and the Baylor College of Medicine s Gene Feature Search http searchlauncher bcm tmc edu seg search gene search html is another nice portal Five popular gene finding services are GrailEXP Geneld NetGene2 GenScan and GeneMark The neural net system GrailEXP Gene recognition and analysis internet link EXPanded http grail lsd ornl gov grailexp is a gene finder an EST alignment utility an exon prediction program a promoter and polyA recognizer a CpG island locater and a repeat masker all combined into one package Geneld http www1 imim es software geneid index html is an ab initio Artificial Intelligence system for predicting gene structu
34. oach However FitConsensus does not incorporate variable weighting depending on positional conservation like profile analysis does nor does it allow gapping to occur within its pattern However these types of patterns probably should not be allowed to gap anyway and all positions of the pattern may be almost equally important since the patterns are generally quite small GCG has pre assembled consensus weight matrices of the donor and acceptor site sequences at exon intron splice junctions for use with FitConsensus available in their public data files However they do not provide any others therefore have reformatted the four weight matrix descriptions of eukaryotic RNA polymerase II promoter elements reported by Bucher 1990 into a form appropriate for the Wisconsin Package Additionally McLauchlan et al 1985 assembled a eukaryotic terminator weight matrix that have reformatted for GCG use have placed all of these files in GCG s logical directory location GenMoreData on the FSU GCG server They have the file names tata csn cap csn ccaat csn gc csn and terminator csn Specifically take a look at the donor csn file The matrix describes the probability at each base position to be either A C U or G in percentages indicate the cut site and the 100 GT consensus below CONSENSUS from Donor Splice site sequences from Stephen Mount NAR 10 2 459 472 figure 1 page 460 Exon Intron G 20 9 11 74 100 0 29 12 84 9 18 2
35. ok at the other five matrices that have made available First the CCAAT site The cat box usually occurs around 75 base pairs upstream of the start point of eukaryotic transcription preferred region 212 to 57 it may be involved in the initial binding of RNA polymerase II and CCAAT binding proteins have been identified Eukaryotic promoter CCAAT region Base freguencies according to Philipp Bucher 1990 J Mol Biol 212 563 578 Preferred region motif within 212 to 57 Optimized cut off value 87 2 SG 7 25 14 40 57 1 0 0 12 9 34 30 SA 32 18 14 58 29 0 0 100 68 10 13 66 SU 30 27 45 1 11 1 1 0 15 82 2 1 SC 31 30 27 1 3 99 99 0 5 0 51 3 Total 175 175 175 175 175 175 175 175 175 175 175 175 CONSENSUS sequence to a certainty level of 68 percent at each position Ccaat Csn Length 12 October 7 1992 12 17 Type N Check 5922 1 HBYRRCCAAT SR Next the famous TATA site aka Hogness box The tata box is a conserved A T rich sequence found about 25 base pairs upstream of the start point of eukaryotic transcription preferred region 36 to 20 It may be involved in positioning RNA polymerase II for correct initiation and it binds Transcription Factor IID proteins Eukaryotic promoter TATA region Base freguencies according to Philipp Bucher 1990 J Mol Biol 212 563 578 Preferred region center between 36 and 20 Optimized cut off value 79 SG 39 5 1 1 1 0 5 11 40 39 33 33 33 36 36 SA 16 4 90 1 91 69 9
36. ons Coding regions must have certain periodicities and patterns These constraints arise in a number of ways the three base genetic code the wobble hypothesis an unequal use of synonymous codons translational factors the amino acid content of the encoded proteins themselves and possibly because of remnants of an ancient genetic code The problem all comes down to figuring out all of your DNA s URFs and ORFs what s the difference Do any of them actually code for a protein URF Unidentified Reading Frame any potential string of amino acids encoded by a stretch of DNA Any given stretch of DNA has potential URFs on any combination of six potential reading frames three forward and three backward ORF Open Reading Frame by definition any continuous reading frame that starts with a start codon and stops with a stop codon Not usually relevant to discussions of genomic eukaryotic DNA but very relevant when dealing with mRNA cDNA or prokaryotic DNA The first order of business is to translate all six reading frames of the sequence because there is no way of knowing where any genes may lay upon it DNA often has genes on opposite reading frames This will generate all URFs as opposed to ORFs This is an especially important distinction when dealing with 4 organisms that have exons and introns since many exons will not begin with a start codon only the first will necessarily begin with one therefore URFs are th
37. ost appropriate table in the Chooser for Codon Frequency Table window for your system and then press OK Alternative tables are also discussed in the introduction Next tell Frames that you want to Show all start and stop signals not just open frames to activate the All command line option lt Click gt Close in the Option window and then Run in the program window My example Frames output follows below oO users thompson seqlab fas_consensus_24figure Aa FRAMES of Input _24 ref lt FAS_cormareus gt Ck S322 tot 4 695 February 9 2003 12138 Coden Table humanhigh cod Threshold 0 00 mo rrr rr rh it ro ott a r iror r rii Boma tte oe aot too rr rrr h ti ttt 2 MaaaaaauMila Me rrr rha tt Fit In Out Crosshairs Print Close Page The plot shows ATG start signals with hash marks rising above the horizontal strand axes and stop signals with hash marks falling below the axes and it indicates rare codon choices above some specified threshold with a dot above each individual reading frame Notice that it shows all six frames both forward and reverse The final GCG content analysis program that we ll run is CodonPreference It also uses a default E coli codon usage table so change it to something more appropriate Launch CodonPreference and change 21 its codon frequency table just like in Frames It also allows a Show all start and stop signals no
38. ptional regulatory sites such as promoters and other transcription factor and enhancer binding sequences can help identify the beginnings of genes however some of these motifs can be quite distant from the actual start of transcription The prokaryote Shine Dalgarno consensus AGG GAG GGA x 6 9 ATG Stormo et al 1982 based on complementarity to 16s rRNA obviously relates to translation initiation as does the methionine start codon itself Eukaryote ribosomes seem to initiate translation at the first AUG encountered following the modified guanosine 5 cap and do not appear to be based on 18s complementarity Kozak 1984 has compiled a Eukaryote start consensus of cc A g ccAUGg that seems to hold true in many situations However matters can be complicated by alternative start codons AUG works in about 90 of cases but there are exceptions in some prokaryotes and organellar genomes Exon Intron junctions Well characterized splice site donor acceptor consensus sequences can point to intron exon borders The exon intron junction has the following consensus structure around its donor and acceptor sites Donor Site Acceptor Site Exon Intron ______ gt Exon A646073 6Py74 87NC65 The splice cut sites occur before a 100 GT consensus at the donor site and after a 100 AG consensus at the acceptor site GCG s weight matrices for these sites do not start at the cut site rather they start a varying distanc
39. ptions window and Run the program After the program finishes the framealign file will be displayed Notice how the introns have been successfully jumped over by the algorithm Write down the nucleotide positions where each exon starts and stops for use in the homework Close the framealign file and use the Output Manager to give it a more sensible name and then Close the Output Manager to return to your Editor display If this were a real lab experience with uncharacterized eukaryotic genomic DNA then you would want to go back to your SeqLab Editor and use its ability to add custom feature annotation beyond what can be done automatically by those programs that can produce RSF output After getting all of your results in one spot the Editor display you would decide what regions are exons and translate them For the sake of time will not require you to do this today but realize that even with a very close or identical homologue as we have here its not a trivial chore Nonetheless study the results from today s tutorial note how the various programs outputs either agree or disagree with those regions that FrameAlign nailed down as the true translated regions of your consensus sequence These observations will be in today s homework 23 Exit SeqLab with the File menu Exit choice and save your RSF file and any changes in your list with appropriate responses Accept the suggested changes and design
40. quence with all of the relevant results from all analyses in order to see how it all comes together Show where the various signals and content biases you found are located Indicate precise positions where you believe relevant features lay Note where you feel the actual starts and stops of the coding regions are in your sequence Develop a coding system to represent various attributes Used in combination text and color can be very helpful Similarities discovered through database searching will greatly assist your interpretation especially if you are dealing with a system that has much available data More annotation added to the map results in greater consensus between the various methods and therefore more trust in their combined inference More data is almost always good in computational molecular biology Wherever a preponderance of data suggests a gene is located believe one is there where the data is contradictory decisions can t be made and where lots of data argues against the location of genes believe one is not there You need to synthesize all this data together to decide what portions of the tentative URFs actually code for proteins The validity of your interpretations will relate directly to your understanding of the molecular biology of the system Putative coding regions CDSs that the analyses have indicated can then be translated Analyze genomic data carefully It won t be as easy as you would have hoped for For
41. re optimized in genomic Drosophila or Homo DNA NetGene2 http www cbs dtu dk services NetGene2 another ab initio program predicts splice site likelihood using neural net techniques in human C elegans and A thaliana DNA GenScan http genes mit edu GENSCAN html is perhaps the most trusted server these days with vertebrate genomes The GeneMark http opal biology gatech edu GeneMark family of gene prediction programs is based on Hidden Markov Chain modeling techniques originally developed in a prokaryotic context the programs have now been expanded to include eukaryotic modeling as well Summary The combinatorial approach The chore of identifying coding sequences is far from trivial and is a long way from being solved in an unambiguous manner however it is extremely important anytime anyone starts sequencing genomic DNA and doesn t have the luxury of a cDNA library and or a very near fully characterized genomic homologue 12 About the best way to make any sense out of all of this data is to get it all in one spot Automated solutions exist e g Gaasterland s Magpie system and its relatives http genomes rockefeller edu research shtml but often you ll be forced to do it manually Either prepare a paper map or a text file map or use the annotation capabilities of some of the specialized sequences editors such as the Wisconsin Package s SeqLab However you do it annotate your se
42. roblem is simple one dimensional consensus pattern type searching is often either overly or insufficiently stringent because of the variable and loosely defined nature of these types of sites An advantage is they are quick and easy Two dimensional weight matrix approaches can be much more powerful and sensitive but they are not nearly as easy to set up in most sequence analysis packages Both types of signal searches pinpoint exact locations on the DNA strand A main point consensus type searches emphasize is Don t believe everything your computer tells you von Heijne 1987a A computer can provide guidance and insight but the limitations can sometimes be overwhelming One dimensional signal searching The Wisconsin Package s FindPatterns program provides a simple consensus style pattern matching tool have written and placed the prokaryote promoter consensus pattern TTGACwx 15 21 TAtAaT based on the E coli data of Hawley and McClure 1983 in the GCG logical directory location GenMoreData promoter dat that encompasses both the 35 and 10 regions The Pribnow box pattern file follows so that you can see it s format and content The standard E coli RNA polymerase promoter Pribnow box file for the program FINDPATTERNS This pattern includes both the 35 amp the 10 region For an incredibly extensive list of eukaryotic transcription factor recognition sites see the GCG public datafile tfsites dat To specify one of these files
43. second type of gene finding technique searching by content is more reliable at least it is much less fraught with false positives but its answers aren t concise either They do not provide exact starting and stopping positions just trends However used in concert the two can be quite powerful tools Adding in the third inference through homology often clinches the story Searching by content utilizes the fact that genes necessarily have many implicit biological constraints imposed on their genetic code This induces certain periodicities and patterns in coding sequences as opposed to noncoding stretches of DNA These factors create distinctly unique coding sequences non coding stretches do not exhibit this type of periodic compositional bias These principles can serve to help discriminate structural genes from all the rest of the so called but misnamed junk DNA found in most genomes depending on what the sequence looks like in two ways 1 based on the local non randomness of a stretch and 2 based on the known codon usage of a particular life form The first the non randomness test does not tell us anything about the particular strand or reading frame however it does not require a previously built codon usage table The second approach is based on the fact that different organisms use different frequencies of codons to code for particular amino acids This requires a codon usage table built up from known trans
44. sker array Exper iments for NCI 69 Cel Lines ncIe9 Click onateature for details Click on base position to zoomin around Cursor Click on left mini buttons fortrack specitic options Guidelines SA Labets lett A center A Track Controls Base Position Chromosome Band FISH Clones STS Markers GC Percent one tscape University of Wisconsin Madison E coli Genome Project Back Reload Home Search Netscape Print Security Shop 7 Location dy Gal What s Related E coli Genome Proje University of Wisconsin Madi http www genome w TOOLS We are presenting two new tools for viewing our genomic sequencing data We feel that these tools will aid us our collaborators and the public in mining information from the forts from our lib Genome Comparison Browser This tool displays an interactive visualization of genome data with access to sequences armotations and comparisons to other genomes Users can nalk through detailed linear maps of the entire genome or search for particular regions Pop up boxes appear to describe the features ofthe data as usere mouse over elements of the display Clidring on elements leads to either amore detailed visualisation or annotation and sequence infomation Each dataset is distinct and a legend with additional instructions descriptions and appropriate references for published data is Provided by a link to
45. t just open frames option take advantage of this through the Options menu However CodonPreference only shows forward translation frames Therefore if you had to analyze the opposite strand you would have to reverse complement your sequence and run the program a second time We will not be doing that here The plot from CodonPreference run with this option on my example s forward strand is shown below 0 users thompson seqlab fas_consensus_25 figure f POOOMPREFERENCE of fuara thongaon seqlab nerdel input_25 rst FAS corseraes Chi S822 1 to 4695 February 9 2008 12352 Codon Table fuer local gag gegara datarmeredata human_h gh od PrefWindon 25 Rare Codon Threshold 0 10 Blasindows 25 Denalty 154 0 Bao 3 1 Third Pesttlon QC Blas Fit In Out Crosshairs Print crose A Page lof 1 gt The plot shows two color coded curves a red codon preference curve and a blue third position GC bias curve for each forward reading frame of the sequence in question These curves rise above background scatter in areas of strong probability of coding potential In general coding regions will show a propensity of preferred codons and will have more G s or C s in their third position The horizontal lines within each plot are the average values of each attribute CodonPreference moves its window in increments of three recalculating its statistic at each position to generate a
46. tch The position and quality are tremendously helpful Position is where in your sequence the specified weight matrix begins perhaps not where the site that you are most concerned is occurs rather only where the matrix begins This is particularly important in the donor and acceptor matrices as these both begin in front of the splice site not at it Quality is the percentage fit to the matrix The higher the percentage the more probability the site is an actual signal The frame designation is troubling it is quite misleading as it identifies the frame of the best fit to the matrix not to the reading frame so disregard it It ll only confuse you Don t worry about trying to incorporate these results into your SeqLab sequence s annotation at this point We ll take care of that later on Content approaches TestCode Frames CodonPreference Let s begin investigating gene finding content approaches with a method based only on the randomness of every third position within a given DNA sequence Make sure that your FAS consensus sequence is still selected and then launch TestCode off of the Gene Finding and Pattern Recognition Functions menu Accept all of the program defaults and press Run The results will quickly return as seen below Oo users thompson seqlab fas_consensus_23 figgce JA TESTCODE of usersa thompaon seqldb mendel input_23 rsf FAS_consensua ck 8322 1 to 4695 Win
47. ted into SeqLab Launch them from the command line with pileup_10000 pileup_15000 and pileup_20000 respectively Take home message You can make pretty big alignments with GCG it s all up to what you really need to do to answer the biological questions that you are asking Fortunately for those cases where GCG and similar software won t do the job there are some very good Web resources available for these types of global view genomic analyses NCBI http www ncbi nim nih gov presents a good starting point in North America Their Entrez Genome Map View presents the chromosome context of a gene _ Netscape Entrez Map View E TI Baek Reload Home Search Netscape imay rs Location dp tps Fee nob a S246 astr UZE CHR BM APS lool 25701047 BGOOOORTADEGSTA7O 2000018 QED What s Relate Ai Frain this Vew OS SGenome Fina Advanced Search Home sapiens Map View build 28 BLAST search the human genome Y i Loc1273024 sv seam P 1p36 11 hypothetical gene LOC 127302 i Ds t svevhmseamm C 1p36 13 p38 12intibitor of DNA binding 3 dominant negative helbcoop helix protein i OCI48899t svev seam E 1p38 11 sim ertointibitor of DNA binding 3 dominant negative hetoop helh protein H sapiens D E e ona DE And their Locus Link p
48. tunately often in a lab situation cDNA data is also available on a given sequence although with the increased emphasis on genomic sequencing this is becoming less and less true A careful application and interpretation of the many resources at your disposal can go a long way to increasing your understanding of gene structure and function But as always carry a healthy dose of skepticism to and be extremely wary at any session with the computer as the naive can easily be misled into accepting inappropriate or downright wrong results All available methods should be used together to help reinforce and or reject the others findings SeqLab is a great place to get all of this annotation together in one spot SeqLab s Graphic Features Display mode represents annotation with colors and shapes in a cartoon fashion Sequence Features windows describe the annotation The following SeqLab screen snapshot shows the Editor in this mode being used for genomic annotation 13 SeqLab Main Window on me O sequence Features SS PE File Edit Functions Options Windows Edit Add Raise List users thompson seqlab working list Show Features at cursor 1 source 1 2484 3 665 1038 9 739 1038 102 FindPatterns_Match 1611 1816 ch_Alignment Mode Editor Display Graphic Features Insert copy ale cs cle annotation human_id3 FeatureStart 739 FeatureEnd 1282 B aa SubjectSpec S
49. u do exactly what is written it will work However this requires two things 1 you must read very carefully and not skim over vital steps and 2 you mustn t take offense if you already know what I m discussing I m not insulting your intelligence This also makes the tutorials longer than otherwise necessary Sorry use three writing conventions in the tutorials besides my casual style use bold type for those commands and keystrokes that you are to type in at your keyboard or for buttons or menus that you are to click in a GUI also use bold type for section headings Screen traces are shown in a typewriter style Courier font and 7 indicates abridged data The arrow symbol gt indicates the system prompt and should not be typed as a part of commands Really important statements may be underlined As you ve learned specialized X server graphics communications software is required to use GCG s SeqLab I ll remind you of a few user hints while using X X Windows are only active when the mouse cursor is in that window and always close X Windows when you are through with them to conserve system memory Furthermore to activate X items just lt click gt on them rather than holding your mouse button down Also X buttons are turned on when they are pushed in and shaded Finally don t close X Windows with the X server software s close icon in the upper right or left hand window corner rather always
50. ut Manager to display the find output file Quickly scroll through it and see if any of the patterns names are recognizable Notice the output is huge of which most are probably false positives How do you sort out which are relevant and which are not It s not trivial but The Ghosh Transcription Factor Sites data file is available on line in the GCG public data directories it can help you decide whether an entry is relevant or not by listing the pertinent reference After finding the reference in the file you can investigate further in a science library or online with resources such as MEDLINE through PubMed at NCBI http Avww ncbi nlm nih gov entrez query fcgi Two dimensional signal searching with FitConsensus Now that you ve seen how problematic signal searches are with a one dimensional pattern search approach let s see how well a two dimensional matrix approach works Refer to this tutorial s introduction for a description of the matrices to be used in this section As discussed there GCG s FitConsensus program enables this type of a search to be performed Be sure that your FAS consensus sequence is selected and then launch FitConsensus off of the Gene Finding and Pattern Recognition Functions menu Unfortunately GCG has not updated this program to produce RSF output so we can t take advantage of SeqLab s ability to automatically update it s annotation based on the results of these progra
51. what is known as a codon usage or frequency table In order to utilize the codon usage gene finding strategy a codon usage table for the particular organism in question must be accessible GCG provides six tables in the public data library GenMoreData The available codon usage tables in addition to the default E coli highly expressed genes table ecohigh cod are celegans_high cod celegans_low cod drosophila_high cod human_high cod maize_high cod and yeast_high cod Even more tables are available at various molecular biology data servers such as IUBIO http iubio bio indiana edu soft molbio codon The TRANSTERM database at the European Bioinformatics Institute ftp ftp ebi ac uk pub databases transterm also contains several and an especially good selection derived from recent GenBank versions comes from the CUTG database http www kazusa or jp codon available in GCG format through various SRS servers e g see htip srs sanger ac uk srsbin cgi bin wgetz page LibInfo id 2keC31K_ fg2 lib CUTG Furthermore if you are not satisfied with any of the available options GCG has a program CodonFrequency that enables you to create your own custom codon frequency table Two GCG content analysis programs use codon usage tables in this context Frames a very simple open reading frame identifier that can utilize codon frequency tables to show rare codon usages and the quite sophisticated codon frequency analyzer
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