High throughput omics and BIOINFORMATICS
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1 High throughput omics and BIOINFORMATICS Giuseppe D'Auria Seville, February 2009
2 Genomes from isolated bacteria
3 $ $ $ $ $ $ $ $ $$ $ $ $ $ $ $ $ se q se uen q c se uen ing q c se uen ing qu c en ing c in g ols pro toco l pro toco s toc ls pro Genomes from isolated bacteria $ $ $$ $ $ $ $
4 Genomes from isolated bacteria $ $$ $ $ $ $ se q se uen q c se uen ing q c se uen ing q c as uenc ing se mb ing lin co g f f ee as se mb c as offe ling se mb e co ffe ling co e f f e c as offe e e se mb lin g ols pro toco l pro toco s toc ls $ pro $ $ $ $ $ $ $ $ $$ $ $ $ $ $ $ $ Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformatics Bioinformatics
5 Sequencing Cloning Fragmentation Sanger Sequencing Reads (0.8Kb)
6 Contig (90Kb) Sequencing Cloning Fragmentation Sanger Sequencing Reads (0.8Kb) Assembling
7 Contig (90Kb) Sequencing Cloning Fragmentation Sanger Sequencing Reads (0.8Kb) Assembling
8 Sanger Sequencing ABI Methods 96 wells 384 wells For an average genome of 3.5Mb we need a coverage of ~8 X 3.5 x 8 = 28 Mb. 28Mb / 800bp [read] = single reads : 96wells = 364 plates [ :384 = 91 plates]
9 Sanger Sequencing High Throughput Sequencing
10 Sanger Sequencing High Throughput Sequencing 2 Euro x 96 Reads = 192Euro / Plate 364 plates[96] x 192Euros = Euro
11 Fragmentation Total Dna Extraction 454 Roche Sequencing
12 BCc SCc Fragmentation Total Dna Extraction ri 454 Roche Sequencing Very High Throughput Sequencing
13 Fragmentation 454 Roche Sequencing Total Dna Extraction ri BCc SCc Total Number of Reads (Half Run): Average Sequence Length: total Bases: coverage for a 3.5Kb genome bp (200Mb) 57 X About 8.000Euros In about 10 hours (~8 fold less than ABI) Very High Throughput Sequencing
14 454 Roche Sequencing Actually the reads are of about 400bp (Titanium) 454 Main problem RRRRRREEEEEPPPPPEEEEAAAAATTTTTSSSSSSSS rp1 rp2
15 454 Roche Sequencing Actually the reads are of about 400bp (Titanium) 454 Main problem RRRRRREEEEEPPPPPEEEEAAAAATTTTTSSSSSSSS rp1 rp2
16 454 Roche Sequencing Actually the reads are of about 400bp (Titanium) 454 Main problem RRRRRREEEEEPPPPPEEEEAAAAATTTTTSSSSSSSS rp1 rp2
17 454 Roche Sequencing Actually the reads are of about 400bp (Titanium) 454 Main problem RRRRRREEEEEPPPPPEEEEAAAAATTTTTSSSSSSSS rp1 rp2
18 454 Roche Sequencing Actually the reads are of about 400bp (Titanium) 454 Main problem RRRRRREEEEEPPPPPEEEEAAAAATTTTTSSSSSSSS rp1 rp2
19 Joining the two technologies searching for a compromise A good first scaffolding made by ABI A half run of 454 could be good enough for the bioinformatics team Strategies
20 Joining the two technologies searching for a compromise A good first scaffolding made by ABI A half run of 454 could be good enough for the bioinformatics team The new 454 Titanium in Paired Ends is really fruitful Strategies
21 Joining the two technologies searching for a compromise A good first scaffolding made by ABI A half run of 454 could be good enough for the bioinformatics team The new 454 Titanium in Paired Ends is really fruitful Strategies
22 Strategies Joining the two technologies searching for a compromise A good first scaffolding made by ABI A half run of 454 could be good enough for the bioinformatics team The new 454 Titanium in Paired Ends is really fruitful L R 3 Kb 3 Kb
23 Strategies Joining the two technologies searching for a compromise A good first scaffolding made by ABI A half run of 454 could be good enough for the bioinformatics team The new 454 Titanium in Paired Ends is really fruitful L R 3 Kb 3 Kb L R
24 Joining the two technologies searching for a compromise A good first scaffolding made by ABI A half run of 454 could be good enough for the bioinformatics team The new 454 Titanium in Paired ends is really fruitful Strategies
25 Joining the two technologies searching for a compromise A good first scaffolding made by ABI A half run of 454 could be good enough for the bioinformatics team The new 454 Titanium in Paired ends is really fruitful Strategies
26 Joining the two technologies searching for a compromise A good first scaffolding made by ABI A half run of 454 could be good enough for the bioinformatics team The new 454 Titanium in Paired Ends is really fruitful A good bioinformatics work trying to join contigs Strategies
27 . After sequencing Assembling Bioinformatics starts. Linux or Windows? Both can allow good bioinformatics analysis Linux is more stable for massive data crunching analysis and it is FREE Windows is not FREE For both systems is possible to find the way to run all of the bioinformatics tools
28 Sanger (ABI) Assembling overview Sequencing 454 Solexa Solid
29 Assembling overview Sequencing Sanger (ABI) 454 Solexa Base Calling Phred Pregap4(Staden) Solid
30 Assembling overview Sequencing Sanger (ABI) 454 Solexa Solid Base Calling Phred Pregap4(Staden) Asembling Newbbler (Roche) Phrap CAP (Staden) MIRA
31 Assembling overview Sequencing Sanger (ABI) 454 Solexa Solid Base Calling Phred Pregap4(Staden) Asembling Newbbler (Roche) Phrap CAP (Staden) MIRA Finishing Editor / Viewer Eagle view Consed Gap4 (Staden)...
32 Assembling Staden Package
33 Assembling Staden Package From an overload of sequences (but not so much, let say thousands) First assembling for ABI sequences Staden Package Pregap4 Prepare the sequences to be introduced into the Database
34 Preparing sequences Assembling Staden Package From an overload of sequences (but not so much, let say thousands) First assembling for ABI sequences Staden Package Pregap4 Prepare the sequences to be introduced into the Database
35 Assembling Staden Package From an overload of sequences (but not so much, let say thousands) First assembling for ABI sequences Staden Package Pregap4 Prepare the sequences to be introduced into the Database
36 Assembling Staden Package From an overload of sequences (but not so much, let say thousands) First assembling for ABI sequences Staden Package Pregap4 Prepare the sequences to be introduced into the Database Gap4 Maintains the Database and provides a huge amount of analysis tools.
37 Assembling Staden Package From an overload of sequences (but not so much, let say thousands) First assembling for ABI sequences Staden Package Pregap4 Prepare the sequences to be introduced into the Database Gap4 Maintains the Database and provides a huge amount of analysis tools.
38 Assembling Staden Package From an overload of sequences (but not so much, let say thousands) First assembling for ABI sequences Staden Package Pregap4 Prepare the sequences to be introduced into the Database Gap4 Trev Maintains the Database and provides a huge amount of analysis tools. Displays Chromatograms (ABI) or Fluograms (454) from inside or outside of Gap4 program
39 Assembling Staden Package From an overload of sequences (but not so much, let say thousands) First assembling for ABI sequences Staden Package Pregap4 Gap4 Trev Prepare the sequences to be introduced into the Database ABI Chromatograms Maintains the Database and provides a huge amount of analysis tools. Displays Chromatograms (ABI) or Fluograms (454) from inside or outside of Gap4 program 454 SFF Standard Flowgrams Format
40 From Legionella pneumophila, Alcoy Assembly Assembling Staden Package
41 Assembling Staden Package From Legionella pneumophila, Alcoy Assembly Trying to assemble a contig Contig made by 2200 sequences Just a simulation with really few sequences..
42 96 Assembling Staden Package
43 Assembling Staden Package
44 Assembling Staden Package
45 Assembling Staden Package
46 Assembling Contigs 286 Assembling Staden Package Reads
47 Assembling MIRA Mimicking Intelligent Read Assembly
48 Assembling MIRA Is independent from sequencing technology Permits to assemble big big big bunch of sequences The last version supports 10 millions of reads (supposed) Allows a really fine modulation of assembly parameters according to the (meta)genome necessity (highly repetitive, various kinds of paired ends, EST assembly, etc. ) Permits a complete integration between formats There is an active open source community continuously working improving the system Works only on Linux machines (64bit)
49 Assembling strategy Sanger (ABI) Single reads Paired Ends 454 Single reads Paired Ends
50 Assembling strategy Sanger (ABI) Single reads Paired Ends Abi Pregap4 (base calling) Exp (input) Ztr(chromat.) Sequences one by one First assembly or as file of file names (fofn) 454 Single reads Paired Ends
51 Assembling strategy Sanger (ABI) Single reads Paired Ends 454 Single reads Paired Ends Abi Pregap4 (base calling) Sff (one only big file) Exp (input) Ztr(chromat.) sff_extract Sequences one by one First assembly or as file of file names (fofn)
52 Assembling strategy Sanger (ABI) Single reads Paired Ends 454 Single reads Paired Ends Abi Pregap4 (base calling) Sff (one only big file) Exp (input) Ztr(chromat.) sff_extract Sequences one by one First assembly or as file of file names (fofn) CAF (Common Assembly Format) Sequences in fasta format quality file xml for clipping information
53 Assembling strategy Sanger (ABI) Single reads Paired Ends 454 Single reads Paired Ends Abi Pregap4 (base calling) Sff (one only big file) Exp (input) Ztr(chromat.) sff_extract Sequences one by one First assembly or as file of file names (fofn) CAF (Common Assembly Format) Sequences in fasta format quality file xml for clipping information MIRA unique hybrid assembly
54 Assembling MIRA Working with 454 technology the obtained sequences are stored in one unique big big big file something.sff SFF
55 Assembling MIRA Working with 454 technology the obtained sequences are stored in one unique big big big file something.sff SFF sff_extract Jose Blanca >sff_extract E6RAXER04.sff o test_in Fasta Fasta.qual xml
56 The simplest >mira fasta genomedraft project=test Assembling MIRA
57 Assembling MIRA The simplest >mira fasta genomedraft project=sff Format conversion CAFTOOLS >caf2gap project test_out version 0 ace test_out.caf >gap2caf project test2 version 0 ace abi_out.caf >cafmerge caf1 abi_out.caf caf2 sff_out.caf out merged_in.caf
58 Assembling MIRA The simplest >mira fasta genomedraft project=sff Format conversion CAFTOOLS >caf2gap project sff_out version 0 ace sff_out.caf >gap2caf project abi version 0 ace abi_out.caf >cafmerge caf1 abi_out.caf caf2 sff_out.caf out merged_in.caf Assembling again > mira caf genomedraft project=merged > caf2gap project merged version 0 ace merged_out.caf
59 Assembling MIRA The simplest >mira fasta genomedraft project=sff Format conversion CAFTOOLS >caf2gap project sff_out version 0 ace sff_out.caf >gap2caf project abi version 0 ace abi_out.caf >cafmerge caf1 abi_out.caf caf2 sff_out.caf out merged_in.caf Assembling again > mira caf genomedraft project=merged > caf2gap project merged version 0 ace merged_out.caf Cleaning > convert_project f caf t caf x 500 y 5 merged_out.caf merged_out_x500_y10.caf > caf2gap project merged_x500_y10 version 0 ace merged_out_x500_y10.caf
60 Assembling MIRA Generating our first genbank file : Extracting contigs consensus from Staden
61 Assembling MIRA Generating our first genbank file : Extracting contigs consensus from Staden Something of perl >concatenator_contig.pl i cons p F a 1
62 Assembling MIRA Generating our first genbank file : Extracting contigs consensus from Staden Something of perl >concatenator_contig.pl i cons p F a 1 Searching for ORF GLIMMER >g3 from scratch cons.concat cons step1
63 Assembling MIRA Generating our first genbank file : Extracting contigs consensus from Staden Something of perl >concatenator_contig.pl i cons p F a 1 Searching for ORF GLIMMER >g3 from scratch cons.concat cons step1 orf00001 orf00002 orf00003 orf00004 orf >cons taaataaataattttatttatttagccatggattta aatgccaatttaattaggacagtcacaagaacaatt agtataaattgctttcattaaagaaaataataacga agtccaccgttaaatccagagataaggatatgcctt Creating our first GenBank file >Annotator.pl i cons.concat p cons.predict t Seville
64 For INTREPID and BRAVE people PERL Perl is a scripting language widely used for system administration and programming on the World Wide Web. It originated in the UNIX community and has a strong UNIX slant, but usage on Windows has grown rapidly. ActivePerl is a quality assured binary distribution of Perl for popular UNIX platforms and Windows. perl (small 'p') is the program used to interpret the Perl language.
65 For INTREPID and BRAVE people PERL
66 For INTREPID and BRAVE people PERL
67 For INTREPID and BRAVE people Bio PERL
68 Just reset Time...
69 Viewer Editor Artemis To visualize the genome(s) or contigs Artemis (Linux Windows Whatever) Extracts features from an annotated file jointly with the sequence, performs 6 frames translation and allows to navigate easily (really easily) through the data. Permits adding or changing features
70 Viewer Editor Artemis To visualize the genome(s) or contigs Artemis (Linux Windows Whatever) Extracts features from an annotated file jointly with the sequence, performs 6 frames translation and allows to navigate easily (really easily) through the data. Permits adding or changing features
71 Viewer Editor Artemis To visualize the genome(s) or contigs Artemis (Linux Windows Whatever) Extracts features from an annotated file jointly with the sequence, performs 6 frames translation and allows to navigate easily (really easily) through the data. Permits adding or changing features Genome view
72 Viewer Editor Artemis To visualize the genome(s) or contigs Artemis (Linux Windows Whatever) Extracts features from an annotated file jointly with the sequence, performs 6 frames translation and allows to navigate easily (really easily) through the data. Permits adding or changing features GC content
73 Viewer Editor Artemis To visualize the genome(s) or contigs Artemis (Linux Windows Whatever) Extracts features from an annotated file jointly with the sequence, performs 6 frames translation and allows to navigate easily (really easily) through the data. Permits adding or changing features GC Skew
74 Viewer Editor Artemis To visualize the genome(s) or contigs Artemis (Linux Windows Whatever) Extracts features from an annotated file jointly with the sequence, performs 6 frames translation and allows to navigate easily (really easily) through the data. Permits adding or changing features Artemis Comparative Tool (ACT) (Linux Windows Whatever) Same structure of Artemis, permits to compare several genomes at one time, specially useful in contigs orientation, primer design, etc.. Need BLAST results files coming from genome genome comparisons
75 Artemis Comparative tool Genome 1 file Genome 1 versus Genome 2 blast file Genome 2 file
76 Artemis Comparative tool Genome 1 file Genome 1 versus Genome 2 blast file Genome 2 file Genome 2 versus Genome 3 blast file Genome 3 file Genome 3 versus Genome 4 blast file Genome 4 file
77 Artemis Comparative tool
78 Genome 1 file Genome 2 file Genome 3 file Genome 4 file Genome 5 file Genome 6 file Artemis Comparative tool
79 Artemis Comparative tool Genome 1 file Genome 1 versus Genome 2 blast file Genome 2 file Genome 2 versus Genome 3 blast file Genome 3 file Genome 3 versus Genome 4 blast file Genome 4 file Genome 4 versus Genome 5 blast file Genome 5 file Genome 5 versus Genome 6 blast file Genome 6 file
80 BLAST (Basic Local Alignment Search Tool) BLAST Permits to align sequences searching for overlapping regions Is the base of great part of bioinformatics analysis. There are alternatives but the basic version works quite well
81 BLAST BLAST (Basic Local Alignment Search Tool) Paris (Genome 1) Paris vs Lens comparative BLAST file Lens (Genome 2) Lens vs Corby comparative BLAST file Corby (Genome 3) Paris_Lens.blastn Permits to align sequences searching for overlapping regions Is the base of great part of bioinformatics analysis. There are alternatives but the basic version works quite well Artemis Comparative Tool (ACT) Lens_Corby.blastn
82 BLAST BLAST (Basic Local Alignment Search Tool) Paris (Genome 1) Paris vs Lens comparative BLAST file Lens (Genome 2) Lens vs Corby comparative BLAST file Corby (Genome 3) Paris_Lens.blastn Permits to align sequences searching for overlapping regions Is the base of great part of bioinformatics analysis. There are alternatives but the basic version works quite well Artemis Comparative Tool (ACT) Lens_Corby.blastn $>bl2seq i Paris.fna j Lens.fna p blastn D 1 o Paris_Lens.blastn $>bl2seq i Lens.fna j Corby.fna p blastn D 1 o Lens_Corby.blastn
83 Genome 1 file Genome 2 file Genome 3 file Mauve Aligner
84 Mauve Aligner Genome 1 file Genome 2 file Genome 3 file Locally Collinear Blocks
85 Thank you And now Questions..
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