Overview of Generated Files Courtesy of Dr. Jon Keebler
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1 Overview of Generated Files Courtesy of Dr. Jon Keebler Fastq Files What s inside of them How they are built Phred Quality Score Strings Quality Analysis of Fastq libraries
2 Fastq File What s Inside Read 1 Read 2 Read TGGAAGCTCTTTGGAGAATGTCAA! +! YVW\eea_]cdcc CAGAAGTTCAAATTTCATAAATAC! +! GAAGCTCAAGGTCGCAACGAAATA! +! ggccff[cfeffd_eg_gcfak]^! Base Call Sequence Base Call Quality
3 Fastq File How is it built? (Illumina TGGAAGCTCTTTGGAGAATGTCAA! +! YVW\eea_]cdcc CAGAAGTTCAAATTTCATAAATAC! +! GAAGCTCAAGGTCGCAACGAAATA! +! ggccff[cfeffd_eg_gcfak]^! GATGCATGTAGTGTCCGAAGTGAA! TGTAGATGATTGCAGATCCTAACA! TCTTCTCCAAGTGGGATATGGTTC!
4 TGGAAGCTCTTTGGAGAATGTCAA! CAGAAGTTCAAATTTCATAAATAC! GAAGCTCAAGGTCGCAACGAAATA! GATGCATGTAGTGTCCGAAGTGAA! TGTAGATGATTGCAGATCCTAACA! TCTTCTCCAAGTGGGATATGGTTC! TCTTCTTTAACGTGTAATGGACTT! TATCGTTGAAGGATTCTGCCTATG! CACATCCCACTTGCCCGATGCATT!
5 TGGAAGCTCTTTGGAGAATGTCAA! CAGAAGTTCAAATTTCATAAATAC! GAAGCTCAAGGTCGCAACGAAATA! TCTTCTTTAACGTGTAATGGACTT! TATCGTTGAAGGATTCTGCCTATG! CACATCCCACTTGCCCGATGCATT! GA2 FLOW CELL Lanes, Tiles, Clusters TCTTCTCCAAGTGGGATATGGTTC! TGTAGATGATTGCAGATCCTAACA! GATGCATGTAGTGTCCGAAGTGAA! LANE 2 LANE 1
6 CAGAAGTTCAAATTTCATAAATAC TGGAAGCTCTTTGGAGAATGTCAA! LANE 1 LANE 2 GA2 FLOW CELL Lanes, Tiles, Clusters GAAGCTCAAGGTCGCAACGAAATA! TCTTCTCCAAGTGGGATATGGTTC! TGTAGATGATTGCAGATCCTAACA! GATGCATGTAGTGTCCGAAGTGAA! CACATCCCACTTGCCCGATGCATT! TATCGTTGAAGGATTCTGCCTATG! TCTTCTTTAACGTGTAATGGACTT! Metzker, M. L. (2009). Sequencing technologies the next generayon. Nature Reviews GeneYcs, 11(1), doi: /nrg2626
7 GAAGCTCAAGGTCGCAACGAAATA! CAGAAGTTCAAATTTCATAAATAC! TGGAAGCTCTTTGGAGAATGTCAA! LANE 1 LANE 2 TILE 1 TILE Tiles per Lane TCTTCTCCAAGTGGGATATGGTTC! TGTAGATGATTGCAGATCCTAACA! GATGCATGTAGTGTCCGAAGTGAA! TILE 3 TCTTCTTTAACGTGTAATGGACTT! TATCGTTGAAGGATTCTGCCTATG! CACATCCCACTTGCCCGATGCATT!
8 LANE 1 GAAGCTCAAGGTCGCAACGAAATA! CAGAAGTTCAAATTTCATAAATAC! TGGAAGCTCTTTGGAGAATGTCAA! TCTTCTCCAAGTGGGATATGGTTC! TGTAGATGATTGCAGATCCTAACA! GATGCATGTAGTGTCCGAAGTGAA! CACATCCCACTTGCCCGATGCATT! LANE 2 TATCGTTGAAGGATTCTGCCTATG! Machine Cycle 1 Machine Cycle 2 Machine Cycle 3 TCTTCTTTAACGTGTAATGGACTT! Metzker, M. L. (2009). Sequencing technologies the next generayon. Nature Reviews GeneYcs, 11(1), doi: /nrg2626
9 Typical Run Folder directory structure aaer image analysis and base calling Files are grouped by Tile or by Cycle. Fastq files are built from the contents of these directories.
10 Fastq Precursor Files Files created by the Real Time Analysis soaware: *.stats binary file containing base call staysycs *.filter binary file containing filter results *.bcl binary file containing base calls *_pos.txt X & Y coordinates of clusters (within each Tile) * = s_<lane>_<yle>
11 Fastq Precursor Files Illumina BCL Converter soaware creates *_qseq.txt * = s_<lane>_<pair>_<yle> Reads grouped by Lane & Tile
12 Fastq Precursor Files Contents of each s_<lane>_<pair>_<yle>_qseq.txt (120 per lane) Machine_ID Run_Number Lane_Number Tile_Number X_coordinate Y_coordinate Index_sequence (0) Pair_number (1,2) Base- Call Sequence Quality Scores Pass_filter? (0 or 1)
13 Fastq File How is it built? *.stats *.filter *.bcl *_pos.txt (one file per Yle) BCL- Converter (Illumina) *_qseq.txt (one file per Yle) Read Pass Filter (Y or TGGAAGCTCTTTGGAGAATGTCAA! +! YVW\eea_]cdcc CAGAAGTTCAAATTTCATAAATAC! +! GAAGCTCAAGGTCGCAACGAAATA! +! ggccff[cfeffd_eg_gcfak]^! Perl script (wrilen by me)
14 Fastq File How will it be built? *.stats *.filter *.bcl *_pos.txt (one file per Yle) CASAVA 1.8 TGGAAGCTCTTTGGAGAATGTCAA! +! YVW\eea_]cdcc CAGAAGTTCAAATTTCATAAATAC! +! GAAGCTCAAGGTCGCAACGAAATA! +! ggccff[cfeffd_eg_gcfak]^!
15 Overview Fastq Files What s inside of them How they are built Phred Quality Scores Quality Analysis of Fastq GAAGCTCAAGGTCGCAACGAAATA! +! ggccff[cfeffd_eg_gcfak]^!
16 Quality Scoring String of ASCII encoded integer scores decimal_ascii_value Offset = Quality Score hlp://
17 Quality Score Example Quality String: fak]^! ASCII values: ! Illumina v1.5! Offset = 64! Quality Scores! f = 38! a = 33! K = 11! ] = 29! ^ = 30! hlp://
18 Phred Quality Scoring Higher score = beler quality GATK Base Quality RecalibraYon: Base_quality_score_recalibraYon hlp://en.wikipedia.org/wiki/phred_quality_score
19 Quality Score Offset Offset = 33 (Sanger standard) or 64 (Illumina/Solexa) Offset 33 Range: # to I Offset 64 Range: B to h! # and B are No- score / ambiguous base calls! With Illumina RTA 1.9 & CASAVA 1.8, the offset will return to Sanger scaling (Q2 2011). If in doubt, ASK! hlp://en.wikipedia.org/wiki/fastq_format
20 Sequencing Data Quality Control Illumina RTA / SCS soaware (real- Yme, on- instrument)
21 Sequencing Data Quality Control Illumina RTA / SCS soaware FastX Toolkit (hlp://hannonlab.cshl.edu/fastx_toolkit/)
22 Sequencing Data Quality Control Illumina RTA / SCS soaware FastX Toolkit (hlp://hannonlab.cshl.edu/fastx_toolkit/) FastQC (hlp://
23 Sequencing Data Quality Control Illumina RTA / SCS soaware FastX Toolkit (hlp://hannonlab.cshl.edu/fastx_toolkit/) FastQC (hlp:// Basic StaYsYcs: 40 Millions of Reads Total reads Non- Ambiguous reads Fully Quality- Scored reads High- Quality (q20) reads
24 Now the real work begins. QuesYons? McPherson, J. D. (2009). Next- generayon gap. Nature Methods, 6(11s), S2- S5. doi: /nmeth.f.268
25
26 Experimental Design and Sources of VariaYon Slide from Dr. Ross Whelen guest lecture last year
27 Experimental Design and Sources of VariaYon Million Reads Million Reads
28 Experimental Design and Sources of VariaYon Million Reads Illumina: ~8.3 million reads/sample Roche:? Who knows?
29 Experimental Design and Sources of VariaYon Illumina: Equal representayon of data/sample Roche:? Who knows? RL1: reads RL2: reads RL3: reads RL4: 3 reads RL5: reads RL6: reads RL7: reads RL8: reads
30 ComputaYonal Resources Consider your computayonal resources!!! 1 lane of 100 base single pass is 8-10Gb de novo assembly at least 1 gigabyte of RAM per million of reads for a sample. **remember, we give out at least 30million reads/lane at least gigabytes of hard drive space per full lane of sequence.
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