2 Amira A. AL-Hosary PhD of infectious diseases Department of Animal Medicine (Infectious Diseases) Faculty of Veterinary Medicine Assiut University Egypt
3 Interpretation of sequence results
4 An overview on DNA sequencing: DNA sequencing involves the determination of the sequence of nucleotides in a sample of DNA. It use a modified PCR reaction where both normal and labeled dideoxy-nucleotides are included in the reaction mix. Each dideoxy-nucleotides were labeled with fluorescent dyes (Each nucleotide has a different color). At the end of the sequencing reaction, Using a polyacrylamide gel (either a big thin slab gel or a narrow capillary tube filled with gel solution) that is scanned with a laser detection device. As each band moves past a viewer, the laser excites the dye, and the color of fluorescence is read by a photocell and recorded on a computer.
5 An overview on DNA sequencing:
6 Sequencer Ion Torrent PGM 454 GS FLX HiSeq 2000 SOLiDv4 PacBio Sanger 3730xl Manufacturer Ion Torrent (Life Technologies) 454 Life Sciences (Roche) Illumina Applied Biosystems (Life Technologies) Pacific Biosciences Applied Biosystems (Life Technologies) Amplification approach Emulsion PCR Emulsion PCR Bridge amplification Emulsion PCR Single-molecule; no amplification PCR Data output per run Mb 0.7 Gb 600 Gb 120 Gb Mb Kb Accuracy 99% 99.9% 99.9% 99.94% 88.0% (>99.9% CCS) % Time per run 2 hours 24 hours 3 10 days 7 14 days 2-3 hours 20 minutes - 3 hours Read length bp 700 bp 100x100 bp paired end 50x50 bp paired end 5,500-10,000 bp bp Cost per run $350 USD $7,000 USD $6,000 USD (30x human genome) $4,000 USD $ USD $4 USD (single read/reaction) Cost per Mb $1.00 USD $10 USD $0.07 USD $0.13 USD $ $3.00 USD $2400 USD Cost per instrument $80,000 USD $500,000 USD $690,000 USD $495,000 USD $695,000 USD $95,000 USD
7 1- The Band:
8 Interpreting Sequencing Results Automated DNA Sequencers generate 1- A four-color chromatogram showing the results of the sequencing run. 2- In addition to a text file of sequence data.
9 Interpreting Sequencing Results When you obtain a sequence you should proofread it to ensure that all ambiguous sites are correctly called and determine the overall quality of your data. Base Designations A designation green peaks G designation black peaks T designation red peaks C designation blue peaks N designation peaks that, for whatever reason, are not clear enough to designate as A, G, T, or C.
10 Interpreting Sequencing Chromatograms Good sequence generally begins roughly around base 20. Beginning of Sequence End of sequence
11 Interpreting Sequencing Chromatograms With a little practice, you can scan a chromatogram in less than a minute and spot problems. It is not necessary to read each and every base. An example of excellent sequence. Note the evenly-spaced peaks and the lack of baseline 'noise'
12 Interpreting Sequencing Chromatograms Background noise This example has a little baseline noise, but the 'real' peaks are still easy to call, so there's no problem with this sample
13 Interpreting Sequencing Chromatograms Noise like the above most commonly arises when the sample itself is too dim.
14 Types of Polymorphisms 1- Transitions: A G or C T (purines to purines OR pyrimidines to pyrimidines) 2-Insertions: an extra base is present when compared to the Anderson reference sequence. 3- Deletions: a base is missing when compared to the Anderson reference sequence. 4- Mis-Called (a) Irregular spacing: Common one for us is a G-A dinucleotide, which leaves a little extra space between them.
15 4- Mis-Called (b) Mis-call a nucleotide: Sometimes the computer will mis-call a nucleotide when a human could do better. Most often, this occurs when the basecaller calls a specific nucleotide, when the peak really was ambiguous and should have been called as 'N'.
16 4- Mis-Called (b) The real problem comes when the base caller attempts to interpret a gap as a real nucleotide. Note the real T peak (nt 58) and the real C peak (nt 60), with the G barely visible between them. Despite it size, the baseline-noise G peak was picked as if it were real. The clues to spot are (i) the oddly-spaced letters, with the G squeezed in, and (ii) the gap in the 'real' peaks, containing a low noise peak. This is a great example of why a weak sample, with its consequent noisy chromatogram, is untrustworthy.
17 5- Heterozygous (double) peaks: A single peak position within a trace may have but two peaks of different colors instead of just one. This is common when sequencing a PCR product derived from diploid genomic DNA, where polymorphic positions will show both nucleotides simultaneously. Note that the base caller may list that base position as an 'N', or it may simply call the larger of the two peaks. Here's a great example of a PCR amplicon from genomic DNA, with a clear heterozygous single-nucleotide polymorphism (SNP).
18 6- Negative samples / No DNA chromatograms displaying peaks from which no useable sequence can be obtained may be due to an absence of DNA. These chromatograms generally have one or two predominant colors.
19 7- Loss of resolution later in the gel: As the gel progresses, it loses resolution. This is normal; peaks broaden and shift, making it harder to make them out and call the bases accurately. The sequencer will continue attempting to "read" this data, but errors become more and more frequent. This is a typical example of data from a very good sample the spacing between the basecall letters at top is regular, which is often a good indication of the reliability of the data.
20 There are only a few base calls that can be considered reliable. The G at 981 may in fact be two G's, the N could be a G or an A, and who knows how many A's there are afterwards.
21 8- Non-discrete peaks these may occur when several of the same nucleotide appears in a row. For example, if the sequence includes the region TAAAAAT, it may be represented by one wavy peak as opposed to 5 distinct peaks.
22 9- Good sequence with bad base calling: Failed analysis Ask the Sequencing Service to reanalyze the sequence.
23 10- DNA template has a secondary structure: Secondary structures create a distortion that makes it impossible for elongation to continue and so the sequence ends abruptly. The sequence ends after approximately 200 bp
24 11- DNA contamination:
25 12- Excess dye peaks at the beginning of the sequence Cause related to sequencing: Poor removal of unincorporated dye terminators during the post-sequencing clean up
26 13- Sharp peaks in the sequence: 14- Sequence with "G" dye blob
27 15- Reaction failed, No sequencing data
28 Realize, too, that it's easy for a human to miss these. If you want to be sure you've detected all of the polymorphic positions, you should be using a computer program to scan your chromatograms!
29 Interpreting of Sequencing Results
30 Interpreting of Sequencing Results Determining homology: In other words, is your sequence similar to any other published sequences and if so, to what degree? This can be accomplished using BLAST, (Basic Local Alignment Search Tool): This program supported by the National Center for Biotechnology Information (NCBI). The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches. This program is accessible at: (GenBank database; National Center for Biotechnology Information, National Institutes of health).
32 BLAST: Basic Local Alignment Search Tool blast.ncbi.nlm.nih.gov/ The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between sequences. The program compares nucleotide or protein sequences to... Align two or more - Protein BLAST: ***search... - Nucleotide BLAST Rat - sequences Nucleotide BLAST: Search nucleotide databases using a nucleotide... blast.ncbi.nlm.nih.gov/blast.cgi?...blastn...blastsearch... No BLAST database contains all the sequences at NCBI. BLAST databases... BLAST - Wikipedia, the free encyclopedia en.wikipedia.org/wiki/blast In bioinformatics, Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary biological sequence information, such as the amino-acid... Process - Output - Input - Background
34 Click the Blast! button at the bottom to submit your sequence data.
35 This screen will come up next. Finally (sometimes after a lengthy wait), a new window will appear showing any hits your sequence made. The results will be color coded and annotated
36 The bars show what places along your sequence are similar to other published sequences; the colors indicate how many bases were involved in homology determination.
37 Clicking on a gi link at the beginning of any line will take you to the GenBank accession page for a sequence showing similarity to yours. There you can find a wealth of information about the published sequence to which yours showed some homology.
39 INTERPRETATION OF SEQUENCES WHICH CODING FOR PROTEIN
40 Translation and Open Reading Frame Search Regions of DNA that encode proteins are first transcribed into messenger RNA and then translated into protein. By examining the DNA sequence alone we can determine the sequence of amino acids that will appear in the final protein. In translation codons of three nucleotides determine which amino acid will be added next in the growing protein chain. It is important then to decide which nucleotide to start translation, and when to stop, this is called an open reading frame.
41 Once a gene has been sequenced it is important to determine the correct open reading frame (ORF). Every region of DNA has six possible reading frames, three in each direction. The reading frame that is used determines which amino acids will be encoded by a gene. Typically only one reading frame is used in translating a gene and this is often the longest open reading frame. Once the open reading frame is known the DNA sequence can be translated into its corresponding amino acid sequence. An open reading frame starts with an ATG (Met) in most species and ends with a stop codon (TAA, TAG or TGA).
42 For example, the following sequence of DNA can be read in six reading frames. Three in the forward and three in the reverse direction. The three reading frames in the forward direction are shown with the translated amino acids below each DNA seqeunce. Frame 1 starts with the "a", Frame 2 with the "t" and Frame 3 with the "g". Stop codons are indicated by an "*" in the protein sequence.
43 5' 3' atgcccaagctgaatagcgtagaggggttttcatcatttgaggacgatgtataa 1 atg ccc aag ctg aat agc gta gag ggg ttt tca tca ttt gag gac gat gta taa M P K L N S V E G F S S F E D D V * 2 tgc cca agc tga ata gcg tag agg ggt ttt cat cat ttg agg acg atg tat C P S * I A * R G F H H L R T M Y 3 gcc caa gct gaa tag cgt aga ggg gtt ttc atc att tga gga cga tgt ata A Q A E * R R G V F I I * G R C I
45 Translation: Each sequence must be translate to its amino acids (aa) by using Expasy.translatesoftware
50 BLAST: Basic Local Alignment Search Tool blast.ncbi.nlm.nih.gov/ The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between sequences. The program compares nucleotide or protein sequences to... Align two or more - Protein BLAST: ***search... - Nucleotide BLAST Rat - sequences Nucleotide BLAST: Search nucleotide databases using a nucleotide... blast.ncbi.nlm.nih.gov/blast.cgi?...blastn...blastsearch... No BLAST database contains all the sequences at NCBI. BLAST databases... BLAST - Wikipedia, the free encyclopedia en.wikipedia.org/wiki/blast In bioinformatics, Basic Local Alignment Search Tool, or BLAST, is an algorithm for comparing primary biological sequence information, such as the amino-acid... Process - Output - Input - Background
53 The bars show what places along your aa are similar to other published; the colors indicate how many bases were involved in homology determination.
55 Thanks a lot with my Best Regards and My Best wishes Amira A. AL-Hosary Mob. (002)
(http://genomes.urv.es/caical) TUTORIAL (July 2006) CAIcal manual 2 Table of contents Introduction... 3 Required inputs... 5 SECTION A Calculation of parameters... 8 SECTION B CAI calculation for FASTA
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