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Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 1 subPSEC (substitution position-specific evolutionary conservation) estimates the.

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Presentation on theme: "Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 1 subPSEC (substitution position-specific evolutionary conservation) estimates the."— Presentation transcript:

1 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 1 subPSEC (substitution position-specific evolutionary conservation) estimates the likelihood of a functional effect. Values are 0 to - 10, (-10 most likely to be deleterious). -3 is the previously identified cutoff point for functional significance. P deleterious (anything above 0.5 is considered deleterious) substitution D538G EVOLUTIONARY ANALYSIS OF CODING SNPS ESR1_HUMAN: D538G

2 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 2 ESR1_HUMAN: D538G

3 11 possible candidate SNPs were selected for their potential relevance to breast cancer. rs , which resides in a predicted binding site for 3 miRNAs in the estrogen receptor- α (ESR1) gene, was associated with a 27% reduction in breast cancer risk in premenopausal women. When the C allele is present, miR-453 binds with greater affinity to ESR1, thus leading to decreased levels of ERα protein. Postmenopausal women already have reduced levels of endogenous estrogen, perhaps explaining why this SNP is relevant only in premenopausal women. Would carriers of the ancestral T allele respond better to endocrine therapy ? given that they will naturally express increased levels of the receptor. References: Tchatchou, S. et al. A variant affecting a putative miRNA target site in estrogen receptor (ESR) 1 is associated with breast cancer risk in premenopausal women. Carcinogenesis 30, 59–64 (2009). Adams, B. D., Furneaux, H. & White, B. A. The micro-ribonucleic acid (miRNA) miR-206 targets the human estrogen receptor-α (ERα) and represses ERα messenger RNA and protein expression in breast cancer cell lines. Mol. Endocrinol. 21, 1132–1147 (2007). Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 3 SNPs in miRNA Binding Sites

4 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 4

5 5 Before you design your own primers – Don’t reinvent the wheels! Essential Bioinformatics Resources for Designing PCR Primers for Various Applications:

6 1.Use NCBI Gene or UCSC genome browser to find gene variants: Transcript variants Alternative isoforms Exon-intron boundaries Pseusogenes 2.Gene conservation considerations 3. SNPs- There are approximately 56 million SNPs in the human genome, 16 million are in gene introns and exons, most are silent mutations. Are we aiming at these locations ? jPCR: Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 6 Basic considerations before designing primers

7 Primer length determines the specificity and affects annealing to the template:  Short primer => low specificity, non-specific amplification  Long primer => decreased binding efficiency at normal annealing temperature (due to high probability of forming secondary structures such as hairpins). Primer design and primer characteristics Primer length: bps, complete sequence identity to template G/C content: 40-60% Avoid mismatches at the 3’ end The presence of G or C bases within the last five bases from the 3' end of primers (GC clamp) helps promote specific binding at the 3' end. Avoid 3 or more G or C at the 3’ end because high primer-dimer probability Avoid a 3’ end T Always have a reference gene (GAPDH, actin, RPLPO (Large Ribosomal Protein)) performed with your query genes Optimal amplicon size: bps Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 7

8 Primer design: Melting temperature (T m )  T m is the temperature at which 50% of the DNA duplex dissociates to become single stranded  Determined by primer length, base composition and concentration  Affected by the salt concentration of the PCR reaction mix  Optimal melting temperature: 52°C - 60°C T m above 65°C may cause secondary annealing, higher T m (75°C - 80°C) is recommended for amplifying high GC content targets  Primer pair T m mismatch  Significant primer pair T m mismatch can lead to poor amplification (desirable T m difference < 5°C between primer pairs) Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 8

9 Primer design: Annealing temperature T a (Annealing temperature) vs. T m  T a is determined by the T m of both primers and amplicons: optimal T a =0.3 x T m (primer)+0.7 x T m (product)-25  General rule: T a is 5°C lower than T m  Higher T a enhances specific amplification but may lower yields  Crucial in detecting polymorphisms Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 9

10 Primer design: Specificity and cross homology  Specificity: Determined primarily by primer length and sequence  Cross homology: Cross homology may become a problem when PCR template is DNA with highly repetitive sequences  Avoid non-specific amplification: BLAST PCR primers against NCBI non-redundant sequence database Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 10

11 Primer design: Avoid secondary structures  Hairpins are formed via intra-molecular interactions, negatively affect primer-template binding, leading to poor or no amplification  Self-Dimer (homodimer)  Formed by inter-molecular interactions between the two same primers  Cross-Dimer (heterodimer)  Formed by inter-molecular interactions between the sense and antisense primers  Avoid Template Secondary Structure Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 11

12 Web Site: Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 12

13 Web Site: Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 13

14 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 14

15 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 15 Web Site: SNP primers: 0 Design specific primers for each transcript:

16 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 16 SNPs Copy number variation and InDels

17 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 17

18 Primer Design Tools for Degenerate PCR– CODEHOP Web Site: More Info: Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 18

19 Cross hybridization and specificity of primers Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 19

20 Resources for PCR Primer Specificity Analysis: NCBI BLAST 20

21 21 Primer specificity and Mapping: The UCSC In-Silico PCR Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University

22 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 22 PCR reaction setup calculators

23 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 23 ESR1 human Public PCR Primers/Oligo Probes Repository: The NCBI Probe Database

24 Resources for real time PCR: RTPrimerDB Web Site: Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 24 Shows pre-calculated primers on all gene transcripts !

25 Web Site: More Info: Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 25

26 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 26

27 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 27

28 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 28 Dilution Calculator Takes an oligo stock solution of higher concentration and determines how much volume to dilute down to final (desired) lower concentration. Input of the volumes of the stock solution (Start Volume) and the diluted solution (End Volume) are not required, but recommended.

29 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 29 Exome Analysis Identify genetic disease causes: Sequence the human coding regions of patient and healthy (1-2% of the human genome (~30Mb)), find the genomic cause of diseases.

30 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 30 >A8KAF4_HUMAN A8KAF4 Estrogen receptor OS=Homo sapiens PE=2 SV=1 ATGACCATGACCCTGCACACCAAGGCCAGCGGCATGGCCCTGCTGCACCAGATCCAGGGC AACGAGCTGGAGCCCCTGAACAGGCCCCAGCTGAAGATCCCCCTGGAGAGGCCCCTGGGC GAGGTGTACCTGGACAGCAGCAAGCCCGCCGTGTACAACTACCCCGAGGGCGCCGCCTAC GAGTTCAACGCCGCCGCCGCCGCCAACGCCCAGGTGTACGGCCAGACCGGCCTGCCCTAC GGCCCCGGCAGCGAGGCCGCCGCCTTCGGCAGCAACGGCCTGGGCGGCTTCCCCCCCCTG AACAGCGTGAGCCCCAGCCCCCTGATGCTGCTGCACCCCCCCCCCCAGCTGAGCCCCTTC CTGCAGCCCCACGGCCAGCAGGTGCCCTACTACCTGGAGAACGAGCCCAGCGGCTACACC GTGAGGGAGGCCGGCCCCCCCGCCTTCTACAGGCCCAACAGCGACAACAGGAGGCAGGGC GGCAGGGAGAGGCTGGCCAGCACCAACGACAAGGGCAGCATGGCCATGGAGAGCGCCAAG GAGACCAGGTACTGCGCCGTGTGCAACGACTACGCCAGCGGCTACCACTACGGCGTGTGG AGCTGCGAGGGCTGCAAGGCCTTCTTCAAGAGGAGCATCCAGGGCCACAACGACTACATG TGCCCCGCCACCAACCAGTGCACCATCGACAAGAACAGGAGGAAGAGCTGCCAGGCCTGC AGGCTGAGGAAGTGCTACGAGGTGGGCATGATGAAGGGCATCAGGAAGGACAGGAGGGGC GGCAGGATGCTGAAGCACAAGAGGCAGAGGGACGACGGCGAGGGCAGGGGCGAGGTGGGC AGCGCCGGCGACATGAGGGCCGCCAACCTGTGGCCCAGCCCCCTGATGATCAAGAGGAGC AAGAAGAACAGCCTGGCCCTGAGCCTGACCGCCGACCAGATGGTGAGCGCCCTGCTGGAC GCCGAGCCCCCCATCCTGTACCCCGAGTACGACCCCACCAGGCCCTTCAGCGAGGCCAGC ATGATGGGCCTGCTGACCAACCTGGCCGACAGGGAGCTGGTGCACATGATCAACTGGGCC AAGAGGGTGCCCGGCTTCGTGGACCTGACCCTGCACGACCAGGTGCACCTGCTGGAGTGC GCCTGGCTGGAGATCCTGATGATCGGCCTGGTGTGGAGGAGCATGGAGCACCCCGGCAAG CTGCTGTTCGCCCCCAACCTGCTGCTGGACAGGAACCAGGGCAAGTGCGTGGAGGGCATG GTGGAGATCTTCGACATGCTGCTGGCCACCAGCAGCAGGTTCAGGATGATGAACCTGCAG GGCGAGGAGTTCGTGTGCCTGAAGAGCATCATCCTGCTGAACAGCGGCGTGTACACCTTC CTGAGCAGCACCCTGAAGAGCCTGGAGGAGAAGGACCACATCCACAGGGTGCTGGACAAG ATCACCGACACCCTGATCCACCTGATGGCCAAGGCCGGCCTGACCCTGCAGCAGCAGCAC CAGAGGCTGGCCCAGCTGCTGCTGATCCTGAGCCACATCAGGCACATGAGCAACAAGGGC ATGGAGCACCTGTACAGCATGAAGTGCAAGAACGTGGTGCCCCTGTACGACCTGCTGCTG GAGATGCTGGACGCCCACAGGCTGCACGCCCCCACCAGCAGGGGCGGCGCCAGCGTGGAG GAGACCGACCAGAGCCACCTGGCCACCGCCGGCAGCACCAGCAGCCACAGCCTGCAGAAG TACTACATCACCGGCGAGGCCGAGGGCTTCCCCGCCACCGTG => 6 frames translation

31 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 31 Format Conversion tools:  Reverse and\or Complement of DNA sequences ( )  Split FASTA: divides FASTA sequence records into smaller FASTA sequences of the size you specify ( ) Sequence Analysis:  DNA Pattern Find: accepts one or more sequences along with a search pattern and returns the number and positions of sites that match the pattern ( )  PCR Primer Stats: accepts a list of PCR primer sequences and returns a report describing the properties of each primer, including melting temperature, percent GC content, and PCR suitability ( )  PCR Products: accepts one or more DNA sequence templates and two primer sequences. The program searches for perfectly matching primer annealing sites that can generate a PCR product. Any resulting products are sorted by size, and they are given a title specifying their length, their position in the original sequence, and the primers that produced them ( )  Reverse Translate ( )  Translate ( )  Primer Map: accepts a DNA sequence and returns a textual map showing the annealing positions of PCR primers ( ) Resources for PCR Primer Mapping/Amplicon Size

32 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University 32 x total127x only62x-y total overlap y total628y only566x-z total overlap z total0z only0y-z total overlap Comparing gene-lists Venny

33 Microarray Experiments Probes for genes are located on the chip. Hybridization of mRNA to the probes on the chip is performed and results are recorded. Various platforms ! Next generation sequencing bypass the rate-limiting step of conventional DNA sequencing (separating randomly terminated DNA polymers by gel electrophoresis) by physically arraying DNA molecules on solid surfaces and determining the DNA sequence in situ, without the need for gel separation. Anchor DNA single molecule to solid surface Amplify template by in situ PCR Add 4 color labeled reverse terminators, polymerase, universal primer Reverse termination, repeat 1…100 times, the number of cycles determines the length of sequence. Remove un-incorporated nucleotide Detect with laser Next Generation Sequencing In both technologies, the great advantage is achieved by novel bio-technologies for producing high throughput data !!! However, both have pros and cons… Microarray and Next Generation Sequencing Technologies 33 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University

34 conspros detection of only known transcripts limited to sequenced organisms, no de-novo higher background low expressed genes are less accurately detected relatively cheap mature biotechnology and analysis tools (since the late 90’s) fixed probes, no heterogeneity of coverage highly reproducible Arrays still expensive technical bias in mRNA library preparation and in transcripts of different length pre-mature bioinformatics tools de-novo analysis is tricky, ambiguity in mapping reads to the genome very high coverage is needed for low expressed genes variable sequence coverage for different genomic regions very sensitive if sufficient sequence depth direct read-out of all transcripts paired-end reads, better accuracy de-novo sequencing, new genomes highly reproducible new and exciting NGS In both, consistent biological interpretation ! 34

35 Marioni J C et al. Genome Res. 2008;18: Copyright © 2008, Cold Spring Harbor Laboratory Press Consistent Biological Interpretation ? 35 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University

36 NGS are becoming the technology of choice for a wide range of applications, but the transition away from microarrays is still long. Different applications have different requirements, so researchers need to carefully weigh their options when making the choice for using a platform. 36 Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University

37 37 TAU Bioinformatics unit: who are we and what do we do ?

38 Tel: Dr. Metsada Pasmanik-Chor, Bioinformatics Unit, Tel Aviv University


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