1 / 18

1. Bacterial genomes - genes tightly packed, no introns...

HOW TO FIND GENES WITHIN A DNA SEQUENCE?. 1. Bacterial genomes - genes tightly packed, no introns. Scan for ORFs (open reading frames). check all 6 reading frames (both strands). Fig. 5.2. look for significant distance between potential start and stop codon (eg 100 codons).

heavynne-mays
Télécharger la présentation

1. Bacterial genomes - genes tightly packed, no introns...

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. HOW TO FIND GENES WITHIN A DNA SEQUENCE? 1. Bacterial genomes - genes tightly packed, no introns... Scan for ORFs (open reading frames) • check all 6 reading frames (both strands) Fig. 5.2 • look for significant distance between potential start • and stop codon (eg 100 codons) … but when examining short sequences, start codon (or stop codon) might be located further upstream (or downstream)

  2. Use computer programs to search for ORFs: Query: 3 kb sequence Potential problems? - if initiation codon other than ATG (relatively rare) - if overlapping genes (rare) - if deviation from standard genetic code (can change default) - if gene contains intron(s)

  3. 2. Eukaryotic genomes (such as human) - genes usually far apart, long introns & short exons Would an ORF scan work here? Fig.5.4

  4. Can also use algorithms to look for: 1. Exon-intron boundaries - “GT-AG” rule, but consensus sequences very short (see Fig.5.5) 2. Regulatory motifs - upstream promoters, downstream polyA addition signals… - but consensus sequences usually very short • 3. Codon bias patterns • - synonymous codons are not all used equally • - patterns differ among • organisms See Fig. 5.10 which shows results from various bioinformatics tools used to analyze 15 kb of human genome Table 5.1, Brown1st ed

  5. 4. Homologous sequences in databank BLAST searches www.ncbi.nlm.nih.gov/BLAST/ Basic Local Alignment Search Tool • search programs to look for similarity between your sequence • of interest (protein or DNA) and entries in global data banks BLASTN – search at nucleotide level BLASTP – search at protein level BLASTX – search nt sequence against protein databases (automatic 6-reading frame conceptual translation) tBLASTN – protein query vs. conceptual translation of DNA database

  6. Fungal Query = yeast mitochondrial ribosomal protein L8 (238 aa) Bacterial

  7. Nomenclature may differ among organisms - called L17 in Streptococcus but L8 in yeast E-values: statistical measure of likelihood that sequences with this degree of similarity occur randomly ie. reflects number of hits expected by chance

  8. What if this search was done at nucleotide (instead of protein) level? Query = yeast mitochondrial ribosomal protein L8 gene (including promoter & UTRs) Only got “hits” with other yeast entries, in this case Homologous genes from divergent organisms typically show greater similarity at amino acid level than at nt level Degeneracy of genetic code Codon bias among organisms Probability of specific stretch of nucleotides occurring by random chance (“spurious hits”) is higher than for the same length of amino acids

  9. Fig. 5.18 To illustrate the power of amino acid level searches, text shows 2 sequences with 76% nt identity … but only 28% aa identity But it’s a rather artificial example… because if 2 DNA stretches of 300 bp or so (normal default length in ORF Finder) showed 76% nt identity, it’s very improbable that such similarity occurred by chance

  10. HOMOLOGOUS GENES (share common evolutionary origin) (p.145) 1. Orthologous - homologous genes in different organisms (eg. b-globin genes from mouse and human) 2. Paralogous - homologous genes in same organism (eg. multi-gene family members, a-globin and b-globin from mouse) Two genes are either evolutionarily related or they are not …. so instead of “…% homologous”,use “… % identity”

  11. ARE TWO SEQUENCES HOMOLOGOUS OR INDEPENDENT IN ORIGIN? Factors to consider: • Length of sequence • - short sequences more likely to occur by chance 2. Base composition - highly biased (eg if only AT) more likely to occur by chance “low complexity regions” 3. Similarity at amino acid level (if protein-coding region) - high % identity is strong argument for homology - usually implies common protein function - nt changes such that minimal effect on aa sequence

  12. Comparison of homologous regions from multiple genomes MultiPipMaker program (percent identity plot) Human chr 7 (1.8 Mbp region) “Numbered boxes correspond to exons” - score of % nt sequence similarity (blocks compared vs. reference sequence) - gives overview of sequence relationships for genomic region shared among organisms Thomas Nature 424:788, 2003

  13. EXPERIMENTAL TECHNIQUES TO FIND GENES 1. Zoo blot (Southern) analysis - find regions homologous to DNA from other organisms - to determine presence/absence of gene among different organisms Heterologous hybridization - use conditions of “reduced stringency” (eg lower temp) so that duplex hybrids with some mismatches are stable Interpretation of data shown in figure? Fig. 5.12

  14. Kidney Brain Heart Lung Liver • 2. Northern blot analysis • - to identify expressed regions of genomes (detect transcripts) In situ hybridization - to determine cellular location gene X probe Probe: tagged DNA (eg. PCR product, restriction fragment, cDNA clone…) in denatured form or oligomer or antisense (synthetic) RNA … 35S-labeled b-myosin antisense probe hybridizing to heart ventricle in 13-day embryonic mouse Strachan & Read Fig. 5.17 (but note that many identical copies of that particular mRNA are present on blot) Fig. 5.11

  15. Some protein genes are constitutively expressed … “housekeeping gene” products needed at all times … whereas others are differentially expressed in specific tissue type during development in response to environmental cues Only a subset of genes are expressed at a given time and mRNA levels can vary greatly among genes ~10,000 – 15,000 different mRNAs present in “typical” mammalian cell type under given condition (may be ~ 20,000 different proteins present) Aside: RNA-sequencing studies suggest ~ 8000 genes ubiquitously expressed in human tissues (RamskoldPLoS 2009) (higher than predicted from microarray analysis, to be discussed in Topic 7)

  16. EXPERIMENTAL TECHNIQUES TO FIND CODING REGIONS WITHIN GENES 1. Sequencing of cDNA (or EST) clones ... & compare to genomic sequences to determine positions of introns eg. for primer can use mixture of “anchored” oligo(dT)s with A, C or G in the 3’ position 5’cap AAAAAAAAAn 3’ 5’ • ESTs • short sequences obtained by • sequence analysis of cDNA clones … but if low abundance mRNA may not be in bank …. or cDNA maybe not full-length If so, which end would you expect to be missing? Fig.3.36

  17. Human phosphatidylinositol glycan gene (chromosome 18) - additional info from RNA level data RefSeq: gene data agreed upon by everyone ~60% of RefSeq genes could be extended at 5’ and 3’ ends (based on additional EST data = UTRs) Nusbaum Nature 437:551, 2005 (Fig S2)

  18. 2. To obtain sequence info corresponding to termini of mRNAs: RACE – rapid amplification of cDNA ends “specialized” RT-PCR strategy where NNN… might be restriction site (eg. to aid in cloning RACE product) 5’ RACE - mapping 5’ end of mRNA useful in locating position of promoter - promoter immediately upstream of transcription start site How would you carry out 3’ RACE (to determine exact position of 3’end of mRNA)? Fig.5.13

More Related