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

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

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

4. Can also use algorithms to look for: 1. Exon-intron boundaries - “GT-AG” rule, but consensus sequences very short 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 Table 5.1, Brown1 st ed (see Fig.5.5) See Fig. 5.10 which shows results from various bioinformatics tools used to analyze 15 kb of human genome

5. 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 BLASTP – search at protein level BLASTN – search at nucleotide level BLASTX – search nt sequence against protein databases (automatic 6-reading frame conceptual translation) 4. Homologous sequences in databank tBLASTN – protein query vs. conceptual translation of DNA database

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

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

8. Query = yeast mitochondrial ribosomal protein L8 gene (including promoter & UTRs) What if this search was done at nucleotide (instead of protein) level? 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. 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… Fig. 5.18 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) 1. Orthologous - homologous genes in different organisms 2. Paralogous - homologous genes in same organism (eg.  -globin genes from mouse and human) (eg. multi-gene family members,  -globin and  -globin from mouse) Two genes are either evolutionarily related or they are not …. so instead of “…% homologous”, use “… % identity” (p.145)

11. ARE TWO SEQUENCES HOMOLOGOUS OR INDEPENDENT IN ORIGIN? Factors to consider: 1.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 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 “low complexity regions”

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

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

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

15. Some protein genes are constitutively expressed … “housekeeping gene” products needed at all times … whereas others are differentially expressed Only a subset of genes are expressed at a given time and mRNA levels can vary greatly among genes during development in specific tissue type in response to environmental cues ~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 (Ramskold PLoS 2009) (higher than predicted from microarray analysis, to be discussed in Topic 7)

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

17. Human phosphatidylinositol glycan gene (chromosome 18) - additional info from RNA level data ~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) RefSeq: gene data agreed upon by everyone

18. RACE – rapid amplification of cDNA ends Fig.5.13 2. To obtain sequence info corresponding to termini of mRNAs: where NNN… might be restriction site (eg. to aid in cloning RACE product) “specialized” RT-PCR strategy 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)?