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Identifying Regulatory Elements Through Genomic Landmark Alignments

This analysis explores how to identify regulatory elements by aligning sequences to genomic landmarks, focusing on cis- and trans-acting elements. The study compares various alignment techniques and databases like TRANSFAC, emphasizing the importance of motif identification methods in gene regulation research. The proximity of binding sites to transcription start sites and the significance of alignment scores are highlighted, with a discussion on the effectiveness of the A-GLAM technique. Discover how to narrow the search range for regulatory elements and consider the implications for gene expression regulation.

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Identifying Regulatory Elements Through Genomic Landmark Alignments

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  1. An analysis of “Alignments anchored on genomic landmarks can aid in the identification of regulatory elements” by Kannan Tharakaraman et al. Sarah Aerni July 8, 2005

  2. Gene Regulation • Transcription factors • Cis-acting elements • Gene expression is regulated by gene itself (gene acts upon itself) • Trans-acting elements • Gene expression is regulated by other genes (gene inhibits another)

  3. Gene Regulation US Department of Energy Office of Science

  4. Motifs • Binding sites • Transcription factors • Zinc Finger • Hard to identify • Relatively short sequences • Some indices well conserved • Usually localized in certain proximity of the gene

  5. Enumerative Methods Align sequences, usually use orthologous genes Depends on local alignments Cannot be too similar or too distant Alignment Methods Create w-mers and find over-represented motifs Frequency may be misconstrued due to repeats Techniques to Identify Regulatory Elements • Tharakaraman Technique • Combine both methods • Include word placement with frequency – is the location of Cis-Regulatory regions correlated?

  6. Initial Steps • Mask repeats • Avoid identifying repeats as motifs • Maintain one position for possible motifs • Align Transcription Start Site (TSS) • Depend on proximity to TSS • Allow for slight shifts – look for clusters

  7. Define Significance • Alignment scores • Assign significance using gap penalties from Mock Set • Jittering – watch for overrepresented octonucleotides • ρ = 5 determined to be significant without jittering

  8. TRANSFAC • Database of Eukaryotic Transcriptional Regulatory Elements • Comparison of TRANSFAC octonucleotides to those identified by paper’s technique

  9. GLAM • Sequence input • Every sequence arbitrary position and window size chosen • Gapless multiple alignment in window sequences • Uses probability to determine whether windows are repositioned or resized (Gibbs Sampling) • “seed” constraints • OOPS (1 occurrence per sequence) • ZOOPS(0 or 1 occurrence per sequence)

  10. Alignment Techniques • Different techniques show different results • A-GLAM determined to be best • Compare to TRANSFAC • AlignACE cannot function computationally at genomic scale

  11. Distance to TSS • Cis-acting element locations determined by blocks • Largest number close to 0 (TSS) • Identified element correlated with TRANSFAC

  12. Further Discussion • Discussion is limited to method results • Little information given on whether location is truly correlated • No Biological discussion • Proximity of TSS and Cis-Acting binding sites • Narrow search range to a smaller field • Use in identification of types of element?

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