Statistical Analysis for Word counting in Drosophila Core Promoters Yogita Mantri April Bioinformatics Capstone presentation

 Introduction & Motivation  Dataset used  Part I – Unbiased word counting  Part II – TCAGT-centric word counting  Conclusions and Future work

Introduction  Regulatory elements are short DNA sequences that control gene expression.  They are often found around the Transcription Start Site (TSS), sometimes further upstream.  Identification of promoters and regulatory elements is a major challenge in bioinformatics: Regulatory elements are not well-conserved Computational discovery of TSS in not straightforward Promoter sequences do not have distinguishable statistical properties Transcription is a highly cooperative process including competitive or cooperative binding which is not completely determined from the rest of the genome’s DNA sequence

“Computational analysis of core promoters in the Drosophila Genome”, Ohler, Rubin et. al, Genome Biology 2002, 3(12):research0087.1– Above image edited from: Drosophila Core Promoters

Motivation for project  Database of Core Promoters with TSS experimentally determined is a huge advantage over other approaches using only gene upstream regions.  Word Counting method to determine significant patterns, inspired by Dr. Peter Cherbas’ earlier work. “The arthropod initiator: the capsite consensus plays an important role in transcription”, Cherbas L, Cherbas P., Insect Biochem Mol Biol Jan;23(1):81-90

 Introduction & Motivation  Dataset used  Part I – Unbiased word counting  Part II – TCAGT-centric word counting  Conclusions and Future work

The Database of Drosophila Core Promoters  Compiled by Sumit Middha. It consists of Drosophila core promoters from three experimental sources.  Ohler, Rubin et al: 1941 promoters Stringent criteria for identifying TSSs, requiring 5’ ends of multiple cDNAs to lie in close proximity.  Kadonaga et al: 205 promoters Changed TSS to coincide with A of Inr consensus TCAGT even if experimental results reported TSS in the vicinity. The discrepancy was fixed by taking the experimentally reported TSS.  Eukaryotic Promoter Database: 1926 promoters Assigned TSS based on experimental data with a precision of +/- 5bp or better.  3458 sequences after removing redundant entries in the dataset.

 Introduction & Motivation  Dataset used  Part I – Unbiased word counting  Part II – TCAGT-centric word counting  Conclusions and Future work

Word Analysis – Part I Unbiased search  Used various statistical measures like Z- score on all possible n-mers in the entire dataset and in specific windows.  The goal was to see whether known patterns of interest were significantly enriched in promoter sequences than other patterns.

Basic Statistics of the dataset  3458 promoter sequences in the database.  First step was a word-frequency analysis (pentamers used for initial analysis)  Performed analysis on the following sets: Entire dataset (DS-1) Subset of above dataset, with only -20 to +20 region (DS-2)  2 types of analyses, differing in “Random” sequences used: 1 st Order Markov Chains based on base and transition probabilities of respective dataset “non-coding” regions

Random set  Generated 100 sets of 1 st order Markov chains  Each set contained same number of sequences as original datase (3458), and having same length (350)  Computed occurrence of each pentamer in actual and random sequences  For random sequences, calculated average and S.D over all sets

Z-score  A test of significance  Mean and S.D calculated over 100 sets  Calculated Z-scores for all pentamers  Looking for pentamers with very high or very low Z- scores

RankPatternZ-Score 1aaaaa ttttt ttttg88.1 4gaaaa aaaac atttt gtttt ttttc aaaat gcagc gcagt tcagt acagt tcatt tataa Rank of TCAGT and variants in entire dataset

PATTERNZ-ScoreRank tcagt tcatt gcagt acagt tataa Summary of known pentamers in different windows PatternZ-scoreRank tcagt tcatt gcagt acagt tataa Sliding Windows PatternZ-scoreRank tcagt tcatt gcagt acagt tataa Non-overlapping windows

Z-score Plots of tcagt and variants using sliding windows of 10 bp

Lesson  Cannot ignore position preference of regulatory motifs!

 Introduction & Motivation  Dataset used  Part I – Unbiased word counting  Part II – TCAGT-centric word counting  Conclusions and Future work

Word Analysis – Part II Guided search, starting with known INR element TCAGT  Identification of INR enriched regions  Identification of synonyms  Correlation analysis of INR synonyms  Guided search

TCAGT-centric word analysis WindowZscore (-3,3) (-4,2) (-2,4) (-5,1) (-6,-1) (-7,-2) (-1,5) (1,6) (2,7) (3,8) 28.79

Group1 CTCAG--- ATCAG--- TTCAG--- GTCAG--- -TCAGT-- ---AGTTG ---AGTCG --CAGTT- --CAGTC- Group 3 ACACT--- -CACTCTG Group 4 -TCACA- GTCAC-- --CACAC Group 6 -CATTC TCATT- INR Synonyms Group 2 TTAGT Group 5 TCACTCT “Computational analysis of core promoters in the Drosophila Genome”, Ohler, Rubin et. al, Genome Biology 2002, 3(12):research0087.1–

TOTAL: INR+INR- TATA+ TATA- TATA+ TATA- DPE-DPE+ DPE- DPE+ DPE- DPE+ DPE Binary Tree Representation of Dataset

3 Clusters in INR-positive set ggtcacact ggtcacac cggtcacac ttcagtcg cggacgtg tataaaag tcagt TATA (-40, -35) DPE (+20, -30) INR (-10, +2)

TATA+TATA- INR INR INR+, TATA+ Log Likelihood: DPE+DPE- INR INR INR+, DPE+ Log Likelihood: DPE+DPE- TATA TATA INR+, DPE+ Log Likelihood: Contingency Matrices for INR, TATA, DPE

tctttcttt ggtcacac ctcgaggg ctatcgat cggtcacac ttctttccg gtcacact Possible Alternative TATA and INR Synonyms ?? TATA – 2 ? INR – 2 ?

actatcgat ctatcgat tatcgataaactatcgat Enrichment further upstream – New Binding Sites?

TOTAL: INR+ INR- TATA+ TATA- INR_2+ INR_2- DPE-DPE+ DPE- DPE+ TATA_2+ TATA_2- DPE+ DPE Next Level of Binary Tree analysis DPE+ DPE- ? DPE+ DPE- ?

Conclusions & Future steps  The main goal of this project was to try to identify significant words based on only statistical over- representation.  The first part of the analysis using an unbiased searching method was successful only in a very narrow range of positions around the TSS.  However, the biased search starting with the Inr consensus revealed the 3 known regulatory elements in that region.  An analysis of the Inr-negative set showed over-expression of patterns in the same positions as the Inr, TATA and DPE should be, and could be possible synonyms.  Thus the word-counting strategy has the potential to reveal: Regulatory motifs and interrelationships that other motif discovery programs cannot Synonyms for regulatory motifs Dependencies among regulatory motifs

Acknowledgements  Dr. Haixu Tang  Dr. Sun Kim  Dr. Peter Cherbas  Sumit Middha  Bioinformatics Research Group