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Gene Prediction in Genomic Studies Ab-initio based methods Angela Pena Gonzalez Lavanya Rishishwar.

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Presentation on theme: "Gene Prediction in Genomic Studies Ab-initio based methods Angela Pena Gonzalez Lavanya Rishishwar."— Presentation transcript:

1 Gene Prediction in Genomic Studies Ab-initio based methods Angela Pena Gonzalez Lavanya Rishishwar

2 INTRODUCTION What Gene Prediction means and a brief background

3 Introduction: Gene Prediction Gene Prediction is the process of detection of the location of open reading frames (ORFs) and delineation of the structures of introns as well as exons if the genes of interest are of eukaryotic origin. The ultimate goal is to describe all the genes computationally with near 100% accuracy

4 Introduction: ORF Reading Frame: A sequence of DNA/RNA that is translated into an amino acid sequence, three bases at a time, each triplet sequence coding for a single amino acid Every region of DNA has six possible reading frames Open Reading Frame (ORF) is the longest frame uninterrupted by a stop codon

5 Introduction: ORF Not all translations have a biochemical support for them, some are merely derived theoretically or computationally In other words, each gene is an ORF but not ever ORF is a gene

6 Introduction: Gene Genes are the functional and physical unit of heredity passed from parent to offspring. Genes are pieces of DNA, and most genes contain the information for making a specific protein.

7 Introduction: Gene Models ProkaryoticEukaryotic

8 Introduction: Coding v/s Noncoding Coding region Coding regions are the parts of DNA which will give rise to a mature messenger RNA that will be translated into the specific amino acids of the protein product Noncoding region Noncoding regions are the parts of DNA which do not encode protein sequences. They may or may not be transcribed into RNA. E.g.: tRNA, rRNA, sRNA genes

9 NECESSITY Why we need gene prediction algorithms?

10 Necessity There have been a sharp up trend in the number of genomes sequenced in the past decade.

11 Necessity KEGG Genome: Release Update of Jan 2012 No. of Genomes in KEGG

12 Necessity There have been a sharp up trend in the number of genomes sequenced in the past decade. Accurately predicting genes can significantly reduce the amount of experimental verification work which is time and labor consuming and expensive to carry out Current state-of-art gene predictors have a high accuracy of ~90-99% (i.e., able to predict >90% of the experimentally validated genes)

13 METHODS How the gene predictors make the predictions?

14 Gene Prediction Methods Gene Prediction represents one of the most difficult problems in the field of pattern recognition, particularly in the case of eukaryotes The principle difficulties are: o Detection of initiation site (AUG) o Alternative start codons o Gene overlap o Undetected small proteins


16 Ab-initio Methods Predicts gene based on the given sequence alone. Consists of two types of models: o Markov based models o Dynamic Programming

17 A brief introduction of HMMs Hidden Markov models (HMMs) are discrete Markov processes where every state generates an observation at each time step. A hidden Markov model (HMM) is statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states.

18 Markov Model (Discrete Markov Process) A discrete Markov process is a sequence of random variables q1,…,qt that take values in a discrete set S={s1,…,sN} where the Markov property holds. Markov property: Parameters – Initial state probabilities: πi – State transition probabilities: aij 18

19 From Markov Model to HMM HMMs are discrete Markov processes where each state also emits an observation according to some probability distribution, we need to augment our model. Parameters – Initial state probabilities: π i – State transition probabilities: a ij – Emission probabilities: e i (k) 19 Markov ModelHidden Markov Model Each state emits an observation with 100% probability Each state emits an observation according to a certain probability distribution


21 Say Adios to your windows and get to Linux!! Say “Si” when you are ready to work on Linux!!! Di que “Si” si tu estas listo para trabajar con Linux!!!

22 A Quick Linux How-To Manual Terminal (and Kernel)! “That’s Linux to me!” – Lava Basic Navigations in Terminal: – Change to a specific directory – cd – List the contents of the folder – ls – Come up one level of the folder – cd.. – Copy a file to one location to another – cp – Move a file from one location to another – mv – Rename a file (file1) to (file2) – mv file1 file2

23 A Quick Linux How-To Manual – Autocomplete – tab! – Extract a file – tar –xvf [file name] – Installing a software: Navigate to the folder where “Makefile” is present Type make Wait for the installer to finish processing Programs will be stored in the same folder or a different folder by the name “bin” (stands for basic input)

24 That’s all Folks, Thank you for coming, Gracious!

25 Naah, Just kidding! Lets get down to business!

26 GeneMark Developed by Dr. Mark Borodovsky (from Georgia Tech!) Works on elegant pseudo-HMMs and HMM Several versions available – prokayotic/eukaryotic, self training

27 Running GeneMark./ --prok --out [output file] [genome file]

28 Glimmer3 Works by creating a variable-length Markov model from a training set of genes Using the model to identify all genes in a DNA sequence

29 Running Glimmer3 It’s a 2 step progress 1.A probability model of coding sequences must be built called an interpolated context model../build-icm [model name] < [genome] 2.Program is run to analyze the sequences and make gene predictions./glimmer3 [genome] [icm_model] [output] o Best results require longest possible training set of genes

30 Glimmer3 programs (if you are curious) Long-orfs  uses an amino-acid distribution model to filter the set of orfs Extract  builds training set from long, nonoverlapping orfs Build-icm  build interpolated context model from training sequences Glimmer3  analyze sequences and make predictions

31 RNA Prediction

32 Running tRNA-Scan-SE tRNAscan-SE –B -o -f -m -B : search for bacterial tRNAs This option selects the bacterial covariace model for tRNA analysis, and loosens the search parameters for EufindtRNA to improve detection o f bacterial tRNAs. -o : save final results in Specifiy this option to write results to. -f : save results and tRNA secondary structures to. -m : save statistics summary for run contains the run options selected as well as statistics on the number of tRNAs detected at each phase of the search, search speed, and other statistics.

33 Output using “–o” parameter Output using “–f” parameter


35 THANK YOU Yes I am serious. We are done. You are saved!

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