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Lecture 19

Biology 2581B Lecture 19: Lec 19 – Bioinformatics

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Biology 2581B
David R Smith

Lecture 19 – Bioinformatics & Future of Genetics Bioinformatics - Most of you will never perform these procedures - Tests are becoming obsolete - Many of you will use digital device to interpret molecular sequence data o E.g in a hospital, having a child, etc - Health care o Personalized genomics ▪ From designer babies to handheld sequences - Genetic technologies and bioinformatics iercing every aspect of daily life - Healthcare will be hugely influenced by bioinformatics Science and healthcare - Many jobs in science and healthcare o Hospital, clinic, governmental environmental agency with water treatment, bioinformatics, nutrition - Don’t have to be a computer program in the back end, can work in the front end too - Back end and front end o Design, development o Instruction, implementation - Front end: go to high schools and give lectures about bioinformatics, pharmaceutical agents 2 topics - Assembling genomes (task of bioinformatics) - Searching genetic sequences Assembling - Assembling genomes is one of the most important and biggest aspect of bioinformatics - It is easy to generate the raw sequence data o Have technology to fire genome sequencing data really fast o 1000Gb – 100 billion bases of DNA in less than 24 hours - BUT most of the data is in tiny pieces o Some new technology can do longer pieces - Have all this data that is easy to get but how do we put it all together? o Not trivial – very hard and challenging - Sequencing reads - Isolated DNA and sent it to sequencing center - Got DNA data back in pieces of 150 nt - Want to put genome together into a full genome - MUST LOOK FOR OVERLAPS - 25 nucleotides overlap come together and assemble into a contig (because of the overlap) o Represents at 275 nt portion of the genome - Challenge: finding overlap and matching data on the reads and putting together the puzzle - Can say that 25 nt that overlap is small o If you have 25 nt that match between reads – good bet that they belong together - Looking at two reads and at a site in one read and comparing it to a site in another read - Chance of looking at one site, and having the two reads to be identical = 25% chance they match - To get 25 nt that match by chance on two pieces of DNA that don’t belong together is VERY rare = 0.25 25 - These reads should be together and they represent a real portion of the genome - Chance that 1 site is identical = 0.25 - Chance that 2 sites are identical = 0.25 x 0.25 = 0.0625 - Chance that 25 sites are identical = 0.25 = 0.000000000000001 - Issue is not aligning into contigs – the issue is the REPEATS IN GENOMES - Repeats in genomes screws up the sequencing process - Repeats are identical o 4 copies of the identical repeats o 6 copies of the identical repeat in another read - Can put the reads together because they share identical sequence (repeats) - it can fit together in many different ways - Sometimes repeats go on for hundreds or thousands of nucleotides - Need another read in your data set that spans the entire repeat and anchors into each one of the reads o Share identical sequence outside the repeat - Resolve this challenge and form the contig - In many genome, the repeats are so long; you can never get a read that goes through the whole thing o 100bp of repetitive DNA  left not knowing what to do – can’t assemble it - There are sections in genomes where they have not been able to assemble - Today’s bioinformatics programs can assemble millions of sequencing reads o In hours to days to weeks… depending on the algorithm and computer power - Pile in millions of the reads into the computer and it assembles them o Either days, weeks or hours - Assemble a tomato genome, would wait a week for the first round of assembly to go through o If using powerful computer, it would take a couple hours Assembling algorithms - Hugely dependent on the computer infrastructure - More powerful computers = better assemblies - Algorithm tries to find overlap o Have one read and its sequence and search it against 100 billion other reads o Different ways to do it - Take things into account: o How long of an overlap are you willing to accept as a genuine overlap – 4 or 25 nt? o How many mistakes are you willing to accept in the overlap ▪ Sequencing machines make mistakes – not perfect ▪ Will be some errors in the reads ▪ Insertion and deletions as mistakes o Some sophisticated algorithms can navigate through repeat elements o Take into account the quality of the read ▪ As the sequencing reads come off the machine, some are going to look good, others will have a lot of errors ▪ Algorithms are given a quality score - Algorithms use quality scores to see how good their assembly is and how to put it together - Hugely dependent of the computing infrastructure… more powerful computers = better assemblies - Sophisticated algorithms that can assemble genomes – need powerful computer 2 topics - searching - SEARCH – BLAST algorithm - Blastn (nucleotide vs. nucleotide) o Searching nucleotide sequence (RNA or DNA) against a database of known nucleotide sequences - Tblastx (translated nt vs. translated nt) Search the translation of the nucleotide sequence against a data base of translated sequences - Blastp (protein vs. protein) o Search protein against protein - Take unknown sequence and search it against a database of knowns (BLAST IT) - Type of bioinformatics that everyone does on a daily basis - Blastp – search protein against protein BLASTN - Blastn: search nucleotide sequence against database of nucleotide sequences o N for nucleotide TBLASTX (6 frames) - Take unknown nucleotide sequence, and you think it may be protein coding - Instead of searching the nucleotide, translate it into all possible frames (6 frames) o Search each one of the amino acid sequences against a data base where you did the same thing for everything in the data base ▪ Taken every nucleotide sequence in the data base and turned it into the 6 possible amino acid strands - If it was protein coding, do all 6 frames because you do not know where it starts or what strand it is on BLASTP - Blast protei
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