Prac%cal Bioinforma%cs for Life Scien%sts. Week 14, Lecture 28. István Albert Bioinforma%cs Consul%ng Center Penn State

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1 Prac%cal Bioinforma%cs for Life Scien%sts Week 14, Lecture 28 István Albert Bioinforma%cs Consul%ng Center Penn State

2 Final project A group of researchers are interested in studying protein binding loca%ons in a yet unknown genome. They hypothesize that observed phenotypic varia%ons will correlate with sequence varia%on of the bound loca%ons in this genome. The researchers perform two experimental procedures. 1. First they sequence the whole genome of the wild- type (normal, wt) phenotype with a paired end sequencing technology. 2. Second, they perform a chroma%n- immuno- precipita%on experiment that isolates DNA fragments corresponding to bound proteins. This second experiment is performed for both the wild- type (wt) phenotype and the mutant phenotype (m1).

3 Final project data (see course webpage) 1. The paired end whole- genome sequencing is stored in datasets r1.fq and r2.fq 2. ChIP- Seq data for the wild- type sample is stored in file p1.fq 3. ChIP- Seq data for the mutant sample is stored in file p2.fq

4 Ques%ons that need to be answered What is the es%mated size of your genome? How many binding loca%ons can you detect for each of the Chip- Seq datasets? Does the number of binding loca%ons vary? Can you observe any genomic varia%on between phenotypes in the bound loca%ons? Include a IGV screenshot of the loca%on of one binding site Tips: 1. there are many ways to solve the project. 2. there are fewer than 10 binding sites 3. at the bare minimum all ques%ons above may be be solved via three tools: velvet + bwa + samtools

5 Due date: next Thursday Project due by next Thursday (Dec 8 th ) Project related office hours on next Monday and Wednesday between 2 and 3pm Turn in all homework you might have missed (par%al credit will be given)

6 Classifica%on of metagenomics data One of the first ques%ons any life scien%st is asking what species is present in my data? Yet it may be the right ques%on just pubng a label makes the data more precise than it might be Current state of meta- genomics does not lend itself to accurate species level characteriza%on too many unknown bacteria too many unknown systema%c effects too many improperly designed experiments

7 We ll use and compare two classifica%on approaches BLAST à LCA (lowest common ancestor) à visualize with Megan (Metagenome Analyzer) Probabilis%c (bayesian) classifica%on for 16S rrna data via the RDP mul%classifier Download the data for lecture 28 from the course webpage

8 Megan Metagenome Analyzer Java based with a neat graphical user interface

9 Find and download the 16S rrna BLAST database

10 Run blast to generate the alignments We have two samples s1.fa and s2.fa that correspond to two condi%ons You can limit the number of alignments and use more threads if your computer can handle that à speeds up the process considerably

11 Our blast files This will need to be recognized by Megan to connect it to a taxonomy (we need to use a gi to taxonomy mapper) Neat feature: your original sequence names may also contain the taxonomy as a list, in which case Megan will parse that out. > [0]Bacteria;[1]Bacteroidetes;[2]Bacteroidia;[3]Bacteroidales;something In that case no addi%onal taxonomical informa%on is needed.

12 Result of the classifica%on You can also load up the s1.rma file directly

13 Classify at RDP Note: this classifies the sequences directly! No alignment step needed!

14 Results of the classifica%on

15 RPD mul%classifier from the command line Find and download the from the classifier webpage. It can generate combine the output for each file

16 Inves%ga%ng a bit more How do these methods work really: MEGAN à lowest common ancestor (read the Megan manual for more details) à but operates solely on sequence similarity - extract reads that map to a certain taxa and analyze the alignments RDP à breaks each sequence into words, and computes the likelyhood that a word comes from a certain taxa (it only works for 16s RNA data!)