Bioinformatics Exam 2 review

Read mapping can be

time-consuming

Mapping hundreds of millions of reads back to the reference genome is

CPU and RAM intensive and slow

Most mappers allow

~2 mismatches (for read mapping) within first 30bp (428 could still uniquely identify most 30bp sequences in a 3GB genome), slower when allowing indels.

BLAST

rapid heuristic approach for local alignment

• can do this faster with some sacrifice

BLAST output: E-value

random hits with alignment

• “chance”

Statistical significance: E-value (Expectation value)

indicates the number of alignments expected to occur by chance with equivalent or better scores

An E-value of 10

suggests you would expect to see 10 random hits of similar quality. These are generally not considered meaningful matches unless the query is very short.

An n E-value of 0.005

means you expect to find one such alignment in every 200 random searches. This is often used as a threshold for statistically significant matches.

Blasting a rat protein sequence

• Use the “Reference proteins” database.

• Choose “human (taxid: 9606)” as the organism.

Transcriptome

All of the expressed RNAs in a cell are its transcriptome

Transcriptome varies by

cell type, tissue and organ, environment and treatment, time of day and age

RNA-seq Experimental Design

• ribo-minus

• PolyA

• strand-specific

ribo-minus

remove too abundant r/tRNA transcripts

• 80% RNA

PolyA

mRNA after splicing, enrich for exons, no r/tRNA, most popular

Strand-specific

directionality of RNA, useful for studying regulatory RNAs

RNA Sequencing: what does $200/sample mean?

• Single-end (SE) or pair-end (PE): PE getting more popular

• Depth: total number of reads

• Read length

Sequence Alignment for RNA

• map RNA reads to DNA reference

• find exon-exon junctions

1. mature mRNA reads

2. standard DNA aligner will not work

RNA sequencing data are

quantitative

• Data are represented as “depth of coverage” plots.

• The higher a gene is expressed, the more reads we find for that gene, the higher the peak.

• Read distribution can be highly non-uniform → complicates statistical analysis.

Data is quantitative but

noisy

• needed to do normalization

Strand-specific RNA sequencing

• helps us resolve the reads

• separate positive and negative

• helps us separate overlapped genes

step 1 for RNA sequencing

reverse transcription from RNA to cDNA

step 2 for RNA sequencing

decide on design factors including polyA selection, single-strand vs double-strand, paired end vs single-end

step 3 for RNA sequencing

perform the actual sequencing

step 4 for RNA sequencing

aligment

step 5 for RNA sequencing

RNA quantification

step 6 for RNA sequencing

normalization

step 7 for RNA sequencing

differential expression analysis

step 8 for RNA sequencing

GO and KEGG enrichment analysis

Normalization is important

• larger library has more reads

• larger transcript/genes have more reads

RPKM stands for

Reads Per Kilobase of transcript per Million mapped reads

Question: Read count is 10 for a gene that is 2 Kb long from a sample that has 5 million reads. What’s the RPKM?

1

• 10 / (2 × 5)

RPKM formula

10⁹ x (number of reads mapped to a region) / (total reads x region length)

total reads

adjust library size difference

region length

adjust transcript/gene length difference

The only difference between RPKM and FPKM is that FPKM takes into account

that two reads can map to one fragment (and so it doesn’t count this fragment twice)

RPKM is for

single-end sequencing

PKM is for

paired-end sequencing

Alternative splicing

• very common

• 90% of gene undergoes

Allele specific expression

• some more expressed than other

• pair of chromosomes

• both can be expressed into RNA, etc.

Detection of allele-specific expression by RNA-seq

• genome is not a reference genome

• 50/50 split

• significant allele expression for one over the other if not 50/50 split

The Genotype-Tissue Expression (GTEx) project aims to

characterize variation in gene expression levels across individuals and diverse tissues of the human body, many of which are not easily accessible

GTEx v8:

RNA-seq data from 15,201 samples across 49 tissues from 838 donors

Expression profiles

• patients= column

• rows= genes

• 2 different directions

• For most of these methods, we can think of the output as a 2D matrix

• ach row/column represents an expression profile

• Differential expression – which genes have different expression levels across two groups?

Differential expression (DE) input

RNA-seq RPKM between two conditions (e.g., healthy v.s. disease). Each gene will be tested separately for DE.

Differential expression (DE) output

DE testing result for each gene.

visualizing differential expression: volcano plo

• Each dot is a gene tested for differential expression

• X-axis: log fold change (logFC)

• Y-axis: -log(p-value)

• Color: sig. up regulation, sig. down regulation, not sig.

X-axis: log fold change (logFC)

direction of change

• reverse order if negative

Wilcoxon rank sum test

Test that is non parametric

• Also know as the Mann-Whitney U test

• Nonparametric version of two-sample t-test

• No distribution assumption of the data (e.g., normally distributed)

Wilcoxon rank sum test example

native american group sum is 75

• takes sum, sorts from smallest ot largest, only take sum of NA groups

MSCE

mean sister chromatid exchange measurments

Gene ontology (GO)

• A hierarchy of roles of genes and gene products.

• Composed of three independent ontologies: molecular function, biological process, cellular component.

• GO itself does not contain any information on genes or gene products.

T or F: Gene Ontology exhibits hierarchy structure?

True

Curators annotate their findings of

genes (known as annotations)

Molecular Function (MF)

Describes activities, such as catalytic or binding activities, that can be performed by individual gene products or assembled complexes of gene products at the molecular level.

Biological Process (BP)

Defined as a biological objective to which the genes or gene products contribute

Cellular Component (CC

Refers to the place in the cell where the gene products are active.

Motivation

• 100 balls, 40 white, draw 10 of them, is there an enrichment if out of the 10 draws 8 of them are white?

• 20,000 genes, 617 genes within the apoptosis GO, ran an RNA-seq experiment between two conditions and got 200 significantly differentially expressed genes, 80 of them are from the apoptosis GO. Is there an enrichment?

Kyoto Encyclopedia of Genes and Genomes (KEGG)

• Can think of this as a graph, with nodes representing compounds and edges representing reactions.

• Can perform KEGG pathway enrichment analysis, similar to GO enrichment analysis

• different way to summarize different genes into pathways

GO and KEGG enrichments

can test for each term one at a time to test if they are significantly enriched

• GO on top

• KEGG on bottom

Supervised learning

• outcome measurement Y (Also called dependent variable, response, target)

• vector of p predictor measurements X (also called inputs, regressors, covariates, features, independent variables)

• regression and classification

type 2 diabetes is what type of supervised learning

classification

classification

categorical Y

• finite, unordered set

Regression problem

Y is quantitative

• continuous

Unsupervised learning

• no outcome variable

• objective is more fuzzy- groups of samples that behave similar

Supervised has

X, y

• Given an observation x, what is the best label y? (e.g., classification, regression)

unsupervised has

X

• Given a set of x’s, output their hidden structure (e.g., clustering)

outlier

a data point whose response y does not follow the general trend of the rest of the data

A data point has high leverage if

it has a "pull" on the regression line and can significantly influence the slope and intercept of the model

Logistic regression with categorial outcome

y = 3 possibilities

• 1 = stroke

• 2 = drug overdose

• 3 = epileptic seizure

• linear regression is not appropriate here