rna-seq experiments for bioinformaticians

RNA-seq experiment for Bioinformaticians

Ashis Kumer Biswas

BioMeCIS at CSE, UT Arlington

April 12, 2012

Ashis Kumer Biswas RNA-seq and Bioinformatics

Outline

1 Basics of RNA-seq technologyQuick facts about RNA-seqRNA-seq steps

2 RNA-seq for BioinformaticiansShort-Read Alignments

Quick facts about RNA-seq

It’s a massively parallel sequencing method for transcriptomeanalysis.

What is transcriptome

Transcriptome T is set of RNA molecules.

What is RNA

Figure: The Cell[1]

What is RNA

Figure: DNA vs. RNA[1]

What is RNA

Figure: RNA secondary structure for the RNA sequence(5’end)–ACCCCCUCCUUCCUUGGAUCAAGGGGCUCAA–(3’end)

What is RNA

Figure: RNA secondary structure for the RNA sequence(5’end)–ACCCCCUCCUUCCUUGGAUCAAGGGGCUCAA–(3’end)

What is RNA

Types of RNA:

mRNA — messenger RNA: it carries the code from the DNAin nucleus for synthesis of one/more proteins into thecytoplasm where the protein manufacturing takes place in theorganelle — “Ribosome”.

tRNA — transfer RNA: it brings amino acids to the ribosome,where the translation of mRNA into Amino Acid sequences.

rRNA — ribosomal RNA: the rRNA and some proteinscombine to form a nucleoprotein called “ribosome” whichserves as the site and carries the necessary enzymes for proteinsynthesis.

What is RNA

Types of RNA:

What is RNA

Types of RNA:

What is RNA

More types of RNA:

ncRNA — non-coding RNAs: They are not translated intoprotein. Examples:

tRNArRNAsnoRNA—small nucleolar RNA: it guides the chemicalmodifications of other RNAs.miRNA—microRNA: it’s a post-transcriptional regulators.siRNA—small interfering RNA: it is involved in RNAinterference pathway (i.e., in certain gene expression pathway).piRNA—piwi-interacting RNA: it forms RNA-proteincomplexes which regulates some post-transcriptional geneexpression.lncRNA — long ncRNA: non-coding RNA longer than 200nucleotides.

What is RNA

More types of RNA:

rRNAsnoRNA—small nucleolar RNA: it guides the chemicalmodifications of other RNAs.miRNA—microRNA: it’s a post-transcriptional regulators.siRNA—small interfering RNA: it is involved in RNAinterference pathway (i.e., in certain gene expression pathway).piRNA—piwi-interacting RNA: it forms RNA-proteincomplexes which regulates some post-transcriptional geneexpression.lncRNA — long ncRNA: non-coding RNA longer than 200nucleotides.

What is RNA

More types of RNA:

tRNArRNA

snoRNA—small nucleolar RNA: it guides the chemicalmodifications of other RNAs.miRNA—microRNA: it’s a post-transcriptional regulators.siRNA—small interfering RNA: it is involved in RNAinterference pathway (i.e., in certain gene expression pathway).piRNA—piwi-interacting RNA: it forms RNA-proteincomplexes which regulates some post-transcriptional geneexpression.lncRNA — long ncRNA: non-coding RNA longer than 200nucleotides.

What is RNA

More types of RNA:

tRNArRNAsnoRNA—small nucleolar RNA: it guides the chemicalmodifications of other RNAs.

miRNA—microRNA: it’s a post-transcriptional regulators.siRNA—small interfering RNA: it is involved in RNAinterference pathway (i.e., in certain gene expression pathway).piRNA—piwi-interacting RNA: it forms RNA-proteincomplexes which regulates some post-transcriptional geneexpression.lncRNA — long ncRNA: non-coding RNA longer than 200nucleotides.

What is RNA

More types of RNA:

tRNArRNAsnoRNA—small nucleolar RNA: it guides the chemicalmodifications of other RNAs.miRNA—microRNA: it’s a post-transcriptional regulators.

siRNA—small interfering RNA: it is involved in RNAinterference pathway (i.e., in certain gene expression pathway).piRNA—piwi-interacting RNA: it forms RNA-proteincomplexes which regulates some post-transcriptional geneexpression.lncRNA — long ncRNA: non-coding RNA longer than 200nucleotides.

What is RNA

More types of RNA:

tRNArRNAsnoRNA—small nucleolar RNA: it guides the chemicalmodifications of other RNAs.miRNA—microRNA: it’s a post-transcriptional regulators.siRNA—small interfering RNA: it is involved in RNAinterference pathway (i.e., in certain gene expression pathway).

piRNA—piwi-interacting RNA: it forms RNA-proteincomplexes which regulates some post-transcriptional geneexpression.lncRNA — long ncRNA: non-coding RNA longer than 200nucleotides.

What is RNA

More types of RNA:

tRNArRNAsnoRNA—small nucleolar RNA: it guides the chemicalmodifications of other RNAs.miRNA—microRNA: it’s a post-transcriptional regulators.siRNA—small interfering RNA: it is involved in RNAinterference pathway (i.e., in certain gene expression pathway).piRNA—piwi-interacting RNA: it forms RNA-proteincomplexes which regulates some post-transcriptional geneexpression.

lncRNA — long ncRNA: non-coding RNA longer than 200nucleotides.

What is RNA

More types of RNA:

tRNArRNAsnoRNA—small nucleolar RNA: it guides the chemicalmodifications of other RNAs.miRNA—microRNA: it’s a post-transcriptional regulators.siRNA—small interfering RNA: it is involved in RNAinterference pathway (i.e., in certain gene expression pathway).piRNA—piwi-interacting RNA: it forms RNA-proteincomplexes which regulates some post-transcriptional geneexpression.lncRNA — long ncRNA: non-coding RNA longer than 200nucleotides.

What is RNA

Roles of RNA in the “central dogma of molecular biology”:

In contrast, a genome does not change in a living cell exceptfor mutation; but a transcriptome varies according to differentexternal environmental conditions or in different stages of cellcycles, or in disease conditions.

What is Genome

The full set of DNA sequences of an organism is called its genome.Humans have 23 pairs of chromosomes.

What is Genome

Why analyze the transcriptome?

The research branch “transcriptomics” deals with:

Examining expression profiles (i.e., expression levels) ofmRNAs in a given cell population.

Interpreting the functional elements of the genome..

Revealing the molecular constituents of cells, tissues

Understanding the disease

The transcriptome can be seen as a precursor for theproteome,i.e., the entire set of proteins expressed by agenome.

What is Massively Parallel Sequencing

This technique allows to simultaneously sequence 1 million toseveral hundred millions of short reads (50-400bases) fromamplified DNA clones.

This technology emerged in late 1996, and commerciallyavailable since 2005.

Sequencing cost decreased: ultimate goal— $1000/genomesequencing.

Outline

RNA-seq steps

Step 1

The RNAs having Poly-A (i.e., many Adenine (A)) tail are isolatedfrom sample cell cytoplasm.

m-mRNA

Mature mRNA:

Step 2

The Poly-A RNAs are reverse transcribed to produce adouble-stranded cDNA (complementary DNA).

Step 2

Reverse Transcription

It is the opposite of transcription.

Transcription

It’s the process of producing single-stranded mRNA from adouble-stranded DNA sequence.

It is a way of acquiring a gene sequence—the double strandedDNA fragment from which the mRNA was transcribed.

After reverse transcription, the produced double strandedDNA is called: cDNA (complementary DNA).

Step 2

Step 3

The cDNAs are subject to random fragmentation of size 35-400base pairs.

Step 4

Using the massively parallel high throughput sequencing machines(e.g, Illumina, SOLiD, Roche etc), the library of the short cDNAfragments are sequenced.

Sequenced files

Suppose this is one short-read sequnce:

Sequenced files

The second section of the file contains the quality of eachcharacters of the sequences.

Phred Quality Score Q = −10.log10P, where P is thebase-calling error probability measured by the sequencingmachine.

In other words, P = 10−Q10

For example, if Q = 30 => P = 10−3010 = 10−3 = 1

So, Base call accuracy would be =

(1− P) = (1− 1

1000) = 99.9%

Sequenced files

For example, if Q = 30 => P = 10−3010 = 10−3 = 1

(1− P) = (1− 1

1000) = 99.9%

Sequenced files

For example, if Q = 30 => P = 10−3010 = 10−3 = 1

(1− P) = (1− 1

1000) = 99.9%

Sequenced files

For example, if Q = 30 => P = 10−3010 = 10−3 = 1

(1− P) = (1− 1

1000) = 99.9%

Sequenced files

The range of Phred scores Q is [0, 93]The Phred scores Q are converted to ASCII characters using shiftof 33 (ASCII Letter = Q + 33).The ASCII letter ranges [33, 126] [!,˜]

Here is the scores after the conversion:

Step 5

Align the short-read sequences to exonic reference sequences.

Types of short-reads

Types of short-reads:

Types of short-reads

Types of short-reads:

Step 6

Quantify the expression levels.

Units of measurements

For each transcript, the measure of expression level is quantifiedusing a metric — RPKM.

RPKM: # of reads per kilobase per million mapped reads

Suppose from an RNA-seq experiment we have,

10 million short-reads, but out of which only 8 million readscould be mapped to the reference genome.

From those mapped reads, 1000 alignments maps to atranscript of size 1 kilobases.

So, RPKM score for that transcript =1000

1× 8= 125

# of RNA-seq reads generated from a transcript ∝ thattranscript’s relative abundance in the sample.

Suppose a sample has 2 transcripts A and B, both of whichare present at the same abundance.

If B is twice as long as A, an RNA-seq will contain twice asmany reads from B as from A.

So, in RPKM calculation the read counts were normalized byeach transcript’s length.

RNA-seq steps

So, this is RNA-seq:

What we can get from an RNA-seq experiment

To quantify (count) the mRNA abundance.

To quantify the changes of expression levels of each transcriptduring the development stages of cells, or under differentconditions.

What we can get from an RNA-seq experiment

To quantify (count) the mRNA abundance.

To quantify the changes of expression levels of each transcriptduring the development stages of cells, or under differentconditions.

RNA-seq for Bioinformaticians

Each RNA-seq experiment (“lane”) produces more than 10million short-reads. These are required to be aligned to thereference genome.

Identifying the non-coding RNA.

and many more...

Outline

The Mapping Problem

Input: m l-bp (base-pair) size short-reads S1,S2, ...,Sm and anapproximate reference genome R.Output: What are the positions x1, x2, ..., xm along R where eachshort read matches?In human genome example,m is usually 107 − 108

Length of each short-reads l is typically 50-200 bp.Length of the genome |R| is 3× 109 bp.

The Mapping Problem: Solution 1

Naive Algorithm:

Scan the reference genome R for each short-reads Si ,

Matching the read at each position p and picking the bestmatch.

Time Complexity: O(ml |R|)For human genome example: if m = 108, l = 50, |R|=3× 109,then complexity = 5× 1018. This is clearly impractical.

KMP (Knuth-Morris-Pratt) Algorithm:

Time Complexity: O(m(l + |R|)) = O(ml + m|R|)For human genome example: if m = 108, l = 50,|R|=3× 109, then complexity ≈ 1017. This is also impractical.

Using Suffix Tree:

First build a suffix tree for R.

Once the tree is built, for each Si we can find matches bytraversing the tree from the root efficiently.

Time Complexity: O(ml + |R|)For human genome example: if m = 108, l = 50,|R|=3× 109, then complexity ≈ 109. This looks practical.

But saving the tree requires O(|R|log |R|) bits, i.e., ∼ 64GBwhich is impractical for most of today’s desktop computers.

This approach also allows only EXACT matching.

BWT (Burrows-Wheeler Transform):

It’s invented by Burrows & Wheeler in 1994.

It’s used in data compression technique — bzip2.

The transformation does not change the character’s value. Itchanges the order of the characters.

Frequent substrings in the original text will repeat multipletimes in a row in the transformed text.

This kind of transformed text can be easily compressed byother algorithm — “Move-To-Front Transform” or“Run-Length-Encoding”.

Input SIX.MIXED.PIXIES.SIFT.SIXTY.PIXIE.DUST.BOXES

Output TEXYDST.E.IXIXIXXSSMPPS.B..E.S.EUSFXDIIOIIIT

BWT (Burrows-Wheeler Transform) :

This shows the BWT is reversible!

BWT (Burrows-Wheeler Transform) : Can we answer thesequestions now?

Is the letter “B” followed by “A” or vice versa?

Does the substring “ANA” present in the original text?

The Mapping Problem

The popular program TopHat [2] uses the BWT algorithm todo the mapping of short-reads to the reference genome.

To store the transformed text of the human genome we needonly 2× 3× 109 bits.

Questions?

Thanks!

References

Introduction and basic molecular biology. [Online]. Available:http://compbio.pbworks.com/w/page/16252897/Introduction%20and%20Basic%20Molecular%20Biology

C. Trapnell, A. Roberts, L. Goff, G. Pertea, D. Kim, D. Kelley,H. Pimentel, S. Salzberg, J. Rinn, and L. Pachter,“Differential gene and transcript expression analysis of rna-seqexperiments with tophat and cufflinks,” Nature Protocols,vol. 7, no. 3, pp. 562–578, 2012.

rna-seq experiments for bioinformaticians

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