RNA-seq notes

rnaseq-notes.md

RNA-Seq Notes

This document summarizes some of the distinguishing features of tools used to analyze RNA-Seq data.

Aligners:

Name	Type	Algorithm	Links
bowtie2	aligner	seed and extend	Manual, Download
subread	aligner	seed and vote	Manual, Download
TopHat2	aligner	seed and extend	Manual, Download

Transcript quantifiers:

Name	Type	Algorithm	Links
cufflinks	quantifier	?	Manual, Download
featureCounts	counter	overlap	Manual, Download
RSEM	quantifier	expectation maximization	Manual, Download

Aligners

bowtie2

Map quality score

A read's map quality score Q depends on the probability P that the read is not mapped to the true point of origin. A mapping quality of 10 or less indicates at least a 1 in 10 chance that the read truly originated elsewhere.

• The alignment score for a paired-end alignment is the sum of the two mate scores.

• Additional alignments are flagged as secondary (256) in the SAM column 2.

• Commands:

    # Count unique alignments:
    samtools view -c -F 256 file.bam
  

Subread

Map quality score

A read's map quality score Q is a function of its sequencing base-call qualities and alignment:

L      read length
p_i    base-call P(incorrect) for the ith base
b_m    set of match positions
b_{mm} set of mismatched positions

• Base-call P(incorrect) depends on base quality Q reported by the sequencer:

• Read bases found to be insertions are treated as matched bases in the MQS calculation.

• The MQS is read-length normalized in the range [0, 60).

TopHat2

Map quality score

TopHat2 does not have mapping quality scores.

Instead, the MAPQ column describes the number of different alignments for each read.

MAPQ	Alignments
50	1
3	2
2	3
1	4-9
0	>=10

Source

• Commands:

    # Count unique alignments:
    samtools view -c -q 50 file.bam
  

Variance-stabilizing transformation

Count values span several orders of magnitude.

• After logarithmic transformation, low abundance fragments will tend to show large standard deviations across samples.
• With untransformed data, high abundance fragments will tend to show large standard deviations across samples.

This heteroscedasticity skews the analysis.

The variance-stabilizing transformation was introduced by Anders and Huber (2010), and implemented in the DESeq2 package (Loveet al.,2014). After this transformation, the standard deviations show less dependence on the fragment abundance.