This means you canвЂ™t sum the counts over a set of features to get the expression of that set (e.g. Since counts are NOT scaled by the length of the feature, all units in this category are not comparable within a sample without adjusting for the feature length. If the abundance estimation method youвЂ™re using incorporates sequence bias modeling (such as eXpress or Cufflinks), the bias is often incorporated into the effective length by making the feature shorter or longer depending on the effect of the bias. Where is the mean of the fragment length distribution which was learned from the aligned read. In practice, the effective length is usually computed as: Effective length refers to the number of possible start sites a feature could have generated a fragment of that particular length. These numbers are heavily dependent on two things: (1) the amount of fragments you sequenced (this is related to relative abundances) and (2) the length of the feature, or more appropriately, the effective length.

IвЂ™ll refer to counts by the random variable. вЂњCountsвЂќ usually refers to the number of reads that align to a particular feature. Unfortunately, with alternative splicing you do not directly observe, so often is used, which is estimated using the EM algorithm by a method like eXpress, RSEM, Sailfish, Cufflinks, or one of many other tools. gene, isoform, exon).ДЇinally, I use the random variable to denote the counts you observe from a feature of interest. When saying вЂњfeatureвЂќ, IвЂ™m referring to an expression feature, by which I mean a genomic region containing a sequence that can normally appear in an RNA-Seq experiment (e.g.

The concept of counting is the same with either type of read, as each read represents a fragment that was sequenced. Throughout this post вЂњreadвЂќ refers to both single-end or paired-end reads. This is a result of RNA-Seq being a relative measurement, not an absolute one. The first thing one should remember is that without between sample normalization (a topic for a later post), NONE of these units are comparable across experiments. IвЂ™ll try to clear up a bit of the confusion here. This post covers the units used in RNA-Seq that are, unfortunately, often misused and misunderstood.

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