FPKM is analogous to RPKM but does not use read counts directly.  Instead, FPKM estimates the relative abundance of transcripts in terms of fragments observed which may not be represented by a single read (as in a paired end experiment).

I would not use RPKM at all for expression analysis, and would even steer clear of FPKM methods.  RPKM at least is a poor normalization technique with known issues that can introduce significant bias in relative expression estimates, and has been replaced by far better approaches.  FPKM was an invention of the folks who developed the CuffLinks analysis program (see Nature Biotechnology 2010, vol.28, 511–515).  In my experience though, it does not perform, as an analytical method, nearly as well as others, namely DESeq2 or edgeR.

Look into those Bioconductor programs:  DESeq2 and edgeR.  DESeq2 offers analyses far superior to simple RPKM methods.  Both of these tools start directly from raw count data.

Also, if you have questions about DESeq2 or edgeR, their developers are regular participants in the forums at  www.seqanswers.com and will readily respond to questions.

FPKM is analogous to RPKM but does not use read counts directly.  Instead, FPKM estimates the relative abundance of transcripts in terms of fragments observed which may not be represented by a single read (as in a paired end experiment).

I would not use RPKM at all for expression analysis, and would even steer clear of FPKM methods.  RPKM at least is a poor normalization technique with known issues that can introduce significant bias in relative expression estimates, and has been replaced by far better approaches.  FPKM was an invention of the folks who developed the CuffLinks analysis program (see Nature Biotechnology 2010, vol.28, 511–515).  In my experience though, it does not perform, as an analytical method, nearly as well as others, namely DESeq2 or edgeR.

Look into those Bioconductor programs:  DESeq2 and edgeR.  DESeq2 offers analyses far superior to simple RPKM methods.  Both of these tools start directly from raw count data.

Also, if you have questions about DESeq2 or edgeR, their developers are regular participants in the forums at  www.seqanswers.com and will readily respond to questions.

FPKM is analogous to RPKM

FPKM is more appropriate for paired end seq

FPKM is essentially analogous to RPKM but, rather than using read counts, approximates the relative abundance of transcripts in terms of fragments observed from an RNA-Seq experiment, which may not be represented by a single read, such as in paired-end RNA-Seq experiments

The best answer to this question by a great speaker and the author of these methods. 

On another subject I just released a small video which attempts to explain what our website does (we pre-process loads of RNASeq public data). I would appreciate your feedback and please share if you find it relevant.

Best,

Alain

https://www.youtube.com/watch?v=5NiFibnbE8o

https://vimeo.com/125323142

It used to be when you did RNA-seq, you reported your results in RPKM (Reads Per Kilobase Million) or FPKM (Fragments Per Kilobase Million). However, TPM (Transcripts Per Kilobase Million) is now becoming quite popular. Since there seems to be a lot of confusion about these terms, I thought I’d use a StatQuest to clear everything up.

These three metrics attempt to normalize for sequencing depth and gene length. Here’s how you do it for RPKM:

FPKM is very similar to RPKM. RPKM was made for single-end RNA-seq, where every read corresponded to a single fragment that was sequenced. FPKM was made for paired-end RNA-seq. With paired-end RNA-seq, two reads can correspond to a single fragment, or, if one read in the pair did not map, one read can correspond to a single fragment. 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).

TPM is very similar to RPKM and FPKM. The only difference is the order of operations. Here’s how you calculate TPM:

So you see, when calculating TPM, the only difference is that you normalize for gene length first, and then normalize for sequencing depth second. However, the effects of this difference are quite profound.

When you use TPM, the sum of all TPMs in each sample are the same. This makes it easier to compare the proportion of reads that mapped to a gene in each sample. In contrast, with RPKM and FPKM, the sum of the normalized reads in each sample may be different, and this makes it harder to compare samples directly.

Here’s an example. If the TPM for gene A in Sample 1 is 3.33 and the TPM in sample B is 3.33, then I know that the exact same proportion of total reads mapped to gene A in both samples. This is because the sum of the TPMs in both samples always add up to the same number (so the denominator required to calculate the proportions is the same, regardless of what sample you are looking at.)

With RPKM or FPKM, the sum of normalized reads in each sample can be different. Thus, if the RPKM for gene A in Sample 1 is 3.33 and the RPKM in Sample 2 is 3.33, I would not know if the same proportion of reads in Sample 1 mapped to gene A as in Sample 2. This is because the denominator required to calculate the proportion could be different for the two samples.

Source – StatQuest

RPKM and FPKM are analogous. RPKM used in single-end RNA sequencing, whereas FPKM is more suitable for paired-end sequencing. Both units use the same formula for calculation. See more here for details regarding RPKM and FPKM

https://www.reneshbedre.com/blog/expression_units.html