I have RNA-Seq data with raw counts extracted from bam files using featureCounts. I wanted to convert counts to tpm, because I need TPM for some analysis.

Initially, I got the coding length for each Gene like below:

library(GenomicFeatures)
gtf_txdb <- makeTxDbFromGFF("gencode.v27.annotation.gtf")
# then collect the exons per gene id
exons.list.per.gene <- exonsBy(gtf_txdb,by="gene")
# then for each gene, reduce all the exons to a set of non overlapping exons, calculate their lengths (widths) and sum then
exonic.gene.sizes <- sum(width(reduce(exons.list.per.gene)))
exonic.gene.sizes <- as.data.frame(sum(width(reduce(exons.list.per.gene))))

Now, I have two data frames like data is with raw counts and other data2 with gene name and Coding_Length

tpm <- function(counts, lengths) {
  rate <- counts / lengths
  rate / sum(rate, na.rm = TRUE) * 1e6
} 

final_tpm <- apply(data, 2, function(x) tpm(x, data2$Coding_Length))

final_tpm using the function for some of the genes:

gene            tpm
RPSAP8          3.79
AL645608.8      4.14
RNF223          1.38

And I also used other function like below on dataframe df which have two columns raw counts and effLength calculated from Coding_Length

df$effLength <- df$Coding_Length - 203.7 + 1

countToTpm <- function(counts, effLen)
{
  rate <- log(counts) - log(effLen)
  denom <- log(sum(exp(rate)))
  exp(rate - denom + log(1e6))
}

df$tpm <- with(df, countToTpm(counts, effLength))

With the above way the output of few genes is like below:

gene_name       tpm
RPSAP8          1.57
AL645608.8      1.46
RNF223          0.496

Along with the above way, I also used kallisto to get tpm. And when I compared both results, they doesn't match and not even close.

target_id          length      eff_length        est_counts     tpm
RPSAP8             889          747.358           4.10538       0.0635304
AL645608.8        2086          1944.36              116         0.689984
RNF223            1902          1760.36           50.0024        0.328508

I did the sanity check, the results from both functions give sum to one million.

I understand that kallisto tpm is better, But I have the raw counts and instead of running kallisto again on all 300 samples, I wanted to convert raw counts to tpm.

Which of these functions is to be used for conversion of counts to tpm? Why they don't match to kallisto tpm?