Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • dietmar13
    Senior Member
    • Mar 2010
    • 107

    RUM - feature quantification

    hello,

    can someone point me to a place where I can find comprehensive information about the meaning of all the values in this file (or can give me the answers here):

    RUM - feature quantification (see an example below):
    1) Ucount for the transcript is the number of all unique mapped reads to this transcript, and therefore the sum of the numbers for each exon is higher, because some reads map to two exons (i.e. are split) - true?

    2) maps to introns are also counted for the transcript - true?

    3) maps to different transcripts of one gene are counted for each transcript, therefore the numbers for isoforms are similar, i.e. from the example below: 160 and 149 - true?

    4) how are the min and max values calculated exactly? from the U- and NUcounts (to exons and introns?) following some statistical rational (e.g. mapping quality of multiple mapped reads) - if yes, this would be influenced by the max number of reported mapped reads (by default: 100).

    5) would it be possible to get a robust count number for each transcript like you get with HTseq-count and for each gene (as sum of all transcripts) not for each transcript)?

    6) why do I get with the script <featurequant2geneprofiles.pl> if I use -cnt and -sformat always the max values, and not the Ucount numbers for the transcripts?



    CREB3L4_ENST00000461688(ensembl) 0
    Type Location min max Ucount NUcount Length
    transcript 1:153940401-153945244 2.3635 2.4816 160 8 870
    exon 1 1:153940401-153940640 3.427 3.427 64 0 240
    intron 1 1:153940641-153940997 0.6839 0.6839 19 0 357
    exon 2 1:153940998-153941115 2.8317 2.8317 26 0 118
    intron 2 1:153941116-153941405 1.3294 1.3294 30 0 290
    exon 3 1:153941406-153941652 4.5266 4.5266 87 0 247
    intron 3 1:153941653-153941809 0 0 0 0 157
    exon 4 1:153941810-153941931 6.4257 7.2685 61 8 122
    intron 4 1:153941932-153942111 0 0 0 0 180
    exon 5 1:153942112-153942229 0 0 0 0 118
    intron 5 1:153942230-153945219 0 0 0 0 2990
    exon 6 1:153945220-153945244 15.9359 20.0484 31 8 25
    --------------------------------------------------------------------
    CREB3L4_ENST00000368601(ensembl) 0
    Type Location min max Ucount NUcount Length
    transcript 1:153940713-153945548 2.0156 2.1238 149 8 950
    exon 1 1:153940713-153940786 0 0 0 0 74
    intron 1 1:153940787-153940997 0 0 0 0 211
    exon 2 1:153940998-153941175 3.0323 3.0323 42 0 178
    intron 2 1:153941176-153941405 0 0 0 0 230
    exon 3 1:153941406-153941652 4.5266 4.5266 87 0 247
    intron 3 1:153941653-153941809 0 0 0 0 157
    exon 4 1:153941810-153941931 6.4257 7.2685 61 8 122
    intron 4 1:153941932-153945219 0 0 0 0 3288
    exon 5 1:153945220-153945548 2.3828 2.6953 61 8 329

Latest Articles

Collapse

  • SEQadmin2
    Advanced Sequencing Platforms Tackle Neuroscience’s Toughest Genomics Problems
    by SEQadmin2



    Genomics studies in neuroscience face a special challenge due to the brain’s complexity and scarcity of samples. Mapping changes in cell type and state using conventional next-generation sequencing methods remains challenging. Advances in technologies like single-cell sequencing, spatial transcriptomics, and long-read sequencing have opened the door to deeper studies of the brain and diseases like Alzheimer’s, amyotrophic lateral sclerosis (ALS), and schizophrenia.
    ...
    07-09-2026, 11:10 AM
  • SEQadmin2
    Cancer Drug Resistance: The Lingering Barrier to Rising Survival
    by SEQadmin2



    Cancer survival rates have significantly increased in the last few decades in the United States, reaching a combined 70% 5-year survival rate by 2021. Behind this number, there are years of research to find new therapies, drug targets, and early detection methods. But there is one core challenge that keeps slowing down these advances, and it’s about drug resistance.

    There is no single reason why many patients don’t respond to treatment as expected. Cancer is...
    07-08-2026, 05:17 AM
  • GATTACAT
    Reply to Nine Things a Sample Prep Scientist Thinks About Before Sequencing
    by GATTACAT
    Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.
    07-01-2026, 11:43 AM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by SEQadmin2, Today, 10:26 AM
0 responses
10 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 07-09-2026, 10:04 AM
0 responses
25 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 07-08-2026, 10:08 AM
0 responses
16 views
0 reactions
Last Post SEQadmin2  
Started by SEQadmin2, 07-07-2026, 11:05 AM
0 responses
33 views
0 reactions
Last Post SEQadmin2  
Working...