Seqanswers Leaderboard Ad

Collapse

Announcement

Collapse
No announcement yet.
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • Understanding VCF output from mpileup

    Hello,

    As a start, I would like to mention that I just passed 2 hours reading older threads here and on biostar as well as the VCF specification sheet.

    That been said, I have a question upon calling a SNPs from RNA-seq data (Illumina signle-read, bacterial) with mpileup. I got my VCF file and I'm struggling a little bit to understand the output. If I have:

    Code:
    gi|xxx|emb|xxx|	143630	.	C	T	999	.	DP=490	VDB=0.0004	AF1=1	AC1=4	DP4=1,0,211,276	MQ=20	FQ=-286	PV4=0.43,1.5e-11,1,0.048		GT:PL:GQ	1/1:255,255,0:99	1/1:255,255,0:99
    I'm pretty sure of the following:
    1. 143630 is the position of my SNPs
    2. C is the base in the reference genome and T the alternate variant (actual SNPs)
    3. 999 is the score. The higher it is, better the chances that the call is genuine
    4. DP is the actual coverage on that specific position
    5. DP4 are reads fwd and rev for reference and fwd and rev for alternate call
    6. MQ is the quality


    Now, here are my questions:
    1. VDB is supposed to be Variant Distance Bias. What exaclt does it means and how I interpret it?
    2. AF1 is Allele Frequency. By 1 it means that all the reads are calling the SNPs? If I have AF1=0.5, it means that half of the reads are calling ref nucleotide while the other half is calling SNP?
    3. What the heck is AC1? Max likelihood okay, but how you interpret it?
    4. How do you interpret FQ (Phred probability), i.e. lower vs higher?
    5. PV4 is a total mess... Any insight would be greatly appreciated.
    6. GT:PL:GQ: same as above.


    I know that this is probably very basic for most of you, but I'm just trying to make some sense out of it...

    Thank you all in advance,

    TP

Latest Articles

Collapse

  • seqadmin
    Best Practices for Single-Cell Sequencing Analysis
    by seqadmin



    While isolating and preparing single cells for sequencing was historically the bottleneck, recent technological advancements have shifted the challenge to data analysis. This highlights the rapidly evolving nature of single-cell sequencing. The inherent complexity of single-cell analysis has intensified with the surge in data volume and the incorporation of diverse and more complex datasets. This article explores the challenges in analysis, examines common pitfalls, offers...
    06-06-2024, 07:15 AM
  • seqadmin
    Latest Developments in Precision Medicine
    by seqadmin



    Technological advances have led to drastic improvements in the field of precision medicine, enabling more personalized approaches to treatment. This article explores four leading groups that are overcoming many of the challenges of genomic profiling and precision medicine through their innovative platforms and technologies.

    Somatic Genomics
    “We have such a tremendous amount of genetic diversity that exists within each of us, and not just between us as individuals,”...
    05-24-2024, 01:16 PM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by seqadmin, 06-14-2024, 07:24 AM
0 responses
12 views
0 likes
Last Post seqadmin  
Started by seqadmin, 06-13-2024, 08:58 AM
0 responses
14 views
0 likes
Last Post seqadmin  
Started by seqadmin, 06-12-2024, 02:20 PM
0 responses
17 views
0 likes
Last Post seqadmin  
Started by seqadmin, 06-07-2024, 06:58 AM
0 responses
186 views
0 likes
Last Post seqadmin  
Working...
X