Header Leaderboard Ad

Collapse

SNP calling from RRBS data

Collapse

Announcement

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

  • SNP calling from RRBS data

    Hello,

    I am interested in calling SNPs from reduced-representation bisulfite sequencing (RRBS) read data. The purpose of this analysis is to generate a FST value among study groups to quantify divergence in the genome in comparison to that of the methylome.

    I am aware of certain programs that can identify SNPs within RRBS data such as BS-SNPer and Bis-SNP but I was wondering if anyone had any advice about which program would be most effective at doing this?

    Once SNPs have been identified, what programs would be most suitable for the next stages of the analysis, for instance visualizing the SNPs and creating a SNP panel for measuring genetic differentiation. Would GATC be suitable for this purpose?

    Finally, if there were genomic reads (i.e. not bisulfite converted) available from the same populations but different individuals, how could this data be best used to verify the RRBS derived SNPs, given some of the biases involved in generating this data?

    Thanks in advance for any advice you can give,

    Alan

Latest Articles

Collapse

  • seqadmin
    A Brief Overview and Common Challenges in Single-cell Sequencing Analysis
    by seqadmin


    ​​​​​​The introduction of single-cell sequencing has advanced the ability to study cell-to-cell heterogeneity. Its use has improved our understanding of somatic mutations1, cell lineages2, cellular diversity and regulation3, and development in multicellular organisms4. Single-cell sequencing encompasses hundreds of techniques with different approaches to studying the genomes, transcriptomes, epigenomes, and other omics of individual cells. The analysis of single-cell sequencing data i...

    01-24-2023, 01:19 PM
  • seqadmin
    Introduction to Single-Cell Sequencing
    by seqadmin
    Single-cell sequencing is a technique used to investigate the genome, transcriptome, epigenome, and other omics of individual cells using high-throughput sequencing. This technology has provided many scientific breakthroughs and continues to be applied across many fields, including microbiology, oncology, immunology, neurobiology, precision medicine, and stem cell research.

    The advancement of single-cell sequencing began in 2009 when Tang et al. investigated the single-cell transcriptomes
    ...
    01-09-2023, 03:10 PM

ad_right_rmr

Collapse
Working...
X