Unconfigured Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • Deamonkinge
    Junior Member
    • Dec 2017
    • 1

    RNA_seq Data analysis Help - Trouble viewing files in IGV.

    Hi there, I am new to RNA_seq and to Bioinformatics and I would greatly appreciate if someone could help me out a little with the analysis of some of my RNA_seq data.

    So I am analyzing RNA_seq data from Bacteria using Galaxy - RNA was isolated from wild type bacteria and from a mutant bacteria. RNA was sequenced using Illumina NextSeq 500 system.

    So the first thing I did when given the RNA_seq data was to perform a quality check on the RNA_seq data which was in the form of FastQ files. I performed the quality check using ''FastQC'', after a quality check I found that the reads were of very good quality so I proceeded with mapping of the reads back onto the bacterial reference genome. For this I used ''Bowtie2'' - I allowed Bowtie to perform soft clipping during the mapping step.

    I then obtained a BAM file from the mapping stage - I used the ''BamCoverage'' Tool to change the Bam files into BigWig files. I then used ''IGV'' to visualize the mapping of my reads onto the reference genome.

    You can see this in the picture I attached to this question -



    - In the picture you can see ''Wild Type'' forward and reverse files and also ''Mutant'' forward and reverse files. At the bottom of the picture you can see the Gene / Genes that the reads are mapping to. You can see in the picture that there are many reads from the Mutant forward file mapping to the gene ''NWMN_RS14115'' which is a gene that is on the forward strand.

    My question is : Why are there Big blocks of reads mapping in and around the ends of the gene and less reads mapping to the middle of the gene? Shouldn't all the reads be falling evenly within the confines of the gene? Why are the reads mapping in such uneven batches - especially at the ends of the gene?

    Any help would be greatly appreciated! Thanks
  • sdriscoll
    I like code
    • Sep 2009
    • 436

    #2
    This locus looks like it has pretty low read coverage based on the very blocky looking pileup. I'll say that a lot erratic coverage results from low read depth at a locus. Also if this is your first dip into RNA-Seq be ready to come across many things that seem strange or aren't what you expected when it comes to the raw level alignments like you're looking at.
    /* Shawn Driscoll, Gene Expression Laboratory, Pfaff
    Salk Institute for Biological Studies, La Jolla, CA, USA */

    Comment

    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, 07-13-2026, 10:26 AM
    0 responses
    27 views
    0 reactions
    Last Post SEQadmin2  
    Started by SEQadmin2, 07-09-2026, 10:04 AM
    0 responses
    37 views
    0 reactions
    Last Post SEQadmin2  
    Started by SEQadmin2, 07-08-2026, 10:08 AM
    0 responses
    24 views
    0 reactions
    Last Post SEQadmin2  
    Started by SEQadmin2, 07-07-2026, 11:05 AM
    0 responses
    34 views
    0 reactions
    Last Post SEQadmin2  
    Working...