Header Leaderboard Ad

Collapse

Questions about TopHat

Collapse

Announcement

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

  • yjlui
    started a topic Questions about TopHat

    Questions about TopHat

    I must say I'm new to bioinformatics...

    - Since I'm using paired-end (Illumina 36bp) data, I need to provide the expected (mean) inner distance between mate pairs when using TopHat. For example, the median of the insert size of a lane in my data is 170 (low threshold: 149 and high threshold: 3629), so I provided 170 as the -r value (I'm aware that the mean is larger than 170 as the data are positively skewed). However, filesl8B1D.log and fileCVzqGw.log (two TopHat log files) show that only about 11% of the reads have at least one reported alignment, which I think is really low (the FPKM values provided by Cufflinks using the generated SAM file seem OK though)! Is there anything I can do to improve this? Also, is there any simple way I can tell how well TopHat did the mapping? Or I can only do this by examining accepted_hits.sam (output of TopHat)? Any way to tell if a read is uniquely mapped to a gene?

    - I'm aware that transcripts.gtf (output of Cufflinks) gives the estimated depth of read coverage across a transcript, but what I want is actually the depth of read coverage across a gene. I've built a GFF file (containing only Ensembl genes) and used it together with accepted_hits.sam to get the raw counts of reads using coverageBed (as Cufflinks only reports abundance in FPKM). However, the number of genes with raw count > 0 is larger than the number of genes with FPKM > 0 in genes.expr (output of Cufflinks) (I've tried using all the reads in accepted_hits.sam and reads mapped in a proper pair, i.e. 0x2 flag set to 1, but Cufflinks seems to filter reads in a different way).

    Thanks very much for your time and help!
    Last edited by yjlui; 07-30-2010, 12:28 AM.

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