Seqanswers Leaderboard Ad

Collapse

Announcement

Collapse
No announcement yet.
This topic is closed.
X
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • Corydoras
    replied
    Hi Brian,

    That worked like a charm, thank you! The normalization also greatly improved the assemblies and the kmer-coverage distribution looks much nicer. I was just wondering: by default, bbnorm will use a kmer of 31. But for my assembly I am using 41. The assembly works fine, but is it advisable to normalize the coverage using a kmer of 41?

    Thanks,

    Sarah

    Leave a comment:


  • Brian Bushnell
    replied
    Sarah,

    Yes, BBNorm is the correct tool.

    I'm not sure, but I suspect that your shell is not bash. You could retry the command with "bash" instead of "sh", which may work. But the easier thing is just to skip the shellscript and invoke java manually:

    java -ea -Xmx14g -cp /home/martin/Downloads/bbmap/current/ jgi.KmerNormalize bits=32 in=Fowleri_combined.fastq out=normFowleri.fastq target=15

    That command would work if you had 16g of RAM. Just set the -Xmx parameter (highlighted in purple) to about 85% of however much RAM is on the machine. If you don't know, you should be able to find out like this on a Linux system:

    cat /proc/meminfo

    ...then look at the first line, "MemTotal".

    However, 15x is a fairly low target depth. For velvet I would suggest at least 30x for an optimal assembly, unless you just don't have enough data.

    -Brian

    Leave a comment:


  • Corydoras
    replied
    Hi Brian,

    I got a file with cleaned sequence data and I want to assemble this de-novo using velvet. Due to the nature of the sequencing and the library protocol, my kmer coverage is quite variable and I wanted to use BBnorm to normalize the coverage a bit to aid the assembly. Am I correct that BBnorm is the right thing to use for this?

    Anyway, currently trying to give it a go and I got this error message:

    bbmap$ sh bbnorm.sh in=Fowleri_combined.fastq out=normFowleri.fastq target=15
    bbnorm.sh: 104: bbnorm.sh: Bad substitution
    bbnorm.sh: 112: bbnorm.sh: [[: not found
    bbnorm.sh: 112: bbnorm.sh: [[: not found
    bbnorm.sh: 118: bbnorm.sh: source: not found
    bbnorm.sh: 119: bbnorm.sh: parseXmx: not found
    bbnorm.sh: 120: bbnorm.sh: [[: not found
    bbnorm.sh: 123: bbnorm.sh: freeRam: not found
    java -ea -Xmxm -cp /home/martin/Downloads/bbmap/current/ jgi.KmerNormalize bits=32 in=Fowleri_combined.fastq
    Invalid maximum heap size: -Xmxm
    Could not create the Java virtual machine.

    Any ideas?

    Many thanks,

    Sarah

    Leave a comment:


  • dietmar13
    replied
    Rum

    why is RUM always neglected by comparing RNA-seq mappers?
    In my hands RUM outperforms other pipelines, e.g. tophat, in sensitivity, especially for spliced reads...

    RUM: RNA Seq Unified Mapper
    RNA-Seq Unified Mapper. Contribute to itmat/rum development by creating an account on GitHub.


    RUM is rather slow, but using multithreaded servers allows mapping in tolerable time (compared to sample and library generation and data interpretation)

    dietmar

    Leave a comment:


  • Brian Bushnell
    replied
    Originally posted by dpryan View Post
    It'd be great if you could get in touch with the authors of this paper and just use their test datasets. That would allow comparisons against most of the popular aligners out there.
    Thanks for the suggestion; I'll look into that!

    Leave a comment:


  • GenoMax
    replied
    Originally posted by dpryan View Post
    It'd be great if you could get in touch with the authors of this paper and just use their test datasets. That would allow comparisons against most of the popular aligners out there.
    Data is available here:


    Leave a comment:


  • dpryan
    replied
    It'd be great if you could get in touch with the authors of this paper and just use their test datasets. That would allow comparisons against most of the popular aligners out there.

    Leave a comment:


  • Brian Bushnell
    replied
    I have compared it to tophat, which it greatly outperforms in speed and has higher sensitivity on real RNA-seq data. I have not yet compared it to STAR - I tried to but was unable to get STAR to run without core-dumping so I gave up. I may have compiled it wrong; I'll try again eventually.

    However, I don't have a really good tool for generating and evaluating synthetic RNA-seq data, so it's harder to quantify. The closest I can get is to generate synthetic DNA reads with very large deletions, which is not quite the same thing since RNA-seq data has other strange artifacts and the introns are not distributed randomly.

    Leave a comment:


  • kopi-o
    replied
    Looks very impressive! Can it beat STAR (speed and accuracy wise) for RNA-seq though? (RNA-seq is listed as one of the use cases towards the end)

    Leave a comment:


  • Introducing BBMap, a new short-read aligner for DNA and RNA

    BBMap will be publicly released soon, pending confirmation with LBL's legal department.

    In the meantime feel free to look at these graphs of its performance:



    Note that this is a 50MB powerpoint file. It contains graphs of relative performance of BBMap and other short read aligners (bwa, bowtie2, gsnap, smalt) mapping synthetic data.

    EDIT:
    This thread is now closed; please use this one to post questions.
    Last edited by Brian Bushnell; 11-10-2014, 12:09 PM.

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
13 views
0 likes
Last Post seqadmin  
Started by seqadmin, 06-12-2024, 02:20 PM
0 responses
16 views
0 likes
Last Post seqadmin  
Started by seqadmin, 06-07-2024, 06:58 AM
0 responses
184 views
0 likes
Last Post seqadmin  
Working...
X