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  • wjyzidane
    replied
    Originally posted by Retr0 View Post
    I'm also getting 'Segmentation fault', any ideas ?
    all the files in HMS_source_code should be in the same fold as the executable file.

    Leave a comment:


  • Retr0
    replied
    I'm also getting 'Segmentation fault', any ideas ?

    Leave a comment:


  • danielfortin86
    replied
    I'm also having the same problem. What was the solution?

    Leave a comment:


  • ECO
    replied
    Why not share your solution?

    Leave a comment:


  • demis001
    replied
    Found the problem

    Sorry, I found the problem!

    Disregard this question.....

    DD

    Leave a comment:


  • demis001
    replied
    HMS-Segmentation fault

    Hi,

    I tried to run the test run using the sample data described on the user manual. However, I am keep getting "segmentation fault" error that was some what confusing. Usually, "segmentation fault" error spewed when there is a memory problem. I have tested on two system with 8GB RAM and 32GB RAM Linux workstations and getting the same result.

    I then took the top 5 fasta line from the input and rerun the command described below. Still got the same result.

    ./hms -i top500.nrsf.hpeak.seq -w 21 -dna 4 -iteration 10 -chain 20 -seqprop 0.1 -strand 2 -nobase dep 2

    Would you please comment what might go wrong?

    DD


    Originally posted by GAanalyzer View Post
    Hey, Abhi, thank you for your interest.

    All the source of our data can be found in our supplementary document at


    The one at BCGSC
    ChIP-Seq Transcription Factor Data — by Steven Jones — last modified Dec 05, 2008

    contains dataset that we did not analyze. You can try that.

    I will try to find more and posted here later.


    Best,

    Leave a comment:


  • GAanalyzer
    replied
    Hi Hanat,

    Thank you for your interest in our program. You can find the C source code for background model at the HMS website:



    The command of this C source code can be found in HMS manual page 22.



    Free feel to contact me if you have any question.

    Best,

    Ming

    Leave a comment:


  • hannat
    replied
    hi
    I've read the paper, and i found out that the background model is estimated from human promoter sequence. I wonder if you could also give the source code for background markov chain model estimation, because i am dealing with Drosophila sequence, i cant use the existing markov chain models.

    regards

    Leave a comment:


  • GAanalyzer
    replied
    Hey, Abhi, thank you for your interest.

    All the source of our data can be found in our supplementary document at


    The one at BCGSC
    ChIP-Seq Transcription Factor Data — by Steven Jones — last modified Dec 05, 2008

    contains dataset that we did not analyze. You can try that.

    I will try to find more and posted here later.


    Best,

    Leave a comment:


  • apratap
    replied
    Hey

    I am actually trying to get some hands on Chip-Seq data. Do you happen to know some good dataset @ SRA which I can download and play with HMS.

    Thanks!
    -Abhi

    Leave a comment:


  • GAanalyzer
    started a topic HMS, motif finding tool for ChIP-Seq

    HMS, motif finding tool for ChIP-Seq

    This is such a nice forum. I am impressed by the experience and enthusiasm of you guys. I just want to bring your attention to a paper we just published:

    On the detection and refinement of transcription factor binding sites using ChIP-Seq data

    Abstract


    Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein-DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic “greedy” search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation.

    It is open access at


    Our HMS program is freely available at


    Please give HMS a try. I hope that you find it useful. Questions, comments, suggestions and criticisms are welcomed and can be sent to me at [email protected]. Thank you very much.

    Best,
    Ming

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