CENTDIST: discovery of co-associated factors by motif distribution
نویسندگان
چکیده
منابع مشابه
CENTDIST: discovery of co-associated factors by motif distribution
Transcription factors (TFs) do not function alone but work together with other TFs (called co-TFs) in a combinatorial fashion to precisely control the transcription of target genes. Mining co-TFs is thus important to understand the mechanism of transcriptional regulation. Although existing methods can identify co-TFs, their accuracy depends heavily on the chosen background model and other param...
متن کاملAnalysis of Co-Associated Transcription Factors via Ordered Adjacency Differences on Motif Distribution
Transcription factors (TFs) binding to specific DNA sequences or motifs, are elementary to the regulation of transcription. The gene is regulated by a combination of TFs in close proximity. Analysis of co-TFs is an important problem in understanding the mechanism of transcriptional regulation. Recently, ChIP-seq in mapping TF provides a large amount of experimental data to analyze co-TFs. Sever...
متن کاملCombining phylogenetic motif discovery and motif clustering to predict co-regulated genes
MOTIVATION We present a sequence-based framework and algorithm PHYLOCLUS for predicting co-regulated genes. In our approach, de novo discovery methods are used to find motifs conserved by evolution and then a Bayesian hierarchical clustering model is used to cluster these motifs, thereby grouping together genes that are putatively co-regulated. Our clustering procedure allows both the number of...
متن کاملMotif discovery in promoters of genes co-localized and co-expressed during myeloid cells differentiation
Genes co-expressed may be under similar promoter-based and/or position-based regulation. Although data on expression, position and function of human genes are available, their true integration still represents a challenge for computational biology, hampering the identification of regulatory mechanisms. We carried out an integrative analysis of genomic position, functional annotation and promote...
متن کاملmodeling loss data by phase-type distribution
بیمه گران همیشه بابت خسارات بیمه نامه های تحت پوشش خود نگران بوده و روش هایی را جستجو می کنند که بتوانند داده های خسارات گذشته را با هدف اتخاذ یک تصمیم بهینه مدل بندی نمایند. در این پژوهش توزیع های فیزتایپ در مدل بندی داده های خسارات معرفی شده که شامل استنباط آماری مربوطه و استفاده از الگوریتم em در برآورد پارامترهای توزیع است. در پایان امکان استفاده از این توزیع در مدل بندی داده های گروه بندی ...
ذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Nucleic Acids Research
سال: 2011
ISSN: 0305-1048,1362-4962
DOI: 10.1093/nar/gkr387