Because algorithms for motif prediction have always suffered of low performance issues, there is a constant effort to find better techniques. Finding unknown patterns of unknown lengths in massive amounts of data has long been a major challenge in computational biology. Therefore, in PGC- 1α/β-associated pathologies, PRC, as a metabolic sensor, may ensure mitochondrial homeostasis.ĭe novo motif discovery is essential in understanding the cis-regulatory processes that play a role in gene expression.
Our findings demonstrate that the PRC coactivator can act in complex with several transcription factors and regulate miRNA expression to control the fine regulation of main metabolic functions in the cell. PRC is also involved in transcriptional regulatory complexes targeting 12 miRNAs, five of which are involved in the control of the OXPHOS process. Our data confirm that ERR1 is a key partner of PRC in the regulation of mitochondrial functions and suggest a potential role of this complex in RNA processing. This study showes that PRC induces a complex network of cellular functions interacting with at least one to five of the studied transcription factors (Estrogen Related Receptor alpha, ERR1 Nuclear-Respiratory Factors, NRF1 and NRF2 cAMP Response Element Binding, CREB and Ying Yang, YY1). Here we aim to propose an integrative view of the cellular pathways regulated by PRC through integration of cDNA and miRNA microarray data and chromatin immunoprecipitation results obtained from XTC.UC1 cells invalidated for PRC. The XTC.UC1 cell line is a mitochondria-rich model of thyroid tumors whose biogenesis is almost exclusively dependent on PRC. These coactivators integrate mitochondrial energy production into cell metabolism using complementary pathways.
The PGC-1 (Peroxisome proliferator-activated receptor Gamma Coactivator-1) family of coactivators (PGC-1α, PGC-1β, and PRC) plays a central role in the transcriptional control of mitochondrial biogenesis and oxidative phosphorylation (OXPHOS) processes.
#Pdf comparator tool code#
In addition, the source code is released on a GNU General Public License. It is tailored for regulatory motif detection, however it allows for substantial amount of configuration regarding sequence background, motif database and parameters for motif-finding methods.*Availability:* The method is available online as a webserver at. We propose a new complete service allowing researchers to submit their sequences for analysis by four different motif-finding methods for clustering and comparison with a reference motif database. This leads to the practical problem of combining results of different motif-finding tools, taking into account current knowledge collected in motif databases. Currently, general performance of the model-based motif-finding methods is unsatisfactory however, different methods are successful in different cases. They aim at detecting very weak signals responsible for biological processes requiring robust sequence identification like transcription-factor binding to DNA or docking sites in proteins. Methods for finding overrepresented sequence motifs are useful in several key areas of computational biology. Typical areas of application for T-RegĬomparator are motif and regulatory module finding and annotation of regulatory genomic regions. We provide a user interface to a program for sequence scanning with weight matrices. Our tool delivers a detailed report on similarities between user-supplied motifs and motifs in the database.Īpart from simple one-to-one relationships, T-Reg Comparator is also able to detect similarities between submatrices. Or binding site sequences and our program matches them against the T-Reg database, which is presently built on data from the The user inputs a set of such weight matrices Transcription factor binding sites are usually encoded as position weight matrices.
#Pdf comparator tool software#
T-Reg Comparator is a novel software tool designed to support research into transcriptional regulation.
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