35 research outputs found

    Classification of TFs co-occurring with Twi on Twi-bound genomic regions in stage 10 to 11 <i>Drosophila</i> embryos.

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    <p>The TFs were classified based on their known co-regulatory function with Twi and their involvement in mesoderm specification and muscle development or other developmental processes.</p

    COPS flowchart.

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    <p>COPS first scans the input sequences using all known TF binding motifs annotated in open source databases or retrieved from other resources and builds the frequent pattern (FP)-tree. The statistical significance (Z score) of the motif co-occurrences is calculated by comparing the <i>log</i> likelihood score of the frequent pattern to the <i>log</i> likelihood score distribution of the background. The percentage of overlap between the motifs of the pair is also calculated and reported. Additionally, COPS offers the option to calculate the preferred distance between co-occurring TF binding motifs and its statistical significance (Z score).</p

    COPS: Detecting Co-Occurrence and Spatial Arrangement of Transcription Factor Binding Motifs in Genome-Wide Datasets

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    <div><p>In multi-cellular organisms, spatiotemporal activity of <em>cis</em>-regulatory DNA elements depends on their occupancy by different transcription factors (TFs). In recent years, genome-wide ChIP-on-Chip, ChIP-Seq and DamID assays have been extensively used to unravel the combinatorial interaction of TFs with <em>cis</em>-regulatory modules (CRMs) in the genome. Even though genome-wide binding profiles are increasingly becoming available for different TFs, single TF binding profiles are in most cases not sufficient for dissecting complex regulatory networks. Thus, potent computational tools detecting statistically significant and biologically relevant TF-motif co-occurrences in genome-wide datasets are essential for analyzing context-dependent transcriptional regulation. We have developed COPS (<u>C</u>o-<u>O</u>ccurrence <u>P</u>attern <u>S</u>earch), a new bioinformatics tool based on a combination of association rules and Markov chain models, which detects co-occurring TF binding sites (BSs) on genomic regions of interest. COPS scans DNA sequences for frequent motif patterns using a Frequent-Pattern tree based data mining approach, which allows efficient performance of the software with respect to both data structure and implementation speed, in particular when mining large datasets. Since transcriptional gene regulation very often relies on the formation of regulatory protein complexes mediated by closely adjoining TF binding sites on CRMs, COPS additionally detects preferred short distance between co-occurring TF motifs. The performance of our software with respect to biological significance was evaluated using three published datasets containing genomic regions that are independently bound by several TFs involved in a defined biological process. In sum, COPS is a fast, efficient and user-friendly tool mining statistically and biologically significant TFBS co-occurrences and therefore allows the identification of TFs that combinatorially regulate gene expression.</p> </div

    Observed overlap of the genomic regions bound by the TF Twi and its co-regulators.

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    <p>A-C: The observed overlap (blue bar) between genomic regions bound by Twi and genomic regions bound by its known co-regulator Snail and its candidate co-regulator Ase is depicted in comparison to the expected random overlap (red curve). The overlap of genomic regions bound by Twi in stage 10 to 11 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052055#pone.0052055-Zinzen1" target="_blank">[6]</a> with the genomic regions bound by Twi/Snail/Dorsal in stage 5–7 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052055#pone.0052055-Zeitlinger1" target="_blank">[28]</a> (A), Snail (stage 10 to 11 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052055#pone.0052055-Southall1" target="_blank">[7]</a>) (B) and Ase (stage 10 to 11 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052055#pone.0052055-Southall1" target="_blank">[7]</a>) (C) is shown. A’-C’: Distribution and overlap (red) of Twi- and Twi/Snail/Dorsal- (A’), Twi- and Snail- (B’) and Twi- and Ase- (C’), bound genomic regions. The regions bound by Twi are shown in blue, the regions bound by the co-regulatory TFs in green and the overlapping regions bound by both Twi and the co-regulatory TFs in red.</p

    Comparison of COPS to two other computational approaches.

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    <p>The plots depict the Matthews Correlation Coefficient (MCC) values as determined using COPS and the published tools CPModule and ModuleDigger, for the co-occurrence patterns Bap/Twi, Tin/Twi, Ase/Pros and Snail/Pros in sequence-sets of increasing size (ranging from 50 to 300 sequences). The MCC values used for evaluating the performance of the different computational tools were calculated as described in the Materials and Methods. The red line illustrates the performance of COPS, the blue line CPModule and the green line ModuleDigger.</p

    Classification of TFs co-occurring with Nkx2.5 in ChIP-on-Chip identified genomic regions in HL-1 cells.

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    <p>The TFs were classified based on their known interactions with Nkx2.5 and their involvement in muscle or heart development.</p

    Classification of TFs co-occurring with Pros in DamID identified genomic regions in stage 10 to 11 <i>Drosophila</i> embryos.

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    <p>The TFs were classified based on their combinatorial activity with Pros and their involvement in nervous system development and other developmental processes.</p

    Short distance arrangements between BS of known co-regulatory TFs.

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    <p>The table lists the preferred short distance arrangements (Δbp) between the BSs of known co-regulatory TF pairs, as reported by COPS by analyzing different genome-wide datasets (first column). The last column shows the statistical significance (p-value) of the short distance arrangement for each pair of TFBSs. Only pairs with a motif spacing of <20 bp are listed in the table.</p

    Practical Approaches towards Complete Real-time Gaze Tracking

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    Visual context plays a key role in many computer vision tasks, and performance of eye/gaze-tracking methods also benefit from it. However, the size of contextual information (e.g. full face image) is very large w.r.t the primary input i.e. cropped image of the eye. This adds large computational costs to the algorithm and makes it inefficient, severely lim- iting its utility in real-time applications. In this paper, we perform a (computational) cost vs benefit analysis of var- ious input types that include context, leaning towards an efficient gaze-tracking system. We further study the effect of an alternate ranking loss based training strategy. Finally, we demonstrate some practical calibration techniques that can convert gaze-vectors into points-on-screen, an impor- tant application that is often overlooked in literature. We examine how data-efficient these techniques are in terms of how well they utilise expensive calibration data.Computer Science | Data Science and Technolog