212 research outputs found
De novo reconstruction of satellite repeat units from sequence data
Satellite DNA are long tandemly repeating sequences in a genome and may be
organized as high-order repeats (HORs). They are enriched in centromeres and
are challenging to assemble. Existing algorithms for identifying satellite
repeats either require the complete assembly of satellites or only work for
simple repeat structures without HORs. Here we describe Satellite Repeat Finder
(SRF), a new algorithm for reconstructing satellite repeat units and HORs from
accurate reads or assemblies without prior knowledge on repeat structures.
Applying SRF to real sequence data, we showed that SRF could reconstruct known
satellites in human and well-studied model organisms. We also found satellite
repeats are pervasive in various other species, accounting for up to 12% of
their genome contents but are often underrepresented in assemblies. With the
rapid progress on genome sequencing, SRF will help the annotation of new
genomes and the study of satellite DNA evolution even if such repeats are not
fully assembled
MicroRNA-126 regulates the expression of inflammationrelated genes in vascular smooth muscle cells of diabetic rats
Purpose: To investigate the regulatory role of miR-126 in inflammation-associated gene expression in vascular smooth muscle (VSMCs) cells of diabetic rats.Methods: Diabetes was induced in rats by intraperitoneal injection of streptozocin (STZ) at a dose of 50 mg/kg. VSMCs were isolated from the aortic intimal-medial layers of the rats by a standard protocol. Expression of miR-126 was determined by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Transfection was performed with Lipofectamine 2000 reagent.Results: Expression of miR-126 was significantly (p < 0.05) downregulated in the VSMCs of diabetic rats. Similarly, expressions of SOD, APX and CAT were downregulated, while those of COX, LOX and NOS were significantly upregulated in VSMCs. However, transfection-induced miR-126 overexpression in the VSMCs of diabetic rats led to significant (p < 0.05) upregulation of SOD, CAT, APX and downregulation of COX, LOX and NOS. TargetScan analysis revealed that miR-126 exerted these effects by targeting SIRT1 gene. Furthermore, qRT-PCR and western blotting revealed that miR-126 overexpression in diabetic VMSCs caused significant (p < 0.05) upregulation of the expression of SIRT-1.Conclusion: The results indicate that miR-126 regulates the expression of inflammation-related genes by targeting SIRT-1 genes in vascular smooth muscle cells of diabetic rats. Thus, miR-126 may be beneficial in the management of diabetes.Keywords: Diabetes, Inflammation, Genes, microRNA, vascular smooth muscle cell
Inferring Transcriptional Interactions by the Optimal Integration of ChIP-chip and Knock-out Data
How to combine heterogeneous data sources for reliable prediction of transcriptional regulation is a challenge. Here we present an easy but powerful method to integrate Chromatin immunoprecipitation (ChIP)-chip and knock-out data. Since these two types of data provide complementary (physical and functional) information about transcription, the method combining them is expected to achieve high detection rates and very low false positive rates. We try to seek the optimal integration of these two data using hyper-geometric distribution. We evaluate our method on yeast data and compare our predictions with YEASTRACT, high-quality ChIP-chip data, and literature. The results show that even using low-quality ChIP-chip data, our method uncovers more relations than those inferred before from high-quality data. Furthermore our method achieves a low false positive rate. We find experimental and computational evidence in literature for most transcription factor (TF)-gene relations uncovered by our method
Scalable telomere-to-telomere assembly for diploid and polyploid genomes with double graph
Despite recent advances in the length and the accuracy of long-read data,
building haplotype-resolved genome assemblies from telomere to telomere still
requires considerable computational resources. In this study, we present an
efficient de novo assembly algorithm that combines multiple sequencing
technologies to scale up population-wide telomere-to-telomere assemblies. By
utilizing twenty-two human and two plant genomes, we demonstrate that our
algorithm is around an order of magnitude cheaper than existing methods, while
producing better diploid and haploid assemblies. Notably, our algorithm is the
only feasible solution to the haplotype-resolved assembly of polyploid genomes.Comment: 14 pages, 4 fuhire
Learning a Stable Dynamic System with a Lyapunov Energy Function for Demonstratives Using Neural Networks
Autonomous Dynamic System (DS)-based algorithms hold a pivotal and
foundational role in the field of Learning from Demonstration (LfD).
Nevertheless, they confront the formidable challenge of striking a delicate
balance between achieving precision in learning and ensuring the overall
stability of the system. In response to this substantial challenge, this paper
introduces a novel DS algorithm rooted in neural network technology. This
algorithm not only possesses the capability to extract critical insights from
demonstration data but also demonstrates the capacity to learn a candidate
Lyapunov energy function that is consistent with the provided data. The model
presented in this paper employs a straightforward neural network architecture
that excels in fulfilling a dual objective: optimizing accuracy while
simultaneously preserving global stability. To comprehensively evaluate the
effectiveness of the proposed algorithm, rigorous assessments are conducted
using the LASA dataset, further reinforced by empirical validation through a
robotic experiment
Frame-wise Cross-modal Matching for Video Moment Retrieval
Video moment retrieval targets at retrieving a moment in a video for a given
language query. The challenges of this task include 1) the requirement of
localizing the relevant moment in an untrimmed video, and 2) bridging the
semantic gap between textual query and video contents. To tackle those
problems, early approaches adopt the sliding window or uniform sampling to
collect video clips first and then match each clip with the query. Obviously,
these strategies are time-consuming and often lead to unsatisfied accuracy in
localization due to the unpredictable length of the golden moment. To avoid the
limitations, researchers recently attempt to directly predict the relevant
moment boundaries without the requirement to generate video clips first. One
mainstream approach is to generate a multimodal feature vector for the target
query and video frames (e.g., concatenation) and then use a regression approach
upon the multimodal feature vector for boundary detection. Although some
progress has been achieved by this approach, we argue that those methods have
not well captured the cross-modal interactions between the query and video
frames.
In this paper, we propose an Attentive Cross-modal Relevance Matching (ACRM)
model which predicts the temporal boundaries based on an interaction modeling.
In addition, an attention module is introduced to assign higher weights to
query words with richer semantic cues, which are considered to be more
important for finding relevant video contents. Another contribution is that we
propose an additional predictor to utilize the internal frames in the model
training to improve the localization accuracy. Extensive experiments on two
datasets TACoS and Charades-STA demonstrate the superiority of our method over
several state-of-the-art methods. Ablation studies have been also conducted to
examine the effectiveness of different modules in our ACRM model.Comment: 12 pages; accepted by IEEE TM
- β¦