MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features

Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

/pdf/bioinformatics-enrichment-tools-paths-toward-the-2yo1xl3r2j.pdf

Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

/pdf/integrative-analysis-of-111-reference-human-epigenomes-4bavnjaon6.pdf

Integrative analysis of 111 reference human epigenomes

We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.

/pdf/gene-ontology-analysis-for-rna-seq-accounting-for-selection-456k6bns5w.pdf

Gene ontology analysis for RNA-seq: accounting for selection bias

Outcomes of high-throughput biological experiments are typically interpreted by statistical testing for enriched gene functional categories defined by the Gene Ontology (GO). The resulting lists of GO terms may be large and highly redundant, and thus difficult to interpret.

REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures. Furthermore, REVIGO visualizes this non-redundant GO term set in multiple ways to assist in interpretation: multidimensional scaling and graph-based visualizations accurately render the subdivisions and the semantic relationships in the data, while treemaps and tag clouds are also offered as alternative views. REVIGO is freely available at http://revigo.irb.hr/.

/pdf/revigo-summarizes-and-visualizes-long-lists-of-gene-ontology-3taarmiz8n.pdf

REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

Transfer metric learning (TML) aims to improve the metric learning in target domains by transferring knowledge from related tasks, where the distance metrics are strong and reliable. Existing TML approaches only focus on how to transfer the source metric knowledge, which is often prone to be over-fitting to the source domain. In this paper, we study how to train a source metric that is appropriate for transfer and then design a general deep TML method for effective metric transfer. In particular, we propose to learn the source metric parameterized by a deep neural network in an adversarial way and then transfer the metric to the target domain by embedding imitation, which allows the inputs of source and target domains to be heterogeneous. Besides, we restrict the size of the target metric network to be small so that the inference is efficient in the target domain. Results in the popular face verification application demonstrate the effectiveness of our method.

Robust Metric Boosts Transfer

The Loess Plateau is one of the three most severely affected geological disaster areas in China. Water sensitivity is the most significant feature of the loess. Under the action of continuous heavy rainfall, rainwater infiltrates the loess, resulting in a rapid increase in soil saturation and changes in soil moisture. This affects the shear strength of the soil and induces shallow loess landslides. Therefore, it is significant to our country’s disaster prevention and mitigation efforts to effectively monitor the deformation and inducement of such landslides. At present, the global navigation satellite system (GNSS) is widely used in the field of landslide disaster monitoring as a technical means to directly obtain real-time three-dimensional vector deformation of the surface. At the same time, GNSS can also provide a steady stream of L-band microwave signals to obtain surface environmental information, such as soil moisture around the station. In past landslide disaster monitoring research, GNSS was only used to provide three-dimensional deformation information, and its ability to provide environmental information around the station was almost completely ignored. This study proposes a ground-based GNSS remote sensing comprehensive monitoring system integrating “three-dimensional deformation and soil moisture content” combined with a rainfall-type shallow loess landslide event in Linxia City. The ability of ground-based GNSS to comprehensively monitor shallow loess landslide disasters was analysed. Experiments show that GNSS can provide high-precision deformation time series characteristics and monitor the changes in soil moisture content around the station at the same time; the two have a certain response relationship, which can comprehensively evaluate the stability of shallow loess landslides. As heavy rainfall is a key factor affecting the change in soil water content, this study adds the atmospheric water vapour content calculated by ground-based GNSS refraction remote sensing in the discussion chapter and analyses the relationship between precipitable water vapour and rainfall in this area to give full play to ground-based GNSS remote sensing. In the role of landslide disaster monitoring, we hope to build a more comprehensive ground-based GNSS remote sensing monitoring system to better serve the monitoring of landslide disasters.

/pdf/research-of-deformation-and-soil-moisture-in-loess-landslide-2l6swdew.pdf

Research of Deformation and Soil Moisture in Loess Landslide Simultaneous Retrieved with Ground-Based GNSS


 α-synuclein (α-syn) is the main protein component of Lewy bodies, the major pathological hallmarks of Parkinson’s disease (PD). C-terminal truncated forms of α-syn are found in the brain of PD patients, and reduce cell viability, as well as tend to form fibrils. Nevertheless, little is known about the molecular mechanisms underlying the role of C-terminal truncation on the cytotoxicity and aggregation of α-syn. The N-terminal domain of α-syn interacts with membranes and molecular chaperones, and these interactions are critical for the protein’s physiological function and its pathological effects in PD. Here, we use nuclear magnetic resonance spectroscopy, a co-flotation assay, dynamic light scattering, and thioflavin T fluorescence to show that the truncation alters α-syn conformation, resulting in a new attractive interaction of the N-terminus with membranes and molecular chaperone, protein disulfide isomerase (PDI). Remarkably, the truncated protein is more toxic to mitochondria than full-length protein and diminishes the effect of PDI on α-syn fibrillation. Our findings reveal a modulatory role for the C-terminus in the cytotoxicity and aggregation of α-syn by interfering the interaction of the N-terminus with membrane and chaperone.

https://www.researchsquare.com/article/rs-1151643/latest.pdf

α-synuclein C-terminal Truncation Modulates Its Cytotoxicity and Aggregation by Promoting Its N-terminal Interactions With Membrane and Chaperone

A new method for measuring thermal resistance of building walls and analyses of influencing factors

Existing Spatio-temporal graph models consider traffic flow prediction as a "node-level" task, and it is difficult to make decisions based on the results of point prediction alone. The modeling idea is to explore the hidden graph structure that better reflects the spatial dependency of the road network from the traffic flow "signal" to replace the less accurate graph structure that is calculated directly based on the geographical location. Here, we propose the Dilated SpatioTemporal Graph Convolutional Network (DSTGCN) model and the Adaptive Graph Recurrent Neural Network (AGRNN) model for traffic flow prediction. The main work is to propose a self-learning transfer matrix for directed graphs that can automatically discover potential graph structures from the historical traffic of each node, improve the gating mechanism in temporal convolution, and introduce probabilistic prediction into the traffic flow prediction problem to enhance the dimensionality of information used for decision making. DSTGCN and AGRNN are evaluated on two publicly available traffic datasets. METRLA and PEMS-BAY were evaluated and achieved the fastest computational speed (compared to other Spatio-temporal graph models) and the best prediction results, respectively.

Xin Zhou

Papers

Robust Metric Boosts Transfer

Research of Deformation and Soil Moisture in Loess Landslide Simultaneous Retrieved with Ground-Based GNSS

α-synuclein C-terminal Truncation Modulates Its Cytotoxicity and Aggregation by Promoting Its N-terminal Interactions With Membrane and Chaperone

A new method for measuring thermal resistance of building walls and analyses of influencing factors

Research on traffic probabilistic forecasting with spatial-temporal graph neural networks