scholarly journals ortho2align: a sensitive approach for searching for orthologues of novel lncRNAs

2021 ◽  
Author(s):  
Dmitry Evgenevich Mylarshchikov ◽  
Andrey Alexandrovich Mironov
Keyword(s):  
High Throughput Sequencing ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Noncoding Rnas ◽  
Sequence Conservation ◽  
Search Process ◽  
Versatile Tool ◽  
Search Tool ◽  
Living Organisms ◽  
Genomic Regions

Background: Many novel long noncoding RNAs have been discovered in recent years due to advances in high-throughput sequencing experiments. Finding orthologues of these novel lncRNAs might facilitate clarification of their functional role in living organisms. However, lncRNAs exhibit low sequence conservation, so specific methods for enhancing the signal-to-noise ratio were developed. Nevertheless, current methods such as transcriptomes comparison approaches or searches for conserved secondary structures are not applicable to novel lncRNAs dy design. Results: We present ortho2align - a versatile sensitive synteny-based lncRNA orthologue search tool with statistical assessment of sequence conservation. This tool allows control of the specificity of the search process and optional annotation of found orthologues. ortho2align shows similar performance in terms of sensitivity and resource usage as the state-of-the-art method for aligning orthologous lncRNAs but also enables scientists to predict unannotated orthologous sequences for lncRNAs in question. Using ortho2align, we predicted orthologues of three distinct classes of novel human lncRNAs in six Vertebrata species to estimate their degree of conservation. Conclusions: Being designed for the discovery of unannotated orthologues of novel lncRNAs in distant species, ortho2align is a versatile tool applicable to any genomic regions, especially weakly conserved ones. A small amount of input files makes ortho2align easy to use in orthology studies as a single tool or in bundle with other steps that researchers will consider sensible.

scholarly journals Utilizing networks for differential analysis of chromatin interactions

2017 ◽  
Vol 15 (06) ◽  
pp. 1740008 ◽  
Author(s):  
Lu Liu ◽  
Jianhua Ruan
Keyword(s):  
High Throughput Sequencing ◽  
Signal To Noise Ratio ◽  
Chromatin Interaction ◽  
Differential Analysis ◽  
Local Method ◽  
Global Method ◽  
Chromatin Interactions ◽  
Normalization Strategy ◽  
Genome Wide ◽  
Genomic Regions

Chromatin conformation capture with high-throughput sequencing (Hi-C) is a powerful technique to detect genome-wide chromatin interactions. In this paper, we introduce two novel approaches to detect differentially interacting genomic regions between two Hi-C experiments using a network model. To make input data from multiple experiments comparable, we propose a normalization strategy guided by network topological properties. We then devise two measurements, using local and global connectivity information from the chromatin interaction networks, respectively, to assess the interaction differences between two experiments. When multiple replicates are present in experiments, our approaches provide the flexibility for users to either pool all replicates together to therefore increase the network coverage, or to use the replicates in parallel to increase the signal to noise ratio. We show that while the local method works better in detecting changes from simulated networks, the global method performs better on real Hi-C data. The local and global methods, regardless of pooling, are always superior to two existing methods. Furthermore, our methods work well on both unweighted and weighted networks and our normalization strategy significantly improves the performance compared with raw networks without normalization. Therefore, we believe our methods will be useful for identifying differentially interacting genomic regions.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

2019 ◽  
Vol 15 (3) ◽  
pp. 216-230 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Javad Zahiri ◽  
Mehrdad Asadian ◽  
Mehrdad Behmanesh ◽  
Barat A. Fakheri ◽  
...  
Keyword(s):  
Computational Methods ◽  
State Of The Art ◽  
Noncoding Rnas ◽  
Research Field ◽  
Design Strategies ◽  
Structural Prediction ◽  
Computational Costs ◽  
Cellular Processes ◽  
Laboratory Techniques ◽  
Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

scholarly journals Brain Long Noncoding RNAs: Multitask Regulators of Neuronal Differentiation and Function

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3951
Author(s):  
Sarva Keihani ◽  
Verena Kluever ◽  
Eugenio F. Fornasiero
Keyword(s):  
Neuronal Differentiation ◽  
Neuronal Excitability ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Regulatory Mechanisms ◽  
Control Robustness ◽  
Living Organisms ◽  
And Function ◽  
Regulatory Functions ◽  
Neuronal Physiology

The extraordinary cellular diversity and the complex connections established within different cells types render the nervous system of vertebrates one of the most sophisticated tissues found in living organisms. Such complexity is ensured by numerous regulatory mechanisms that provide tight spatiotemporal control, robustness and reliability. While the unusual abundance of long noncoding RNAs (lncRNAs) in nervous tissues was traditionally puzzling, it is becoming clear that these molecules have genuine regulatory functions in the brain and they are essential for neuronal physiology. The canonical view of RNA as predominantly a ‘coding molecule’ has been largely surpassed, together with the conception that lncRNAs only represent ‘waste material’ produced by cells as a side effect of pervasive transcription. Here we review a growing body of evidence showing that lncRNAs play key roles in several regulatory mechanisms of neurons and other brain cells. In particular, neuronal lncRNAs are crucial for orchestrating neurogenesis, for tuning neuronal differentiation and for the exact calibration of neuronal excitability. Moreover, their diversity and the association to neurodegenerative diseases render them particularly interesting as putative biomarkers for brain disease. Overall, we foresee that in the future a more systematic scrutiny of lncRNA functions will be instrumental for an exhaustive understanding of neuronal pathophysiology.

scholarly journals Widespread occurrence of microRNA-mediated target cleavage on membrane-bound polysomes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiaoyu Yang ◽  
Chenjiang You ◽  
Xufeng Wang ◽  
Lei Gao ◽  
Beixin Mo ◽  
...  
Keyword(s):  
Endoplasmic Reticulum ◽  
Gene Silencing ◽  
High Frequency ◽  
Small Rnas ◽  
High Throughput Sequencing ◽  
Noncoding Rnas ◽  
Small Interfering Rnas ◽  
Widespread Occurrence ◽  
Membrane Bound ◽  
Different Tissues

Abstract Background Small RNAs (sRNAs) including microRNAs (miRNAs) and small interfering RNAs (siRNAs) serve as core players in gene silencing at transcriptional and post-transcriptional levels in plants, but their subcellular localization has not yet been well studied, thus limiting our mechanistic understanding of sRNA action. Results We investigate the cytoplasmic partitioning of sRNAs and their targets globally in maize (Zea mays, inbred line “B73”) and rice (Oryza sativa, cv. “Nipponbare”) by high-throughput sequencing of polysome-associated sRNAs and 3′ cleavage fragments, and find that both miRNAs and a subset of 21-nucleotide (nt)/22-nt siRNAs are enriched on membrane-bound polysomes (MBPs) relative to total polysomes (TPs) across different tissues. Most of the siRNAs are generated from transposable elements (TEs), and retrotransposons positively contributed to MBP overaccumulation of 22-nt TE-derived siRNAs (TE-siRNAs) as opposed to DNA transposons. Widespread occurrence of miRNA-mediated target cleavage is observed on MBPs, and a large proportion of these cleavage events are MBP-unique. Reproductive 21PHAS (21-nt phasiRNA-generating) and 24PHAS (24-nt phasiRNA-generating) precursors, which were commonly considered as noncoding RNAs, are bound by polysomes, and high-frequency cleavage of 21PHAS precursors by miR2118 and 24PHAS precursors by miR2275 is further detected on MBPs. Reproductive 21-nt phasiRNAs are enriched on MBPs as opposed to TPs, whereas 24-nt phasiRNAs are nearly completely devoid of polysome occupancy. Conclusions MBP overaccumulation is a conserved pattern for cytoplasmic partitioning of sRNAs, and endoplasmic reticulum (ER)-bound ribosomes function as an independent regulatory layer for miRNA-induced gene silencing and reproductive phasiRNA biosynthesis in maize and rice.

scholarly journals Films and Materials Derived from Aminomalononitrile

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 82
Author(s):  
Helmut Thissen ◽  
Richard A. Evans ◽  
Vincent Ball
Keyword(s):  
Surface Chemistry ◽  
Surface Functionalization ◽  
State Of The Art ◽  
Chemical Reactivity ◽  
The State ◽  
Deposition Kinetics ◽  
Conformal Coating ◽  
Early Atmosphere ◽  
Living Organisms ◽  
Hypothetical Structure

In recent years major advances in surface chemistry and surface functionalization have been performed through the development, most often inspired by living organisms, of versatile methodologies. Among those, the contact of substrates with aminomalononitrile (AMN) containing solutions at pH = 8.5 allows a conformal coating to be deposited on the surface of all known classes of material. Since AMN is a molecule probably formed in the early atmosphere of our planet and since HCN-based compounds have been detected on many comets and Titan (Saturn’s largest moon) it is likely that such molecules will open a large avenue in surface functionalization mostly for bio-applications. This mini review describes the state of the art of AMN-based coatings from their deposition kinetics, composition, chemical reactivity, hypothetical structure to their first applications as biomaterials. Finally, the AMN-based versatile coatings are compared to other kinds of versatile coating based on catecholamines and polyphenols.

scholarly journals A state-of-the-art perspective on microgliopathic pain

Open Biology ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 180154 ◽  
Author(s):  
Kazuhide Inoue
Keyword(s):  
Chronic Pain ◽  
Neuropathic Pain ◽  
State Of The Art ◽  
Warning System ◽  
Physiological Significance ◽  
Principal Role ◽  
Activated Microglia ◽  
Clinical Burden ◽  
Synaptic Functions ◽  
Living Organisms

Acute nociceptive pain is an undesirable feeling but has a physiological significance as a warning system for living organisms. Conversely, chronic pain is lacking physiological significance, but rather represents a confusion of nerve functions. The neuropathic pain that occurs after peripheral nerve injury (PNI) is perhaps the most important type of chronic pain because it is refractory to available medications and thus remains a heavy clinical burden. In recent decades, studies have shown that spinal microglia play a principal role in the alterations in synaptic functions evoking this pain. It is also clear that the P2X4 receptor (P2X4R), a subtype of ionotropic ATP receptors, is upregulated exclusively in spinal microglia after PNI and plays a key role in evoking neuropathic pain. Neuropathic pain is caused by several conditions associated with activated microglia without nerve damage. ‘Microgliopathic pain’ is a new concept indicating such abnormal pain related to activated microglia.

scholarly journals Electronic records management – a state of the art review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Babatunde Oladejo ◽  
Sunčica Hadžidedić
Keyword(s):  
State Of The Art ◽  
Academic Research ◽  
Research Area ◽  
Electronic Records ◽  
Records Management ◽  
Content Type ◽  
Search Results ◽  
Search Tool ◽  
Research And Education ◽  
Electronic Records Management

Purpose This paper aims to examine the state of the art in electronic records management (ERM) with the goal of identifying the prevailing research topics, gaps and issues in the field. Design/methodology/approach First, a wide search was performed on academic research databases, limited to the period between 2008–2018. Second, the search results were reviewed for relevance and duplicates. Finally, the study sources were checked against the list of journals and conferences ranked by computing research and education and JourQual. The final sample of 55 selected studies was analyzed in depth. Findings ERM has lost some research momentum due to being deeply embedded in affiliate information systems areas and the changing records management landscape. Additionally, the requirement models specified by Governmental/National Archives might have constrained technology innovation in ERM. A lack of application was identified for the social media research area. Research limitations/implications Limitations were encountered in available search tool functionality and keyword confusion leading to inflated search results. While effort has been made to obtain optimal search results, some relevant articles may have been omitted. Originality/value The last ERM state-of-the-art review was in 1997. A lot has changed since then. This paper will help researchers understand the current state of ERM research, its understudied areas and identify gaps for future studies.

scholarly journals Comprehensive analysis of miRNA-mRNA/lncRNA during gonadal development of triploid female rainbow trout (Oncorhynchus mykiss)

2020 ◽  
Author(s):  
Tianqing Huang ◽  
Wei Gu ◽  
Enhui Liu ◽  
Xiulan Shi ◽  
Bingqian Wang ◽  
...  
Keyword(s):  
Rainbow Trout ◽  
High Throughput Sequencing ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Noncoding Rnas ◽  
Gonadal Development ◽  
Differentially Expressed ◽  
Rainbow Trout Oncorhynchus Mykiss ◽  
Oocyte Meiosis ◽  
Non Coding Rna

Abstract Background: Chromosomal ploidy manipulation is one of the means to create excellent germplasm. Triploid fish could provide an ideal sterile model for the mechanism research of abnormality in meiosis. The complete understanding of the coding and noncoding RNAs regulating sterility caused by meiosis abnormality is still not well understood.Results: By high-throughput sequencing, we compared the expression profiles of gonadal mRNA, long non-coding RNA (lncRNA), and microRNA (miRNA) at different developmental stages [65 days post fertilisation (dpf), 180 dpf, and 600 dpf] between the diploid (XX) and triploid (XXX) female rainbow trout. A majority of differentially expressed (DE) RNAs were identified, and 22 DE mRNAs related to oocyte meiosis and homologous recombination were characterized. The predicted miRNA-mRNA/lncRNA networks of 3 developmental stages were constructed based on the target pairs of DE lncRNA-miRNA and DE mRNA-miRNA. According to the networks, meiosis-related gene of ccne1 was targeted by dre-miR-15a-5p_R+1, and 6 targeted DE lncRNAs were identified. Also, RT-qPCR was performed to validate the credibility of the network.Conclusions: This study explored the potential interplay between coding and noncoding RNAs during the gonadal development of polyploid fish. It provides full insights into polyploidy-associated effects on fertility of fish. These differentially expressed coding and noncoding RNAs provide a novel resource for studying genome diversity of polyploid induction.

scholarly journals Discovery of allenyl acetylene, H2CCCHCCH, in TMC-1

2021 ◽  
Vol 647 ◽  
pp. L3 ◽  
Author(s):  
J. Cernicharo ◽  
C. Cabezas ◽  
M. Agúndez ◽  
B. Tercero ◽  
N. Marcelino ◽  
...  
Keyword(s):  
Column Density ◽  
State Of The Art ◽  
Rotational Temperature ◽  
Signal To Noise Ratio ◽  
Laboratory Data ◽  
The Other ◽  
Signal To Noise ◽  
Chemical Models ◽  
Upper Limit

We present the discovery in TMC-1 of allenyl acetylene, H2CCCHCCH, through the observation of nineteen lines with a signal-to-noise ratio ∼4–15. For this species, we derived a rotational temperature of 7 ± 1 K and a column density of 1.2 ± 0.2 × 1013 cm−2. The other well known isomer of this molecule, methyl diacetylene (CH3C4H), has also been observed and we derived a similar rotational temperature, Tr = 7.0 ± 0.3 K, and a column density for its two states (A and E) of 6.5 ± 0.3 × 1012 cm−2. Hence, allenyl acetylene and methyl diacetylene have a similar abundance. Remarkably, their abundances are close to that of vinyl acetylene (CH2CHCCH). We also searched for the other isomer of C5H4, HCCCH2CCH (1.4-Pentadiyne), but only a 3σ upper limit of 2.5 × 1012 cm−2 to the column density can be established. These results have been compared to state-of-the-art chemical models for TMC-1, indicating the important role of these hydrocarbons in its chemistry. The rotational parameters of allenyl acetylene have been improved by fitting the existing laboratory data together with the frequencies of the transitions observed in TMC-1.

ANN softmax

2021 ◽  
Vol 15 (1) ◽  
pp. 1-10
Author(s):  
Kang Zhao ◽  
Liuyihan Song ◽  
Yingya Zhang ◽  
Pan Pan ◽  
Yinghui Xu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Training Sample ◽  
Locality Sensitive Hashing ◽  
Search Process ◽  
Inverted File ◽  
Deep Model ◽  
Learning Tasks ◽  
Improve Accuracy ◽  
Small Set ◽  
Kernel Design

Thanks to the popularity of GPU and the growth of its computational power, more and more deep learning tasks, such as face recognition, image retrieval and word embedding, can take advantage of extreme classification to improve accuracy. However, it remains a big challenge to train a deep model with millions of classes efficiently due to the huge memory and computation consumption in the last layer. By sampling a small set of classes to avoid the total classes calculation, sampling-based approaches have been proved to be an effective solution. But most of them suffer from the following two issues: i) the important classes are ignored or only partly sampled, such as the methods using random sampling scheme or retrieval techniques of low recall (e.g., locality-sensitive hashing), resulting in the degradation of accuracy; ii) inefficient implementation owing to incompatibility with GPU, like selective softmax. It uses hashing forest to help select classes, but the search process is implemented in CPU. To address the above issues, we propose a new sampling-based softmax called ANN Softmax in this paper. Specifically, we employ binary quantization with inverted file system to improve the recall of important classes. With the help of dedicated kernel design, it can be totally parallelized in mainstream training framework. Then, we find the size of important classes that are recalled by each training sample has a great impact on the final accuracy, so we introduce sample grouping optimization to well approximate the full classes training. Experimental evaluations on two tasks (Embedding Learning and Classification) and ten datasets (e.g., MegaFace, ImageNet, SKU datasets) demonstrate our proposed method maintains the same precision as Full Softmax for different loss objectives, including cross entropy loss, ArcFace, CosFace and D-Softmax loss, with only 1/10 sampled classes, which outperforms the state-of-the-art techniques. Moreover, we implement ANN Soft-max in a complete GPU pipeline that can accelerate the training more than 4.3X. Equipped our method with a 256 GPUs cluster, the time of training a classifier of 300 million classes on our SKU-300M dataset can be reduced to ten days.

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