bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transcription factor regulatory modules provide the molecular mechanisms for functional redundancy observed among transcription factors in yeast
Authors: Yang Tzu-Hsien 
Published: December 2019

Abstract
Background
Current technologies for understanding the transcriptional reprogramming in cells include the transcription factor (TF) chromatin immunoprecipitation (ChIP) experiments and the TF knockout experiments. The ChIP experiments show the binding targets of TFs against which the antibody directs while the knockout techniques find the regulatory gene targets of the knocked-out TFs. However, it was shown that these two complementary results contain few common targets. Researchers have used the concept of TF functional redundancy to explain the low overlap between these two techniques. But the detailed molecular mechanisms behind TF functional redundancy remain unknown. Without knowing the possible molecular mechanisms, it is hard for biologists to fully unravel the cause of TF functional redundancy.

Results
To mine out the molecular mechanisms, a novel algorithm to extract TF regulatory modules that help explain the observed TF functional redundancy effect was devised and proposed in this research. The method first searched for candidate TF sets from the TF binding data. Then based on these candidate sets the method utilized the modified Steiner Tree construction algorithm to construct the possible TF regulatory modules from protein-protein interaction data and finally filtered out the noise-induced results by using confidence tests. The mined-out regulatory modules were shown to correlate to the concept of functional redundancy and provided testable hypotheses of the molecular mechanisms behind functional redundancy. And the biological significance of the mined-out results was demonstrated in three different biological aspects: ontology enrichment, protein interaction prevalence and expression coherence. About 23.5% of the mined-out TF regulatory modules were literature-verified. Finally, the biological applicability of the proposed method was shown in one detailed example of a verified TF regulatory module for pheromone response and filamentous growth in yeast.

Conclusion
In this research, a novel method that mined out the potential TF regulatory modules which elucidate the functional redundancy observed among TFs is proposed. The extracted TF regulatory modules not only correlate the molecular mechanisms to the observed functional redundancy among TFs, but also show biological significance in inferring TF functional binding target genes. The results provide testable hypotheses for biologists to further design subsequent research and experiments.
Topological structure analysis of chromatin interaction networks
Authors: Viksna Juris , Melkus Gatis , Celms Edgars , Čerāns Kārlis , Freivalds Karlis , Kikusts Paulis , Lace Lelde , Opmanis Mārtiņš , Rituma Darta , Rucevskis Peteris 
Published: December 2019

Abstract
Background
Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks.

Results
It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, such evidence was based on manual analysis and was limited. Here we present a novel method for analysis of chromatin interaction networks aimed towards identifying characteristic topological features of interaction graphs and confirming their potential significance in chromatin architecture. Our method automatically identifies all connected components with an assigned significance score above a given threshold. These components can be subjected afterwards to different assessment methods for their biological role and/or significance. The method was applied to the largest PCHi-C data set available to date that contains interactions for 17 haematopoietic cell types. The results demonstrate strong evidence of well-pronounced component structure of chromatin interaction networks and provide some characterisation of this component structure. We also performed an indicative assessment of potential biological significance of identified network components with the results confirming that the network components can be related to specific biological functionality.

Conclusions
The obtained results show that the topological structure of chromatin interaction networks can be well described in terms of isolated connected components of the network and that formation of these components can be often explained by biological features of functionally related gene modules. The presented method allows automatic identification of all such components and evaluation of their significance in PCHi-C dataset for 17 haematopoietic cell types. The method can be adapted for exploration of other chromatin interaction data sets that include information about sufficiently large number of different cell types, and, in principle, also for analysis of other kinds of cell type-specific networks.
Fast and accurate microRNA search using CNN
Authors: Tang Xubo , Sun Yanni 
Published: December 2019

Abstract
Background
There are many different types of microRNAs (miRNAs) and elucidating their functions is still under intensive research. A fundamental step in functional annotation of a new miRNA is to classify it into characterized miRNA families, such as those in Rfam and miRBase. With the accumulation of annotated miRNAs, it becomes possible to use deep learning-based models to classify different types of miRNAs. In this work, we investigate several key issues associated with successful application of deep learning models for miRNA classification. First, as secondary structure conservation is a prominent feature for noncoding RNAs including miRNAs, we examine whether secondary structure-based encoding improves classification accuracy. Second, as there are many more non-miRNA sequences than miRNAs, instead of assigning a negative class for all non-miRNA sequences, we test whether using softmax output can distinguish in-distribution and out-of-distribution samples. Finally, we investigate whether deep learning models can correctly classify sequences from small miRNA families.

Results
We present our trained convolutional neural network (CNN) models for classifying miRNAs using different types of feature learning and encoding methods. In the first method, we explicitly encode the predicted secondary structure in a matrix. In the second method, we use only the primary sequence information and one-hot encoding matrix. In addition, in order to reject sequences that should not be classified into targeted miRNA families, we use a threshold derived from softmax layer to exclude out-of-distribution sequences, which is an important feature to make this model useful for real transcriptomic data. The comparison with the state-of-the-art ncRNA classification tools such as Infernal shows that our method can achieve comparable sensitivity and accuracy while being significantly faster.

Conclusion
Automatic feature learning in CNN can lead to better classification accuracy and sensitivity for miRNA classification and annotation. The trained models and also associated codes are freely available at https://github.com/HubertTang/DeepMir.
Gencore: an efficient tool to generate consensus reads for error suppressing and duplicate removing of NGS data
Authors: Chen Shifu , Zhou Yanqing , Chen Yaru , Huang Tanxiao , Liao Wenting , Xu Yun , Li Zhicheng , Gu Jia 
Published: December 2019

Abstract
Background
Removing duplicates might be considered as a well-resolved problem in next-generation sequencing (NGS) data processing domain. However, as NGS technology gains more recognition in clinical application, researchers start to pay more attention to its sequencing errors, and prefer to remove these errors while performing deduplication operations. Recently, a new technology called unique molecular identifier (UMI) has been developed to better identify sequencing reads derived from different DNA fragments. Most existing duplicate removing tools cannot handle the UMI-integrated data. Some modern tools can work with UMIs, but are usually slow and use too much memory. Furthermore, existing tools rarely report rich statistical results, which are very important for quality control and downstream analysis. These unmet requirements drove us to develop an ultra-fast, simple, little-weighted but powerful tool for duplicate removing and sequence error suppressing, with features of handling UMIs and reporting informative results.

Results
This paper presents an efficient tool gencore for duplicate removing and sequence error suppressing of NGS data. This tool clusters the mapped sequencing reads and merges reads in each cluster to generate one single consensus read. While the consensus read is generated, the random errors introduced by library construction and sequencing can be removed. This error-suppressing feature makes gencore very suitable for the application of detecting ultra-low frequency mutations from deep sequencing data. When unique molecular identifier (UMI) technology is applied, gencore can use them to identify the reads derived from same original DNA fragment. Gencore reports statistical results in both HTML and JSON formats. The HTML format report contains many interactive figures plotting statistical coverage and duplication information. The JSON format report contains all the statistical results, and is interpretable for downstream programs.

Conclusions
Comparing to the conventional tools like Picard and SAMtools, gencore greatly reduces the output data’s mapping mismatches, which are mostly caused by errors. Comparing to some new tools like UMI-Reducer and UMI-tools, gencore runs much faster, uses less memory, generates better consensus reads and provides simpler interfaces. To our best knowledge, gencore is the only duplicate removing tool that generates both informative HTML and JSON reports. This tool is available at: https://github.com/OpenGene/gencore
The phylogenetic significance of the morphology of the syrinx, hyoid and larynx, of the southern cassowary, Casuarius casuarius (Aves, Palaeognathae)
Authors: McInerney Phoebe L. , Lee Michael S. Y. , Clement Alice M. , Worthy Trevor H. 
Published: December 2019

AbstractBackgroundPalaeognathae is a basal clade within Aves and include the large and flightless ratites and the smaller, volant tinamous. Although much research has been conducted on various aspects of palaeognath morphology, ecology, and evolutionary history, there are still areas which require investigation. This study aimed to fill gaps in our knowledge of the Southern Cassowary,Casuarius casuarius, for which information on the skeletal systems of the syrinx, hyoid and larynx is lacking - despite these structures having been recognised as performing key functional roles associated with vocalisation, respiration and feeding. Previous research into the syrinx and hyoid have also indicated these structures to be valuable for determining evolutionary relationships among neognath taxa, and thus suggest they would also be informative for palaeognath phylogenetic analyses, which still exhibits strong conflict between morphological and molecular trees.ResultsThe morphology of the syrinx, hyoid and larynx ofC. casuariusis described from CT scans. The syrinx is of the simple tracheo-bronchial syrinx type, lacking specialised elements such as the pessulus; the hyoid is relatively short with longer ceratobranchials compared to epibranchials; and the larynx is comprised of entirely cartilaginous, standard avian anatomical elements including a concave, basin-like cricoid and fused cricoid wings. As in the larynx, both the syrinx and hyoid lack ossification and all three structures were most similar toDromaius.We documented substantial variation across palaeognaths in the skeletal character states of the syrinx, hyoid, and larynx, using both the literature and novel observations (e.g. ofC. casuarius). Notably, new synapomorphies linking Dinornithiformes and Tinamidae are identified, consistent with the molecular evidence for this clade. These shared morphological character traits include the ossification of the cricoid and arytenoid cartilages, and an additional cranial character, the articulation between the maxillary process of the nasal and the maxilla.ConclusionSyrinx, hyoid and larynx characters of palaeognaths display greater concordance with molecular trees than do other morphological traits. These structures might therefore be less prone to homoplasy related to flightlessness and gigantism, compared to typical morphological traits emphasised in previous phylogenetic studies.
A genome-skimmed phylogeny of a widespread bryozoan family, Adeonidae
Authors: Orr Russell J. S. , Haugen Marianne N. , Berning Björn , Bock Philip , Cumming Robyn L. , Florence Wayne K. , Hirose Masato , Di Martino Emanuela , Ramsfjell Mali H. , Sannum Maja M. , Smith Abigail M. , Vieira Leandro M. , Waeschenbach Andrea , Liow Lee Hsiang 
Published: December 2019

Abstract
Background
Understanding the phylogenetic relationships among species is one of the main goals of systematic biology. Simultaneously, credible phylogenetic hypotheses are often the first requirement for unveiling the evolutionary history of traits and for modelling macroevolutionary processes. However, many non-model taxa have not yet been sequenced to an extent such that statistically well-supported molecular phylogenies can be constructed for these purposes. Here, we use a genome-skimming approach to extract sequence information for 15 mitochondrial and 2 ribosomal operon genes from the cheilostome bryozoan family, Adeonidae, Busk, 1884, whose current systematics is based purely on morphological traits. The members of the Adeonidae are, like all cheilostome bryozoans, benthic, colonial, marine organisms. Adeonids are also geographically widely-distributed, often locally common, and are sometimes important habitat-builders.

Results
We successfully genome-skimmed 35 adeonid colonies representing 6 genera (Adeona, Adeonellopsis, Bracebridgia, Adeonella, Laminopora and Cucullipora). We also contributed 16 new, circularised mitochondrial genomes to the eight previously published for cheilostome bryozoans. Using the aforementioned mitochondrial and ribosomal genes, we inferred the relationships among these 35 samples. Contrary to some previous suggestions, the Adeonidae is a robustly supported monophyletic clade. However, the genera Adeonella and Laminopora are in need of revision: Adeonella is polyphyletic and Laminopora paraphyletically forms a clade with some Adeonella species. Additionally, we assign a sequence clustering identity using cox1 barcoding region of 99% at the species and 83% at the genus level.

Conclusions
We provide sequence data, obtained via genome-skimming, that greatly increases the resolution of the phylogenetic relationships within the adeonids. We present a highly-supported topology based on 17 genes and substantially increase availability of circularised cheilostome mitochondrial genomes, and highlight how we can extend our pipeline to other bryozoans.
D3GRN: a data driven dynamic network construction method to infer gene regulatory networks
Authors: Chen Xiang , Li Min , Zheng Ruiqing , Wu Fang-Xiang , Wang Jianxin 
Published: December 2019

Abstract
Background
To infer gene regulatory networks (GRNs) from gene-expression data is still a fundamental and challenging problem in systems biology. Several existing algorithms formulate GRNs inference as a regression problem and obtain the network with an ensemble strategy. Recent studies on data driven dynamic network construction provide us a new perspective to solve the regression problem.

Results
In this study, we propose a data driven dynamic network construction method to infer gene regulatory network (D3GRN), which transforms the regulatory relationship of each target gene into functional decomposition problem and solves each sub problem by using the Algorithm for Revealing Network Interactions (ARNI). To remedy the limitation of ARNI in constructing networks solely from the unit level, a bootstrapping and area based scoring method is taken to infer the final network. On DREAM4 and DREAM5 benchmark datasets, D3GRN performs competitively with the state-of-the-art algorithms in terms of AUPR.

Conclusions
We have proposed a novel data driven dynamic network construction method by combining ARNI with bootstrapping and area based scoring strategy. The proposed method performs well on the benchmark datasets, contributing as a competitive method to infer gene regulatory networks in a new perspective.
An efficient simulated annealing algorithm for the RNA secondary structure prediction with Pseudoknots
Authors: Kai Zhang , Yuting Wang , Yulin Lv , Jun Liu , Juanjuan He 
Published: December 2019

Abstract
Background
RNA pseudoknot structures play an important role in biological processes. However, existing RNA secondary structure prediction algorithms cannot predict the pseudoknot structure efficiently. Although random matching can improve the number of base pairs, these non-consecutive base pairs cannot make contributions to reduce the free energy.

Result
In order to improve the efficiency of searching procedure, our algorithm take consecutive base pairs as the basic components. Firstly, our algorithm calculates and archive all the consecutive base pairs in triplet data structure, if the number of consecutive base pairs is greater than given minimum stem length. Secondly, the annealing schedule is adapted to select the optimal solution that has minimum free energy. Finally, the proposed algorithm is evaluated with the real instances in PseudoBase.

Conclusion
The experimental results have been demonstrated to provide a competitive and oftentimes better performance when compared against some chosen state-of-the-art RNA structure prediction algorithms.
Investigating host-bacterial interactions among enteric pathogens
Authors: Bose Tungadri , Venkatesh K. V. , Mande Sharmila S. 
Published: December 2019

AbstractBackgroundIn 2017, World Health Organization (WHO) published a catalogue of 12 families of antibiotic-resistant “priority pathogens” that are posing the greatest threats to human health. Six of these dreaded pathogens are known to infect the human gastrointestinal system. In addition to causing gastrointestinal and systemic infections, these pathogens can also affect the composition of other microbes constituting the healthy gut microbiome. Such aberrations in gut microbiome can significantly affect human physiology and immunity. Identifying the virulence mechanisms of these enteric pathogens are likely to help in developing newer therapeutic strategies to counter them.ResultsUsing our previously published in silico approach, we have evaluated (and compared) Host-Pathogen Protein-Protein Interaction (HPI) profiles of four groups of enteric pathogens, namely, different species ofEscherichia,Shigella,SalmonellaandVibrio. Results indicate that in spite of genus/ species specific variations, most enteric pathogens possess a common repertoire of HPIs. This core set of HPIs are probably responsible for the survival of these pathogen in the harsh nutrient-limiting environment within the gut. Certain genus/ species specific HPIs were also observed.ConslusionsThe identified bacterial proteins involved in the core set of HPIs are expected to be helpful in understanding the pathogenesis of these dreaded gut pathogens in greater detail. Possible role of genus/ species specific variations in the HPI profiles in the virulence of these pathogens are also discussed. The obtained results are likely to provide an opportunity for development of novel therapeutic strategies against the most dreaded gut pathogens.
Effect of growth rate on transcriptomic responses to immune stimulation in wild-type, domesticated, and GH-transgenic coho salmon
Authors: Kim Jin-Hyoung , Macqueen Daniel J. , Winton James R. , Hansen John D. , Park Hyun , Devlin Robert H. 
Published: December 2019

AbstractBackgroundTranscriptomic responses to immune stimulation were investigated in coho salmon (Oncorhynchus kisutch) with distinct growth phenotypes. Wild-type fish were contrasted to strains with accelerated growth arising either from selective breeding (i.e. domestication) or genetic modification. Such distinct routes to accelerated growth may have unique implications for relationships and/or trade-offs between growth and immune function.ResultsRNA-Seq was performed on liver and head kidney in four ‘growth response groups’ injected with polyinosinic-polycytidylic acid (Poly I:C; viral mimic), peptidoglycan (PGN; bacterial mimic) or PBS (control). These groups were: 1) ‘W’: wild-type, 2) ‘TF’: growth hormone (GH) transgenic salmon with ~ 3-fold higher growth-rate than W, 3) ‘TR’: GH transgenic fish ration restricted to possess a growth-rate equal to W, and 4) ‘D’: domesticated non-transgenic fish showing growth-rate intermediate to W and TF. D and TF showed a higher similarity in transcriptomic response compared to W and TR. Several immune genes showed constitutive expression differences among growth response groups, including perforin 1 and C-C motif chemokine 19-like. Among the affected immune pathways, most were up-regulated by Poly I:C and PGN. In response to PGN, the c-type lectin receptor signalling pathway responded uniquely in TF and TR. In response to stimulation with both immune mimics, TR responded more strongly than other groups. Further, group-specific pathway responses to PGN stimulation included NOD-like receptor signalling in W and platelet activation in TR. TF consistently showed the most attenuated immune response relative to W, and more DEGs were apparent in TR than TF and D relative to W, suggesting that a non-satiating ration coupled with elevated circulating GH levels may cause TR to possess enhanced immune capabilities. Alternatively, TF and D salmon are prevented from acquiring the same level of immune response as TR due to direction of energy to high overall somatic growth. Further study of the effects of ration restriction in growth-modified fishes is warranted.ConclusionsThese findings improve our understanding of the pleiotropic effects of growth modification on the immunological responses of fish, revealing unique immune pathway responses depending on the mechanism of growth acceleration and nutritional availability.