bluuebunny/crossref_metadata_2025_split huggingface dataset:
The study of muscle remodeling in Drosophila metamorphosis using in vivo microscopy and bioimage informatics
Authors: Chinta Rambabu , Tan Joo Huang , Wasser Martin 
Published: December 2019

Abstract

Background
Metamorphosis in insects transforms the larval into an adult body plan and comprises the destruction and remodeling of larval and the generation of adult tissues. The remodeling of larval into adult muscles promises to be a genetic model for human atrophy since it is associated with dramatic alteration in cell size. Furthermore, muscle development is amenable to 3D in vivo microscopy at high cellular resolution. However, multi-dimensional image acquisition leads to sizeable amounts of data that demand novel approaches in image processing and analysis.

Results
To handle, visualize and quantify time-lapse datasets recorded in multiple locations, we designed a workflow comprising three major modules. First, the previously introduced TLM-converter concatenates stacks of single time-points. The second module, TLM-2D-Explorer, creates maximum intensity projections for rapid inspection and allows the temporal alignment of multiple datasets. The transition between prepupal and pupal stage serves as reference point to compare datasets of different genotypes or treatments. We demonstrate how the temporal alignment can reveal novel insights into the east gene which is involved in muscle remodeling. The third module, TLM-3D-Segmenter, performs semi-automated segmentation of selected muscle fibers over multiple frames. 3D image segmentation consists of 3 stages. First, the user places a seed into a muscle of a key frame and performs surface detection based on level-set evolution. Second, the surface is propagated to subsequent frames. Third, automated segmentation detects nuclei inside the muscle fiber. The detected surfaces can be used to visualize and quantify the dynamics of cellular remodeling. To estimate the accuracy of our segmentation method, we performed a comparison with a manually created ground truth. Key and predicted frames achieved a performance of 84% and 80%, respectively.

Conclusions
We describe an analysis pipeline for the efficient handling and analysis of time-series microscopy data that enhances productivity and facilitates the phenotypic characterization of genetic perturbations. Our methodology can easily be scaled up for genome-wide genetic screens using readily available resources for RNAi based gene silencing in Drosophila and other animal models.
TranSeqAnnotator: large-scale analysis of transcriptomic data
Authors: Menon Ranjeeta , Garg Gagan , Gasser Robin B , Ranganathan Shoba 
Published: December 2019

Abstract

Background
The transcriptome of an organism can be studied with the analysis of expressed sequence tag (EST) data sets that offers a rapid and cost effective approach with several new and updated bioinformatics approaches and tools for assembly and annotation. The comprehensive analyses comprehend an organism along with the genome and proteome analysis. With the advent of large-scale sequencing projects and generation of sequence data at protein and cDNA levels, automated analysis pipeline is necessary to store, organize and annotate ESTs.

Results
TranSeqAnnotator is a workflow for large-scale analysis of transcriptomic data with the most appropriate bioinformatics tools for data management and analysis. The pipeline automatically cleans, clusters, assembles and generates consensus sequences, conceptually translates these into possible protein products and assigns putative function based on various DNA and protein similarity searches. Excretory/secretory (ES) proteins inferred from ESTs/short reads are also identified. The TranSeqAnnotator accepts FASTA format raw and quality ESTs along with protein and short read sequences and are analysed with user selected programs. After pre-processing and assembly, the dataset is annotated at the nucleotide, protein and ES protein levels.

Conclusion
TranSeqAnnotator has been developed in a Linux cluster, to perform an exhaustive and reliable analysis and provide detailed annotation. TranSeqAnnotator outputs gene ontologies, protein functional identifications in terms of mapping to protein domains and metabolic pathways. The pipeline is applied to annotate large EST datasets to identify several novel and known genes with therapeutic experimental validations and could serve as potential targets for parasite intervention. TransSeqAnnotator is freely available for the scientific community at http://estexplorer.biolinfo.org/TranSeqAnnotator/.
Matching phosphorylation response patterns of antigen-receptor-stimulated T cells via flow cytometry
Authors: Azad Ariful , Pyne Saumyadipta , Pothen Alex 
Published: December 2019

AbstractBackgroundWhen flow cytometric data on mixtures of cell populations are collected from samples under different experimental conditions, computational methods are needed (a) to classify the samples into similar groups, and (b) to characterize the changes within the corresponding populations due to the different conditions. Manual inspection has been used in the past to study such changes, but high-dimensional experiments necessitate developing new computational approaches to this problem. A robust solution to this problem is to construct distinct templates to summarize all samples from a class, and then to compare these templates to study the changes across classes or conditions.ResultsWe designed a hierarchical algorithm,flowMatch, to first match the corresponding clusters across samples for producing robust meta-clusters, and to then construct a high-dimensional template as a collection of meta-clusters for each class of samples. We applied the algorithm on flow cytometry data obtained from human blood cells before and after stimulation with anti-CD3 monoclonal antibody, which is reported to change phosphorylation responses of memory and naive T cells. TheflowMatchalgorithm is able to construct representative templates from the samples before and after stimulation, and to match corresponding meta-clusters across templates. The templates of the pre-stimulation and post-stimulation data corresponding to memory and naive T cell populations clearly show, at the level of the meta-clusters, the overall phosphorylation shift due to the stimulation.ConclusionsWe concisely represent each class of samples by a template consisting of a collection of meta-clusters (representative abstract populations). UsingflowMatch, the meta-clusters across samples can be matched to assess overall differences among the samples of various phenotypes or time-points.
Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE)
Authors: Chowdhury Sharif , Kandhavelu Meenakshisundaram , Yli-Harja Olli , Ribeiro Andre S 
Published: December 2019

AbstractBackgroundCell imaging is becoming an indispensable tool for cell and molecular biology research. However, most processes studied are stochastic in nature, and require the observation of many cells and events. Ideally, extraction of information from these images ought to rely on automatic methods. Here, we propose a novel segmentation method, MAMLE, for detecting cells within dense clusters.MethodsMAMLE executes cell segmentation in two stages. The first relies on state of the art filtering technique, edge detection in multi-resolution with morphological operator and threshold decomposition for adaptive thresholding. From this result, a correction procedure is applied that exploits maximum likelihood estimate as an objective function. Also, it acquires morphological features from the initial segmentation for constructing the likelihood parameter, after which the final segmentation is obtained.ConclusionsWe performed an empirical evaluation that includes sample images from different imaging modalities and diverse cell types. The new method attained very high (above 90%) cell segmentation accuracy in all cases. Finally, its accuracy was compared to several existing methods, and in all tests, MAMLE outperformed them in segmentation accuracy.
Widespread evidence of viral miRNAs targeting host pathways
Authors: Carl Joseph W , Trgovcich Joanne , Hannenhalli Sridhar 
Published: December 2019

Abstract

Background
MicroRNAs (miRNA) are regulatory genes that target and repress other RNA molecules via sequence-specific binding. Several biological processes are regulated across many organisms by evolutionarily conserved miRNAs. Plants and invertebrates employ their miRNA in defense against viruses by targeting and degrading viral products. Viruses also encode miRNAs and there is evidence to suggest that virus-encoded miRNAs target specific host genes and pathways that may be beneficial for their infectivity and/or proliferation. However, it is not clear whether there are general patterns underlying cellular targets of viral miRNAs.

Results
Here we show that for several of the 135 known viral miRNAs in human viruses, the human genes targeted by the viral miRNA are enriched for specific host pathways whose targeting is likely beneficial to the virus. Given that viral miRNAs continue to be discovered as technologies evolve, we extended the investigation to 6809 putative miRNAs encoded by 23 human viruses. Our analysis further suggests that human viruses have evolved their miRNA repertoire to target specific human pathways, such as cell growth, axon guidance, and cell differentiation. Interestingly, many of the same pathways are also targeted in mice by miRNAs encoded by murine viruses. Furthermore, Human Cytomegalovirus (CMV) miRNAs that target specific human pathways exhibit increased conservation across CMV strains.

Conclusions
Overall, our results suggest that viruses may have evolved their miRNA repertoire to target specific host pathways as a means for their survival.
An enhanced computational platform for investigating the roles of regulatory RNA and for identifying functional RNA motifs
Authors: Chang Tzu-Hao , Huang Hsi-Yuan , Hsu Justin Bo-Kai , Weng Shun-Long , Horng Jorng-Tzong , Huang Hsien-Da 
Published: December 2019

Abstract

Background
Functional RNA molecules participate in numerous biological processes, ranging from gene regulation to protein synthesis. Analysis of functional RNA motifs and elements in RNA sequences can obtain useful information for deciphering RNA regulatory mechanisms. Our previous work, RegRNA, is widely used in the identification of regulatory motifs, and this work extends it by incorporating more comprehensive and updated data sources and analytical approaches into a new platform.

Methods and results
An integrated web-based system, RegRNA 2.0, has been developed for comprehensively identifying the functional RNA motifs and sites in an input RNA sequence. Numerous data sources and analytical approaches are integrated, and several types of functional RNA motifs and sites can be identified by RegRNA 2.0: (i) splicing donor/acceptor sites; (ii) splicing regulatory motifs; (iii) polyadenylation sites; (iv) ribosome binding sites; (v) rho-independent terminator; (vi) motifs in mRNA 5'-untranslated region (5'UTR) and 3'UTR; (vii) AU-rich elements; (viii) C-to-U editing sites; (ix) riboswitches; (x) RNA cis-regulatory elements; (xi) transcriptional regulatory motifs; (xii) user-defined motifs; (xiii) similar functional RNA sequences; (xiv) microRNA target sites; (xv) non-coding RNA hybridization sites; (xvi) long stems; (xvii) open reading frames; (xviii) related information of an RNA sequence. User can submit an RNA sequence and obtain the predictive results through RegRNA 2.0 web page.

Conclusions
RegRNA 2.0 is an easy to use web server for identifying regulatory RNA motifs and functional sites. Through its integrated user-friendly interface, user is capable of using various analytical approaches and observing results with graphical visualization conveniently. RegRNA 2.0 is now available at http://regrna2.mbc.nctu.edu.tw.
How to inherit statistically validated annotation within BAR+ protein clusters
Authors: Piovesan Damiano , Martelli Pier Luigi , Fariselli Piero , Profiti Giuseppe , Zauli Andrea , Rossi Ivan , Casadio Rita 
Published: December 2019

Abstract

Background
In the genomic era a key issue is protein annotation, namely how to endow protein sequences, upon translation from the corresponding genes, with structural and functional features. Routinely this operation is electronically done by deriving and integrating information from previous knowledge. The reference database for protein sequences is UniProtKB divided into two sections, UniProtKB/TrEMBL which is automatically annotated and not reviewed and UniProtKB/Swiss-Prot which is manually annotated and reviewed. The annotation process is essentially based on sequence similarity search. The question therefore arises as to which extent annotation based on transfer by inheritance is valuable and specifically if it is possible to statistically validate inherited features when little homology exists among the target sequence and its template(s).

Results
In this paper we address the problem of annotating protein sequences in a statistically validated manner considering as a reference annotation resource UniProtKB. The test case is the set of 48,298 proteins recently released by the Critical Assessment of Function Annotations (CAFA) organization. We show that we can transfer after validation, Gene Ontology (GO) terms of the three main categories and Pfam domains to about 68% and 72% of the sequences, respectively. This is possible after alignment of the CAFA sequences towards BAR+, our annotation resource that allows discriminating among statistically validated and not statistically validated annotation. By comparing with a direct UniProtKB annotation, we find that besides validating annotation of some 78% of the CAFA set, we assign new and statistically validated annotation to 14.8% of the sequences and find new structural templates for about 25% of the chains, half of which share less than 30% sequence identity to the corresponding template/s.

Conclusion
Inheritance of annotation by transfer generally requires a careful selection of the identity value among the target and the template in order to transfer structural and/or functional features. Here we prove that even distantly remote homologs can be safely endowed with structural templates and GO and/or Pfam terms provided that annotation is done within clusters collecting cluster-related protein sequences and where a statistical validation of the shared structural and functional features is possible.
The phenylalanine ammonia lyase (PAL) gene family shows a gymnosperm-specific lineage
Authors: Bagal Ujwal R , Leebens-Mack James H , Lorenz W Walter , Dean Jeffrey FD 
Published: December 2019

AbstractBackgroundPhenylalanine ammonia lyase (PAL) is a key enzyme of the phenylpropanoid pathway that catalyzes the deamination of phenylalanine to trans-cinnamic acid, a precursor for the lignin and flavonoid biosynthetic pathways. To date, PAL genes have been less extensively studied in gymnosperms than in angiosperms. Our interest in PAL genes stems from their potential role in the defense responses ofPinus taeda, especially with respect to lignification and production of low molecular weight phenolic compounds under various biotic and abiotic stimuli. In contrast to all angiosperms for which reference genome sequences are available,P. taedahas previously been characterized as having only a single PAL gene. Our objective was to re-evaluate this finding, assess the evolutionary history of PAL genes across major angiosperm and gymnosperm lineages, and characterize PAL gene expression patterns inPinus taeda.MethodsWe compiled a large set of PAL genes from the largest transcript dataset available forP. taedaand other conifers. The transcript assemblies forP. taedawere validated through sequencing of PCR products amplified using gene-specific primers based on the putative PAL gene assemblies. Verified PAL gene sequences were aligned and a gene tree was estimated. The resulting gene tree was reconciled with a known species tree and the time points for gene duplication events were inferred relative to the divergence of major plant lineages.ResultsIn contrast to angiosperms, gymnosperms have retained a diverse set of PAL genes distributed among three major clades that arose from gene duplication events predating the divergence of these two seed plant lineages. Whereas multiple PAL genes have been identified in sequenced angiosperm genomes, all characterized angiosperm PAL genes form a single clade in the gene PAL tree, suggesting they are derived from a single gene in an ancestral angiosperm genome. The five distinct PAL genes detected and verified inP. taedawere derived from a combination of duplication events predating and postdating the divergence of angiosperms and gymnosperms.ConclusionsGymnosperms have a more phylogenetically diverse set of PAL genes than angiosperms. This inference has contrasting implications for the evolution of PAL gene function in gymnosperms and angiosperms.
A Bayesian decision fusion approach for microRNA target prediction
Authors: Yue Dong , Guo Maozu , Chen Yidong , Huang Yufei 
Published: December 2019

Abstract
MicroRNAs (miRNAs) are 19-25 nucleotides non-coding RNAs known to have important post-transcriptional regulatory functions. The computational target prediction algorithm is vital to effective experimental testing. However, since different existing algorithms rely on different features and classifiers, there is a poor agreement among the results of different algorithms. To benefit from the advantages of different algorithms, we proposed an algorithm called BCmicrO that combines the prediction of different algorithms with Bayesian Network. BCmicrO was evaluated using the training data and the proteomic data. The results show that BCmicrO improves both the sensitivity and the specificity of each individual algorithm. All the related materials including genome-wide prediction of human targets and a web-based tool are available at http://compgenomics.utsa.edu/gene/gene_1.php.
Automatically clustering large-scale miRNA sequences: methods and experiments
Authors: Wan Linxia , Ding Jiandong , Jin Ting , Guan Jihong , Zhou Shuigeng 
Published: December 2019

Abstract

Background
Since the initial annotation of microRNAs (miRNAs) in 2001, many studies have sought to identify additional miRNAs experimentally or computationally in various species. MiRNAs act with the Argonaut family of proteins to regulate target messenger RNAs (mRNAs) post-transcriptionally. Currently, researches mainly focus on single miRNA function study. Considering that members in the same miRNA family might participate in the same pathway or regulate the same target(s) and thus share similar biological functions, people can explore useful knowledge from high quality miRNA family architecture.

Results
In this article, we developed an unsupervised clustering-based method miRCluster to automatically group miRNAs. In order to evaluate this method, several data sets were constructed from the online database miRBase. Results showed that miRCluster can efficiently arrange miRNAs (e.g identify 354 families in miRBase16 with an accuracy of 92.08%, and can recognize 9 of all 10 newly-added families in miRBase 17). By far, ~30% mature miRNAs registered in miRBase are unclassified. With miRCluster, over 85% unclassified miRNAs can be assigned to certain families, while ~44% of these miRNAs distributed in ~300novel families.

Conclusions
In short, miRCluster is an automatic and efficient miRNA family identification method, which does not require any prior knowledge. It can be helpful in real use, especially when exploring functions of novel miRNAs. All relevant materials could be freely accessed online (http://admis.fudan.edu.cn/projects/miRCluster).
Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes
Authors: Trimarchi Michael P , Murphy Mark , Frankhouser David , Rodriguez Benjamin AT , Curfman John , Marcucci Guido , Yan Pearlly , Bundschuh Ralf 
Published: December 2019

Abstract

Background
DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized.

Methods
We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status.

Results
We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level.

Conclusions
We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation.
FitSearch: a robust way to interpret a yeast fitness profile in terms of drug's mode-of-action
Authors: Lee Minho , Han Sangjo , Chang Hyeshik , Kwak Youn-Sig , Weller David M , Kim Dongsup 
Published: December 2019

Abstract

Background
Yeast deletion-mutant collections have been successfully used to infer the mode-of-action of drugs especially by profiling chemical-genetic and genetic-genetic interactions on a genome-wide scale. Although tens of thousands of those profiles are publicly available, a lack of an accurate method for mining such data has been a major bottleneck for more widespread use of these useful resources.

Results
For general usage of those public resources, we designed FitRankDB as a general repository of fitness profiles, and developed a new search algorithm, FitSearch, for identifying the profiles that have a high similarity score with statistical significance for a given fitness profile. We demonstrated that our new repository and algorithm are highly beneficial to researchers who attempting to make hypotheses based on unknown modes-of-action of bioactive compounds, regardless of the types of experiments that have been performed using yeast deletion-mutant collection in various types of different measurement platforms, especially non-chip-based platforms.

Conclusions
We showed that our new database and algorithm are useful when attempting to construct a hypothesis regarding the unknown function of a bioactive compound through small-scale experiments with a yeast deletion collection in a platform independent manner. The FitRankDB and FitSearch enhance the ease of searching public yeast fitness profiles and obtaining insights into unknown mechanisms of action of drugs. FitSearch is freely available at http://fitsearch.kaist.ac.kr.