bluuebunny/crossref_metadata_2025_split huggingface dataset:
Survey of metaproteomics software tools for functional microbiome analysis
Authors: Sajulga R. , Easterly C. , Riffle M. , Mesuere B. , Muth T. , Mehta S. , Kumar P. , Johnson J. , Gruening B. , Schiebenhoefer H. , Kolmeder C. A. , Fuchs S. , Nunn B. L. , Rudney J. , Griffin T. J. , Jagtap P. D. 
Published: January 2020

AbstractTo gain a thorough appreciation of microbiome dynamics, researchers characterize the functional role of expressed microbial genes/proteins. This can be accomplished through metaproteomics, which characterizes the protein complement of the microbiome. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, so that researchers can make informed decisions regarding software choice based on their research goals.Tandem mass spectrometry-based proteomic data obtained from dental carie plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! Tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN6, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms.Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the Universal Protein Knowledgebase revealed that the sensitivity and specificity of functional annotation differed between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated the most precise GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.
Macroevolutionary analysis of discrete traits with rate heterogeneity
Authors: Grundler Michael C. , Rabosky Daniel L. 
Published: January 2020

AbstractOrganismal traits show dramatic variation in phylogenetic patterns of origin and loss across the Tree of Life. Understanding the causes and consequences of this variation depends critically on accounting for heterogeneity in rates of trait evolution among lineages. Here, we describe a method for modeling among-lineage evolutionary rate heterogeneity in a trait with two discrete states. The method assumes that the present-day distribution of a binary trait is shaped by a mixture of stochastic processes in which the rate of evolution varies among lineages in a phylogeny. The number and location of rate changes, which we refer to as rate-shift events, are inferred automatically from the data. Simulations reveal that the method accurately reconstructs rates of trait evolution and ancestral character states even when simulated data violate model assumptions. We apply the method to an empirical dataset of mimetic coloration in snakes and find elevated rates of trait evolution in two clades of harmless snakes that are broadly sympatric with dangerously venomous New World coral snakes, recapitulating an earlier analysis of the same dataset. Although the method performed well on many simulated data sets, we caution that overall power for inferring heterogeneous dynamics of single binary traits is low.
Preliminary Evaluation of the Utility of Deep Generative Histopathology Image Translation at a Mid-Sized NCI Cancer Center
Authors: Levy Joshua J. , Jackson Christopher R. , Sriharan Aravindhan , Christensen Brock C. , Vaickus Louis J. 
Published: January 2020

AbstractEvaluation of a tissue biopsy is often required for the diagnosis and prognostic staging of a disease. Recent efforts have sought to accurately quantitate the distribution of tissue features and morphology in digitized images of histological tissue sections, Whole Slide Images (WSI). Generative modeling techniques present a unique opportunity to produce training data that can both augment these models and translate histologic data across different intra-and-inter-institutional processing procedures, provide cost-effective ways to perform computational chemical stains (synthetic stains) on tissue, and facilitate the creation of diagnostic aid algorithms. A critical evaluation and understanding of these technologies is vital for their incorporation into a clinical workflow. We illustrate several potential use cases of these techniques for the calculation of nuclear to cytoplasm ratio, synthetic SOX10 immunohistochemistry (IHC, sIHC) staining to delineate cell lineage, and the conversion of hematoxylin and eosin (H&E) stain to trichome stain for the staging of liver fibrosis.
Anomalous reverse transcription through chemical modifications in polyadenosine stretches
Authors: Kladwang Wipapat , Topkar Ved V. , Liu Bei , Hodges Tracy L. , Keane Sarah C. , al-Hashimi Hashim , Das Rhiju 
Published: January 2020

AbstractThermostable reverse transcriptases are workhorse enzymes underlying nearly all modern techniques for RNA structure mapping and for transcriptome-wide discovery of RNA chemical modifications. Despite their wide use, these enzymes’ behaviors at chemical modified nucleotides remain poorly understood. Wellington-Oguri et al. recently reported an apparent loss of chemical modification within putatively unstructured polyadenosine stretches modified by dimethyl sulfate or 2’ hydroxyl acylation, as probed by reverse transcription. Here, re-analysis of these and other publicly available data, capillary electrophoresis experiments on chemically modified RNAs, and nuclear magnetic resonance spectroscopy on A12and variants show that this effect is unlikely to arise from an unusual structure of polyadenosine. Instead, tests of different reverse transcriptases on chemically modified RNAs and molecules synthesized with single 1-methyladenosines implicate a previously uncharacterized reverse transcriptase behavior: near-quantitative bypass through chemical modifications within polyadenosine stretches. All tested natural and engineered reverse transcriptases (MMLV; SuperScript II, III, and IV; TGIRT-III; and MarathonRT) exhibit this anomalous bypass behavior. Accurate DMS-guided structure modeling of the polyadenylated HIV-1 3’ untranslated region RNA requires taking into account this anomaly. Our results suggest that poly(rA-dT) hybrid duplexes can trigger unexpectedly effective reverse transcriptase bypass and that chemical modifications in poly(A) mRNA tails may be generally undercounted.
Enhanced triacylglycerol (TAG) and protein accumulation in transgenic diatomThalassiosira pseudonanawith altered photosynthetic pigmentation
Authors: Gaidarenko Olga , Yee Daniel P. , Hildebrand Mark 
Published: January 2020

ABSTRACTMicroalgal productivity in mass cultures is limited by the inefficiency with which available light energy is utilized. In dense cultures, cells closest to the light source absorb more light energy than they can use and dissipate the excess, while light penetrance into the culture is steeply attenuated. Reducing microalgal light harvesting and/or dissipating capacity per cell may improve total light utilization efficiency in mass cultures. In this study, two transgenic lines of the diatomThalassiosira pseudonanawith altered photosynthetic pigment content are evaluated with respect to photosynthetic parameters, growth, and macromolecule accumulation. In one line, violaxanthin de-epoxidase-like 2 (VDL2) is overexpressed (OE), resulting in a reduction of the diadinoxanthin cycle pigments, which are involved in light energy dissipation (non-photochemical quenching, NPQ), accompanied by a stoichiometric increase in the light-harvesting pigment fucoxanthin. No differences in the maximum potential quantum yield of photosystem II (Fv/Fm) or light-limited photosynthetic rate (α) were found. However, when adapted to 30 µmol photons m−2sec−1, the VDL2 OE maximum relative electron transport rate (rETRmax) upon exposure to saturating light intensities was 86-95% of wild type (WT). When adapted to 300 µmol photons m−2sec−1, VDL2 OE saturated photosynthesis at 62-71% of the light intensity needed to saturate WT (Ek). NPQ was substantially lower at and below 300 µmol photons m−2sec−1. VDL2 OE accumulated up to 3.4 times as much triacylglycerol (TAG) as WT during exponential growth, and up to twice as much protein. Growth in terms of culture density was up to 7% slower. TAG and protein accumulation inversely correlated with NPQ. The second line evaluated was obtained by using antisense RNA to simultaneously silence or knock down (KD) both LUT1-like (LTL) genes, hypothesized to catalyze an intermediate carotenoid biosynthesis step of converting β-carotene to zeaxanthin. Overall reduction of photosynthetic pigment content without altering the relative abundance of individual pigments resulted. No significant differences in photosynthetic parameters compared to WT were found. LTL KD grew at a rate comparable to WT and accumulated up to 40% more TAG during exponential growth, while protein content was reduced by 11-19%. LTL KD cells were elongated and 5-10% smaller than WT, and cultures contained auxospores, indicating stress that may relate to a cell cycle progression defect.
TLmutation: predicting the effects of mutations using transfer learning
Authors: Shamsi Zahra , Chan Matthew , Shukla Diwakar 
Published: January 2020

AbstractA reoccurring challenge in bioinformatics is predicting the phenotypic consequence of amino acid variation in proteins. With the recent advancements in sequencing techniques, sufficient genomic data has become available to train models that predict the evolutionary statistical energies, but there is still inadequate experimental data to directly predict functional effects. One approach to overcome this data scarcity is to apply transfer learning and train more models with available datasets. In this study, we propose a set of transfer learning algorithms we call TLmutation, which implements a supervised transfer learning algorithm that transfers knowledge from survival data of a protein to a particular function of that protein. This is followed by an unsupervised transfer learning algorithm that extends the knowledge to a homologous protein. We explore the application of our algorithms in three cases. First, we test the supervised transfer on 17 previously published deep mutagenesis datasets to complete and refine missing datapoints. We further investigate these datasets to identify which mutations build better predictors of variant functions. In the second case, we apply the algorithm to predict higher-order mutations solely from single point mutagenesis data. Finally, we perform the unsupervised transfer learning algorithm to predict mutational effects of homologous proteins from experimental datasets. These algorithms are generalized to transfer knowledge between Markov random field models. We show the benefit of our transfer learning algorithms to utilize informative deep mutational data and provide new insights into protein variant functions. As these algorithms are generalized to transfer knowledge between Markov random field models, we expect these algorithms to be applicable to other disciplines.
Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
Authors: Chu Tinyi , Wang Zhong , Pe’er Dana , Danko Charles G. 
Published: January 2020

AbstractUnderstanding the interactions between cells in their environment is a major challenge in genomics. Here we developed BayesPrism, a Bayesian method to jointly predict cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 1,412 bulk RNA-seq samples in primary glioblastoma, head and neck squamous cell carcinoma, and melanoma using single-cell datasets of 85 patients. We identified cell types correlated with clinical outcomes and explored spatial heterogeneity in malignant cell states and non-malignant cell type composition. We refined subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T cells, fibroblasts, and endothelial cells across multiple tumor types. Our work introduces a new lens that uses scRNA-seq to accurately infer cellular composition and expression in large cohorts of bulk data.
General DNA methylation patterns and environmentally-induced differential methylation in the eastern oyster (Crassostrea virginica)
Authors: Venkataraman Yaamini R. , Downey-Wall Alan M. , Ries Justin , Westfield Isaac , White Samuel J. , Roberts Steven B. , Lotterhos Kathleen E. 
Published: January 2020

AbstractEpigenetic modification, specifically DNA methylation, is one possible mechanism for intergenerational plasticity. Before inheritance of methylation patterns can be characterized, we need a better understanding of how environmental change modifies the parental epigenome. To examine the influence of experimental ocean acidification on eastern oyster (Crassostrea virginica) gonad tissue, oysters were cultured in the laboratory under control (491 ± 49 μatm) or high (2550 ± 211 μatm) pCO2 conditions for four weeks. DNA from reproductive tissue was isolated from five oysters per treatment, then subjected to bisulfite treatment and DNA sequencing. Irrespective of treatment, DNA methylation was primarily found in gene bodies with approximately 22% of CpGs (2.7% of total cytosines) in the C. virginica genome predicted to be methylated. In response to elevated pCO2, we found 598 differentially methylated loci primarily overlapping with gene bodies. A majority of differentially methylated loci were in exons (61.5%) with less intron overlap (31.9%). While there was no evidence of a significant tendency for the genes with differentially methylated loci to be associated with distinct biological processes, the concentration of these loci in gene bodies, including genes involved in protein ubiquitination and biomineralization suggests DNA methylation may be important for transcriptional control in response to ocean acidification. Changes in gonad methylation also indicate potential for these methylation patterns to be inherited by offspring. Understanding how experimental ocean acidification conditions modify the oyster epigenome, and if these modifications are inherited, allows for a better understanding of how ecosystems will respond to environmental change.
A validation framework for neuroimaging software: the case of population receptive fields
Authors: Lerma-Usabiaga Garikoitz , Benson Noah , Winawer Jonathan , Wandell Brian A. 
Published: January 2020

AbstractNeuroimaging software methods are complex, making it a near certainty that some implementations will contain errors. Modern computational techniques (i.e., public code and data repositories, continuous integration, containerization) enable the reproducibility of the analyses and reduce coding errors, but they do not guarantee the scientific validity of the results. It is difficult, nay impossible, for researchers to check the accuracy of software by reading the source code; ground truth test datasets are needed. Computational reproducibility means providing software so that for the same input anyone obtains the same result, right or wrong. Computational validity means obtaining the right result for the ground-truth test data. We describe a framework for validating and sharing software implementations. We apply the framework to an application: population receptive field (pRF) methods for functional MRI data. The framework is composed of three main components implemented with containerization methods to guarantee computational reproducibility: (1) synthesis of fMRI time series from ground-truth pRF parameters, (2) implementation of four public pRF analysis tools and standardization of inputs and outputs, and (3) report creation to compare the results with the ground truth parameters. We identified realistic conditions that lead to imperfect parameter recovery in all four implementations, and we provide means to reduce this problem. The computational validity framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. The framework and methods can be extended to other critical neuroimaging algorithms. Having validation frameworks help (1) developers to build new software, (2) research scientists to verify the software’s accuracy, and (3) reviewers to evaluate the methods used in publications and grants.Author SummaryComputer science provides powerful tools and techniques for implementing and deploying software. These techniques support software collaboration, reduce coding errors and enable reproducibility of the analyses. A further question is whether the software estimates are correct (valid). We describe a framework for validating and sharing software implementations based on ground-truth testing. We applied the framework to four separate applications that implemented population receptive field (pRF) estimates for functional MRI data. We quantified the validity, and we also documented limitations with these applications. Finally, we provide ways to mitigate these limitations. Implementing a software validation framework along with sharing and reproducibility is an important step for the complex methods used in neuroscience. Validation will help developers to build new software, researchers verify that the results are valid, and reviewers to evaluate the precision of methods in publications and grants.
Spinal dI2 interneurons regulate the stability of bipedal stepping
Authors: Haimson Baruch , Hadas Yoav , Kania Artur , Daley Monica , Cinnamon Yuval , Lev-Tov Aharon , Klar Avihu 
Published: January 2020

SummaryPeripheral and intraspinal feedback is required to shape and update the output of spinal networks that execute motor behavior. We report that lumbar dI2 spinal interneurons of the chick receive synaptic input from afferents and pre-motoneurons. They innervate contralateral premotor networks in the lumbar and brachial spinal cord and their ascending projections innervate the cerebellum. These findings suggest that dI2 neurons function as interneurons in local lumbar circuits and involved in lumbo-brachial coupling and that part of them deliver peripheral and intraspinal feedback to the cerebellum. Silencing of dI2 neurons leads to destabilized stepping in P8 hatchlings with occasional collapses, variable step-profiles and wide-base walking, suggesting that the dI2 neurons may contribute to stabilization of the bipedal gait.
The Gene-Rich Genome of the ScallopPecten maximus
Authors: Kenny Nathan J , McCarthy Shane A , Dudchenko Olga , James Katherine , Betteridge Emma , Corton Craig , Dolucan Jale , Mead Dan , Oliver Karen , Omer Arina D , Pelan Sarah , Ryan Yan , Sims Ying , Skelton Jason , Smith Michelle , Torrance James , Weisz David , Wipat Anil , Aiden Erez L , Howe Kerstin , Williams Suzanne T 
Published: January 2020

AbstractBackgroundThe King Scallop,Pecten maximus, is distributed in shallow waters along the Atlantic coast of Europe. It forms the basis of a valuable commercial fishery and its ubiquity means that it plays a key role in coastal ecosystems and food webs. Like other filter feeding bivalves it can accumulate potent phytotoxins, to which it has evolved some immunity. The molecular origins of this immunity are of interest to evolutionary biologists, pharmaceutical companies and fisheries management.FindingsHere we report the genome sequencing of this species, conducted as part of the Wellcome Sanger 25 Genomes Project. This genome was assembled from PacBio reads and scaffolded with 10x Chromium and Hi-C data, and its 3,983 scaffolds have an N50 of 44.8 Mb (longest scaffold 60.1 Mb), with 92% of the assembly sequence contained in 19 scaffolds, corresponding to the 19 chromosomes found in this species. The total assembly spans 918.3 Mb, and is the best-scaffolded marine bivalve genome published to date, exhibiting 95.5% recovery of the metazoan BUSCO set. Gene annotation resulted in 67,741 gene models. Analysis of gene content revealed large numbers of gene duplicates, as previously seen in bivalves, with little gene loss, in comparison with the sequenced genomes of other marine bivalve species.ConclusionsThe genome assembly ofPecten maximusand its annotated gene set provide a high-quality platform for a wide range of investigations, including studies on such disparate topics as shell biomineralization, pigmentation, vision and resistance to algal toxins. As a result of our findings we highlight the sodium channel geneNav1, known as a gene conferring resistance to saxitoxin and tetrodotoxin, as a candidate for further studies investigating immunity to domoic acid.
Word predictability, prosody, gesture and mouth movements in face-to-face language comprehension
Authors: Zhang Ye , Frassinelli Diego , Tuomainen Jyrki , Skipper Jeremy I , Vigliocco Gabriella 
Published: January 2020

AbstractThe ecology of human language is face-to-face interaction, comprising cues, like prosody, cospeech gestures, and mouth movements. Yet, the multimodal context is usually stripped away in experiments as dominant paradigms focus on linguistic processing only. In two studies we presented video-clips of an actress producing naturalistic passages to participants whose electroencephalographic activity was recorded. We quantified each cue and determined their effect on a well-established electroencephalographic marker of cognitive load in comprehension (N400). We found that brain responses to words were affected by informativeness of co-occurring multimodal cues, indicating that comprehension relies on linguistic and non-linguistic cues. Moreover, brain responses were affected by interactions between the multimodal cues, indicating that the impact of each cue dynamically changes based on the informativeness of other available cues. Thus, results show that multimodal cues are integral to comprehension, hence, our theories must move beyond the limited focus on speech and linguistic processing.