scholarly journals Decontamination, pooling and dereplication of the 678 samples of the Marine Microbial Eukaryote Transcriptome Sequencing Project

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Mick Van Vlierberghe ◽  
Arnaud Di Franco ◽  
Hervé Philippe ◽  
Denis Baurain
Keyword(s):  
Transcriptome Sequencing ◽  
Large Scale ◽  
High Quality ◽  
Data Description ◽  
Sequencing Project ◽  
Microbial Eukaryote ◽  
Photosynthetic Organisms ◽  
Tree Inference ◽  
Context Tree ◽  
Phylogenomic Analyses

Abstract Objectives Complex algae are photosynthetic organisms resulting from eukaryote-to-eukaryote endosymbiotic-like interactions. Yet the specific lineages and mechanisms are still under debate. That is why large scale phylogenomic studies are needed. Whereas available proteomes provide a limited diversity of complex algae, MMETSP (Marine Microbial Eukaryote Transcriptome Sequencing Project) transcriptomes represent a valuable resource for phylogenomic analyses, owing to their broad and rich taxonomic sampling, especially of photosynthetic species. Unfortunately, this sampling is unbalanced and sometimes highly redundant. Moreover, we observed contaminated sequences in some samples. In such a context, tree inference and readability are impaired. Consequently, the aim of the data processing reported here is to release a unique set of clean and non-redundant transcriptomes produced through an original protocol featuring decontamination, pooling and dereplication steps. Data description We submitted 678 MMETSP re-assembly samples to our parallel consolidation pipeline. Hence, we combined 423 samples into 110 consolidated transcriptomes, after the systematic removal of the most contaminated samples (186). This approach resulted in a total of 224 high-quality transcriptomes, easy to use and suitable to compute less contaminated, less redundant and more balanced phylogenies.

Tiered Sampling

2021 ◽  
Vol 15 (5) ◽  
pp. 1-52
Author(s):  
Lorenzo De Stefani ◽  
Erisa Terolli ◽  
Eli Upfal
Keyword(s):  
Large Scale ◽  
Analysis Of Algorithms ◽  
Base Layer ◽  
Single Edge ◽  
Real World Data ◽  
High Quality ◽  
Large Graphs ◽  
Massive Graphs ◽  
Variance Estimate ◽  
Low Probability

We introduce Tiered Sampling , a novel technique for estimating the count of sparse motifs in massive graphs whose edges are observed in a stream. Our technique requires only a single pass on the data and uses a memory of fixed size M , which can be magnitudes smaller than the number of edges. Our methods address the challenging task of counting sparse motifs—sub-graph patterns—that have a low probability of appearing in a sample of M edges in the graph, which is the maximum amount of data available to the algorithms in each step. To obtain an unbiased and low variance estimate of the count, we partition the available memory into tiers (layers) of reservoir samples. While the base layer is a standard reservoir sample of edges, other layers are reservoir samples of sub-structures of the desired motif. By storing more frequent sub-structures of the motif, we increase the probability of detecting an occurrence of the sparse motif we are counting, thus decreasing the variance and error of the estimate. While we focus on the designing and analysis of algorithms for counting 4-cliques, we present a method which allows generalizing Tiered Sampling to obtain high-quality estimates for the number of occurrence of any sub-graph of interest, while reducing the analysis effort due to specific properties of the pattern of interest. We present a complete analytical analysis and extensive experimental evaluation of our proposed method using both synthetic and real-world data. Our results demonstrate the advantage of our method in obtaining high-quality approximations for the number of 4 and 5-cliques for large graphs using a very limited amount of memory, significantly outperforming the single edge sample approach for counting sparse motifs in large scale graphs.

scholarly journals Large-Scale and Deep-Seated Gravitational Slope Deformations on Mars: A Review

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 174
Author(s):  
Marco Emanuele Discenza ◽  
Carlo Esposito ◽  
Goro Komatsu ◽  
Enrico Miccadei
Keyword(s):  
Large Scale ◽  
High Quality ◽  
Surface Data ◽  
Geomorphological Processes ◽  
Gravitational Processes ◽  
Quality Surface ◽  
High Quality Surface ◽  
Mars Missions

The availability of high-quality surface data acquired by recent Mars missions and the development of increasingly accurate methods for analysis have made it possible to identify, describe, and analyze many geological and geomorphological processes previously unknown or unstudied on Mars. Among these, the slow and large-scale slope deformational phenomena, generally known as Deep-Seated Gravitational Slope Deformations (DSGSDs), are of particular interest. Since the early 2000s, several studies were conducted in order to identify and analyze Martian large-scale gravitational processes. Similar to what happens on Earth, these phenomena apparently occur in diverse morpho-structural conditions on Mars. Nevertheless, the difficulty of directly studying geological, structural, and geomorphological characteristics of the planet makes the analysis of these phenomena particularly complex, leaving numerous questions to be answered. This paper reports a synthesis of all the known studies conducted on large-scale deformational processes on Mars to date, in order to provide a complete and exhaustive picture of the phenomena. After the synthesis of the literature studies, the specific characteristics of the phenomena are analyzed, and the remaining main open issued are described.

57 Precision neoantigen discovery using novel algorithms and expanded HLA-ligandome datasets

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A62-A62
Author(s):  
Dattatreya Mellacheruvu ◽  
Rachel Pyke ◽  
Charles Abbott ◽  
Nick Phillips ◽  
Sejal Desai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Lines ◽  
Antigen Processing ◽  
Large Scale ◽  
Prediction Models ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
High Quality ◽  
Tissue Samples

BackgroundAccurately identified neoantigens can be effective therapeutic agents in both adjuvant and neoadjuvant settings. A key challenge for neoantigen discovery has been the availability of accurate prediction models for MHC peptide presentation. We have shown previously that our proprietary model based on (i) large-scale, in-house mono-allelic data, (ii) custom features that model antigen processing, and (iii) advanced machine learning algorithms has strong performance. We have extended upon our work by systematically integrating large quantities of high-quality, publicly available data, implementing new modelling algorithms, and rigorously testing our models. These extensions lead to substantial improvements in performance and generalizability. Our algorithm, named Systematic HLA Epitope Ranking Pan Algorithm (SHERPA™), is integrated into the ImmunoID NeXT Platform®, our immuno-genomics and transcriptomics platform specifically designed to enable the development of immunotherapies.MethodsIn-house immunopeptidomic data was generated using stably transfected HLA-null K562 cells lines that express a single HLA allele of interest, followed by immunoprecipitation using W6/32 antibody and LC-MS/MS. Public immunopeptidomics data was downloaded from repositories such as MassIVE and processed uniformly using in-house pipelines to generate peptide lists filtered at 1% false discovery rate. Other metrics (features) were either extracted from source data or generated internally by re-processing samples utilizing the ImmunoID NeXT Platform.ResultsWe have generated large-scale and high-quality immunopeptidomics data by using approximately 60 mono-allelic cell lines that unambiguously assign peptides to their presenting alleles to create our primary models. Briefly, our primary ‘binding’ algorithm models MHC-peptide binding using peptide and binding pockets while our primary ‘presentation’ model uses additional features to model antigen processing and presentation. Both primary models have significantly higher precision across all recall values in multiple test data sets, including mono-allelic cell lines and multi-allelic tissue samples. To further improve the performance of our model, we expanded the diversity of our training set using high-quality, publicly available mono-allelic immunopeptidomics data. Furthermore, multi-allelic data was integrated by resolving peptide-to-allele mappings using our primary models. We then trained a new model using the expanded training data and a new composite machine learning architecture. The resulting secondary model further improves performance and generalizability across several tissue samples.ConclusionsImproving technologies for neoantigen discovery is critical for many therapeutic applications, including personalized neoantigen vaccines, and neoantigen-based biomarkers for immunotherapies. Our new and improved algorithm (SHERPA) has significantly higher performance compared to a state-of-the-art public algorithm and furthers this objective.

scholarly journals A Novel and Efficient High-Yield Method for Preparing Bacterial Ghosts

Toxins ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 420
Author(s):  
Yi Ma ◽  
Liu Cui ◽  
Meng Wang ◽  
Qiuli Sun ◽  
Kaisheng Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Expression System ◽  
High Yield ◽  
Wild Type ◽  
High Quality ◽  
Efficient Protection ◽  
Bacterial Ghosts ◽  
Cell Envelopes ◽  
Extracellular Structures ◽  
First Time

Bacterial ghosts (BGs) are empty cell envelopes possessing native extracellular structures without a cytoplasm and genetic materials. BGs are proposed to have significant prospects in biomedical research as vaccines or delivery carriers. The applications of BGs are often limited by inefficient bacterial lysis and a low yield. To solve these problems, we compared the lysis efficiency of the wild-type protein E (EW) from phage ΦX174 and the screened mutant protein E (EM) in the Escherichia coli BL21(DE3) strain. The results show that the lysis efficiency mediated by protein EM was improved. The implementation of the pLysS plasmid allowed nearly 100% lysis efficiency, with a high initial cell density as high as OD600 = 2.0, which was higher compared to the commonly used BG preparation method. The results of Western blot analysis and immunofluorescence indicate that the expression level of protein EM was significantly higher than that of the non-pLysS plasmid. High-quality BGs were observed by SEM and TEM. To verify the applicability of this method in other bacteria, the T7 RNA polymerase expression system was successfully constructed in Salmonella enterica (S. Enterica, SE). A pET vector containing EM and pLysS were introduced to obtain high-quality SE ghosts which could provide efficient protection for humans and animals. This paper describes a novel and commonly used method to produce high-quality BGs on a large scale for the first time.

scholarly journals Mesophotic.org: a repository for scientific information on mesophotic ecosystems

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Pim Bongaerts ◽  
Gonzalo Perez-Rosales ◽  
Veronica Z Radice ◽  
Gal Eyal ◽  
Andrea Gori ◽  
...  
Keyword(s):  
Large Scale ◽  
Scientific Information ◽  
Temporal Trends ◽  
Light Availability ◽  
Research Community ◽  
Future Research ◽  
Photosynthetic Organisms ◽  
Future Research Directions ◽  
Available Information ◽  
Reduced Light

Abstract Mesophotic coral ecosystems (MCEs) and temperate mesophotic ecosystems (TMEs) occur at depths of roughly 30–150 m depth and are characterized by the presence of photosynthetic organisms despite reduced light availability. Exploration of these ecosystems dates back several decades, but our knowledge remained extremely limited until about a decade ago, when a renewed interest resulted in the establishment of a rapidly growing research community. Here, we present the ‘mesophotic.org’ database, a comprehensive and curated repository of scientific literature on mesophotic ecosystems. Through both manually curated and automatically extracted metadata, the repository facilitates rapid retrieval of available information about particular topics (e.g. taxa or geographic regions), exploration of spatial/temporal trends in research and identification of knowledge gaps. The repository can be queried to comprehensively obtain available data to address large-scale questions and guide future research directions. Overall, the ‘mesophotic.org’ repository provides an independent and open-source platform for the ever-growing research community working on MCEs and TMEs to collate and expedite our understanding of the occurrence, composition and functioning of these ecosystems. Database URL: http://mesophotic.org/

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