Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
695,167
result(s) for
"Databases"
Sort by:
Databases : organizing information
Describes how databases work and how to use tables, files, and relational databases.
Book
RESCRIPt: Reproducible sequence taxonomy reference database management
Nucleotide sequence and taxonomy reference databases are critical resources for widespread applications including marker-gene and metagenome sequencing for microbiome analysis, diet metabarcoding, and environmental DNA (eDNA) surveys. Reproducibly generating, managing, using, and evaluating nucleotide sequence and taxonomy reference databases creates a significant bottleneck for researchers aiming to generate custom sequence databases. Furthermore, database composition drastically influences results, and lack of standardization limits cross-study comparisons. To address these challenges, we developed RESCRIPt, a Python 3 software package and QIIME 2 plugin for reproducible generation and management of reference sequence taxonomy databases, including dedicated functions that streamline creating databases from popular sources, and functions for evaluating, comparing, and interactively exploring qualitative and quantitative characteristics across reference databases. To highlight the breadth and capabilities of RESCRIPt, we provide several examples for working with popular databases for microbiome profiling (SILVA, Greengenes, NCBI-RefSeq, GTDB), eDNA and diet metabarcoding surveys (BOLD, GenBank), as well as for genome comparison. We show that bigger is not always better, and reference databases with standardized taxonomies and those that focus on type strains have quantitative advantages, though may not be appropriate for all use cases. Most databases appear to benefit from some curation (quality filtering), though sequence clustering appears detrimental to database quality. Finally, we demonstrate the breadth and extensibility of RESCRIPt for reproducible workflows with a comparison of global hepatitis genomes. RESCRIPt provides tools to democratize the process of reference database acquisition and management, enabling researchers to reproducibly and transparently create reference materials for diverse research applications. RESCRIPt is released under a permissive BSD-3 license at https://github.com/bokulich-lab/RESCRIPt .
Journal Article
Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder
Survey of postzygotic mosaic mutations (PZMs) in 5,947 trios with autism spectrum disorders (ASD) discovers differences in mutational properties between germline mutations and PZMs. Spatiotemporal analyses of the PZMs also revealed the association of the amygdala with ASD and implicated risk genes, including recurrent potential gain-of-function mutations in
SMARCA4
.
We systematically analyzed postzygotic mutations (PZMs) in whole-exome sequences from the largest collection of trios (5,947) with autism spectrum disorder (ASD) available, including 282 unpublished trios, and performed resequencing using multiple independent technologies. We identified 7.5% of
de novo
mutations as PZMs, 83.3% of which were not described in previous studies. Damaging, nonsynonymous PZMs within critical exons of prenatally expressed genes were more common in ASD probands than controls (
P
< 1 × 10
−6
), and genes carrying these PZMs were enriched for expression in the amygdala (
P
= 5.4 × 10
−3
). Two genes (
KLF16
and
MSANTD2
) were significantly enriched for PZMs genome-wide, and other PZMs involved genes (
SCN2A
,
HNRNPU
and
SMARCA4
) whose mutation is known to cause ASD or other neurodevelopmental disorders. PZMs constitute a significant proportion of
de novo
mutations and contribute importantly to ASD risk.
Journal Article
A survey on deep learning approaches for text-to-SQL
To bridge the gap between users and data, numerous text-to-SQL systems have been developed that allow users to pose natural language questions over relational databases. Recently, novel text-to-SQL systems are adopting deep learning methods with very promising results. At the same time, several challenges remain open making this area an active and flourishing field of research and development. To make real progress in building text-to-SQL systems, we need to de-mystify what has been done, understand how and when each approach can be used, and, finally, identify the research challenges ahead of us. The purpose of this survey is to present a detailed taxonomy of neural text-to-SQL systems that will enable a deeper study of all the parts of such a system. This taxonomy will allow us to make a better comparison between different approaches, as well as highlight specific challenges in each step of the process, thus enabling researchers to better strategise their quest towards the “holy grail” of database accessibility.
Journal Article
Blockchain enabled applications : understand the blockchain ecosystem and how to make it work for you
\"This book looks at the consequences of machine-to-machine transactions using the blockchain socially, technologically, economically and politically\"--Back cover.
Book
Introducing the trier univalence neutrality ambivalence
Using validated stimulus material is crucial for ensuring research comparability and replicability. However, many databases rely solely on bidimensional valence ratings, ranging from negative to positive. While this material might be appropriate for certain studies, it does not reflect the complexity of attitudes and therefore might hamper the unambiguous interpretation of some study results. In fact, most databases cannot differentiate between neutral (i.e., neither positive nor negative) and ambivalent (i.e., simultaneously positive and negative) attitudes. Consequently, even presumably univalent (only positive or negative) stimuli cannot be clearly distinguished from ambivalent ones when selected via bipolar rating scales. In the present research, we introduce the Trier Univalence Neutrality Ambivalence (TUNA) database, a database containing 304,262 validation ratings from heterogeneous samples of 3,232 participants and at least 20 (M = 27.3, SD = 4.84) ratings per self-report scale per picture for a variety of attitude objects on split semantic differential scales. As these scales measure positive and negative evaluations independently, the TUNA database allows to distinguish univalence, neutrality, and ambivalence (i.e., potential ambivalence). TUNA also goes beyond previous databases by validating the stimulus materials on affective outcomes such as experiences of conflict (i.e., felt ambivalence), arousal, anger, disgust, and empathy. The TUNA database consists of 796 pictures and is compatible with other popular databases. It sets a focus on food pictures in various forms (e.g., raw vs. cooked, non-processed vs. highly processed), but includes pictures of other objects that are typically used in research to study univalent (e.g., flowers) and ambivalent (e.g., money, cars) attitudes for comparison. Furthermore, to facilitate the stimulus selection the TUNA database has an accompanying desktop app that allows easy stimulus selection via a multitude of filter options.
Journal Article
