Despite recent advances in natural language processing, many statistical models for processing text perform extremely poorly under domain shift. Processing biomedical and clinical text is a critically important application area of natural language processing, for which there are few robust, practical, publicly available models. This paper describes scispaCy, a new tool for practical biomedical/scientific text processing, which heavily leverages the spaCy library. We detail the performance of two packages of models released in scispaCy and demonstrate their robustness on several tasks and datasets. Models and code are available at this https URL

ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing

The integron is a powerful system which, by capturing, stockpiling, and rearranging new functions carried by gene encoding cassettes, confers upon bacteria a rapid adaptation capability in changing environments. Chromosomally located integrons (CI) have been identified in a large number of environmental Gram-negative bacteria. Integron evolutionary history suggests that these sedentary CIs acquired mobility among bacterial species through their association with transposable elements and conjugative plasmids. As a result of massive antibiotic use, these so-called mobile integrons are now widespread in clinically relevant bacteria and are considered to be the principal agent in the emergence and rise of antibiotic multiresistance in Gram-negative bacteria. Cassette rearrangements are catalyzed by the integron integrase, a site-specific tyrosine recombinase. Central to these reactions is the single-stranded DNA nature of one of the recombination partners, the attC site. This makes the integron a unique recombination system. This review describes the current knowledge on this atypical recombination mechanism, its implications in the reactions involving the different types of sites, attC and attI, and focuses on the tight regulation exerted by the host on integron activity through the control of attC site folding. Furthermore, cassette and integrase expression are also highly controlled by host regulatory networks and the bacterial stress (SOS) response. These intimate connections to the host make the integron a genetically stable and efficient system, granting the bacteria a low cost, highly adaptive evolution potential “on demand”.

The Integron: Adaptation On Demand.

Transcriptomic analyses of postmortem brains have begun to elucidate molecular abnormalities in autism spectrum disorder (ASD). However, a crucial pathway involved in synaptic development, RNA editing, has not yet been studied on a genome-wide scale. Here we profiled global patterns of adenosine-to-inosine (A-to-I) editing in a large cohort of postmortem brains of people with ASD. We observed a global bias for hypoediting in ASD brains, which was shared across brain regions and involved many synaptic genes. We show that the Fragile X proteins FMRP and FXR1P interact with RNA-editing enzymes (ADAR proteins) and modulate A-to-I editing. Furthermore, we observed convergent patterns of RNA-editing alterations in ASD and Fragile X syndrome, establishing this as a molecular link between these related diseases. Our findings, which are corroborated across multiple data sets, including dup15q (genomic duplication of 15q11.2-13.1) cases associated with intellectual disability, highlight RNA-editing dysregulation in ASD and reveal new mechanisms underlying this disorder.

https://escholarship.org/content/qt46r0r1jv/qt46r0r1jv.pdf?t=pwn331

Widespread RNA editing dysregulation in brains from autistic individuals.

As the generation and use of genomic datasets is becoming increasingly common in all areas of biology, the need for resources to collate, analyse and present data from independent (Tier 1) species-level genome projects into well supported clade-oriented (Tier 2) databases and provide a mechanism for these data to be propagated to pan-taxonomic (Tier 3) databases is becoming more pressing. Lepbase is a Tier 2 genomic resource for the Lepidoptera, supporting a research community using genomic approaches to understand evolution, speciation, olfaction, behaviour and pesticide resistance in a wide range of target species. Lepbase offers a core set of tools to make genomic data widely accessible including an Ensembl genome browser, text and sequence homology searches and bulk downloads of consistently presented and formatted datasets. As a part of the taxonomic community that we serve, we are working directly with Lepidoptera researchers to prioritise analyses and add tools that will be of most value to current research questions.

/pdf/lepbase-the-lepidopteran-genome-database-43anw3e9m4.pdf

Lepbase: the Lepidopteran genome database

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don't have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, "SFPEL-LPI", to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

/pdf/sfpel-lpi-sequence-based-feature-projection-ensemble-47l86k7p6z.pdf

SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions

Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available.Database URL: http://www.ensembl.org.

AJ Vilella

Papers

Database: The Journal of Biological Databases and Curation