LIRcentral: a manually curated online database of experimentally validated functional LIR motifs.

Several selective macroautophagy receptor and adaptor proteins bind members of the Atg8 (autophagy related 8) family using short linear motifs (SLiMs), most often referred to as Atg8-family interacting motifs (AIMs) or LC3-interacting regions (LIRs). AIM/LIR motifs have been extensively studied during the last fifteen years, since they can uncover the underlying biological mechanisms and possible substrates for this key catabolic process of eukaryotic cells. Prompted by the fact that experimental information regarding LIR motifs can be found scattered across heterogeneous literature resources, we have developed LIRcentral (https://lircentral.

Rfam 14: expanded coverage of metagenomic, viral and microRNA families.

Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40.

Non-Coding RNA Analysis Using the Rfam Database.

Rfam is a database of non-coding RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. Using a combination of manual and literature-based curation and a custom software pipeline, Rfam converts descriptions of RNA families found in the scientific literature into computational models that can be used to annotate RNAs belonging to those families in any DNA or RNA sequence. Valuable research outputs that are often locked up in figures and supplementary information files are encapsulated in Rfam entries and made accessible through the Rfam Web site.

Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families.

The Rfam database is a collection of RNA families in which each family is represented by a multiple sequence alignment, a consensus secondary structure, and a covariance model. In this paper we introduce Rfam release 13.0, which switches to a new genome-centric approach that annotates a non-redundant set of reference genomes with RNA families.

RNAcentral: a comprehensive database of non-coding RNA sequences.

RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species.

LiSIs: An Online Scientific Workflow System for Virtual Screening.

Modern methods of drug discovery and development in recent years make a wide use of computational algorithms. These methods utilise Virtual Screening (VS), which is the computational counterpart of experimental screening. In this manner the in silico models and tools initial replace the wet lab methods saving time and resources.

iLIR: A web resource for prediction of Atg8-family interacting proteins.

Macroautophagy was initially considered to be a nonselective process for bulk breakdown of cytosolic material. However, recent evidence points toward a selective mode of autophagy mediated by the so-called selective autophagy receptors (SARs). SARs act by recognizing and sorting diverse cargo substrates (e.

Discovering genetic polymorphism associated with gene expression levels across the whole genome.

Genetic differences have been shown to contribute to gene expression variability. A complete evaluation of the associations between a whole genome scan with 550k Single Nucleotide Polymorphisms (SNPs) and 54k detectable expression levels (probesets) was performed on 176 human peripheral blood samples. The results are presented along with visualizations that reveal cis and trans gene expression regulatory effects.