MicroRNAs: Genomics, Biogenesis, Mechanism, and Function

We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w1118; iso-2; iso-3 strain and the reference y1; cn1 bw1 sp1 strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in...

A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Proteins are built by using the information contained in molecules of messenger RNA (mRNA). Cells have several ways of controlling the amounts of different proteins they make. For example, a so-called ‘microRNA’ molecule can bind to an mRNA molecule to cause it to be more rapidly degraded and less efficiently used, thereby reducing the amount of protein built from that mRNA. Indeed, microRNAs are thought to help control the amount of protein made from most human genes, and biologists are working to predict the amount of control imparted by each microRNA on each of its mRNA targets. All RNA molecules are made up of a sequence of bases, each commonly known by a single letter—‘A’, ‘U’, ‘C’ or ‘G’. These bases can each pair up with one specific other base—‘A’ pairs with ‘U’, and ‘C’ pairs with ‘G’. To direct the repression of an mRNA molecule, a region of the microRNA known as a ‘seed’ binds to a complementary sequence in the target mRNA. ‘Canonical sites’ are regions in the mRNA that contain the exact sequence of partner bases for the bases in the microRNA seed. Some canonical sites are more effective at mRNA control than others. ‘Non-canonical sites’ also exist in which the pairing between the microRNA seed and mRNA does not completely match. Previous work has suggested that many non-canonical sites can also control mRNA degradation and usage. Agarwal et al. first used large experimental datasets from many sources to investigate microRNA activity in more detail. As expected, when mRNAs had canonical sites that matched the microRNA, mRNA levels and usage tended to drop. However, no effect was observed when the mRNAs only had recently identified non-canonical sites. This suggests that microRNAs primarily bind to canonical sites to control protein production. Based on these results, Agarwal et al. further developed a statistical model that predicts the effects of microRNAs binding to canonical sites. The updated model considers 14 different features of the microRNA, microRNA site, or mRNA—including the mRNA sequence around the site—to predict which sites within mRNAs are most effectively targeted by microRNAs. Tests showed that Agarwal et al.'s model was as good as experimental approaches at identifying the effective target sites, and was better than existing computational models. The model has been used to power the latest version of a freely available resource called TargetScan, and so could prove a valuable resource for researchers investigating the many important roles of microRNAs in controlling protein production.

/pdf/predicting-effective-microrna-target-sites-in-mammalian-4ivmgdccgc.pdf

Predicting effective microRNA target sites in mammalian mRNAs

MicroRNAs are a family of small, non-coding RNAs that regulate gene expression in a sequence-specific manner. The two founding members of the microRNA family were originally identified in Caenorhabditis elegans as genes that were required for the timed regulation of developmental events. Since then, hundreds of microRNAs have been identified in almost all metazoan genomes, including worms, flies, plants and mammals. MicroRNAs have diverse expression patterns and might regulate various developmental and physiological processes. Their discovery adds a new dimension to our understanding of complex gene regulatory networks.

/pdf/micrornas-small-rnas-with-a-big-role-in-gene-regulation-3a05xihg95.pdf

MicroRNAs: small RNAs with a big role in gene regulation

Despite their widespread roles as regulators of gene expression, important questions remain about target regulation by microRNAs. Animal microRNAs were originally thought to repress target translation, with little or no influence on mRNA abundance, whereas the reverse was thought to be true in plants. Now, however, it is clear that microRNAs can induce mRNA degradation in animals and, conversely, translational repression in plants. Recent studies have made important advances in elucidating the relative contributions of these two different modes of target regulation by microRNAs. They have also shed light on the specific mechanisms of target silencing, which, although it differs fundamentally between plants and animals, shares some common features between the two kingdoms.

Gene silencing by microRNAs: contributions of translational repression and mRNA decay.

MicroRNAs (miRNAs) are a conserved class of small non-coding RNAs that assemble with Argonaute proteins into miRNA-induced silencing complexes (miRISCs) to direct post-transcriptional silencing of complementary mRNA targets. Silencing is accomplished through a combination of translational repression and mRNA destabilization, with the latter contributing to most of the steady-state repression in animal cell cultures. Degradation of the mRNA target is initiated by deadenylation, which is followed by decapping and 5'-to-3' exonucleolytic decay. Recent work has enhanced our understanding of the mechanisms of silencing, making it possible to describe in molecular terms a continuum of direct interactions from miRNA target recognition to mRNA deadenylation, decapping and 5'-to-3' degradation. Furthermore, an intricate interplay between translational repression and mRNA degradation is emerging.

Towards a molecular understanding of microRNA-mediated gene silencing

MicroRNAs (miRNAs) silence the expression of target genes post-transcriptionally. Their function is mediated by the Argonaute proteins (AGOs), which colocalize to P-bodies with mRNA degradation enzymes. Mammalian P-bodies are also marked by the GW182 protein, which interacts with the AGOs and is required for miRNA function. We show that depletion of GW182 leads to changes in mRNA expression profiles strikingly similar to those observed in cells depleted of the essential Drosophila miRNA effector AGO1, indicating that GW182 functions in the miRNA pathway. When GW182 is bound to a reporter transcript, it silences its expression, bypassing the requirement for AGO1. Silencing by GW182 is effected by changes in protein expression and mRNA stability. Similarly, miRNAs silence gene expression by repressing protein expression and/or by promoting mRNA decay, and both mechanisms require GW182. mRNA degradation, but not translational repression, by GW182 or miRNAs is inhibited in cells depleted of CAF1, NOT1, or the decapping DCP1:DCP2 complex. We further show that the N-terminal GW repeats of GW182 interact with the PIWI domain of AGO1. Our findings indicate that GW182 links the miRNA pathway to mRNA degradation by interacting with AGO1 and promoting decay of at least a subset of miRNA targets.

/pdf/mrna-degradation-by-mirnas-and-gw182-requires-both-ccr4-not-30tdm1fy1k.pdf

mRNA degradation by miRNAs and GW182 requires both CCR4:NOT deadenylase and DCP1:DCP2 decapping complexes

Getting to the Root of miRNA-Mediated Gene Silencing

Post-transcriptional processes have a central role in the regulation of eukaryotic gene expression. Although it has been known for a long time that these processes are functionally linked, often by the use of common protein factors, it has only recently become apparent that many of these processes are also physically connected. Indeed, proteins that are involved in mRNA degradation, translational repression, mRNA surveillance and RNA-mediated gene silencing, together with their mRNA targets, colocalize within discrete cytoplasmic domains known as P bodies. The available evidence indicates that P bodies are sites where mRNAs that are not being translated accumulate, the information carried by associated proteins and regulatory RNAs is integrated, and their fate - either translation, silencing or decay - is decided.

/pdf/p-bodies-at-the-crossroads-of-post-transcriptional-pathways-4c42tpx5yq.pdf

P bodies: at the crossroads of post-transcriptional pathways

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Elisa Izaurralde

Author Tools

Chat about Author