The FLIGHT Drosophila RNAi database: 2010 update

FLIGHT (http://flight.icr.ac.uk/) is an online resource compiling data from high-throughput Drosophila in vivo and in vitro RNAi screens. FLIGHT includes details of RNAi reagents and their predicted off-target effects, alongside RNAi screen hits, scores and phenotypes, including images from high-content screens. The latest release of FLIGHT is designed to enable users to upload, analyze, integrate and share their own RNAi screens. Users can perform multiple normalizations, view quality control plots, detect and assign screen hits and compare hits from multiple screens using a variety of methods including hierarchical clustering. FLIGHT integrates RNAi screen data with microarray gene expression as well as genomic annotations and genetic/physical interaction datasets to provide a single interface for RNAi screen analysis and datamining in Drosophila.Key words: RNAi, database, integration, bioinformatics, phenotype     

Scrutinizing MHC-I Binding Peptides and Their Limits of Variation

Designed peptides that bind to major histocompatibility protein I (MHC-I) allomorphs bear the promise of representing epitopes that stimulate a desired immune response. A rigorous bioinformatical exploration of sequence patterns hidden in peptides that bind to the mouse MHC-I allomorph H-2K^b is presented. We exemplify and validate these motif findings by systematically dissecting the epitope SIINFEKL and analyzing the resulting fragments for their binding potential to H-2K^b in a thermal denaturation assay. The results demonstrate that only fragments exclusively retaining the carboxy- or amino-terminus of the reference peptide exhibit significant binding potential, with the N-terminal pentapeptide SIINF as shortest ligand. This study demonstrates that sophisticated machine-learning algorithms excel at extracting fine-grained patterns from peptide sequence data and predicting MHC-I binding peptides, thereby considerably extending existing linear prediction models and providing a fresh view on the computer-based molecular design of future synthetic vaccines. The server for prediction is available at http://modlab-cadd.ethz.ch (SLiDER tool, MHC-I version 2012).     

ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

Background

With next-generation sequencing technologies, experiments that were considered prohibitive only a few years ago are now possible. However, while these technologies have the ability to produce enormous volumes of data, the sequence reads are prone to error. This poses fundamental hurdles when genetic diversity is investigated.

Results

We developed ShoRAH, a computational method for quantifying genetic diversity in a mixed sample and for identifying the individual clones in the population, while accounting for sequencing errors. The software was run on simulated data and on real data obtained in wet lab experiments to assess its reliability.

Conclusions

ShoRAH is implemented in C++, Python, and Perl and has been tested under Linux and Mac OS X. Source code is available under the GNU General Public License at http://www.cbg.ethz.ch/software/shorah.     

Identification of Genes That Promote or Inhibit Olfactory Memory Formation in Drosophila

Genetic screens in Drosophila melanogaster and other organisms have been pursued to filter the genome for genetic functions important for memory formation. Such screens have employed primarily chemical or transposon-mediated mutagenesis and have identified numerous mutants including classical memory mutants, dunce and rutabaga. Here, we report the results of a large screen using panneuronal RNAi expression to identify additional genes critical for memory formation. We identified >500 genes that compromise memory when inhibited (low hits), either by disrupting the development and normal function of the adult animal or by participating in the neurophysiological mechanisms underlying memory formation. We also identified >40 genes that enhance memory when inhibited (high hits). The dunce gene was identified as one of the low hits and further experiments were performed to map the effects of the dunce RNAi to the α/β and γ mushroom body neurons. Additional behavioral experiments suggest that dunce knockdown in the mushroom body neurons impairs memory without significantly affecting acquisition. We also characterized one high hit, sickie, to show that RNAi knockdown of this gene enhances memory through effects in dopaminergic neurons without apparent effects on acquisition. These studies further our understanding of two genes involved in memory formation, provide a valuable list of genes that impair memory that may be important for understanding the neurophysiology of memory or neurodevelopmental disorders, and offer a new resource of memory suppressor genes that will aid in understanding restraint mechanisms employed by the brain to optimize resources.     

GUItars: A GUI Tool for Analysis of High-Throughput RNA Interference Screening Data

Background

High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.

Results

The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.

Conclusion

With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/.     

Large-Scale Proteomics of the Cassava Storage Root and Identification of a Target Gene to Reduce Postharvest Deterioration

Cassava (Manihot esculenta) is the most important root crop in the tropics, but rapid postharvest physiological deterioration (PPD) of the root is a major constraint to commercial cassava production. We established a reliable method for image-based PPD symptom quantification and used label-free quantitative proteomics to generate an extensive cassava root and PPD proteome. Over 2600 unique proteins were identified in the cassava root, and nearly 300 proteins showed significant abundance regulation during PPD. We identified protein abundance modulation in pathways associated with oxidative stress, phenylpropanoid biosynthesis (including scopoletin), the glutathione cycle, fatty acid α-oxidation, folate transformation, and the sulfate reduction II pathway. Increasing protein abundances and enzymatic activities of glutathione-associated enzymes, including glutathione reductases, glutaredoxins, and glutathione S-transferases, indicated a key role for ascorbate/glutathione cycles. Based on combined proteomics data, enzymatic activities, and lipid peroxidation assays, we identified glutathione peroxidase as a candidate for reducing PPD. Transgenic cassava overexpressing a cytosolic glutathione peroxidase in storage roots showed delayed PPD and reduced lipid peroxidation as well as decreased H₂O₂ accumulation. Quantitative proteomics data from ethene and phenylpropanoid pathways indicate additional gene candidates to further delay PPD. Cassava root proteomics data are available at www.pep2pro.ethz.ch for easy access and comparison with other proteomics data.     

SNES makes sense? Single-cell exome sequencing evolves

Technologies for single-cell sequencing are improving steadily. A recent study describes a new method for interrogating all coding sequences of the human genome at single-cell resolution.See related research by Leung et al., http://genomebiology.com/2015/16/1/55     

A Genome-Wide RNAi Screen for Enhancers of par Mutants Reveals New Contributors to Early Embryonic Polarity in Caenorhabditis elegans

The par genes of Caenorhabditis elegans are essential for establishment and maintenance of early embryo polarity and their homologs in other organisms are crucial polarity regulators in diverse cell types. Forward genetic screens and simple RNAi depletion screens have identified additional conserved regulators of polarity in C. elegans; genes with redundant functions, however, will be missed by these approaches. To identify such genes, we have performed a genome-wide RNAi screen for enhancers of lethality in conditional par-1 and par-4 mutants. We have identified 18 genes for which depletion is synthetically lethal with par-1 or par-4, or both, but produces little embryo lethality in wild type. Fifteen of the 18 genes identified in our screen are not previously known to function in C. elegans embryo polarity and 11 of them also increase lethality in a par-2 mutant. Among the strongest synthetic lethal genes, polarity defects are more apparent in par-2 early embryos than in par-1 or par-4, except for strd-1(RNAi), which enhances early polarity phenotypes in all three mutants. One strong enhancer of par-1 and par-2 lethality, F25B5.2, corresponds to nop-1, a regulator of actomyosin contractility for which the molecular identity was previously unknown. Other putative polarity enhancers identified in our screen encode cytoskeletal and membrane proteins, kinases, chaperones, and sumoylation and deubiquitylation proteins. Further studies of these genes should give mechanistic insight into pathways regulating establishment and maintenance of cell polarity.     

A Bayesian Assignment Method for Ambiguous Bisulfite Short Reads

DNA methylation is an epigenetic modification critical for normal development and diseases. The determination of genome-wide DNA methylation at single-nucleotide resolution is made possible by sequencing bisulfite treated DNA with next generation high-throughput sequencing. However, aligning bisulfite short reads to a reference genome remains challenging as only a limited proportion of them (around 50–70%) can be aligned uniquely; a significant proportion, known as multireads, are mapped to multiple locations and thus discarded from downstream analyses, causing financial waste and biased methylation inference. To address this issue, we develop a Bayesian model that assigns multireads to their most likely locations based on the posterior probability derived from information hidden in uniquely aligned reads. Analyses of both simulated data and real hairpin bisulfite sequencing data show that our method can effectively assign approximately 70% of the multireads to their best locations with up to 90% accuracy, leading to a significant increase in the overall mapping efficiency. Moreover, the assignment model shows robust performance with low coverage depth, making it particularly attractive considering the prohibitive cost of bisulfite sequencing. Additionally, results show that longer reads help improve the performance of the assignment model. The assignment model is also robust to varying degrees of methylation and varying sequencing error rates. Finally, incorporating prior knowledge on mutation rate and context specific methylation level into the assignment model increases inference accuracy. The assignment model is implemented in the BAM-ABS package and freely available at https://github.com/zhanglabvt/BAM_ABS.     

A Mass Spectrometric-Derived Cell Surface Protein Atlas

Cell surface proteins are major targets of biomedical research due to their utility as cellular markers and their extracellular accessibility for pharmacological intervention. However, information about the cell surface protein repertoire (the surfaceome) of individual cells is only sparsely available. Here, we applied the Cell Surface Capture (CSC) technology to 41 human and 31 mouse cell types to generate a mass-spectrometry derived Cell Surface Protein Atlas (CSPA) providing cellular surfaceome snapshots at high resolution. The CSPA is presented in form of an easy-to-navigate interactive database, a downloadable data matrix and with tools for targeted surfaceome rediscovery (http://wlab.ethz.ch/cspa). The cellular surfaceome snapshots of different cell types, including cancer cells, resulted in a combined dataset of 1492 human and 1296 mouse cell surface glycoproteins, providing experimental evidence for their cell surface expression on different cell types, including 136 G-protein coupled receptors and 75 membrane receptor tyrosine-protein kinases. Integrated analysis of the CSPA reveals that the concerted biological function of individual cell types is mainly guided by quantitative rather than qualitative surfaceome differences. The CSPA will be useful for the evaluation of drug targets, for the improved classification of cell types and for a better understanding of the surfaceome and its concerted biological functions in complex signaling microenvironments.     

SANTA: Quantifying the Functional Content of Molecular Networks

Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach (called SANTA) quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html.     

UP-TORR: Online Tool for Accurate and Up-to-Date Annotation of RNAi Reagents

RNA interference (RNAi) is a widely adopted tool for loss-of-function studies but RNAi results only have biological relevance if the reagents are appropriately mapped to genes. Several groups have designed and generated RNAi reagent libraries for studies in cells or in vivo for Drosophila and other species. At first glance, matching RNAi reagents to genes appears to be a simple problem, as each reagent is typically designed to target a single gene. In practice, however, the reagent–gene relationship is complex. Although the sequences of oligonucleotides used to generate most types of RNAi reagents are static, the reference genome and gene annotations are regularly updated. Thus, at the time a researcher chooses an RNAi reagent or analyzes RNAi data, the most current interpretation of the RNAi reagent–gene relationship, as well as related information regarding specificity (e.g., predicted off-target effects), can be different from the original interpretation. Here, we describe a set of strategies and an accompanying online tool, UP-TORR (for Updated Targets of RNAi Reagents; www.flyrnai.org/up-torr), useful for accurate and up-to-date annotation of cell-based and in vivo RNAi reagents. Importantly, UP-TORR automatically synchronizes with gene annotations daily, retrieving the most current information available, and for Drosophila, also synchronizes with the major reagent collections. Thus, UP-TORR allows users to choose the most appropriate RNAi reagents at the onset of a study, as well as to perform the most appropriate analyses of results of RNAi-based studies.     

Functional Genetics in the Post-Genomics Era: Building a Better Roadmap in Drosophila

In this commentary, Rob Kulathinal describes two articles from the Perrimon lab, each describing a new online resource that can assist geneticists with the design of their RNA interference (RNAi) experiments. Hu et al.’s “UP-TORR: online tool for accurate and up-to-date annotation of RNAi reagents” and “FlyPrimerBank: An online database for Drosophila melanogaster gene expression analysis and knockdown evaluation of RNAi reagents” are published, respectively, in this month’s issues of GENETICS and G3.