Biomedical subjects

Qiqi Xu

Publications and source records attributed to Qiqi Xu.

4 recordsLinked to original sources

PubMed2006

Integrated siRNA design based on surveying of features associated with high RNAi effectiveness.

BACKGROUND: Short interfering RNAs have allowed the development of clean and easily regulated methods for disruption of gene expression. However, while these methods continue to grow in popularity, designing effective siRNA experiments can be challenging. The various existing siRNA design guidelines suffer from two problems: they differ considerably from each other, and they produce high levels of false-positive predictions when tested on data of independent origins. RESULTS: Using a distinctly large set of siRNA efficacy data assembled from a vast diversity of origins (the siRecords data, containing records of 3,277 siRNA experiments targeting 1,518 genes, derived from 1,417 independent studies), we conducted extensive analyses of all known features that have been implicated in increasing RNAi effectiveness. A number of features having positive impacts on siRNA efficacy were identified. By performing quantitative analyses on cooperative effects among these features, then applying a disjunctive rule merging (DRM) algorithm, we developed a bundle of siRNA design rule sets with the false positive problem well curbed. A comparison with 15 online siRNA design tools indicated that some of the rule sets we developed surpassed all of these design tools commonly used in siRNA design practice in positive predictive values (PPVs). CONCLUSION: The availability of the large and diverse siRNA dataset from siRecords and the approach we describe in this report have allowed the development of highly effective and generally applicable siRNA design rule sets. Together with ever improving RNAi lab techniques, these design rule sets are expected to make siRNAs a more useful tool for molecular genetics, functional genomics, and drug discovery studies.

Algorithms↗

PubMed2006

SVRMHC prediction server for MHC-binding peptides.

BACKGROUND: The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. RESULTS: Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. CONCLUSION: SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

Amino Acid Sequence↗

PubMed2006

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models.

BACKGROUND: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. RESULTS: We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. CONCLUSION: As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Algorithms↗

PubMed2006

siRecords: an extensive database of mammalian siRNAs with efficacy ratings.

UNLABELLED: Short interfering RNAs (siRNAs) have been gaining popularity as the gene knock-down tool of choice by many researchers because of the clean nature of their workings as well as the technical simplicity and cost efficiency in their applications. We have constructed siRecords, a database of siRNAs experimentally tested by researchers with consistent efficacy ratings. This database will help siRNA researchers develop more reliable siRNA design rules; in the mean time, siRecords will benefit experimental researchers directly by providing them with information about the siRNAs that have been experimentally tested against the genes of their interest. Currently, more than 4100 carefully annotated siRNA sequences obtained from more than 1200 published siRNA studies are hosted in siRecords. This database will continue to expand as more experimentally tested siRNAs are published. AVAILABILITY: The siRecords database can be accessed at http://siRecords.umn.edu/siRecords/

Abstracting and Indexing↗