Towards accurate detection and genotyping of expressed variants from whole transcriptome sequencing data

BACKGROUND: Massively parallel transcriptome sequencing (RNA-Seq) is becoming the method of choice for studying functional effects of genetic variability and establishing causal relationships between genetic variants and disease. However, RNA-Seq poses new technical and computational challenges compared to genome sequencing. In particular, mapping transcriptome reads onto the genome is more challenging than mapping genomic reads due to splicing. Furthermore, detection and genotyping of single nucleotide variants (SNVs) requires statistical models that are robust to variability in read coverage due to unequal transcript expression levels. RESULTS: In this paper we present a strategy to more reliably map transcriptome reads by taking advantage of the availability of both the genome reference sequence and transcript databases such as CCDS. We also present a novel Bayesian model for SNV discovery and genotyping based on quality scores. CONCLUSIONS: Experimental results on RNA-Seq data generated from blood cell tissue of three Hapmap individuals show that our methods yield increased accuracy compared to several widely used methods. The open source code implementing our methods, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/NGSTools/

TRIP: A method for novel transcript reconstruction from paired-end RNA-seq reads

Preliminary experimental results on synthetic datasets generated with various sequencing parameters and distribution assumptions show that TRIP has increased transcriptome reconstruction accuracy compared to previous methods that ignore fragment length distribution information

Linkage disequilibrium based genotype calling from low-coverage shotgun sequencing reads

Background Recent technology advances have enabled sequencing of individual genomes, promising to revolutionize biomedical research. However, deep sequencing remains more expensive than microarrays for performing whole-genome SNP genotyping. Results In this paper we introduce a new multi-locus statistical model and computationally efficient genotype calling algorithms that integrate shotgun sequencing data with linkage disequilibrium (LD) information extracted from reference population panels such as Hapmap or the 1000 genomes project. Experiments on publicly available 454, Illumina, and ABI SOLiD sequencing datasets suggest that integration of LD information results in genotype calling accuracy comparable to that of microarray platforms from sequencing data of low-coverage. A software package implementing our algorithm, released under the GNU General Public License, is available at http://dna.engr.uconn.edu/software/GeneSeq/. Conclusions Integration of LD information leads to significant improvements in genotype calling accuracy compared to prior LD-oblivious methods, rendering low-coverage sequencing as a viable alternative to microarrays for conducting large-scale genome-wide association studies

PrimerHunter: a primer design tool for PCR-based virus subtype identification

Rapid and reliable virus subtype identification is critical for accurate diagnosis of human infections, effective response to epidemic outbreaks and global-scale surveillance of highly pathogenic viral subtypes such as avian influenza H5N1. The polymerase chain reaction (PCR) has become the method of choice for virus subtype identification. However, designing subtype-specific PCR primer pairs is a very challenging task: on one hand, selected primer pairs must result in robust amplification in the presence of a significant degree of sequence heterogeneity within subtypes, on the other, they must discriminate between the subtype of interest and closely related subtypes. In this article, we present a new tool, called PrimerHunter, that can be used to select highly sensitive and specific primers for virus subtyping. Our tool takes as input sets of both target and nontarget sequences. Primers are selected such that they efficiently amplify any one of the target sequences, and none of the nontarget sequences. PrimerHunter ensures the desired amplification properties by using accurate estimates of melting temperature with mismatches, computed based on the nearest neighbor model via an efficient fractional programming algorithm. Validation experiments with three avian influenza HA subtypes confirm that primers selected by PrimerHunter have high sensitivity and specificity for target sequences

PrimerHunter: a primer design tool for PCR-based virus subtype identification

Rapid and reliable virus subtype identification is critical for accurate diagnosis of human infections, effective response to epidemic outbreaks and global-scale surveillance of highly pathogenic viral subtypes such as avian influenza H5N1. The polymerase chain reaction (PCR) has become the method of choice for virus subtype identification. However, designing subtype-specific PCR primer pairs is a very challenging task: on one hand, selected primer pairs must result in robust amplification in the presence of a significant degree of sequence heterogeneity within subtypes, on the other, they must discriminate between the subtype of interest and closely related subtypes. In this article, we present a new tool, called PrimerHunter, that can be used to select highly sensitive and specific primers for virus subtyping. Our tool takes as input sets of both target and nontarget sequences. Primers are selected such that they efficiently amplify any one of the target sequences, and none of the nontarget sequences. PrimerHunter ensures the desired amplification properties by using accurate estimates of melting temperature with mismatches, computed based on the nearest neighbor model via an efficient fractional programming algorithm. Validation experiments with three avian influenza HA subtypes confirm that primers selected by PrimerHunter have high sensitivity and specificity for target sequences

Estimation of alternative splicing isoform frequencies from RNA-Seq data

<p>Abstract</p> <p>Background</p> <p>Massively parallel whole transcriptome sequencing, commonly referred as RNA-Seq, is quickly becoming the technology of choice for gene expression profiling. However, due to the short read length delivered by current sequencing technologies, estimation of expression levels for alternative splicing gene isoforms remains challenging.</p> <p>Results</p> <p>In this paper we present a novel expectation-maximization algorithm for inference of isoform- and gene-specific expression levels from RNA-Seq data. Our algorithm, referred to as IsoEM, is based on disambiguating information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand and read pairing information when available. The open source Java implementation of IsoEM is freely available at <url>http://dna.engr.uconn.edu/software/IsoEM/</url>.</p> <p>Conclusions</p> <p>Empirical experiments on both synthetic and real RNA-Seq datasets show that IsoEM has scalable running time and outperforms existing methods of isoform and gene expression level estimation. Simulation experiments confirm previous findings that, for a fixed sequencing cost, using reads longer than 25-36 bases does not necessarily lead to better accuracy for estimating expression levels of annotated isoforms and genes.</p

Approximation algorithms for VLSI routing

Ph.D.Vijay V. Vaziran