Biology subjects

Lyon, G. J.

Publications and source records attributed to Lyon, G. J..

3 recordsLinked to original sources

bioRxiv2017

Scikit-ribo: Accurate estimation and robust modeling of translation dynamics at codon resolution

Ribosome profiling (Riboseq) is a powerful technique for measuring protein translation, however, sampling errors and biological biases are prevalent and poorly understand. Addressing these issues, we present Scikit-ribo (https://github.com/hanfang/scikit-ribo), the first open-source software for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Riboseq and RNAseq data. Scikit-ribo accurately identifies A-site locations and reproduces codon elongation rates using several digestion protocols (r = 0.99). Next we show commonly used RPKM-derived TE estimation is prone to biases, especially for low-abundance genes. Scikit-ribo introduces a codon-level generalized linear model with ridge penalty that correctly estimates TE while accommodating variable codon elongation rates and mRNA secondary structure. This corrects the TE errors for over 2000 genes in S. cerevisiae, which we validate using mass spectrometry of protein abundances (r = 0.81) and allows us to determine the Kozak-like sequence directly from Riboseq. We conclude with an analysis of coverage requirements needed for robust codon-level analysis, and quantify the artifacts that can occur from cycloheximide treatment.

bioinformatics↗

bioRxiv2017

Not by systems alone: identifying functional outliers in rare disease pedigrees

In characterizing a disease, it is common to search for dysfunctional genes by assaying the transcriptome. The resulting differentially expressed genes are typically assessed for shared features, such as functional annotation or co-expression. While useful, the reliability of these systems methods is hard to evaluate. To better understand shared disease signals, we assess their replicability by first looking at gene-level recurrence and then pathway-level recurrence along with co-expression signals across six pedigrees of a rare homogeneous X-linked disorder, TAF1 syndrome. We find most differentially expressed genes are not recurrent between pedigrees, making functional enrichment largely distinct in each pedigree. However, we find two highly recurrent \"functional outliers\" (CACNA1I and IGFBP3), genes acting atypically with respect to co-expression and therefore absent from a systems-level assessment. We show this occurs in re-analysis of Huntingtons disease, Parkinsons disease and schizophrenia. Our results suggest a significant role for genes easily missed in systems approaches.

systems biology↗

bioRxiv2017

HadoopCNV: A Dynamic Programming Imputation Algorithm To Detect Copy Number Variants From Sequencing Data

BACKGROUNDWhole-genome sequencing (WGS) data may be used to identify copy number variations (CNVs). Existing CNV detection methods mostly rely on read depth or alignment characteristics (paired-end distance and split reads) to infer gains/losses, while neglecting allelic intensity ratios and cannot quantify copy numbers. Additionally, most CNV callers are not scalable to handle a large number of WGS samples.\n\nMETHODSTo facilitate large-scale and rapid CNV detection from WGS data, we developed a Dynamic Programming Imputation (DPI) based algorithm called HadoopCNV, which infers copy number changes through both allelic frequency and read depth information. Our implementation is built on the Hadoop framework, enabling multiple compute nodes to work in parallel.\n\nRESULTSCompared to two widely used tools - CNVnator and LUMPY, HadoopCNV has similar or better performance on both simulated data sets and real data on the NA12878 individual. Additionally, analysis on a 10-member pedigree showed that HadoopCNV has a Mendelian precision that is similar or better than other tools. Furthermore, HadoopCNV can accurately infer loss of heterozygosity (LOH), while other tools cannot. HadoopCNV requires only 1.6 hours for a human genome with 30X coverage, on a 32-node cluster, with a linear relationship between speed improvement and the number of nodes. We further developed a method to combine HadoopCNV and LUMPY result, and demonstrated that the combination resulted in better performance than any individual tools.\n\nCONCLUSIONSThe combination of high-resolution, allele-specific read depth from WGS data and Hadoop framework can result in efficient and accurate detection of CNVs.

bioinformatics↗