Biology subjects

Li, J. J.

Publications and source records attributed to Li, J. J..

6 recordsLinked to original sources

bioRxiv2018

A statistical simulator scDesign for rational scRNA-seq experimental design

Single-cell RNA-sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within an individual cell. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths, and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information. Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and six different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design.

bioinformatics↗

bioRxiv2018

AIDE: annotation-assisted isoform discovery and abundanceestimation from RNA-seq data

Genome-wide accurate identification and quantification of full-length mRNA isoforms is crucial for investigating transcriptional and post-transcriptional regulatory mechanisms of biological phenomena. Despite continuing efforts in developing effective computational tools to identify or assemble full-length mRNA isoforms from second-generation RNA-seq data, it remains a challenge to accurately identify mRNA isoforms from short sequence reads due to the substantial information loss in RNA-seq experiments. Here we introduce a novel statistical method, AIDE (Annotation-assisted Isoform DiscovEry), the first approach that directly controls false isoform discoveries by implementing the testing-based model selection principle. Solving the isoform discovery problem in a stepwise and conservative manner, AIDE prioritizes the annotated isoforms and precisely identifies novel isoforms whose addition significantly improves the explanation of observed RNA-seq reads. We evaluate the performance of AIDE based on multiple simulated and real RNA-seq datasets followed by a PCR-Sanger sequencing validation. Our results show that AIDE effectively leverages the annotation information to compensate the information loss due to short read lengths. AIDE achieves the highest precision in isoform discovery and the lowest error rates in isoform abundance estimation, compared with three state-of-the-art methods Cufflinks, SLIDE, and StringTie. As a robust bioinformatics tool for transcriptome analysis, AIDE will enable researchers to discover novel transcripts with high confidence.

bioinformatics↗

bioRxiv2018

Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness

Facial attractiveness is a complex human trait of great interest in both academia and industry. Literature on sociological and phenotypic factors associated with facial attractiveness is rich, but its genetic basis is poorly understood. In this paper, we conducted a genome-wide association study to discover genetic variants associated with facial attractiveness using 3,928 samples in the Wisconsin Longitudinal Study. We identified two genome-wide significant loci and highlighted a handful of candidate genes, many of which are specifically expressed in human tissues involved in reproduction and hormone synthesis. Additionally, facial attractiveness showed strong and negative genetic correlations with BMI in females and with blood lipids in males. Our analysis also suggested sex-specific selection pressure on variants associated with lower male attractiveness. These results revealed sex-specific genetic architecture of facial attractiveness and provided fundamental new insights into its genetic basis.

genetics↗

bioRxiv2017

Spliceosome profiling visualizes the operations of a dynamic RNP in vivo at nucleotide resolution

Tools to understand how the spliceosome functions in vivo have lagged behind advances in its structural biology. We describe methods to globally profile spliceosome-bound precursor, intermediates and products at nucleotide resolution. We apply these tools to three divergent yeast species that span 600 million years of evolution. The sensitivity of the approach enables detection of novel cases of non-canonical catalysis including interrupted, recursive and nested splicing. Employing statistical modeling to understand the quantitative relationships between RNA features and the data, we uncover independent roles for intron size, position and number in substrate progression through the two catalytic stages. These include species-specific inputs suggestive of spliceosome-transcriptome coevolution. Further investigations reveal ATP-dependent discard of numerous endogenous substrates at both the precursor and lariat-intermediate stages and connect discard to intron retention, a form of splicing regulation. Spliceosome profiling is a quantitative, generalizable global technology to investigate an RNP central to eukaryotic gene expression.\n\nHighlightsO_LIMeasurement of spliceosome-bound precursor and intermediate in three species\nC_LIO_LINon-canonical splicing events revealed\nC_LIO_LIStatistical modeling uncovers substrate features that predict catalytic efficiency\nC_LIO_LIDiscard of suboptimal substrates occurs in vivo and predicts intron-retained mRNAs\nC_LI

molecular biology↗

bioRxiv2017

scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq Data

The emerging single cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at single-cell resolution. The analysis of scRNA-seq data is complicated by excess zero or near zero counts, the so-called dropouts due to the low amounts of mRNA sequenced within individual cells. Downstream analysis of scRNA-seq would be severely biased if the dropout events are not properly corrected. We introduce scImpute, a statistical method to accurately and robustly impute the dropout values in scRNA-seq data. ScImpute automatically identifies gene expression values affected by dropout events, and only perform imputation on these values without introducing new bias to the rest data. ScImpute also detects outlier or rare cells and excludes them from imputation. Evaluation based on both simulated and real scRNA-seq data on mouse embryos, mouse brain cells, human blood cells, and human embryonic stem cells suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropout events. scImpute is shown to correct false zero counts, enhance the clustering of cell populations and subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.

bioinformatics↗

bioRxiv2017

Quantitating Translational Control: mRNA Abundance-Dependent and Independent Contributions

Translation rate per mRNA molecule correlates positively with mRNA abundance. As a result, protein levels do not scale linearly with mRNA levels, but instead scale with the abundance of mRNA raised to the power of an \"amplification exponent\". Here we show that to quantitate translational control, the translation rate must be decomposed into two components. One, TRmD, depends on the mRNA level and defines the amplification exponent. The other, TRmIND, is independent of mRNA amount and impacts the correlation coefficient between protein and mRNA levels. We show that in S. cerevisiae TRmD represents [~]20% of the variance in translation and directs an amplification exponent of 1.20 with a 95% confidence interval [1.14, 1.26]. TRmIND constitutes the remaining [~]80% of the variance in translation and explains [~]5% of the variance in protein expression. We also find that TRmD and TRmIND are preferentially determined by different mRNA sequence features: TRmIND by the length of the open reading frame and TRmD both by a [~]60 nucleotide element that spans the initiating AUG and by codon and amino acid frequency. Our work provides more appropriate estimates of translational control and implies that TRmIND is under different evolutionary selective pressures than TRmD.

genomics↗