Biology subjects

Zhi, D.

Publications and source records attributed to Zhi, D..

3 recordsLinked to original sources

bioRxiv2018

Gene2Vec: Distributed Representation of Genes Based on Co-Expression

Existing functional description of genes are categorical, discrete, and mostly through manual process. In this work, we explore the idea of gene embedding, distributed representation of genes, in the spirit of word embedding. From a pure data-driven fashion, we trained a 300 dimension vector representation of all human genes, using gene co-expression patterns in 984 data sets from the GEO databases. These vectors capture functional relatedness of genes in terms of recovering known pathways - the average inner product (similarity) of genes within a pathway is 1.68X greater than that of random genes. Using t-SNE, we produced a gene co-expression map that shows local concentrations of tissue specific genes. We also illustrated the usefulness of the embedded gene vectors, laden with rich information on gene co-expression patterns, in tasks such as gene-gene interaction prediction. Overall, we believe that this distributed representation of genes may be useful for more bioinformatics applications.

bioinformatics↗

bioRxiv2017

Ultra-fast Identity by Descent Detection in Biobank-Scale Cohorts using Positional Burrows-Wheeler Transform

With the availability of genotyping data of very large samples, there is an increasing need for tools that can efficiently identify genetic relationships among all individuals in the sample. One fundamental measure of genetic relationship of a pair of individuals is identity by descent (IBD), chromosomal segments that are shared among two individuals due to common ancestry. However, the efficient identification of IBD segments among a large number of genotyped individuals is a challenging computational problem. Most existing methods are not feasible for even thousands of individuals because they are based on pairwise comparisons of all individuals and thus scale up quadratically with sample size. Some methods, such as GERMLINE, use fast dictionary lookup of short seed sequence matches to achieve a near-linear time efficiency. However, the number of short seed matches often scales up super-linearly in real population data.\n\nIn this paper we describe a new approach for IBD detection. We take advantage of an efficient population genotype index, Positional BWT (PBWT), by Richard Durbin. PBWT achieves linear time query of perfectly identical subsequences among all samples. However, the original PBWT is not tolerant to genotyping errors which often interrupt long IBD segments into short fragments. We introduce a randomized strategy by running PBWTs over random projections of the original sequences. To boost the detection power we run PBWT multiple times and merge the identified IBD segments through interval tree algorithms. Given a target IBD segment length, RaPID adjust parameters to optimize detection power and accuracy.\n\nSimulation results proved that our tool (RaPID) achieves almost linear scaling up to sample size and is orders of magnitude faster than GERMLINE. At the same time, RaPID maintains a detection power and accuracy comparable to existing mainstream algorithms, GERMLINE and IBDseq. Running multiple times with various target detection lengths over the 1000 Genomes Project data, RaPID can detect population events at different time scales. With our tool, it is feasible to identify IBDs among hundreds of thousands to millions of individuals, a sample size that will become reality in a few years.

genomics↗

bioRxiv2016

Hardy Weinberg Exact Test In Large Scale Variant Calling Quality Control

Hardy Weinberg Equilibrium (HWE) test is widely used as a quality control measure to detect sequencing artifacts like mismapping, allelic dropout and biases. However, in the high throughput sequencing era, where the sample size is beyond a thousand scale, the utility of HWE test in reducing the false positive rate remains unclear. In this paper, we demonstrate that HWE test has limited power in identifying sequencing artifacts when the variant allele frequency is lower than 1% in a variant call set produced from more than five thousand whole genome sequenced samples from two homogeneous populations. We develop a novel strategy of implementing HWE filtering in which we incorporate site frequency spectrum information and determine the p-value cutoff which optimizes the tradeoff between sensitivity and specificity. The novel strategy is shown to outperform the exact test of HWE with an empirical constant p-value cutoff regardless of the sequencing sample size. We also present best practice recommendations for identifying possible sources of false positives from large sequencing datasets based on an analysis of intrinsic biases in the variant calling process. Our novel strategy of determining the HWE test p-value cutoff and applying the test to the common variants provides a practical approach for the variant level quality controls in the upcoming sequencing projects with tens to hundreds of thousand of samples.

bioinformatics↗