Perez-Losada, M. | bioRxiv Science

bioRxiv2019

Evaluation of haplotype callers for next-generation sequencing of viruses

Currently, the standard practice for assembling next-generation sequencing (NGS) reads of viral genomes is to summarize thousands of individual short reads into a single consensus sequence, thus confounding useful intra-host diversity information for molecular phylodynamic inference. It is hypothesized that a few viral strains may dominate the intra-host genetic diversity with a variety of lower frequency strains comprising the rest of the population. Several software tools currently exist to convert NGS sequence variants into haplotypes. However, previous studies suggest that current approaches of haplotype reconstruction greatly underestimate intra-host diversity. Here, we tested twelve NGS haplotype reconstruction methods using viral populations simulated under realistic evolutionary dynamics. Parameters for the simulated data spanned known fast evolving viruses (e.g., HIV-1) diversity estimates to test the limits of the haplotype reconstruction methods and ensured coverage of predicted intra-host viral diversity levels. Using those parameters, we simulated HIV-1 viral populations of 216-1,185 haplotypes per host at a frequency <7%. All twelve investigated haplotype callers showed variable performance and produced drastically different results that were mainly driven by differences in mutation rate and, to a lesser extent, in effective population size. Most methods were able to accurately reconstruct haplotypes when genetic diversity was low. However, under higher levels of diversity (e.g., those seen intra-host HIV-1 infections), haplotype reconstruction accuracy was highly variable and, on average, poor. High diversity levels led to severe underestimation of, with a few tools greatly overestimating, the true number of haplotypes. PredictHaplo and PEHaplo produced estimates close to the true number of haplotypes, although their haplotype reconstruction accuracy was worse than that of the other ten tools. We conclude that haplotype reconstruction from NGS short reads is unreliable due to high genetic diversity of fast-evolving viruses. Local haplotype reconstruction of longer reads to phase variants may provide a more reliable estimation of viral variants within a population.\n\nHighlightsO_LIHaplotype callers for NGS data vary greatly in their performance.\nC_LIO_LIHaplotype callers performance is mainly determined by mutation rate.\nC_LIO_LIHaplotype callers performance is less sensitive to effective population size.\nC_LIO_LIMost haplotype callers perform well with low diversity and poorly with high diversity.\nC_LIO_LIPredictHaplo performs best if genetic diversity is in the range of HIV diversity.\nC_LI

bioinformatics↗

bioRxiv2018

Telescope: Characterization of the retrotranscriptome by accurate estimation of transposable element expression

Characterization of Human Endogenous Retrovirus (HERV) expression within the transcriptomic landscape using RNA-seq is complicated by uncertainty in fragment assignment because of sequence similarity. We present Telescope, a computational software tool that provides accurate estimation of transposable element expression (retrotranscriptome) resolved to specific genomic locations. Telescope directly addresses uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We demonstrate the utility of our approach through single locus analysis of HERV expression in 13 ENCODE cell types. When examined at this resolution, we find that the magnitude and breadth of the retrotranscriptome can be vastly different among cell types. Furthermore, our approach is robust to differences in sequencing technology, and demonstrates that the retrotranscriptome has potential to be used for cell type identification. Telescope performs highly accurate quantification of the retrotranscriptomic landscape in RNA-seq experiments, revealing a differential complexity in the transposable element biology of complex systems not previously observed. Telescope is available at github.com/mlbendall/telescope.\n\nAuthor SummaryAlmost half of the human genome is composed of Transposable elements (TEs), but their contribution to the transcriptome, their cell-type specific expression patterns, and their role in disease remains poorly understood. Recent studies have found many elements to be actively expressed and involved in key cellular processes. For example, human endogenous retroviruses (HERVs) are reported to be involved in human embryonic stem cell differentiation. Discovering which exact HERVs are differentially expressed in RNA-seq data would be a major advance in understanding such processes. However, because HERVs have a high level of sequence similarity it is hard to identify which exact HERV is differentially expressed. To solve this problem, we developed a computer program which addressed uncertainty in fragment assignment by reassigning ambiguously mapped fragments to the most probable source transcript as determined within a Bayesian statistical model. We call this program, \"Telescope\". We then used Telescope to identify HERV expression in 13 well-studied cell types from the ENCODE consortium and found that different cell types could be characterized by enrichment for different HERV families, and for locus specific expression. We also showed that Telescope performed better than other methods currently used to determine TE expression. The use of this computational tool to examine new and existing RNA-seq data sets may lead to new understanding of the roles of TEs in health and disease.

genomics↗