HASLR Fast Hybrid Assembly of Long Reads, bioRxiv, 2020-01-28

AbstractThird generation sequencing technologies from platforms such as Oxford Nanopore Technologies and Pacific Biosciences have paved the way for building more contiguous assemblies and complete reconstruction of genomes. The larger effective length of the reads generated with these technologies has provided a mean to overcome the challenges of short to mid-range repeats. Currently, accurate long read assemblers are computationally expensive while faster methods are not as accurate. Therefore, there is still an unmet need for tools that are both fast and accurate for reconstructing small and large genomes. Despite the recent advances in third generation sequencing, researchers tend to generate second generation reads for many of the analysis tasks. Here, we present HASLR, a hybrid assembler which uses both second and third generation sequencing reads to efficiently generate accurate genome assemblies. Our experiments show that HASLR is not only the fastest assembler but also the one with the lowest number of misassemblies on all the samples compared to other tested assemblers. Furthermore, the generated assemblies in terms of contiguity and accuracy are on par with the other tools on most of the samples.AvailabilityHASLR is an open source tool available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comvpc-ccghaslr>httpsgithub.comvpc-ccghaslr<jatsext-link>.

biorxiv bioinformatics 0-100-users 2020

Post-prediction Inference, bioRxiv, 2020-01-23

AbstractMany modern problems in medicine and public health leverage machine learning methods to predict outcomes based on observable covariates [1, 2, 3, 4]. In an increasingly wide array of settings, these predicted outcomes are used in subsequent statistical analysis, often without accounting for the distinction between observed and predicted outcomes [1, 5, 6, 7, 8, 9]. We call inference with predicted outcomes post-prediction inference. In this paper, we develop methods for correcting statistical inference using outcomes predicted with an arbitrary machine learning method. Rather than trying to derive the correction from the first principles for each machine learning tool, we make the observation that there is typically a low-dimensional and easily modeled representation of the relationship between the observed and predicted outcomes. We build an approach for the post-prediction inference that naturally fits into the standard machine learning framework. We estimate the relationship between the observed and predicted outcomes on the testing set and use that model to correct inference on the validation set and subsequent statistical models. We show our postpi approach can correct bias and improve variance estimation (and thus subsequent statistical inference) with predicted outcome data. To show the broad range of applicability of our approach, we show postpi can improve inference in two totally distinct fields modeling predicted phenotypes in repurposed gene expression data [10] and modeling predicted causes of death in verbal autopsy data [11]. We have made our method available through an open-source R package [<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comSiruoWangpostpi>httpsgithub.comSiruoWangpostpi<jatsext-link>]

biorxiv bioinformatics 0-100-users 2020

 

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