efficient data structures for mobile de novo genome assembly by third generation sequencing

Abstract Mobile/portable (third-generation) sequencing technologies, including Oxford Nanopore's MinION and SmidgION, are revolutionizing once again –after the advent of high-throughput sequencing– biomedical sciences. They combine an increase in sequence length (up to hundred thousands of bases) with extreme portability. While a sequencer now fits the palm of a hand and needs only a USB outlet or a mobile phone/tablet to work, the data analysis phases are bound to an available Internet connection and cloud computing. This somehow hampers the portability paradigm, especially if the technology is used in resource-limited settings or remote areas with limited connectivity. In this work, we introduce efficient data structures to effectively enable portable data analytics by means of third-generation sequencing. Specifically, we show how sequence overlap graphs (fixed length k-mers, with an extension on variable lengths) can be built and stored on a mobile phone, thereby allowing the execution of de novo genome assembly algorithms (along with ad-hoc strategies for error correction) without the need of transfer data over the Internet nor execution on a desktop

Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data

Estimating treatment effects, especially individualized treatment effects (ITE), using observational data is challenging due to the complex situations of confounding bias. Existing approaches for estimating treatment effects from longitudinal observational data are usually built upon a strong assumption of "unconfoundedness", which is hard to fulfill in real-world practice. In this paper, we propose the Variational Temporal Deconfounder (VTD), an approach that leverages deep variational embeddings in the longitudinal setting using proxies (i.e., surrogate variables that serve for unobservable variables). Specifically, VTD leverages observed proxies to learn a hidden embedding that reflects the true hidden confounders in the observational data. As such, our VTD method does not rely on the "unconfoundedness" assumption. We test our VTD method on both synthetic and real-world clinical data, and the results show that our approach is effective when hidden confounding is the leading bias compared to other existing models

third generation sequencing data analytics on mobile devices cache oblivious and out of core approaches as a proof of concept

Abstract Mobile (third-generation) sequencing technologies, including Oxford Nanopore's MinION and SmidgION, have the benefit of outputting long sequence reads (up to hundred thousands of bases) in a portable manner. These sequencing devices fit in the palm of a hand and only require a USB outlet. Unfortunately, the development of data analysis tools for these technologies is in a nascent stage, impeding on the portability of these devices. The objective of this work is to introduce an out-of-core approach to port Nanopore analytics on mobile devices such as tablets or smartphones, often used in extreme experimental settings with special ergonomics needs and ease of sterilization. In this paper, we present a serial k-mer parser/counter for FAST5 files, and a de Bruijn graph construction method which can run on a hand-held device. In order to accomplish this portability we develop novel cache oblivious data structures and out-of-core chunked processing methods. Our toolset, which we refer to as Nanopore Portable Analytics Library (NanoPAL), wase implemented in ISO C++ v.14 and compiled for Android devices. Using MinION data (Zaire Ebolavirus species and others), we evaluate the time required to parse and build the de Bruijn graph with respect to the file sizes and RAM allocation. These metrics were compared to those of minimap/miniasm. On an LG Nexus 5 with 2GB or RAM, 2MB L2 cache and 16GB storage, the out-of-core NanoPAL is able to process FAST5 files at about 30 minutes per 0.5 GB, creating sorted k-mer and de Bruijn graph files. The recompiled minimap/miniasm tool cannot complete FAST5 files larger than 170MB. In conjunction with base calling/error correction, and with addition of assembly procedures downstream, NanoPAL can be effectively used to perform analyses of MinION/SmidgION data locally on a mobile device

The global spread of Middle East respiratory syndrome: an analysis fusing traditional epidemiological tracing and molecular phylodynamics

Table S1. List of sequences used for analysis. Column “Label” corresponds to labels for sequences. presented in Figures 3 and 4 with country (by 2-letter ISO country code) and year of collection; countries, sources, and dates (month-year) are based on information in GenBank or related publication (indicated in Reference column). (DOCX 128 kb

PhyloTempo: A Set of R Scripts for Assessing and Visualizing Temporal Clustering in Genealogies Inferred from Serially Sampled Viral Sequences

Serially-sampled nucleotide sequences can be used to infer demographic history of evolving viral populations. The shape of a phylogenetic tree often reflects the interplay between evolutionary and ecological processes. Several approaches exist to analyze the topology and traits of a phylogenetic tree, by means of tree balance, branching patterns and comparative properties. The temporal clustering (TC) statistic is a new topological measure, based on ancestral character reconstruction, which characterizes the temporal structure of a phylogeny. Here, PhyloTempo is the first implementation of the TC in the R language, integrating several other topological measures in a user-friendly graphical framework. The comparison of the TC statistic with other measures provides multifaceted insights on the dynamic processes shaping the evolution of pathogenic viruses. The features and applicability of PhyloTempo were tested on serially-sampled intra-host human and simian immunodeficiency virus population data sets. PhyloTempo is distributed under the GNU general public license at https://sourceforge.net/projects/phylotempo/

Joint Application of the Target Trial Causal Framework and Machine Learning Modeling to Optimize Antibiotic Therapy: Use Case on Acute Bacterial Skin and Skin Structure Infections due to Methicillin-resistant Staphylococcus aureus

Bacterial infections are responsible for high mortality worldwide. Antimicrobial resistance underlying the infection, and multifaceted patient's clinical status can hamper the correct choice of antibiotic treatment. Randomized clinical trials provide average treatment effect estimates but are not ideal for risk stratification and optimization of therapeutic choice, i.e., individualized treatment effects (ITE). Here, we leverage large-scale electronic health record data, collected from Southern US academic clinics, to emulate a clinical trial, i.e., 'target trial', and develop a machine learning model of mortality prediction and ITE estimation for patients diagnosed with acute bacterial skin and skin structure infection (ABSSSI) due to methicillin-resistant Staphylococcus aureus (MRSA). ABSSSI-MRSA is a challenging condition with reduced treatment options - vancomycin is the preferred choice, but it has non-negligible side effects. First, we use propensity score matching to emulate the trial and create a treatment randomized (vancomycin vs. other antibiotics) dataset. Next, we use this data to train various machine learning methods (including boosted/LASSO logistic regression, support vector machines, and random forest) and choose the best model in terms of area under the receiver characteristic (AUC) through bootstrap validation. Lastly, we use the models to calculate ITE and identify possible averted deaths by therapy change. The out-of-bag tests indicate that SVM and RF are the most accurate, with AUC of 81% and 78%, respectively, but BLR/LASSO is not far behind (76%). By calculating the counterfactuals using the BLR/LASSO, vancomycin increases the risk of death, but it shows a large variation (odds ratio 1.2, 95% range 0.4-3.8) and the contribution to outcome probability is modest. Instead, the RF exhibits stronger changes in ITE, suggesting more complex treatment heterogeneity.Comment: This is the Proceedings of the KDD workshop on Applied Data Science for Healthcare (DSHealth 2022), which was held on Washington D.C, August 14 202