LongISLND is a software package designed to simulate sequencing data according to the characteristics of third generation, single-molecule sequencing technologies. The general software architecture is easily extendable, as demonstrated by the emulation of Pacific Biosciences (PacBio) multi-pass sequencing with P5 and P6 chemistries, producing data in FASTQ, H5, and the latest PacBio BAM format. We demonstrate its utility by downstream processing with consensus building and variant calling. AVAILABILITY AND IMPLEMENTATION: LongISLND is implemented in Java and available at http://bioinform.github.io/longislnd CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

UNLABELLED: Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes. AVAILABILITY AND IMPLEMENTATION: Code in Python is at http://bioinform.github.io/metasv/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: VarSim is a framework for assessing alignment and variant calling accuracy in high-throughput genome sequencing through simulation or real data. In contrast to simulating a random mutation spectrum, it synthesizes diploid genomes with germline and somatic mutations based on a realistic model. This model leverages information such as previously reported mutations to make the synthetic genomes biologically relevant. VarSim simulates and validates a wide range of variants, including single nucleotide variants, small indels and large structural variants. It is an automated, comprehensive compute framework supporting parallel computation and multiple read simulators. Furthermore, we developed a novel map data structure to validate read alignments, a strategy to compare variants binned in size ranges and a lightweight, interactive, graphical report to visualize validation results with detailed statistics. Thus far, it is the most comprehensive validation tool for secondary analysis in next generation sequencing. AVAILABILITY AND IMPLEMENTATION: Code in Java and Python along with instructions to download the reads and variants is at http://bioinform.github.io/varsim. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next-generation sequence analysis has become an important task both in laboratory and clinical settings. A key stage in the majority sequence analysis workflows, such as resequencing, is the alignment of genomic reads to a reference genome. The accurate alignment of reads with large indels is a computationally challenging task for researchers. RESULTS: We introduce SeqAlto as a new algorithm for read alignment. For reads longer than or equal to 100 bp, SeqAlto is up to 10 × faster than existing algorithms, while retaining high accuracy and the ability to align reads with large (up to 50 bp) indels. This improvement in efficiency is particularly important in the analysis of future sequencing data where the number of reads approaches many billions. Furthermore, SeqAlto uses less than 8 GB of memory to align against the human genome. SeqAlto is benchmarked against several existing tools with both real and simulated data. AVAILABILITY: Linux and Mac OS X binaries free for academic use are available at http://www.stanford.edu/group/wonglab/seqalto CONTACT: [email protected].

This paper compares an implementation of a Bayesian inference algorithm across several FPGAs and GPGPUs, while embracing both the execution model and high-level architecture of a GPGPU. Our study is motivated by recent work in template-based programming and architectural models for FPGA computing. The comparison we present is meant to demonstrate the FPGA's potential, while constraining the design to follow the microarchitectural template of more programmable devices such as GPGPUs.

ParaLearn is a scalable, parallel FPGA-based system for learning interaction networks using Bayesian statistics. ParaLearn includes problem specific parallel/scalable algorithms, system software and hardware architecture to address this complex problem.

Aberrant intracellular signaling plays an important role in many diseases. The causal structure of signal transduction networks can be modeled as Bayesian Networks (BNs), and computationally learned from experimental data. However, learning the structure of Bayesian Networks (BNs) is an NP-hard problem that, even with fast heuristics, is too time consuming for large, clinically important networks (20-50 nodes). In this paper, we present a novel graphics processing unit (GPU)-accelerated implementation of a Monte Carlo Markov Chain-based algorithm for learning BNs that is up to 7.5-fold faster than current general-purpose processor (GPP)-based implementations. The GPU-based implementation is just one of several implementations within the larger application, each optimized for a different input or machine configuration. We describe the methodology we use to build an extensible application, assembled from these variants, that can target a broad range of heterogeneous systems, e.g., GPUs, multicore GPPs. Specifically we show how we use the Merge programming model to efficiently integrate, test and intelligently select among the different potential implementations.

Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficiently. However, the accuracy of such methods can be very sensitive to the choice of regularization parameter, and optimal selection of this parameter remains an open problem. Herein, we propose a maximum a posteriori probability (MAP) approach that investigates different priors on the regularization parameter and yields promising empirical results on both synthetic data and real-life application such as brain imaging data (fMRI).

Learning the structure of Bayesian networks(BNs) is known to be NP-complete and most of the recent work in the field is based on heuristics. Many recent approaches to the problem trade correctness and exactness for faster computation and are still computationally infeasible, except for networks with few variables. In this paper we present a software/hardware co-design approach to learning Bayesian networks from experimental data that is scalable to very large networks. Our implementation improves the performance of algorithms that are traditionally developed based on the Von Neumann computing paradigm by more than four orders of magnitude. Through parallel implementation and exploitation of the reconfigurability of Field Programmable Gate Array (FPGA) systems our design enables scientists to apply BN learning techniques to large problems such as studies in molecular biology where the number of variables in the system overwhelms any state of the art software implementations. We describe how we combine Markov Chain Monte Carlo (MCMC) sampling with Bayesian network learning techniques as well as supervised learning methods in a parallel and scalable design. We also present how our design is mapped and customized to run on the Berkeley Emulation Engine 2 (BEE2) multi-FPGA system. Experimental results are presented on synthetic data sets generated from standard Bayesian networks as well as a real life problem in the context of systems biology

In this paper, we review the paradigm of inductive process modeling, which uses background knowledge about possible component processes to construct quantitative models of dynamical systems. We note that previous methods for this task tend to overfit the training data, which suggests ensemble learning as a likely response. However, such techniques combine models in ways that reduce comprehensibility, making their output much less accessible to domain scientists. As an alternative, we introduce a new approach that induces a set of process models from di#erent samples of the training data and uses them to guide a final search through the space of model structures. Experiments with synthetic and natural data suggest this method reduces error and decreases the chance of including unnecessary processes in the model.