iLoci: robust evaluation of genome content and organization for provisional and mature genome assemblies.

We introduce a new framework for genome analyses based on parsing an annotated genome assembly into distinct interval loci (iLoci), available as open-source software as part of the AEGeAn Toolkit (https://github.com/BrendelGroup/AEGeAn). We demonstrate that iLoci provide an alternative coordinate system that is robust to changes in assembly and annotation versions and facilitates granular quality control of genome data. We discuss how statistics computed on iLoci reflect various characteristics of genome content and organization and illustrate how these statistics can be used to establish a baseline for assessment of the completeness and accuracy of the data. We also introduce a well-defined measure of relative genome compactness and compute other iLocus statistics that reveal genome-wide characteristics of gene arrangements in the whole genome context. Given the fast pace of assembly/annotation updates, our AEGeAn Toolkit fills a niche in computational genomics based on deriving persistent and species-specific genome statistics. Gene structure model-centric iLoci provide a precisely defined coordinate system that can be used to store assembly/annotation updates that reflect either stable or changed assessments. Large-scale application of the approach revealed species- and clade-specific genome organization in precisely defined computational terms, promising intriguing forays into the forces of shaping genome structure as more and more genome assemblies are being deposited.

MicroHapDB: A Portable and Extensible Database of All Published Microhaplotype Marker and Frequency Data.

Microhaplotypes are the subject of significant interest in the forensics community as a promising multi-purpose forensic DNA marker for human identification. Microhaplotype markers are composed of multiple SNPs in close proximity, such that a single NGS read can simultaneously genotype the individual SNPs and phase them in aggregate to determine the associated donor haplotype. Abundant throughout the human genome, numerous recent studies have sought to discover and rank microhaplotype markers according to allelic diversity within and among populations. Microhaplotypes provide an appealing alternative to STR markers for human identification and mixture deconvolution, but can also be optimized for ancestry inference or combined with phenotype SNPs for prediction of externally visible characteristics in a multiplex NGS assay. Designing and evaluating panels of microhaplotypes is complicated by the lack of a convenient database of all published data, as well as the lack of population allele frequency data spanning disparate marker collections. We present MicroHapDB, a comprehensive database of published microhaplotype marker and frequency data, as a tool to advance the development of microhaplotype-based human forensics capabilities. We also present population allele frequencies derived from 26 global population samples for all microhaplotype markers published to date, facilitating the design and interpretation of custom multi-source panels. We submit MicroHapDB as a resource for community members engaged in marker discovery, population studies, assay development, and panel and kit design.

Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy.

xGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud.

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today's pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant's Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching.

Genome, transcriptome and methylome sequencing of a primitively eusocial wasp reveal a greatly reduced DNA methylation system in a social insect.

Comparative genomics of social insects has been intensely pursued in recent years with the goal of providing insights into the evolution of social behaviour and its underlying genomic and epigenomic basis. However, the comparative approach has been hampered by a paucity of data on some of the most informative social forms (e.g. incipiently and primitively social) and taxa (especially members of the wasp family Vespidae) for studying social evolution. Here, we provide a draft genome of the primitively eusocial model insect Polistes dominula, accompanied by analysis of caste-related transcriptome and methylome sequence data for adult queens and workers. Polistes dominula possesses a fairly typical hymenopteran genome, but shows very low genomewide GC content and some evidence of reduced genome size. We found numerous caste-related differences in gene expression, with evidence that both conserved and novel genes are related to caste differences. Most strikingly, these -omics data reveal a major reduction in one of the major epigenetic mechanisms that has been previously suggested to be important for caste differences in social insects: DNA methylation. Along with a conspicuous loss of a key gene associated with environmentally responsive DNA methylation (the de novo DNA methyltransferase Dnmt3), these wasps have greatly reduced genomewide methylation to almost zero. In addition to providing a valuable resource for comparative analysis of social insect evolution, our integrative -omics data for this important behavioural and evolutionary model system call into question the general importance of DNA methylation in caste differences and evolution in social insects.

The khmer software package: enabling efficient nucleotide sequence analysis.

The khmer package is a freely available software library for working efficiently with fixed length DNA words, or k-mers. khmer provides implementations of a probabilistic k-mer counting data structure, a compressible De Bruijn graph representation, De Bruijn graph partitioning, and digital normalization. khmer is implemented in C++ and Python, and is freely available under the BSD license at  https://github.com/dib-lab/khmer/.

ParsEval: parallel comparison and analysis of gene structure annotations.

BACKGROUND: Accurate gene structure annotation is a fundamental but somewhat elusive goal of genome projects, as witnessed by the fact that (model) genomes typically undergo several cycles of re-annotation. In many cases, it is not only different versions of annotations that need to be compared but also different sources of annotation of the same genome, derived from distinct gene prediction workflows. Such comparisons are of interest to annotation providers, prediction software developers, and end-users, who all need to assess what is common and what is different among distinct annotation sources. We developed ParsEval, a software application for pairwise comparison of sets of gene structure annotations. ParsEval calculates several statistics that highlight the similarities and differences between the two sets of annotations provided. These statistics are presented in an aggregate summary report, with additional details provided as individual reports specific to non-overlapping, gene-model-centric genomic loci. Genome browser styled graphics embedded in these reports help visualize the genomic context of the annotations. Output from ParsEval is both easily read and parsed, enabling systematic identification of problematic gene models for subsequent focused analysis. RESULTS: ParsEval is capable of analyzing annotations for large eukaryotic genomes on typical desktop or laptop hardware. In comparison to existing methods, ParsEval exhibits a considerable performance improvement, both in terms of runtime and memory consumption. Reports from ParsEval can provide relevant biological insights into the gene structure annotations being compared. CONCLUSIONS: Implemented in C, ParsEval provides the quickest and most feature-rich solution for genome annotation comparison to date. The source code is freely available (under an ISC license) at http://parseval.sourceforge.net/.

TAL Effector-Nucleotide Targeter (TALE-NT) 2.0: tools for TAL effector design and target prediction.

Transcription activator-like (TAL) effectors are repeat-containing proteins used by plant pathogenic bacteria to manipulate host gene expression. Repeats are polymorphic and individually specify single nucleotides in the DNA target, with some degeneracy. A TAL effector-nucleotide binding code that links repeat type to specified nucleotide enables prediction of genomic binding sites for TAL effectors and customization of TAL effectors for use in DNA targeting, in particular as custom transcription factors for engineered gene regulation and as site-specific nucleases for genome editing. We have developed a suite of web-based tools called TAL Effector-Nucleotide Targeter 2.0 (TALE-NT 2.0; https://boglab.plp.iastate.edu/) that enables design of custom TAL effector repeat arrays for desired targets and prediction of TAL effector binding sites, ranked by likelihood, in a genome, promoterome or other sequence of interest. Search parameters can be set by the user to work with any TAL effector or TAL effector nuclease architecture. Applications range from designing highly specific DNA targeting tools and identifying potential off-target sites to predicting effector targets important in plant disease.