SUMMARY: We introduce an in silico PCR (sPCR) method for the assembly of specific genomic regions spanned by PCR primers using raw sequence reads. This allows a user to quickly isolate the exact regions that are abundant in public archives of gene sequences, leveraging the decades of work that have gone into optimizing primer sequences for benchtop PCR. We implement sPCR in sharkmer as a targeted de Bruijn graph assembler seeded with the forward primer sequence and terminated with the reverse primer sequence. This is useful for a variety of routine tasks, including validating the species identity of a dataset, identifying contaminants, and quickly building phylogenies from raw sequence data. AVAILABILITY AND IMPLEMENTATION: sharkmer is written in Rust. Code, instructions for installation and use, tests, and other resources are available in the GitHub repository at https://github.com/caseywdunn/sharkmer and at Zenodo with DOI 10.5281/zenodo.19020708. It can also be installed via bioconda.

Molecular epidemiology and HIV-1 transmission networks reconstruction can provide insights into transmission dynamics and inform public health strategies. Long HIV sequences, such as near full-length (nFL) genomes, can improve the accuracy of phylogenetic inference. However, relatively short pol sequences are still broadly used for inferring molecular HIV clusters. Whether a mix of long and short HIV-1 sequences can improve phylogenetic inference of molecular HIV clusters remains unknown. We propose a flexible approach called T-shaped alignments that incorporates both nFL HIV-1 genomes and partial pol sequences, and investigate whether this approach improves phylogenetic reconstruction of molecular clusters. Under the assumption that clustering from 100% of long sequences is the most accurate, we obtained 1196 subtype B nFL HIV-1 sequences from the Los Alamos National Laboratory Database and a single-study subset, varied the proportion of long and short sequences in our T-shape alignments, systematically masked all non-pol regions with missing characters in proportional increments, and compared tree similarity and cluster inference among datasets. With the full dataset, we found that when more than 50% of available sequences are nFL, the T-shaped alignment gradually yields results closer to the 100% n, with more and larger clusters identified. However, below the 50% threshold accuracy did not increase. Stringent bootstrap thresholds decreased cluster accuracy gaps but also decreased number of clusters found and mean cluster size. For the subset dataset, we found that the introduction of nFL sequences to the T-shaped alignment improves accuracy in clustering either after a 30% threshold or immediately depending on bootstrap choice. Our new approach and results suggest that using T-shape alignments to mix HIV-1 sequences of different lengths can improve phylogenetic and clustering accuracy, with needed nFL proportion depending on analysis goals. The T-shape alignment provides a straightforward method for utilizing all available sequences to improve phylogenetic analysis.

MOTIVATION: Draft de novo genome assemblies are now available for many organisms. These assemblies are point estimates of the true genome sequences. Each is a specific hypothesis, drawn from among many alternative hypotheses, of the sequence of a genome. Assembly uncertainty, the inability to distinguish between multiple alternative assembly hypotheses, can be due to real variation between copies of the genome in the sample, errors and ambiguities in the sequenced data and assumptions and heuristics of the assemblers. Most assemblers select a single assembly according to ad hoc criteria, and do not yet report and quantify the uncertainty of their outputs. Those assemblers that do report uncertainty take different approaches to describing multiple assembly hypotheses and the support for each. RESULTS: Here we review and examine the problem of representing and measuring uncertainty in assemblies. A promising recent development is the implementation of assemblers that are built according to explicit statistical models. Some new assembly methods, for example, estimate and maximize assembly likelihood. These advances, combined with technical advances in the representation of alternative assembly hypotheses, will lead to a more complete and biologically relevant understanding of assembly uncertainty. This will in turn facilitate the interpretation of downstream analyses and tests of specific biological hypotheses.

BACKGROUND: In the past decade, transcriptome data have become an important component of many phylogenetic studies. They are a cost-effective source of protein-coding gene sequences, and have helped projects grow from a few genes to hundreds or thousands of genes. Phylogenetic studies now regularly include genes from newly sequenced transcriptomes, as well as publicly available transcriptomes and genomes. Implementing such a phylogenomic study, however, is computationally intensive, requires the coordinated use of many complex software tools, and includes multiple steps for which no published tools exist. Phylogenomic studies have therefore been manual or semiautomated. In addition to taking considerable user time, this makes phylogenomic analyses difficult to reproduce, compare, and extend. In addition, methodological improvements made in the context of one study often cannot be easily applied and evaluated in the context of other studies. RESULTS: We present Agalma, an automated tool that constructs matrices for phylogenomic analyses. The user provides raw Illumina transcriptome data, and Agalma produces annotated assemblies, aligned gene sequence matrices, a preliminary phylogeny, and detailed diagnostics that allow the investigator to make extensive assessments of intermediate analysis steps and the final results. Sequences from other sources, such as externally assembled genomes and transcriptomes, can also be incorporated in the analyses. Agalma is built on the BioLite bioinformatics framework, which tracks provenance, profiles processor and memory use, records diagnostics, manages metadata, installs dependencies, logs version numbers and calls to external programs, and enables rich HTML reports for all stages of the analysis. Agalma includes a small test data set and a built-in test analysis of these data. In addition to describing Agalma, we here present a sample analysis of a larger seven-taxon data set. Agalma is available for download at https://bitbucket.org/caseywdunn/agalma. CONCLUSIONS: Agalma allows complex phylogenomic analyses to be implemented and described unambiguously as a series of high-level commands. This will enable phylogenomic studies to be readily reproduced, modified, and extended. Agalma also facilitates methods development by providing a complete modular workflow, bundled with test data, that will allow further optimization of each step in the context of a full phylogenomic analysis.

SUMMARY: Phyutility provides a set of phyloinformatics tools for summarizing and manipulating phylogenetic trees, manipulating molecular data and retrieving data from NCBI. Its simple command-line interface allows for easy integration into scripted analyses, and is able to handle large datasets with an integrated database. AVAILABILITY: Phyutility, including source code, documentation, examples, and executables, is available at http://code.google.com/p/phyutility.