High-throughput sequencing platforms provide an approach for detecting rare HIV-1 variants and documenting more fully quasispecies diversity. We applied this technology to the V3 loop-coding region of env in samples collected from 4 chronically HIV-infected subjects in whom CCR5 antagonist (vicriviroc [VVC]) therapy failed. Between 25,000–140,000 amplified sequences were obtained per sample. Profound baseline V3 loop sequence heterogeneity existed; predicted CXCR4-using populations were identified in a largely CCR5-using population. The V3 loop forms associated with subsequent virologic failure, either through CXCR4 use or the emergence of high-level VVC resistance, were present as minor variants at 0.8–2.8% of baseline samples. Extreme, rapid shifts in population frequencies toward these forms occurred, and deep sequencing provided a detailed view of the rapid evolutionary impact of VVC selection. Greater V3 diversity was observed post-selection. This previously unreported degree of V3 loop sequence diversity has implications for viral pathogenesis, vaccine design, and the optimal use of HIV-1 CCR5 antagonists.

Sequencing-based analyses of microbiomes have traditionally focused on addressing the question of community membership and profiling taxonomic abundance through amplicon sequencing of 16 rRNA genes. More recently, shotgun metagenomics, which involves the random sequencing of all genomic content of a microbiome, has dominated this arena due to advancements in sequencing technology throughput and capability to profile genes as well as microbiome membership. While these methods have revealed a great number of insights into a wide variety of microbiomes, both of these approaches only describe the presence of organisms or genes, and not whether they are active members of the microbiome. To obtain deeper insights into how a microbial community responds over time to their changing environmental conditions, microbiome scientists are beginning to employ large-scale metatranscriptomics approaches. Here, we present a comprehensive review on computational metatranscriptomics approaches to study microbial community transcriptomes. We review the major advancements in this burgeoning field, compare strengths and weaknesses to other microbiome analysis methods, list available tools and workflows, and describe use cases and limitations of this method. We envision that this field will continue to grow exponentially, as will the scope of projects (e.g. longitudinal studies of community transcriptional responses to perturbations over time) and the resulting data. This review will provide a list of options for computational analysis of these data and will highlight areas in need of development.

T he assembly of high-quality genomes from mixed microbial samples is a long-standing challenge in genomics and metagenomics. Here, we describe the application of ProxiMeta, a Hi-C-based metagenomic deconvolution method, to deconvolve a human fecal metagenome. This method uses the intra-cellular proximity signal captured by Hi-C as a direct indicator of which sequences originated in the same cell, enabling culture-free de novo deconvolution of mixed genomes without any reliance on a priori information. We show that ProxiMeta deconvolution provides results of markedly high accuracy and sensitivity, yielding 50 near-complete microbial genomes (many of which are novel) from a single fecal sample, out of 252 total genome clusters. ProxiMeta outperforms traditional contig binning at high-quality genome reconstruction. ProxiMeta shows particularly good performance in constructing high-quality genomes for diverse but poorly-characterized members of the human gut. We further use ProxiMeta to reconstruct genome plasmid content and sharing of plasmids among genomes-tasks that traditional binning methods usually fail to accomplish. Our findings suggest that Hi-C-based deconvolution can be useful to a variety of applications in genomics and metagenomics.

chien-chi Lo & patrick S. G. chain * there is growing interest in reconstructing phylogenies from the copious amounts of genome sequencing projects that target related viral, bacterial or eukaryotic organisms. to facilitate the construction of standardized and robust phylogenies for disparate types of projects, we have developed a complete bioinformatic workflow, with a web-based component to perform phylogenetic and molecular evolutionary (phaMe) analysis from sequencing reads, draft assemblies or completed genomes of closely related organisms. furthermore, the ability to incorporate raw data, including some metagenomic samples containing a target organism (e.g. from clinical samples with suspected infectious agents), shows promise for the rapid phylogenetic characterization of organisms within complex samples without the need for prior assembly. The reconstruction of organismal evolutionary history using phylogenetics is a fundamental method applied to many areas of biology. Single nucleotide polymorphisms (SNPs), one of the dominant forms of evolutionary change, have become an indispensable tool for phylogenetic analyses 1-4. Phylogenies in the pre-genomic era relied on SNPs and conserved sites within a single locus, and was later extended to multiple loci, such as in multiple locus sequence typing (MLST). Although still valuable, these methods only consider evolutionary signals originating within a small fraction of the genome, are unable to capture the complete variation within species, and generally provide a weak phylogenetic signal, particularly within a species, and do not always reflect the true evolutionary history of species 5. While phylogenetic analyses that use many conserved genes (orthologs) are a great improvement, these methods require annotated coding regions, whose predictions are not always accurate or available 6. Furthermore, they are impacted by horizontal gene transfer (HGT) 7 , recombination 8 , rate heterogeneity 9 , and incomplete lineage sorting. Genome-wide SNPs are one of the best measures of phylogenetic diversity as they can discriminate among closely related organisms and help resolve both short and long branches in a tree 10,11. Since selectively neutral SNPs accumulate at a uniform rate, they can be used to measure divergence between species as well as strains 12,13. Furthermore, due to the large number of SNPs found along the length of entire genomes, the use of whole-genome SNPs minimizes the impact of random sequencing and assembly errors that can impact individual loci, as well as biases due to individual genes under strong selective pressure. Some inherent biases remain with whole genome SNP approaches that are similar to loci-based phylogenies such as HGT, recombination, and rate heterogeneity. Although genome-wide sequencing now allows examination of the full complement of genomic variation, the number of completed and finished genomes are increasingly falling behind the generation of new draft genomes, due to the lack of computational or other resources. For example, o...