Jamison McCorrison

A catalog of reference genomes from the human microbiome.

News from the Inner Tube of Life A major initiative by the U.S. National Institutes of Health to sequence 900 genomes of microorganisms that live on the surfaces and orifices of the human body has established standardized protocols and methods for such large-scale reference sequencing. By combining previously accumulated data with new data, Nelson et al. (p. 994) present an initial analysis of 178 bacterial genomes. The sampling so far barely scratches the surface of the microbial diversity found on humans, but the work provides an important baseline for future analyses. Standardized protocols and methods are being established for large-scale sequencing of the microorganisms living on humans. The human microbiome refers to the community of microorganisms, including prokaryotes, viruses, and microbial eukaryotes, that populate the human body. The National Institutes of Health launched an initiative that focuses on describing the diversity of microbial species that are associated with health and disease. The first phase of this initiative includes the sequencing of hundreds of microbial reference genomes, coupled to metagenomic sequencing from multiple body sites. Here we present results from an initial reference genome sequencing of 178 microbial genomes. From 547,968 predicted polypeptides that correspond to the gene complement of these strains, previously unidentified (“novel”) polypeptides that had both unmasked sequence length greater than 100 amino acids and no BLASTP match to any nonreference entry in the nonredundant subset were defined. This analysis resulted in a set of 30,867 polypeptides, of which 29,987 (~97%) were unique. In addition, this set of microbial genomes allows for ~40% of random sequences from the microbiome of the gastrointestinal tract to be associated with organisms based on the match criteria used. Insights into pan-genome analysis suggest that we are still far from saturating microbial species genetic data sets. In addition, the associated metrics and standards used by our group for quality assurance are presented.

Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons

A protocol is described for sequencing the transcriptome of a cell nucleus. Nuclei are isolated from specimens and sorted by FACS, cDNA libraries are constructed and RNA-seq is performed, followed by data analysis. Some steps follow published methods (Smart-seq2 for cDNA synthesis and Nextera XT barcoded library preparation) and are not described in detail here. Previous single-cell approaches for RNA-seq from tissues include cell dissociation using protease treatment at 30 °C, which is known to alter the transcriptome. We isolate nuclei at 4 °C from tissue homogenates, which cause minimal damage. Nuclear transcriptomes can be obtained from postmortem human brain tissue stored at -80 °C, making brain archives accessible for RNA-seq from individual neurons. The method also allows investigation of biological features unique to nuclei, such as enrichment of certain transcripts and precursors of some noncoding RNAs. By following this procedure, it takes about 4 d to construct cDNA libraries that are ready for sequencing.

Draft Genome Sequence of Turicibacter sanguinis PC909, Isolated from Human Feces

While the microbiota resident in the human gut is now known to provide a range of functions relevant to host health, many of the microbial members of the community have not yet been cultured or are represented by a limited number of isolates. We describe here the draft genome sequence of Turicibacter sanguinis PC909, isolated from a pooled healthy human fecal sample as part of the Australian Human Gut Microbiome Project.

Draft Genome Sequences of Burkholderia cenocepacia ET12 Lineage Strains K56-2 and BC7

ABSTRACT The Burkholderia cepacia complex (BCC) is a group of closely related bacteria that are responsible for respiratory infections in immunocompromised humans, most notably those with cystic fibrosis (CF). We report the genome sequences for Burkholderia cenocepacia ET12 lineage CF isolates K56-2 and BC7.

Sequencing viral genomes from a single isolated plaque

BackgroundWhole genome sequencing of viruses and bacteriophages is often hindered because of the need for large quantities of genomic material. A method is described that combines single plaque sequencing with an optimization of Sequence Independent Single Primer Amplification (SISPA). This method can be used for de novo whole genome next-generation sequencing of any cultivable virus without the need for large-scale production of viral stocks or viral purification using centrifugal techniques.MethodsA single viral plaque of a variant of the 2009 pandemic H1N1 human Influenza A virus was isolated and amplified using the optimized SISPA protocol. The sensitivity of the SISPA protocol presented here was tested with bacteriophage F_HA0480sp/Pa1651 DNA. The amplified products were sequenced with 454 and Illumina HiSeq platforms. Mapping and de novo assemblies were performed to analyze the quality of data produced from this optimized method.ResultsAnalysis of the sequence data demonstrated that from a single viral plaque of Influenza A, a mapping assembly with 3590-fold average coverage representing 100% of the genome could be produced. The de novo assembled data produced contigs with 30-fold average sequence coverage, representing 96.5% of the genome. Using only 10 pg of starting DNA from bacteriophage F_HA0480sp/Pa1651 in the SISPA protocol resulted in sequencing data that gave a mapping assembly with 3488-fold average sequence coverage, representing 99.9% of the reference and a de novo assembly with 45-fold average sequence coverage, representing 98.1% of the genome.ConclusionsThe optimized SISPA protocol presented here produces amplified product that when sequenced will give high quality data that can be used for de novo assembly. The protocol requires only a single viral plaque or as little as 10 pg of DNA template, which will facilitate rapid identification of viruses during an outbreak and viruses that are difficult to propagate.

Draft Genome Sequence of Bacteroides vulgatus PC510, a Strain Isolated from Human Feces

Although Bacteroides vulgatus is one of the most prevalent microorganisms in the human gastrointestinal tract, little is known about the genetic potential of this species. Here, we describe the annotated draft genome sequence of B. vulgatus PC510 isolated from human feces.

Draft Genome Sequences of Two Pairs of Human Intestinal Bifidobacterium longum subsp. longum Strains, 44B and 1-6B and 35B and 2-2B, Consecutively Isolated from Two Children after a 5-Year Time Period

ABSTRACT We report the genome sequences of four isolates of a human gut symbiont, Bifidobacterium longum. Strains 44B and 35B were isolated from two 1-year-old infants, while 1-6B and 2-2B were isolated from the same children 5 years later. The sequences permit investigations of factors enabling long-term colonization of bifidobacteria.

NeatFreq: reference-free data reduction and coverage normalization for De Novo sequence assembly

BackgroundDeep shotgun sequencing on next generation sequencing (NGS) platforms has contributed significant amounts of data to enrich our understanding of genomes, transcriptomes, amplified single-cell genomes, and metagenomes. However, deep coverage variations in short-read data sets and high sequencing error rates of modern sequencers present new computational challenges in data interpretation, including mapping and de novo assembly. New lab techniques such as multiple displacement amplification (MDA) of single cells and sequence independent single primer amplification (SISPA) allow for sequencing of organisms that cannot be cultured, but generate highly variable coverage due to amplification biases.ResultsHere we introduce NeatFreq, a software tool that reduces a data set to more uniform coverage by clustering and selecting from reads binned by their median kmer frequency (RMKF) and uniqueness. Previous algorithms normalize read coverage based on RMKF, but do not include methods for the preferred selection of (1) extremely low coverage regions produced by extremely variable sequencing of random-primed products and (2) 2-sided paired-end sequences. The algorithm increases the incorporation of the most unique, lowest coverage, segments of a genome using an error-corrected data set. NeatFreq was applied to bacterial, viral plaque, and single-cell sequencing data. The algorithm showed an increase in the rate at which the most unique reads in a genome were included in the assembled consensus while also reducing the count of duplicative and erroneous contigs (strings of high confidence overlaps) in the deliverable consensus. The results obtained from conventional Overlap-Layout-Consensus (OLC) were compared to simulated multi-de Bruijn graph assembly alternatives trained for variable coverage input using sequence before and after normalization of coverage. Coverage reduction was shown to increase processing speed and reduce memory requirements when using conventional bacterial assembly algorithms.ConclusionsThe normalization of deep coverage spikes, which would otherwise inhibit consensus resolution, enables High Throughput Sequencing (HTS) assembly projects to consistently run to completion with existing assembly software. The NeatFreq software package is free, open source and available at https://github.com/bioh4x/NeatFreq.

Draft Genome Sequence of Enterococcus faecalis PC1.1, a Candidate Probiotic Strain Isolated from Human Feces

ABSTRACT Enterococcus faecalis is commonly isolated from the gastrointestinal tract of healthy infants and adults, where it contributes to host health and well-being. We describe here the draft genome sequence of E. faecalis PC1.1, a candidate probiotic strain isolated from human feces.

Cell type discovery and representation in the era of high-content single cell phenotyping

BackgroundA fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology.ResultsIn this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including “context annotations” in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations.ConclusionThe advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.