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The incidence of preterm birth exceeds 10% worldwide. There are significant disparities in the frequency of preterm birth among populations within countries, and women of African ancestry disproportionately bear the burden of risk in the United States. In the present study, we report a community resource that includes 'omics' data from approximately 12,000 samples as part of the integrative Human Microbiome Project. Longitudinal analyses of 16S ribosomal RNA, metagenomic, metatranscriptomic and cytokine profiles from 45 preterm and 90 term birth controls identified harbingers of preterm birth in this cohort of women predominantly of African ancestry. Women who delivered preterm exhibited significantly lower vaginal levels of Lactobacillus crispatus and higher levels of BVAB1, Sneathia amnii, TM7-H1, a group of Prevotella species and nine additional taxa. The first representative genomes of BVAB1 and TM7-H1 are described. Preterm-birth-associated taxa were correlated with proinflammatory cytokines in vaginal fluid. These findings highlight new opportunities for assessment of the risk of preterm birth.

Background Recent advances in next-generation sequencing have revolutionized genomic research. 16S rRNA amplicon sequencing using paired-end sequencing on the MiSeq platform from Illumina, Inc., is being used to characterize the composition and dynamics of extremely complex/diverse microbial communities. For this analysis on the Illumina platform, merging and quality filtering of paired-end reads are essential first steps in data analysis to ensure the accuracy and reliability of downstream analysis. Results We have developed the Merging and Filtering Tool (MeFiT) to combine these pre-processing steps into one simple, intuitive pipeline. MeFiT invokes CASPER (context-aware scheme for paired-end reads) for merging paired-end reads and provides users the option to quality filter the reads using the traditional average Q-score metric or using a maximum expected error cut-off threshold. Conclusions MeFiT provides an open-source solution that permits users to merge and filter paired end illumina reads. The tool has been implemented in python and the source-code is freely available at https://github.com/nisheth/MeFiT. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1358-1) contains supplementary material, which is available to authorized users.

Background Characterizing microbial communities via next-generation sequencing is subject to a number of pitfalls involving sample processing. The observed community composition can be a severe distortion of the quantities of bacteria actually present in the microbiome, hampering analysis and threatening the validity of conclusions from metagenomic studies. We introduce an experimental protocol using mock communities for quantifying and characterizing bias introduced in the sample processing pipeline. We used 80 bacterial mock communities comprised of prescribed proportions of cells from seven vaginally-relevant bacterial strains to assess the bias introduced in the sample processing pipeline. We created two additional sets of 80 mock communities by mixing prescribed quantities of DNA and PCR product to quantify the relative contribution to bias of (1) DNA extraction, (2) PCR amplification, and (3) sequencing and taxonomic classification for particular choices of protocols for each step. We developed models to predict the “true” composition of environmental samples based on the observed proportions, and applied them to a set of clinical vaginal samples from a single subject during four visits. Results We observed that using different DNA extraction kits can produce dramatically different results but bias is introduced regardless of the choice of kit. We observed error rates from bias of over 85% in some samples, while technical variation was very low at less than 5% for most bacteria. The effects of DNA extraction and PCR amplification for our protocols were much larger than those due to sequencing and classification. The processing steps affected different bacteria in different ways, resulting in amplified and suppressed observed proportions of a community. When predictive models were applied to clinical samples from a subject, the predicted microbiome profiles were better reflections of the physiology and diagnosis of the subject at the visits than the observed community compositions. Conclusions Bias in 16S studies due to DNA extraction and PCR amplification will continue to require attention despite further advances in sequencing technology. Analysis of mock communities can help assess bias and facilitate the interpretation of results from environmental samples. Electronic supplementary material The online version of this article (doi:10.1186/s12866-015-0351-6) contains supplementary material, which is available to authorized users.

Loss-of-function phenotypes often hold the key to understanding the connections and biological functions of biochemical pathways. We and others previously constructed libraries of short hairpin RNAs that allow systematic analysis of RNA interference-induced phenotypes in mammalian cells. Here we report the construction and validation of second-generation short hairpin RNA expression libraries designed using an increased knowledge of RNA interference biochemistry. These constructs include silencing triggers designed to mimic a natural microRNA primary transcript, and each target sequence was selected on the basis of thermodynamic criteria for optimal small RNA performance. Biochemical and phenotypic assays indicate that the new libraries are substantially improved over first-generation reagents. We generated large-scale-arrayed, sequence-verified libraries comprising more than 140,000 second-generation short hairpin RNA expression plasmids, covering a substantial fraction of all predicted genes in the human and mouse genomes. These libraries are available to the scientific community.

We and others have chosen to explore the shRNAs for several reasons: first, the considerable cost of chemically synthesized siRNAs; second, the possibility of enforceable and stable expression of shRNAs; and third, the availability of applications of expression constructs in primary cell types (for example, using retroviruses) and in whole organisms (for example, in mouse). We have developed a system to drive expression of shRNAs by placing them under the control of the human RNA polymerase III U6 small nuclear RNA (snRNA) promoter, which normally controls expression of small RNAs in cells. This system has now been demonstrated to be effective both in