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The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

Among insects, symbionts such as bacteria and fungi can be linked to their physiology and immature development, and in some cases are part of a defense system against parasites and diseases. Current bacterial and fungal symbiont associations in solitary bees are understudied, especially in the Pacific Northwest region of the USA. We collected pollen provisions from the native spring-foraging solitary bee, Osmia lignaria Say, across two distinct foraging periods over 2 years at 22 sites along an urban-to-rural gradient in western Washington. We then used next-generation sequencing to identify bacterial and fungi within pollen provisions and assessed the effect of their richness and diversity on O. lignaria larval development success and adult emergence. We detected a significantly positive relationship between bacterial diversity in pollen with O. lignaria larval developmental success, and higher bacterial richness and diversity during the later foraging period. Fungal generic richness and diversity decreased with increasing plant richness. Although neither was associated with O. lignaria developmental success, we did detect Ascosphaera spp. known to be pathogenic to O. lignaria and other bee species. Neither bacterial or fungal richness or diversity was affected by site type when classified as urban or rural. This study provides new information on bacterial and fungal symbionts present in pollen provisions of a native solitary bee when foraging across urban and rural areas of the Pacific Northwest.

SummaryThe precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants.

Background Idiopathic chronic diarrhea (ICD) is a common cause of morbidity and mortality among juvenile rhesus macaques. Characterized by chronic inflammation of the colon and repeated bouts of diarrhea, ICD is largely unresponsive to medical interventions, including corticosteroid, antiparasitic, and antibiotic treatments. Although ICD is accompanied by large disruptions in the composition of the commensal gut microbiome, no single pathogen has been concretely identified as responsible for the onset and continuation of the disease. Results Fecal samples were collected from 12 ICD-diagnosed macaques and 12 age- and sex-matched controls. RNA was extracted for metatranscriptomic analysis of organisms and functional annotations associated with the gut microbiome. Bacterial, fungal, archaeal, protozoan, and macaque (host) transcripts were simultaneously assessed. ICD-afflicted animals were characterized by increased expression of host-derived genes involved in inflammation and increased transcripts from bacterial pathogens such as Campylobacter and Helicobacter and the protozoan Trichomonas. Transcripts associated with known mucin-degrading organisms and mucin-degrading enzymes were elevated in the fecal microbiomes of ICD-afflicted animals. Assessment of colon sections using immunohistochemistry and of the host transcriptome suggests differential fucosylation of mucins between control and ICD-afflicted animals. Interrogation of the metatranscriptome for fucose utilization genes reveals possible mechanisms by which opportunists persist in ICD. Bacteroides sp. potentially cross-fed fucose to Haemophilus whereas Campylobacter expressed a mucosa-associated transcriptome with increased expression of adherence genes. Conclusions The simultaneous profiling of bacterial, fungal, archaeal, protozoan, and macaque transcripts from stool samples reveals that ICD of rhesus macaques is associated with increased gene expression by pathogens, increased mucin degradation, and altered fucose utilization. The data suggest that the ICD-afflicted host produces fucosylated mucins that are leveraged by potentially pathogenic microbes as a carbon source or as adhesion sites.

BackgroundComplex microbial communities are an area of rapid growth in biology. Metatranscriptomics allows one to investigate the gene activity in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and the sequences must be compared with many references.ResultsHere we present SAMSA2, an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution.ConclusionsUsing publicly available example data, we demonstrate that SAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large paired-end RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the specifics of any experiment.

BackgroundAlthough metatranscriptomics—the study of diverse microbial population activity based on RNA-seq data—is rapidly growing in popularity, there are limited options for biologists to analyze this type of data. Current approaches for processing metatranscriptomes rely on restricted databases and a dedicated computing cluster, or metagenome-based approaches that have not been fully evaluated for processing metatranscriptomic datasets. We created a new bioinformatics pipeline, SAMSA, designed specifically for metatranscriptome dataset analysis, which runs either inhouse or in conjunction with Metagenome-RAST (MG-RAST) servers. Designed for use by researchers with relatively little bioinformatics experience, SAMSA offers a breakdown of metatranscriptome activity by organism or transcript function, and is fully open source. We next used this new tool to evaluate best practices for sequencing stool metatranscriptomes.ResultsWorking with the MG-RAST annotation server, we constructed the Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) software package, a complete pipeline for the analysis of gut microbiome data. In creating this package, we determined optimal parameters in data collection and processing. SAMSA can summarize and evaluate raw annotation results, identifying abundant species and significant functional differences between metatranscriptomes.Using pilot data and simulated subsets, we determined experimental requirements for fecal gut metatranscriptomes. Sequences need to be either long reads (longer than 100bp) or paired-end reads that can be joined. Each sample nees 40-50 million raw sequences which can be expected to yield the 5-10 million annotated reads necessary for accurate abundance measures. We also demonstrated that ribosomal RNA depletion does not equally deplete ribosomes from all species within a sample, and remaining rRNA sequences should be discarded. Using publicly available metatranscriptome data in which rRNA was not depleted, we were able to demonstrate that organism activity can be measured using mRNA counts. We were also able to detect significant differences between control and experimental groups in both organism activity and functional activity.ConclusionsBy making this new pipeline publicly available, we have created a powerful new tool for metatranscriptomics research, offering a new method for greater insight into the activity of diverse microbial communities. We further recommend that stool metatranscriptomes be ribodepleted and sequenced in a 100bp paired end format with a minimum of 40 million reads per sample.