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  1. Genomes of the Wolbachia endosymbionts from the human filarial parasites Mansonella perstans and Mansonella ozzardi reveal multiple origins of nematode-Wolbachia symbiosis

    Wolbachia are Gram-negative, obligate intracellular bacteria in some filarial nematodes and about 60% of arthropods. The filarial parasites Mansonella perstans and Mansonella ozzardi harbor WolbachiaWolbachia have been consistently detected in M. ozzardi, but in M. perstans, the presence of Wolbachia may be isolate-dependent. Phylogenetically, the majority of Wolbachia from filarial parasites cluster into supergroups C, D and J, while arthropod Wolbachia are clustered in supergroups A, B, E, H and S. Wolbachia from Mansonella (wMpe, wMoz) are different from other filarial Wolbachia as they are placed in supergroup F, with Wolbachia from insects such as the bed bug. We present here the genomes of wMpe and wMoz, representing the first genomes from filarial Wolbachia of supergroup F. We also present two new genomes of arthropod Wolbachia from supergroup F.

  2. Genome sequences of the human filarial parasites Mansonella perstans and Mansonella ozzardi

    Mansonelliasis is a widespread yet neglected filariasis of humans, caused by infection with any of the three filarial species: Mansonella perstansM. ozzardi and M. streptocerca.

    The goal of the current study was to obtain whole genome sequences of M. perstans and M. ozzardiM. perstans infections are endemic in Central and West Africa, and in a few areas of South America. M. ozzardi infections are highly prevalent in South America and the Caribbean islands. Transmission to humans is via insect vectors - biting midges of Culicoides spp. for M. perstans, and Culicoides midges as well as black flies of Simulium spp. for M. ozzardi.

  3. Comparison of different sequencing platforms using 480 unique dual indexed libraries for 4 different applications

    Advances in sequencing instruments and chemistries continue to evolve, and new sequencers are being released at an unprecedented pace by both the current market leader, Illumina, and challengers, including MGI. Lower run costs, faster run times, and ease of use offered by new instruments often makes switching an appealing and reasonable choice. However, data quality, error rates, and error types vary among sequencers, even from the same manufacturer. In this study, we evaluated the design of 5 sets of 96 unique dual index primers using diverse library types, and characterized the variation in clustering efficiency, error rates, and error types, in addition to abundance levels.

  4. Increasing the sensitivity of transcriptome profiling in eukaryote and blood samples by depleting abundant RNAs

    The large dynamic range of transcript expression within a total RNA sample presents a challenge in whole transcriptome sequencing. Highly expressed transcripts with minimal biological interest can dominate readouts, masking detection of more informative lower abundance transcripts. Here, we present a method to enrich for RNAs of interest by eliminating unwanted RNAs before sequencing.

  5. Enzymatic Methyl-seq enables accurate and robust methylation detection

    DNA methylation is one of the most important epigenetic regulatory mechanisms. The ability to accurately identify 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) gives us greater insight into potential gene regulatory mechanisms. Bisulfite sequencing (BS) is traditionally used to detect methylated cytosines, but the chemical-based conversion of cytosines to uracils leads to DNA damage that subsequently translates to shorter DNA insert sizes and biases in the data. To overcome these drawbacks, we developed NEBNext Enzymatic Methyl-seq (EM-seq™), an enzymatic approach to detecting cytosine methylation.

  6. Selective removal of abundant RNAs enhances the sensitivity of transcript detection across different prokaryotic and archaeabacterial species

    RNA-seq is a widely used technology with a broad range of applications. However, it is not always feasible for unique prokaryotic species or other interesting organisms, where tools to study them can frequently lag. Here, we present a robust method to enrich for RNAs of interest by eliminating rRNAs in diverse bacterial species. We further introduce an approach to customize RNA depletion, eliminating specific RNAs in any organism not well covered by a pre-optimized kit.

  7. – streaming detection and notification of new SARS-CoV-2 variants

    Nucleic acid diagnostic tests for SARS-CoV-2, whether based on RT-qPCR, RT-LAMP, RPA, or other amplification technology, all depend on primers. While the SARS-CoV-2 genome seems to be less variable than some other retroviral genomes, variants with potential effects on amplification efficiency have arisen and become prevalent in local areas. We developed a streaming analysis method to identify variants that may affect specific primers and a website to allow interested users to register primer loci.

  8. Improving multiplex targeted amplicon sequencing of SARS-CoV-2 on Illumina platforms

    The impact of the novel coronavirus SARS-CoV-2 on the global community has produced a critical need to develop reliable and accurate protocols for sequencing emergent pathogens. Here, we present refined protocols and reagents for the sequencing of SARS-CoV-2 on Illumina sequencing platforms with protocols based on the ARTIC consortium methods.

  9. Incorporation of Unique Molecular Identifiers (UMIs) into Unique Dual Indexing (UDI) of samples improves the accuracy of quantitative Next Generation Sequencing

    The use of Unique Molecular Identifiers (UMIs) has become increasingly popular, offering a multitude of advantages, especially when paired with unique dual indexing (UDI). By incorporating UMIs into UDI adaptors, we have assessed their effect on the accuracy of quantitative sequencing assays. We demonstrate that the sensitivity of variant detection is improved with UMI consensus calling.

  10. Improving transcriptome analysis by incorporating Unique Molecular Identifiers in RNA-sequencing libraries

    RNA sequencing is a powerful tool for the study of gene regulation and function. Applications of RNA-seq have expanded to include low-RNA input library prep methods. In this study, we show that the incorporation of UMIs into RNA-seq analysis allows for a more accurate calculation of transcript abundance.

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