38,819 research outputs found

    estMOI: estimating multiplicity of infection using parasite deep sequencing data.

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    Individuals living in endemic areas generally harbour multiple parasite strains. Multiplicity of infection (MOI) can be an indicator of immune status and transmission intensity. It has a potentially confounding effect on a number of population genetic analyses, which often assume isolates are clonal. Polymerase chain reaction-based approaches to estimate MOI can lack sensitivity. For example, in the human malaria parasite Plasmodium falciparum, genotyping of the merozoite surface protein (MSP1/2) genes is a standard method for assessing MOI, despite the apparent problem of underestimation. The availability of deep coverage data from massively parallizable sequencing technologies means that MOI can be detected genome wide by considering the abundance of heterozygous genotypes. Here, we present a method to estimate MOI, which considers unique combinations of polymorphisms from sequence reads. The method is implemented within the estMOI software. When applied to clinical P.falciparum isolates from three continents, we find that multiple infections are common, especially in regions with high transmission

    Deep sequencing approaches for the analysis of prokaryotic transcriptional boundaries and dynamics

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    The identification of the protein-coding regions of a genome is straightforward due to the universality of start and stop codons. However, the boundaries of the transcribed regions, conditional operon structures, non-coding RNAs and the dynamics of transcription, such as pausing of elongation, are non-trivial to identify, even in the comparatively simple genomes of prokaryotes. Traditional methods for the study of these areas, such as tiling arrays, are noisy, labour-intensive and lack the resolution required for densely-packed bacterial genomes. Recently, deep sequencing has become increasingly popular for the study of the transcriptome due to its lower costs, higher accuracy and single nucleotide resolution. These methods have revolutionised our understanding of prokaryotic transcriptional dynamics. Here, we review the deep sequencing and data analysis techniques that are available for the study of transcription in prokaryotes, and discuss the bioinformatic considerations of these analyses

    Genomic analysis suggests that mRNA destabilization by the microprocessor is specialized for the auto-regulation of Dgcr8.

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    BackgroundThe Microprocessor, containing the RNA binding protein Dgcr8 and RNase III enzyme Drosha, is responsible for processing primary microRNAs to precursor microRNAs. The Microprocessor regulates its own levels by cleaving hairpins in the 5'UTR and coding region of the Dgcr8 mRNA, thereby destabilizing the mature transcript.Methodology/principal findingsTo determine whether the Microprocessor has a broader role in directly regulating other coding mRNA levels, we integrated results from expression profiling and ultra high-throughput deep sequencing of small RNAs. Expression analysis of mRNAs in wild-type, Dgcr8 knockout, and Dicer knockout mouse embryonic stem (ES) cells uncovered mRNAs that were specifically upregulated in the Dgcr8 null background. A number of these transcripts had evolutionarily conserved predicted hairpin targets for the Microprocessor. However, analysis of deep sequencing data of 18 to 200nt small RNAs in mouse ES, HeLa, and HepG2 indicates that exonic sequence reads that map in a pattern consistent with Microprocessor activity are unique to Dgcr8.Conclusion/significanceWe conclude that the Microprocessor's role in directly destabilizing coding mRNAs is likely specifically targeted to Dgcr8 itself, suggesting a specialized cellular mechanism for gene auto-regulation

    Sparse panicle1 is required for inflorescence development in Setaria viridis and maize.

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    Setaria viridis is a rapid-life-cycle model panicoid grass. To identify genes that may contribute to inflorescence architecture and thus have the potential to influence grain yield in related crops such as maize, we conducted an N-nitroso-N-methylurea (NMU) mutagenesis of S. viridis and screened for visible inflorescence mutant phenotypes. Of the approximately 2,700 M2 families screened, we identified four recessive sparse panicle mutants (spp1-spp4) characterized by reduced and uneven branching of the inflorescence. To identify the gene underlying the sparse panicle1 (spp1) phenotype, we performed bulked segregant analysis and deep sequencing to fine map it to an approximately 1 Mb interval. Within this interval, we identified disruptive mutations in two genes. Complementation tests between spp1 and spp3 revealed they were allelic, and deep sequencing of spp3 identified an independent disruptive mutation in SvAUX1 (AUXIN1), one of the two genes in the ∼1 Mb interval and the only gene disruption shared between spp1 and spp3. SvAUX1 was found to affect both inflorescence development and root gravitropism in S. viridis. A search for orthologous mutant alleles in maize confirmed a very similar role of ZmAUX1 in maize, which highlights the utility of S. viridis in accelerating functional genomic studies in maize

    Rapid creation and quantitative monitoring of high coverage shRNA libraries.

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    Short hairpin RNA libraries are limited by low efficacy of many shRNAs and by off-target effects, which give rise to false negatives and false positives, respectively. Here we present a strategy for rapidly creating expanded shRNA pools (approximately 30 shRNAs per gene) that are analyzed by deep sequencing (EXPAND). This approach enables identification of multiple effective target-specific shRNAs from a complex pool, allowing a rigorous statistical evaluation of true hits
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