Browsing by Author "Astrovskaya, Irina"

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    Diarrhea in young children from low-income countries leads to large-scale alterations in intestinal microbiota composition
    (Springer Nature, 2014-06-27) Pop, Mihai; Walker, Alan W; Paulson, Joseph; Lindsay, Brianna; Antonio, Martin; Hossain, M Anowar; Oundo, Joseph; Tamboura, Boubou; Mai, Volker; Astrovskaya, Irina; Bravo, Hector Corrada; Rance, Richard; Stares, Mark; Levine, Myron M; Panchalingam, Sandra; Kotloff, Karen; Ikumapayi, Usman N; Ebruke, Chinelo; Adeyemi, Mitchell; Ahmed, Dilruba; Ahmed, Firoz; Alam, Meer Taifur; Amin, Ruhul; Siddiqui, Sabbir; Ochieng, John B; Ouma, Emmanuel; Juma, Jane; Mailu, Euince; Omore, Richard; Morris, J Glenn; Breiman, Robert F; Saha, Debasish; Parkhill, Julian; Nataro, James P; Stine, O Colin
    Diarrheal diseases continue to contribute significantly to morbidity and mortality in infants and young children in developing countries. There is an urgent need to better understand the contributions of novel, potentially uncultured, diarrheal pathogens to severe diarrheal disease, as well as distortions in normal gut microbiota composition that might facilitate severe disease. We use high throughput 16S rRNA gene sequencing to compare fecal microbiota composition in children under five years of age who have been diagnosed with moderate to severe diarrhea (MSD) with the microbiota from diarrhea-free controls. Our study includes 992 children from four low-income countries in West and East Africa, and Southeast Asia. Known pathogens, as well as bacteria currently not considered as important diarrhea-causing pathogens, are positively associated with MSD, and these include Escherichia/Shigella, and Granulicatella species, and Streptococcus mitis/pneumoniae groups. In both cases and controls, there tend to be distinct negative correlations between facultative anaerobic lineages and obligate anaerobic lineages. Overall genus-level microbiota composition exhibit a shift in controls from low to high levels of Prevotella and in MSD cases from high to low levels of Escherichia/Shigella in younger versus older children; however, there was significant variation among many genera by both site and age. Our findings expand the current understanding of microbiota-associated diarrhea pathogenicity in young children from developing countries. Our findings are necessarily based on correlative analyses and must be further validated through epidemiological and molecular techniques.
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    MetAMOS: a modular and open source metagenomic assembly and analysis pipeline
    (Springer Nature, 2013-01-15) Treangen, Todd J; Koren, Sergey; Sommer, Daniel D; Liu, Bo; Astrovskaya, Irina; Ondov, Brian; Darling, Aaron E; Phillippy, Adam M; Pop, Mihai
    We describe MetAMOS, an open source and modular metagenomic assembly and analysis pipeline. MetAMOS represents an important step towards fully automated metagenomic analysis, starting with next-generation sequencing reads and producing genomic scaffolds, open-reading frames and taxonomic or functional annotations. MetAMOS can aid in reducing assembly errors, commonly encountered when assembling metagenomic samples, and improves taxonomic assignment accuracy while also reducing computational cost. MetAMOS can be downloaded from: https://github.com/treangen/MetAMOS .
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    De novo likelihood-based measures for comparing genome assemblies
    (Springer Nature, 2013-08-22) Ghodsi, Mohammadreza; Hill, Christopher M; Astrovskaya, Irina; Lin, Henry; Sommer, Dan D; Koren, Sergey; Pop, Mihai
    The current revolution in genomics has been made possible by software tools called genome assemblers, which stitch together DNA fragments “read” by sequencing machines into complete or nearly complete genome sequences. Despite decades of research in this field and the development of dozens of genome assemblers, assessing and comparing the quality of assembled genome sequences still relies on the availability of independently determined standards, such as manually curated genome sequences, or independently produced mapping data. These “gold standards” can be expensive to produce and may only cover a small fraction of the genome, which limits their applicability to newly generated genome sequences. Here we introduce a de novo probabilistic measure of assembly quality which allows for an objective comparison of multiple assemblies generated from the same set of reads. We define the quality of a sequence produced by an assembler as the conditional probability of observing the sequenced reads from the assembled sequence. A key property of our metric is that the true genome sequence maximizes the score, unlike other commonly used metrics. We demonstrate that our de novo score can be computed quickly and accurately in a practical setting even for large datasets, by estimating the score from a relatively small sample of the reads. To demonstrate the benefits of our score, we measure the quality of the assemblies generated in the GAGE and Assemblathon 1 assembly “bake-offs” with our metric. Even without knowledge of the true reference sequence, our de novo metric closely matches the reference-based evaluation metrics used in the studies and outperforms other de novo metrics traditionally used to measure assembly quality (such as N50). Finally, we highlight the application of our score to optimize assembly parameters used in genome assemblers, which enables better assemblies to be produced, even without prior knowledge of the genome being assembled. Likelihood-based measures, such as ours proposed here, will become the new standard for de novo assembly evaluation.