2,623 research outputs found
The impact of sequence database choice on metaproteomic results in gut microbiota studies
Background: Elucidating the role of gut microbiota in physiological and pathological processes has recently emerged as a key research aim in life sciences. In this respect, metaproteomics, the study of the whole protein complement of a microbial community, can provide a unique contribution by revealing which functions are actually being expressed by specific microbial taxa. However, its wide application to gut microbiota research has been hindered by challenges in data analysis, especially related to the choice of the proper sequence databases for protein identification.
Results: Here, we present a systematic investigation of variables concerning database construction and annotation and evaluate their impact on human and mouse gut metaproteomic results. We found that both publicly available and experimental metagenomic databases lead to the identification of unique peptide assortments, suggesting parallel database searches as a mean to gain more complete information. In particular, the contribution of experimental metagenomic databases was revealed to be mandatory when dealing with mouse samples. Moreover, the use of a "merged" database, containing all metagenomic sequences from the population under study, was found to be generally preferable over the use of sample-matched databases. We also observed that taxonomic and functional results are strongly database-dependent, in particular when analyzing the mouse gut microbiota. As a striking example, the Firmicutes/Bacteroidetes ratio varied up to tenfold depending on the database used. Finally, assembling reads into longer contigs provided significant advantages in terms of functional annotation yields.
Conclusions: This study contributes to identify host- and database-specific biases which need to be taken into account in a metaproteomic experiment, providing meaningful insights on how to design gut microbiota studies and to perform metaproteomic data analysis. In particular, the use of multiple databases and annotation tools has to be encouraged, even though this requires appropriate bioinformatic resources
Analysis of a data matrix and a graph: Metagenomic data and the phylogenetic tree
In biological experiments researchers often have information in the form of a
graph that supplements observed numerical data. Incorporating the knowledge
contained in these graphs into an analysis of the numerical data is an
important and nontrivial task. We look at the example of metagenomic
data---data from a genomic survey of the abundance of different species of
bacteria in a sample. Here, the graph of interest is a phylogenetic tree
depicting the interspecies relationships among the bacteria species. We
illustrate that analysis of the data in a nonstandard inner-product space
effectively uses this additional graphical information and produces more
meaningful results.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS402 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Multiple Comparative Metagenomics using Multiset k-mer Counting
Background. Large scale metagenomic projects aim to extract biodiversity
knowledge between different environmental conditions. Current methods for
comparing microbial communities face important limitations. Those based on
taxonomical or functional assignation rely on a small subset of the sequences
that can be associated to known organisms. On the other hand, de novo methods,
that compare the whole sets of sequences, either do not scale up on ambitious
metagenomic projects or do not provide precise and exhaustive results.
Methods. These limitations motivated the development of a new de novo
metagenomic comparative method, called Simka. This method computes a large
collection of standard ecological distances by replacing species counts by
k-mer counts. Simka scales-up today's metagenomic projects thanks to a new
parallel k-mer counting strategy on multiple datasets.
Results. Experiments on public Human Microbiome Project datasets demonstrate
that Simka captures the essential underlying biological structure. Simka was
able to compute in a few hours both qualitative and quantitative ecological
distances on hundreds of metagenomic samples (690 samples, 32 billions of
reads). We also demonstrate that analyzing metagenomes at the k-mer level is
highly correlated with extremely precise de novo comparison techniques which
rely on all-versus-all sequences alignment strategy or which are based on
taxonomic profiling
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The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.
Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge
Entropy-scaling search of massive biological data
Many datasets exhibit a well-defined structure that can be exploited to
design faster search tools, but it is not always clear when such acceleration
is possible. Here, we introduce a framework for similarity search based on
characterizing a dataset's entropy and fractal dimension. We prove that
searching scales in time with metric entropy (number of covering hyperspheres),
if the fractal dimension of the dataset is low, and scales in space with the
sum of metric entropy and information-theoretic entropy (randomness of the
data). Using these ideas, we present accelerated versions of standard tools,
with no loss in specificity and little loss in sensitivity, for use in three
domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics
(MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search
(esFragBag, 10x speedup of FragBag). Our framework can be used to achieve
"compressive omics," and the general theory can be readily applied to data
science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo
Doctor of Philosophy
dissertationAdvances in technology have produced efficient and powerful scientific instruments for measuring biological phenomena. In particular, modern microscopes and nextgeneration sequencing machines produce data at such a rate that manual analysis is no longer practical or feasible for meaningful scientific inquiries. Thus, there is a great need for computational strategies to organize and analyze huge amounts of data produced by biological experiments. My work presents computational strategies and software solutions for application in image analysis, human variant prioritization, and metagenomics. The information content of images can be leveraged to answer an extremely broad spectrum of questions ranging from inquiries about basic biological processes to highly specific, application-driven inquiries like the efficacy of a pharmaceutical drug. Modern microscopes can produce images at a rate at which rigorous manual analysis is impossible. I have created software pipelines that automate image analysis in two specific applications domains. In addition, I discuss general image analysis strategies that can be applied to a wide variety of problems. There are tens of millions of known human genetic variants. Prioritizing human variants based on how likely they are to cause disease is of huge importance because of the potential impact on human health. Current variant prioritization methods are limited by their scope, efficiency, and accuracy. I present a variant prioritization method, the VAAST variant prioritizer, which is superior in its scope, efficiency, and accuracy to existing variant prioritization methods. The rise of next-generation sequencing enables huge quantities of sequence to be generated in a short period of time. No field of study has been affected by rapid sequencing more than metagenomics. Metagenomics, the genomic analysis of a population v of microorganisms, has important implications for pathogen detection because metagenomics enables the culture-free detection of microorganisms. I have created Taxonomer, a comprehensive metagenomics pipeline that enables the real-time analysis of read datasets derived from environmental samples
Size Doesn't Matter: Towards a More Inclusive Philosophy of Biology
notes: As the primary author, O’Malley drafted the paper, and gathered and analysed data (scientific papers and talks). Conceptual analysis was conducted by both authors.publication-status: Publishedtypes: ArticlePhilosophers of biology, along with everyone else, generally perceive life to fall into two broad categories, the microbes and macrobes, and then pay most of their attention to the latter. ‘Macrobe’ is the word we propose for larger life forms, and we use it as part of an argument for microbial equality. We suggest that taking more notice of microbes – the dominant life form on the planet, both now and throughout evolutionary history – will transform some of the philosophy of biology’s standard ideas on ontology, evolution, taxonomy and biodiversity. We set out a number of recent developments in microbiology – including biofilm formation, chemotaxis, quorum sensing and gene transfer – that highlight microbial capacities for cooperation and communication and break down conventional thinking that microbes are solely or primarily single-celled organisms. These insights also bring new perspectives to the levels of selection debate, as well as to discussions of the evolution and nature of multicellularity, and to neo-Darwinian understandings of evolutionary mechanisms. We show how these revisions lead to further complications for microbial classification and the philosophies of systematics and biodiversity. Incorporating microbial insights into the philosophy of biology will challenge many of its assumptions, but also give greater scope and depth to its investigations
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